CS Seminar Series

Welcome (back)!

When: Thu, 9/26, 12:50-1:50pm
Where: Olin 107

We are kicking off the 2024-25 CS seminar series with an informal get-together. Whether you are new to the CS department or have been with us for a while, please join us to meet and catch up with CS professors and students.

CSC 489 Senior Project Poster Session

When: Thu, 11/7, 12:50-1:50pm
Where: Wold Center Lobby (around the Steinmetz car)

Abstract:

CS seniors who are taking CSC 489 this term will present their senior projects.

(applied) AI in Particle Physics
Thomas Britton
Jefferson Lab

When: Thu, 4/11 12:50-1:50pm
Where: VART 204

Abstract: Artificial Intelligence and Machine Learning technologies have been integrating themselves into people's daily lives. They are often at work behind the scenes, indirectly influencing our daily lives. From Netflix recommendations and Postal sorting to virtual assistants and generative systems it seems that dealing with AI has become unavoidable; not even the sciences are immune from AI/ML. This talk will describe examples of AI systems in deployment at Jefferson Laboratory impacting every step of the scientific workflow. These systems promise to enhance physics by gathering good data more efficiently and drastically reducing pre-analysis work. The total body of ongoing work, while too massive to be described in detail here, all point to the goal of autonomous experimentation.

Bio: Thomas Britton is currently a staff scientist at Jefferson Lab. He received a PhD in Physics from Syracuse University and has a Bachelor's degree in Physics and Mathematics with a minor in Computer Science from Coe College.

CS Research Fair

When: Thu, 1/11, 12:50-1:50pm
Where: Olin 107

Are you curious about getting involved in research in the CS department? Come to this seminar to find out about the different ways in which students can get involved in CS research, hear about research projects the CS professors are currently working on, and get a chance to talk to the CS professors about research opportunities.

Generating Emotive Sounds for Non-verbal Robots
Hannen Wolfe
Colby College

When: Thu, 1/18 12:50-1:50pm
Where: Olin 107

Abstract: Non-Linguistic Utterances are sounds that communicate information and are not associated with words or other parts of language. They are essential parts of emotive exchanges, not only in human-human interactions but also in the context of human-robot interactions. Wolfe’s research aims to deepen our understanding of emotive sounds for the domain of human-robot exchanges. They investigated the connections between certain audio qualities and emotional states, designing a novel mapping of musical and prosodic audio parameters to a dimensional model of emotion which allows a robot to express a range of emotions. In the interactive sculpture “Touching Affectivity” they sonify a robot’s conductive fur signal using research on emotive vocal communication and music. The emotion in the way the robot is touched is tied to and reflected in the sound the robot generates. These findings contribute valuable insights into how to design NLUs which can enhance the emotional depth of human-robot interactions, with potential applications across various domains.

Bio: Hannen Wolfe (they/them) is a media artist and Assistant Professor of Computer Science at Colby College. Their media artwork is at the intersection of art and computation, building interactive art installations and staging robot performances that uplift underrepresented voices, question how we use technology, and dismantle systemic and structural inequalities. Their research on emotive sound for robots has been published in IEEE Transactions on Affective Computing and presented at the International Conference on Human Robot Interaction (HRI), New Interfaces for Musical Expression (NIME) and others. Wolfe earned a PhD in Media Arts and Technology and a M.S. in Computer Science from University of California, Santa Barbara, as well as a Bachelors of Arts from Bennington College with a concentration in Visual Arts.

CSC 498 Senior Project Poster Session

When: Thu, 2/22, 12:50-1:50pm
Where: TBD

Abstract:

CS seniors who are taking CSC 498 this term will present their senior project proposals.

Senior Project Poster Session and Celebration

When: Mon, 3/11, 12:50-1:50pm
Where: Wold Atrium

Abstract:

CS seniors who are taking CSC 499 this term will present their (almost completed) senior projects.

Formal Methods with Theorem Provers
Arjun Viswanathan
University of Iowa

When: Tue, 3/12, 12:50-1:50pm
Where: Olin 107

Abstract: In this talk I will introduce 1. formal methods - mathematical techniques for specifying and verifying hardware/software systems; 2. theorem provers - software tools for performing formal methods. I will introduce an application of theorem provers in software verification, and our research in extending this application.

Bio: Arjun Viswanathan is a PhD candidate at the University of Iowa. His research interests lie in the application and development of formal methods to make software systems reliable.

Welcome (back)!

When: Thu, 9/14, 12:50-1:50pm
Where: Wold Center 128

We are kicking off the 2022-23 CS seminar series with an informal get-together. Whether you are new to the CS department or have been with us for a while, please join us to meet and catch up with CS professors and students.

~~ co-sponsored with ACM-W ~~

Welcome to everyone who is new to the CS department!

When: Thu, 9/21, 12:50-1:50pm
Where: Olin 107

~~ co-sponsored with the Math and Biology departments ~~

Wrangling microbial data
Georgia Doing
Jackson Laboratory

When: Thu, 10/5, 12:50-1:50pm
Where: Olin 115

Abstract: Of the trillions of microbial cells associated with a human body, only a handful have been studied in the laboratory. Understudied microbial genetic diversity, including that represented on the skin by staphylococci, is important for clinical outcomes of human hosts but too expansive to comprehensively study with molecular and biochemical techniques alone. Computational tools are a promising approach for investigating high dimensional microbiological data which are susceptible to machine learning. Novel gene annotations can be inferred by balancing the performance of black-box machine learning and the interpretability of linear correlations in algorithms with scalability to match that of microbial diversity. In this talk I will present how such an approach can help analyze novel genes from two skin microbes: pathogen Staphylococcus aureus and commensal Staphylococcus epidermidis.

Bio: Georgia is in perpetual awe of how microbes influence and react to dynamic environments and each other. After graduating from Bard College with a joint major in Biology and Computer Science advised by Dr. Brooke Jude and Dr. Rebecca Thomas, she did her PhD work at Dartmouth College in the lab of Dr. Deborah Hogan using machine learning approaches to analyze gene expression in opportunistic pathogens. As a postdoc in the lab of Dr. Julia Oh at the Jackson Laboratory for Genomic Medicine she is now studying the skin microbiome and the pathobiont Staphylococcus epidermidis, identifying patterns in large compendia of data and testing molecular mechanisms in the laboratory. She believes there are no limits on the number of times a dataset should be interrogated or the number of minds by whom it should be re-analyzed.

CSC 498 Senior Project Poster Session

When: Thu, 10/26, 12:50-1:50pm
Where: ISEC atrium

Abstract:

CS seniors who are taking CSC 498 this term will present their senior project proposals.

Presenters: Manav Bilakhia, John Daly, Jason D'Amico, Khai Dong, Aidan Goroway, Ryo Hashimoto, Vu Le, Yuxing Liu, Colby Ryan, Emma Vu, Oliver Taylor, Hope Crisafi, Ben Trantanella

Developing Sign Language First Technology to Improve Information Accessibility
Shruti Mahajan
Worcester Polytechnic Institute

When: Thu, 11/9, 12:50-1:50pm
Where: Olin 107

Abstract: In the United States, American Sign Language (ASL) is the primary language of many deaf adults, and many deaf students receive classroom instruction in ASL while learning English as a second language. However, most interactive computing tools are presented and navigated exclusively in English, even those designed for deaf audiences. Making access to technology contingent upon a sufficient command of a second language creates significant barriers and access delays for deaf individuals.

My research takes a human-centered computing approach to build a foundation that advances understanding of how deaf individuals could work and learn in environments that are designed with their needs and preferences at the forefront. It investigates the feasibility and effectiveness of new SL1 technology, which will provide delivery of signed language (SL) content by allowing deaf signers to navigate and interact with technology completely in their first language (L1). I developed an SL-centric survey tool that enables users to create, distribute, and respond to surveys in SL. In my research, I conducted participatory and user-centric research with deaf participants to iteratively optimize user interface technology.

I present novel interaction design paradigms that create truly accessible technology for deaf SL-signers. This facilitates lifelong learning, enhances access to educational content such as STEM topics, improves career opportunities, and allows SL-based organization of SL corpora, assessments, dictionaries, learning and employment resources. Lastly, I contribute to the advancement of collaborative, human-centered methods and research by working toward creating guidelines for conducting inclusive computing research for and with the ASL community.

Bio: Shruti Mahajan is a Ph.D. candidate in the Human-Computer Interaction lab, Computer Science department at Worcester Polytechnic Institute (WPI), MA. Her research interests are in the field of Human-Computer Interaction (HCI). In her PhD dissertation work, she focused on designing and studying user interfaces in American Signed Languages for the Deaf community. Additionally, she also works with tangible user interfaces that include Augmented Reality and smart fabrics of the future. Her work has been published and recognized at top computing HCI conferences like the Conference on Human Factors in Computing Systems (CHI) and the Conference on Computers and Accessibility (ASSETS) conferences. Across all her research projects, she cares deeply about conducting user-centered and participatory research with careful consideration of how technology impacts society.

Besides her research, she actively engages in and initiates mentoring and outreach efforts to promote inclusivity and diversity in the field of computing. Outside work, she enjoys science fiction books/movies, and learning new languages!

CS Survival Skills: git
~~ Presented by ACM-W ~~

When: Thu, 4/6, 12:50-1:50pm
Where: Olin 106

Abstract:
Join ACM-W in a workshop about the git system. GIT is a system used to manage changes in software projects, and is especially useful where many people contribute to projects. Whether used with Github or Gitlab, git is foundational to software development.

CS Survival Skills: LaTeX
~~ Presented by ACM-W ~~

When: Thu, 4/13, 12:50-1:50pm
Where: Olin 106

Abstract:
Join ACM-W in a workshop about Latex. Latex is a system to create professional documents, especially for computer science and mathematics. Learn how to cite sources, create clean figures, write math formulas and more in Latex from a senior computer science student!

Non-Von Neumann Computing : Solving problems in the post Moore's Law era
Ajinkya Borle
University of Maryland Baltimore County

When: Thu, 5/18, 12:50-1:50pm
Where: Olin 107

Abstract:
Moore's law is a prediction where the number of transistors on a chip doubles every couple of years. Converting this law into computational power are machines built using the von-Neumann architecture. However, it is also predicted that Moore's law will soon fail to hold true in the coming years. This poses a problem in a world that has an ever-growing need of computational power (particularly for AI). One approach to tackle this problem is non-von Neumann computing: a broad term used to describe unconventional computing paradigms; from application specific circuits to exotic technologies like quantum computing.

In this talk, I will discuss how near-term quantum devices can help in solving practical problems in linear algebra. I will then be talking about two problems I am currently working on. Firstly, a hybrid quantum-classical heuristic for discrete optimization and secondly, exploring associative memory with quantum and quantum-inspired computing for K-nearest-neighbors (for storing, in-theory, an exponential amount of data on a polynomial sized memory).

Bio:
Ajinkya Borle is a Visiting Assistant Professor at the University of Maryland Baltimore County (UMBC). He has a Ph. D and Masters in computer science from the same institution with Dr. Samuel Lomonaco as his advisor. His research interests are primarily in the areas of leveraging quantum and other unconventional computing paradigms for solving computational problems in domains such as optimization, machine learning and cybersecurity.

Senior Project Poster Session and Celebration

When: Tue, 6/6, 3-4pm
Where: Crochet Lab (Wold 013)

Abstract:
CS seniors who are taking CSC 499 this term will present their senior projects.

Designing Voice Assistants Inclusively
Andrea Cuadra
Stanford University

When: Thu, 1/5, 12:50-1:50pm
Where: Olin 107

Abstract:
Not everyone’s needs receive sufficient attention when building the technology of the future. For example, voice assistants, like Alexa or Siri, have unfulfilled potential to make a difference in the lives of some marginalized groups, such as older adults. Voice assistants are a promising technology because users only need to speak to them to get a response. However, older adults’ needs and preferences are underrepresented in their design, which results in usability and usefulness challenges. In this talk, I will describe voice assistants, inclusive design, and some of the challenges older adults face with this technology. Then I will introduce two projects that explicitly account for older adults’ needs in the design of voice assistants. This talk aims to enrich students’ understanding of inclusive design, and empower them to carry out this type of work themselves.

Bio:
Andrea Cuadra is a postdoc at Stanford University. Her work in Human-Computer Interaction lies at the intersection of interaction design, inclusivity, and artificial intelligence. Her research focuses on representing the needs and preferences of those that are marginalized in mainstream interactive products and services. Andrea has a PhD in Information Science from Cornell University, an MSc in Information Science from Cornell University, an MSc in Engineering: Product Design from Stanford University, and a BSc in Engineering: Interaction Design from Olin College of Engineering.

Protecting Data In Use Via Trusted Hardware
Lauren Biernacki
University of Michigan

When: Tue, 1/10, 12:50-1:50pm
Where: Olin 107

Abstract:
Data breaches that penetrate web-facing servers and exfiltrate sensitive user data have become pervasive. The data we share online is vulnerable at three points: when it is sent to a server, when it is stored on the device, and when it is used during computation. While we have effective solutions for protecting data in the first two cases, we lack a tractable solution for the third. In this talk, I will discuss the challenges associated with protecting data during computation, including how modern processors can leak data through their typical operations. Then, I will present emerging privacy-preserving computation techniques that seek to protect data in use. Specifically, we will examine how hardware approaches can emerge as data privacy solutions that are dynamic, expressive, and performant.

Bio:
Lauren Biernacki is a Ph.D. candidate in Computer Science and Engineering (CSE) at the University of Michigan. Her research lies at the intersection of computer architecture and security and focuses on integrating confidentiality and integrity protections in modern processor architectures. Lauren is a passionate educator and has received awards for her teaching and service efforts, including recognition for developing a first-year graduate course to help students from marginalized backgrounds gain equal footing in the Michigan Ph.D. program. Lauren received her bachelor's degree in CSE from the University of Connecticut in 2017 and a master's degree in CSE from the University of Michigan in 2019.

The Use of Automatic Sign Recognition to Support Look-up Technologies for Sign Language Learners: A Human-Computer Interaction Perspective
Saad Hassan
Rochester Institute of Technology

When: Thu, 1/12, 12:50-1:50pm
Where: Olin 107

Abstract:
Despite some prior research and commercial systems, if someone who is viewing sign language sees an unfamiliar sign, it remains a difficult task to look up its meaning. There is no standard label a user can type to search for a sign, and formulating a query based on linguistic properties is challenging for students learning sign language. Advances in sign-language recognition technology have enabled the design of search systems for sign look-up in dictionaries, by allowing users to generate a query by submitting a video of themselves performing the word or by submitting a video segment containing the unknown sign. Despite the improvements in sign-recognition, it is unlikely that the user will only see one correct result, given: (a) the technical difficulty of recognizing linguistically complex 3D signs from 2D video; (b) poor lighting, camera motion, or cluttered backgrounds; or (c) learners who may struggle to accurately perform a sign that they are attempting to recall. In practice, it is much more likely that the users will need to browse a list of possible “matches" in a results list to find the word they seek. As artificial intelligence researchers are working on sign recognition technologies, Human-Computer Interaction (HCI) research is needed to investigate how to best structure the user experience for sign language learners, given the imperfect nature of sign recognition.

Methodologically, this research has involved interviews, prototyping, observational studies, and task-based experiments to investigate the user experience of sign-language search systems. I have uncovered challenges that ASL learners face with existing sign-language look-up technologies and proposed metrics to evaluate the performance of underlying sign-recognition models for these technologies. I have also iteratively implemented several prototype sign look-up systems, including “Hybrid Search System”, and “Sub-span Search Search”, to support task-based experimental studies with sign language users and investigated settings of key performance and design variables. The focus of my future research in this area is on designing technologies to support bi-directional ASL learning including both receptive and expressive skills.

Bio:
Saad Hassan is a PhD candidate in the Computing and Information Science program at Rochester Institute of Technology; his research focuses on Computing Accessibility, Human-computer Interaction, and Computational Linguistics. He is a graduate research assistant at the Center for Accessibility and Inclusion Research and Linguistic and Assistive Technologies Laboratory at RIT. He is also a visiting student researcher at Google AI. In the past, he was a research scientist intern with other teams at Google AI as well as Meta Reality Labs. Hassan is one of the recipients of the 2022 Duolingo dissertation research grant. His research has explored the (1) applications of sign recognition to support interactions with smart devices and designing sign language learning applications, (2) enhancements and improvements to captioning to convey underlying prosodic and emotive information, and (3) detection and mitigation of biases against individuals with disabilities in large language models. He has authored 12 peer-reviewed scientific journal articles, book chapters, and conference papers, and he has been nominated for the Best Paper Award at the ACM SIGACCESS Conference on Computers and Accessibility (ASSETS), the major computer science conference on assistive technology for people with disabilities. He has also served on the program committees for ASSETS and the ACM Conference on Human Factors in Computing Systems (CHI).

Scalable and Effective CS Education
Georgiana Haldeman
Colgate University

When: Tue, 1/17, 12:50-1:50pm
Where: Olin 107

Abstract:
As the global economy's demand for computer applications soars, the demand for well-trained computer science (CS) professionals is rising. This trend fuels a surge in CS undergraduate degree production, which, in turn, puts a strain on departmental resources. In addition, CS departments have to contend with poor retention, high dropout and failure rates, fragile learning, and bimodal outcomes. Thus, the need for scalable and effective teaching techniques in CS education is more significant than ever. For most of my talk, I will focus on how to make an existing scalable practice (that is, the automated grading of programming assignments) to be more effective by automatically providing formative feedback to students and a summary of the errors students make in their programs and their associated concepts and skills to instructors. Providing feedback and correcting misconceptions are remediating techniques. Some concepts and skills are more challenging for students, and remediating techniques are insufficient. We need effective techniques for teaching these complex concepts and skills. Toward the end of my talk, I will briefly discuss some preliminary results on effective teaching techniques. I will end my talk with current and future research interests.

Bio:
Georgiana Haldeman has received her Ph.D. and Bachelor of Science in computer science at Rutgers University. She has conducted research primarily in computer science education (CSed). In particular, in her dissertation, she leverages machine learning and code repair techniques to provide feedback to programming assignments automatically. Before CSed research, she conducted research in cryptography and distributed systems.

Cherokee Language Exercises: an open source project for language learning and preservation
Charlie McVicker
Union College

When: Thu, 2/9, 12:50-1:50pm
Where: Olin 107

Abstract:
41% of modern languages are endangered today. As communities organize to protect their languages, there is space for open-source software to fit into community-driven efforts to document, preserve, teach, and revitalize their languages. This talk will discuss an open-source project I run called Cherokee Language Exercises, which focuses on helping "at-large" Cherokee language learners who may not live in a community with a first-language speaker. There will be a focus on techniques for open-source collaboration and standards for the public licensing of data.

Bio:
Charlie McVicker ('24) is a student at Union College in the Gender, Sexuality, and Women's Studies program, a member of Cherokee Nation, a Cherokee language learner of a few years, and a lifelong hobbyist-linguist. S/he also works at DAILP, the Digital Archive of Indigenous Language Persistence, at Northeastern University in Boston as an audio annotator for their project Cherokees Writing the Keetoowah Way. S/he has 18 months of industry experience in full-stack development and has been helping people realize their software dreams professionally since high school.

CSC 498 Senior Project Poster Session

When: Thu, 2/16, 12:50-2:30pm
Where: Olin 107

Abstract:

CS seniors who are taking CSC 498 this term will present their senior project proposals.

~~ Co-sponsored with ACM-W ~~

Women in Computing Career Panel

When: Thu, 2/23, 5-6pm
Where: Becker Career Center

Abstract:
Wonder what to do with a Union degree? Want to break into Tech? FAANG, start-up or grad school? Join ACM-W to get advice from three alumnae: Larissa Umulinga (Software Engineer at Microsoft), Akriti Dhasmana (Data Engineer at Chartbeat), Sharifa Sahai (PhD Student at Harvard in Systems Biology)

~~ Co-sponsored with Visual Arts ~~

Art x Tech -- My career journey and design experiences
Soo Yun Kim
Cisco

When: Thu, 3/2, 12:50-1:50pm
Where: VART 204
Lunch will be served.

Abstract:
I will talk about my professional journey and two design case studies from IBM and Google.

Bio:
Soo Yun Kim is an Emmy Award-winning designer based in New York, focusing on user experience, visual design, and interaction design. She is a Principal Designer at Cisco, creating intranet services and tools to support Cisco employees worldwide. Previously, she was a Mobile Design Lead at Google and shaped the future of productivity and collaboration in Google Drive. As a Design Principal at IBM, she led multiple human-centered design projects, and as a founding member of Triller, she launched a social video-making application with over 250 million installs.

In the past, she has worked with various companies and clients, including Nickelodeon, MTV Networks, USA Today, The Weather Company, Chanel, Michael Kors, J.Crew, and Godiva. Her experiences have led her to present at a variety of design conferences, organizations, meetups, and companies, including UXPA, AHFE, Adobe, BKProductDesign, and Girls Who Code for STEM. She has also been a guest speaker at multiple universities, including Binghamton University, Fashion Institute of Technology, The New School, Pratt Institute, University of Connecticut, Cornell University, and the University of Florida.

Senior Project Poster Session and Celebration

When: Thu, 3/9, 12:50-2:30pm
Where: ISEC Atrium

Abstract:

CS seniors who are taking CSC 499 this term will present their (almost completed) senior projects.

Welcome back!

When: Thu, 9/15, 12:50-1:50pm
Where: Wold 225

We are kicking off the 2022-23 CS seminar series with an informal get-together. Whether you are new to the CS department or have been with us for a while, please join us to meet and catch up with CS professors and students.

Co-sponsored by ACM-W.

New students, meet the department!

When: Thu, 9/22, 12:50-1:50pm
Where: Wold Center 225 or on Zoom.
The Zoom link will be sent out by email. If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu.
Note: There will be a grab-and-go lunch.

ACM-W invites all first-year students and others who are new to the CS department to an orientation event. Learn about ACM-W, meet the CS professors, hear about the CS curriculum, and ask any questions you might have about studying CS at Union.

Co-sponsored by the Modern Languages Department.

The Human-Computer Tradeoff in Language Learning Technology
Kaitlyn Tagarelli
Mango Languages

When: Thu, 10/6, 12:50-1:50pm
Where: Karp 005

Abstract:
Can a computer teach you how to learn a new language? Yes and no. With the recent rise of educational technology and incredible strides in natural language processing, it's important to consider how much is possible with computers alone, and where humans — particularly teachers and education experts — need to step in. This talk will discuss the role of computers and humans in the development of educational technology, and how they interact with and complement each other, with a focus on language learning technology.

Bio:
Kaitlyn Tagarelli (‘07) is a Linguist and the Head of Research at Mango Languages, an online language learning company. After majoring in French and Neuroscience at Union College, Kaitlyn earned a PhD in Linguistics from Georgetown University in Washington, D.C., specializing in how the mind and brain learn languages. She pursued postdoctoral training in Psychology and Neuroscience at Dalhousie University in Halifax, Canada. She has published and presented her research in Second Language Acquisition, Psychology, and Neuroscience journals and conferences, and has researched the development and efficacy of several different language learning softwares. Kaitlyn has taught English to learners of all ages in France and the US, as well as courses in psychology and linguistics at Georgetown and SUNY New Paltz. She currently hosts Mango’s "Science Behind Language Learning" series.

CSC 498 Senior Project Poster Session

When: Thu, 10/27, 12:50-2:30pm
Where: ISEC atrium

Abstract:

CS seniors who are taking CSC 498 this term will present their senior project proposals.

Presenters: Blake Luther, Leah Piscitelli, Celia Lane, Daniella Massa, Daniel Tyebkhan, Jordan An, Leonardo Ferrisi, Talha Mushtaq, Jing Chen, Zachary Dubinsky, Kyle Hannibal, Son Nguyen, Logan Walker, Haley Kresch, Nikos Perdikogiannis, Fiona Shyne

CS Research Fair

When: Thu, 11/10, 12:50-1:50pm
Where: Wold Center 225

Abstract:
Are you curious about getting involved in research in the CS department? Come to this seminar to find out about the different ways in which students can get involved in CS research, hear about research projects the CS professors are currently working on, and get a chance to talk to the CS professors about research opportunities.

Parallel Sorting Using Discrete-Time Neural Network
Pavlo Tymoshchuk
Silesian University of Technology

When: Tue, 4/5, 12:50-1:50pm
Where: Olin 107

Abstract:
A discrete-time parallel sorting neural network is presented. The network is described by set of recurrence equations and by output equations. Corresponding block-diagram of the network is given. The network has high operation speed, arbitrary finite high resolution of input data, and it can process unknown inputs located in any known finite range. The network is characterized by moderate complexity. Computer simulations illustrating performance of the network are provided.

Bio:
Pavlo Tymoshchuk is the Professor of the Department of Electrical Engineering and Computer Science at Silesian University of Technology, Gliwice, Poland. Earlier employers were: St. Mary’s College of Maryland, MD; Institute of Computer Sciences and Information Technologies at L’viv Polytechnic National University, L’viv, Ukraine; State University of Telecommunications, L’viv, Ukraine; Lodz University, Lodz, Poland; and University of Computer Sciences and Skills, Lodz, Poland. His education includes: D.Sc., Telecommunications Engineering - Odesa National Academy of Communication, Odesa, Ukraine; Ph.D., Electrical Engineering - L’viv Polytechnic National University; M.S., Electrical Engineering (same institution). Key research contributions are: continuous-time and discrete-time neural networks; parallel sorting; parallel rank-order filtering; telecommunication systems; and nonlinear dynamic systems.

Heuristics & Hedonic Games
TJ Schlueter
Kyushu University

When: Thu, 4/7, 12:50-1:50pm
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
We propose Anchored Team Formation Games (ATFGs), a new class of hedonic game inspired by tabletop role playing games such as Dungeons & Dragons. We begin with a general introduction to hedonic games, then introduce complete and heuristic solvers. In more detail, we introduce Anchored Team Formation Games. We then discuss the implementation of one complete solver and one heuristic solver and the experimental results from both algorithms.

Bio:
TJ Schlueter is a postdoctoral researcher in the Multi-Agent Laboratory of the Information Science and Electrical Engineering Department at Kyushu University working with Prof. Makoto Yokoo. Their research interests lie in computational social choice, a research area at the intersection of artificial intelligence and theoretical computer science, with a particular focus on hedonic coalition formation games.

Machine Learning Product Development
Akshay Kashyap
Peloton

When: Thu, 4/14, 12:50-1:50pm
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
An overview of building products and features that use Machine Learning at their core. We'll go through how ML Engineering differs from both ML in academia and traditional software engineering. And we'll discuss design patterns and technologies that are commonly used in developing these systems, as well as user-facing considerations for ensuring success. We'll end with what a career in ML might look like and how to prepare for it.

Bio:
Akshay Kashyap '18 is a Staff Machine Learning Engineer at Peloton, where he has worked for almost 4 years now and developed some of Peloton's earliest ML technology. He recently led the Computer Vision team and helped launch their first AI-enabled product.

A Multi-tool for your Quantum Computing Toolbox
Shelby Kimmel
Middlebury College

When: Thu, 5/19, 12:50-1:50pm
Where: Karp 105 [<<< note location]

Abstract:
Quantum computers have the potential to transform the way we solve many types of problems. However, it can be challenging to design quantum algorithms. In this talk, I will take the quantum out of quantum algorithm design by showing how to create and characterize quantum algorithms for a range of problems, from Boolean formula evaluation to cycle detection, by constructing and analyzing certain graphs.

Bio:
Shelby Kimmel is an Assistant Professor of Computer Science at Middlebury College. Previously, she was a Hartree Postdoctoral Fellow at the University of Maryland in QuICS - the Joint Center for Quantum Information and Computer Science. She earned a PhD in physics from MIT and a BA in astrophysics from Williams College.

Technical Interviews: Structure and How to Get Prepared
Erdem Alparslan
Principal Software Engineer and Software Architect for eHealth

When: Thu, 1/27, 12:45-1:45pm
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
I will talk about the structure of technical interviews for Software Engineers. I will explain the hiring process of well-known Bay Area tech companies and how the IT Society may help (theitsociety.org, a non-profit organization helping people to succeed in tech).

The talk will also cover the steps for a recent graduate to get prepared for technical interviews with many resources and important hints. The presentation will end with a Q&A session.

Bio:
Erdem Alparslan received his BS degree in Computer Science from Galatasaray University, Istanbul, Turkey, in 2006 and his MS degree in Artificial Intelligence from Bahcesehir University, Istanbul, Turkey, in 2010. Erdem Alparslan is the Principal Software Engineer at eHealth Inc. Santa Clara, CA. He also worked at various companies in California and Turkey. He worked as a Staff Software Engineer at Vida Health Inc. San Francisco, as the Director of Engineering at Sano Tech Inc. Sunnyvale, as a Staff Software Engineer at Silvercloud Technologies, Inc. Irvine, as a Software Engineer at TUBITAK, Cyber Security Institute Kocaeli, Turkey, and as a Software Engineer at TURKCELL, Istanbul, Turkey. He served as an adjunct instructor at Bahcesehir University, Istanbul, Turkey as well. He was the President and the Founder of the Poetry and Writing Club And a member of the Computer Engineering Club at Galatasaray University. His experience in research and development spans various domains including Cybersecurity, System Design, Data Mining, Natural Language Processing, Databases and Data Warehouses, Mobile and Web Applications, Improvement of the Customer Engagement on Health Coaching Programs by Designing Profile Based Content.

Embedding Ethics in Computer Science Education
Trystan S. Goetze
Harvard

When: Thu, 2/10, 12:45-1:45pm
Where: Karp Hall 005

Abstract:
How can computer science education prepare students to reason about the social and ethical impacts of computing and information technology? This talk will discuss three approaches to integrating ethics education into computer science curricula: required computer ethics courses, the modular Embedded EthiCS approach created at Harvard, and fully transdisciplinary curriculum design. Successes and challenges of each approach will be highlighted.

Bio:
Trystan S. Goetze is a Postdoctoral Fellow of Embedded EthiCS at Harvard University. Their research concentrates on moral and epistemic responsibility, epistemic injustice, cyberethics, and the ethics of the tech industry. At Harvard, they work in the interdisciplinary Embedded EthiCS teaching lab, which designs, workshops, and delivers ethics modules that are integrated into computer science courses.

Designing to Support Volunteers in Online Communities
Sneha Narayan
Carleton College

When: Thu, 2/17, 12:45-1:45pm
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
With the growing use of (and reliance on) computers and cellphones for communication, people's interactions with one another are increasingly being mediated through technological platforms. Entire communities come together online, and the features of the platforms they use can affect what they do, and how they work together. In particular, technological infrastructure that supports distributed, collaborative work has played a crucial role in the success of large-scale, volunteer-run projects such as Wikipedia, and fan wikis. In this talk, I will discuss how the design of wiki-based platforms has shaped the experience of users who wish to contribute to such projects, and some ways in which these collaborative communities have attempted to better design the experience for a variety of users, particularly newcomers. I will then present research that measures the impact of these design interventions through user surveys and field experiments, and explain how this work informs our understanding of online collaborative communities.

Bio:
Sneha Narayan is part of the faculty in the Computer Science department at Carleton College. Before that she received a PhD in the Technology and Social Behavior program at Northwestern University. Her research is focused on understanding how technical systems foster participation and collaboration in decentralized volunteer communities such as Wikipedia, and fan wikis. She is also a member of the Community Data Science Collective. Prior to beginning her doctoral work, Sneha received an MA in Sociology and Social Anthropology from Central European University, and a BA in Mathematics from Oberlin College.

Sensing the Cyberspace Using Network Science and Explainable AI
M. Abdullah Canbaz
Indiana University Kokomo

When: Thu, 3/3, 12:45-1:45pm
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Data Science has become essential to industry, academia, government, and individuals, launched primarily by massive data currents that can influence how humans make decisions. Increasing amounts of data from mobile devices, scientific instruments, computer simulations, home appliances, wearable technologies, and other technologies are constantly released onto the web, with or without the consent of the data owners. Unbeknownst to end users, that flood of their data is mined by third parties for valuable information. Hence, it all comes down to understanding the infrastructure of the Internet. In this talk, Dr. Canbaz presents his work towards sensing cyberspace with the magnifying glasses of Network Science and Explainable AI by (i) investigating the network topology, the security and privacy investigation on household smart devices for healthcare and emergency preparedness; and (ii) understanding and enhancing the models and algorithms to eliminate the polarization, sensational, hateful, divisive, and provocative content in the context of online media.

Bio:
M. Abdullah Canbaz is an Assistant Professor of Computer Science at the School of Sciences at Indiana University Kokomo. He is a data scientist with experience designing, implementing, launching, and maintaining highly scalable products while leading and mentoring fellow engineers/problem solvers.

Welcome (back) CS get-together!

When: Thu, 9/16, 12:45-1:45pm
Where: the tent by Memorial Chapel

Abstract:
We are kicking off the 2021-22 CS seminar series with an informal get-together. Whether you are new to the CS department or not, please join us to meet and catch up with CS professors and students.

CS Research Fair

When: Thu, 9/23, 12:45-1:45pm
Where: the tent by Memorial Chapel

Abstract:
Are you curious about getting involved in research in the CS department? Come to this seminar to find out about the different ways in which students can get involved in CS research, hear about research projects the CS professors are currently working on, and get a chance to talk to the CS professors about research opportunities.

GraphMore: A Software Library for Managing Graph Data
Christine Reilly
Skidmore College

When: Tue, 4/28, 12:45-1:45pm
Where: Karp 005

Abstract:
Graph data is found in a wide range of applications including social networks, scientific simulations, and business transactions. Existing graph data management approaches focus on huge-scale data and are not appropriate for or accessible to the vast majority of users and organizations who want to collect and analyze graph data. Our goal is to create a software architecture that enables academic scientists and other users with similar skills to easily store and interact with graph data. This talk describes our preliminary work on this project. We designed GraphMore: a data model and application programming interface that serves as a layer between the storage system and the application. Our initial experiments demonstrate that GraphMore has reasonable performance, and that it is feasible to implement GraphMore on different storage systems. This talk will conclude with an overview of how our future work on this project will create user-friendly interfaces for interacting with graph data.

Bio:
Dr. Christine Reilly is an Assistant Professor in the Computer Science Department and the Charles Lubin Family Chair for Women in Science at Skidmore College. Her research interests include database systems, distributed systems, and computer science education. In her free time she enjoys hiking the Adirondack high peaks and gardening.

Grad School Panel

When: Thu, 10/7, 4-5pm EDT
Where: Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:

Interested in applying to grad schools? Want to talk to Union graduates about their perspectives on grad school?

Featuring:

• Kyle Doney (PhD Student, CS, UMass Amherst)
• Tim Kuehn (Software Engineer at Google; M.S., CS, Carnegie Mellon University)
• Ryan Muther (PhD Student, CS, Northeastern University)
• Elizabeth Ricci (PhD Student, CS, Cornell University)
• Sharifa Sahai (PhD Student, Systems Biology, Harvard)

This will be a round-table discussion, so please come prepared with questions!

Motion Planning Algorithms and Applications: An Algebraic Topology Perspective
Chinwe Ekenna
University at Albany

When: Thu, 10/14, 12:45-1:45pm EDT
Where: moved to Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract: Motion planning techniques have progressed to handle high-dimensional and complicated settings. However, comprehending the approximations used in producing possible robot configurations and how much sampling is required to assure that a path is generated if one exists remains a challenge. My talk will cover improvements in the topological representation of planning spaces for robots, as well as the topological tools I created to help explore, measure, and provide an upper-bound . In addition application to trajectory planning for UAVs, with possible applications in surveying and monitoring of environments will be discussed. Finally, I'll discuss about my computational biology extension using these topology tools and applied towards predicting protein-protein interactions (PPI) and gene expression analyses.

Bio: Chinwe Ekenna is an Assistant Professor in the Department of Computer Science at the University at Albany, State University of New York, and the Director of the Robotics, Algorithm and Computable Systems (RACS) Laboratory. Chinwe's research centers on intelligent motion planning applied to robotics and proteins. She has explored intelligent adaptation of robotic motion planning to improve planning time and topological data analysis methods to capture important features of robot planning spaces. Her research interest includes Machine learning, computational geometry, and computational biology. Chinwe is a recipient of the NSF-CRII award on "Topology aware configuration spaces" and has gone on to publish several works in ICRA and IROS on this subject. She is currently an Associate Editor for IEEE-RAL and has served on several program committees for the ICRA, IROS and WAFR conferences. She is a committee member of the IEEE RAS Committee to Explore Synergies in Automation and Robotics (CESAR), which comprises top researchers in the field of automation and robotics.

Femtech: Friend or Foe?
Amanda Menking

Joint seminar with Gender, Sexuality, and Women's Studies.

When: Thu, 10/21, 12:45-1:45pm EDT
Where: on Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
From mobile menstrual tracking apps to wearable breast pumps to smart vibrators with biofeedback sensors, “femtech" describes a range of technology-centric products and services ostensibly designed and produced for women. But does this growing sector really signal progress and gender equality? Or does it further reify gender binaries and allow investors (who are mostly men) to profit from a new niche? Drawing from two studies about menstrual tracking apps and conversations with femtech entrepreneurs and activists, Dr. Menking asks whether femtech is friend or foe and what kinds of roles software engineers might play in the answers.

Bio:
Amanda Menking is a researcher with interests at the intersections of social computing systems (like Wikipedia), knowledge production, and gender. Her dissertation focused on how women's health information on the English language Wikipedia is created, curated, and neglected. Prior to pursuing her doctorate, she received a BA in Humanities from Trinity Western University and an MA in English from Hardin-Simmons University and then taught high school and college-level English and Literature courses. She's also a veteran of the Seattle tech scene, having worked for two start-ups and as a vendor at Microsoft. Recently, she's been working in the area of “femtech.”

Feeling, smelling, and seeing the wind: A multi-sensory story of 60,000 fruit flies' journeys across the barren desert.
Floris Van Breugel
University of Nevada, Reno

Joint seminar with Biology.

When: Thu, 11/11, 12:45-1:45pm EDT
Where: on Zoom. The Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
How are flies so effective at finding abandoned bananas, glasses of wine, or potential mates? A large part of their success depends on tracking odor plumes over great distances, which depends on two key sensory outputs: odor encounters, and an estimate of the ambient wind direction. To understand their behavior across a range of spatial scales I will describe recent release and recapture experiments from a dry lake bed in Southern California, which show that tiny fruit flies are capable of navigating over scales of more than a kilometer by using a simple set of feedback control laws. To better understand the olfactory experience of these flies, and what information they might extract from it, we will then take a look at recent data collected using a mobile chemical sensor on a similar dry lake bed. Ultimately, a significant part of following an odor plume is determining what the ambient wind direction is. The final portion of this talk will integrate recent discoveries in neuroscience with a nonlinear control theoretic framework to understand how insects might solve this particular task.

Bio:
Floris van Breugel earned his PhD from Caltech in 2014 in Control and Dynamical Systems under the support of NSF and Hertz graduate fellowships while working with Michael H Dickinson on insect flight biomechanics, control, and multi-sensory integration. He subsequently went to the University of Washington to work with Jeff Riffell and J Nathan Kutz as a Postdoc to work on insect search strategies and machine learning approaches to system identification of complex systems, supported by a Sackler Fellowship in Biophysics and a Moore-Sloan-WRF Fellowship in DataScience. Floris joined the Dept. of Mechanical Engineering at UNR in January 2019, and received a Sloan Fellowship in Neuroscience in 2020, and Air Force Young Investigator Award in 2021.

Deep Ranking for Style-Aware Product Recommendation in Interior Design
Esra Ataer-Cansızoğlu
Facebook

When: Thu, 4/8, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.

Bio:
Esra Cansizoglu is a machine learning engineer at Facebook working on geospatial image analysis for mapping. She received her PhD in Electrical Engineering from Northeastern University and her MS in Computer Science from Boston University. Her research interests are in Computer Vision and Machine Learning. She co-authored several patents and publications in peer-reviewed conferences and journals. Her experience in research and development spans various domains including medical image processing, robotics and recommendation systems.

The Next Generation Sequencing Era: Applications, Challenges and Threats
Huseyin Demirci
University of Luxembourg

When: Thu, 4/15, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
In this talk we will briefly introduce the genomics sequencing era. With the availability of genome sequencing, new applications for diagnosis and therapy are now possible. We will give examples from health, biotechnology and food industries as applications of next generation sequencing. Genome sequencing is inevitably becoming a part of our lives. We will present our results from a 1.100 exome project, which helped to identify many disease-causing mutations for rare diseases and diagnose patients. We will point out software tools assisting with this type of research. We will finally mention the privacy issue of the genomics data which is becoming an increasingly important topic.

Bio:
Huseyin Demirci received his PhD degree from Marmara University, Turkey in 2004 with the thesis "Stochastic Analysis of Block Ciphers". From 1998 to 2016 he worked as a senior researcher at the National Scientific and Technological Research Center of Turkey. He worked on cryptology and cryptanalysis of symmetric ciphers including well known ciphers AES and RC4. He participated in national and EU projects on wireless and lightweight security. Between 2013-2016, he contributed to the foundation of first Genome Sequencing and Bioinformatics analysis Center of Turkey. Between 2016-2019 he worked as a post-doctoral researcher at the University of Minho, Portugal. Since 2020, he is a research associate at the University of Luxembourg. His research interests include genome sequence analysis, information security and genomics privacy.

How to be(come) a software engineer or data scientist
Can Yilmaz
Amazon

When: Thu, 4/22, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Computer Science is a major where you learn the fundamentals of computing. Students sometimes can have hard times figuring out how to translate what they learned into skills that are desired in the current job market or what skills are the most important for their dream job. Computer science degrees can translate into many different job titles: software developer, data scientist, technical program/product manager etc. Although one can think almost all these titles are similar and about coding, they are quite different and coding is not a fundamental skill for some. In this talk, I will discuss software engineer and data scientist titles: what companies pay them to do, fundamental skills, salary expectations and most importantly, strategies to use in the job market to become one.

Bio:
Can is Software Development Engineer working at Amazon. He worked at Microsoft as a Data Scientist before. At Microsoft, Can helped bring Cortana to Outlook as a virtual assistant to help users with their daily email tasks. He also involved in the efforts to have Cortana and Alexa to talk to each other to give users a complete voice assistant experience where they can control their smart home and computers at once. At Amazon he is a part of a small team that creates a confidential platform that will be a breakthrough for AI oriented data management tools. Can is self-taught engineer and data scientist. He has a master degree in Computer Science and bachelors in Mathematics.

A Probabilistic Perspective on Deep Neural Networks
Patrick McClure
National Institute of Mental Health

When: Thu, 4/29, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Deep neural networks (DNNs) are machine learning models that are being applied to increasingly diverse areas. Many of these models are used to make decisions that have substantial impact, such as those related to disease diagnosis from medical images or autonomous driving. However, can their predictions be trusted? A key component of the trustworthiness of a prediction model is proper estimation of prediction uncertainty. Improving DNN uncertainty estimation also leads to better performance for a variety of problems, including continual learning, distributed learning, and anomaly detection. Bayesian probability tools offer a principled solution for improving DNN uncertainty estimation by learning distributions of DNNs (i.e. Bayesian DNNs). In this talk, we will briefly introduce Bayesian DNNs and demonstrate their practical usefulness using neuroimaging examples.

Bio:
Dr. Patrick McClure is a Machine Learning Project Lead at the National Institute of Mental Health (NIMH). He received his PhD in Computational Neuroscience from the University of Cambridge under the supervision of Dr. Nikolaus Kriegeskorte. Before attending Cambridge, he received his MS in Computer Science and his BS in Bioengineering from the University of Louisville. Patrick’s research has primarily focused on developing deep learning and probabilistic modeling tools and applying them to the areas of computational neuroscience, medical image analysis, and computer vision.

Explaining AI for Decision-Making
Iris Howley
Williams College

When: Thu, 5/6, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Users of artificial intelligence (AI) decision-making systems rely on algorithms to help them make day-to-day decisions, but may not understand the potential flaws and biases of these algorithms, even if the algorithm is open and not blackbox. This talk provides an overview of explainable AI followed by a discussion of how our research group expands on current post-hoc methods for explainable AI. We use a method from the learning sciences and human-computer interaction communities, Cognitive Task Analysis (CTA), to identify what knowledge components comprise expert understanding of an algorithm. We apply CTA to Bayesian Knowledge Tracing, an AI algorithm commonly used in learning analytics systems, so that more systematic, rigorous post-hoc explanations for AI algorithms can be developed and evaluated.

Bio:
Iris Howley is a human-computer interaction, artificial intelligence, and learning science researcher focusing on enabling users to overcome obstacles to effective decision-making and community participation through the design of technologies. She received her B.S. in Computer Science from Drexel University and her M.S. and Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Prior to becoming an Assistant Professor of Computer Science at Williams College in 2017, she was a postdoctoral research fellow with the LINK Research Lab at the University of Texas Arlington and the Lytics Lab at Stanford University. At the moment, she is researching the design and deployment of interactive explainables for users of algorithmic systems in educational contexts with the support of an NSF grant.

Towards a Realistic Understanding Of Personal Visualization
Poorna Talkad Sukumar
Notre Dame

When: Tue, 5/11, 1:20-2:20pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Personal data comprises any data that is relevant to one's personal life, such as, health and fitness data and social media interactions. Personal visualizations refer to interactive visual representations of personal data and present a separate class of visualizations with distinctive goals and usage characteristics. They aim to support self-reflection, are consumed in less formal contexts and often on mobile devices, and by people with different motivations, interests, and resources. However, studying personal visualizations can be challenging and traditional evaluation methods may not be suitable for assessing their distinctive goals. To study personal visualizations, it is important to study people interacting with their own data and in realistic settings.

In this talk, I will present two user studies, employing different methods, conducted with a web-based interface presenting visualizations of the personal data gathered as part of a large-scale, longitudinal sensing study. The first study characterizes users' exploratory behaviors on the interface by analyzing logged interactions (mouse hovers and clicks) from 369 participants as they each explored their own data. The second study employs a think-aloud method to identify the personal insights gained by the participants, contextual information recalled by the participants to interpret their data, and usability issues with the interface. Coalescing the findings from the two studies, I will discuss possible design directions for improving personal visualizations and expanding their impact.

Bio:
Poorna Talkad Sukumar is a Ph.D. Candidate in the Department of Computer Science and Engineering at the University of Notre Dame. Her main areas of research are Human-Computer Interaction (HCI) and Information Visualization. She has worked on a number of diverse projects, including the mitigation of cognitive biases of admissions reviewers using visualization tools, exploration of research methods suitable for studying personal visualizations, improving equity in STEM classrooms using visualizations, and understanding and facilitating team behavior using unobtrusive sensing technologies. Her research focuses on emerging topics in HCI and visualization having broader impacts and she has published in top venues, including ACM CHI, EuroVis, and CSCW. She is generally interested in designing and building interactive visualization systems and well-versed in applying various human-centered methods to evaluate these systems.
She also has a Master’s degree in Mobile and Ubiquitous Computing from Lancaster University, UK.

Opportunities and barriers to facilitating evidence-based discussion about civic issues
Brian McInnis
UC San Diego

When: Thu, 5/13, 1:20-2:20pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
News websites have the potential to facilitate large scale evidence-based discussion about civic issues, like climate change, but the online discussions are often overrun by toxicity and misinformation. Even when people are not shouting at each other, online discussions about civic issues rarely focus on evidence, such as data and visualizations. In this talk, I will share our analysis of how people comment about climate change data in the discussion threads at three news websites (i.e., Breitbart News, the Guardian, the New York Times). The findings illustrate that data-centered talk, while rare, can provide valuable insights that could help center an online discussion around evidence—collection, analysis, and visual representation—to engage with an article's narrative. My research explores how techniques from crowdsourcing offer a potential way to promote and encourage evidence-based discussion. I will draw examples from my own work studying news websites, collaboration platforms, and in-the-wild civic initiatives to review critical system design opportunities and barriers.

Bio:
Dr. Brian McInnis is a postdoctoral scholar at UC San Diego and a member of the Design Lab. Brian explores how to help people collaboratively build insights around policy concerns. Brian earned his PhD in Information Science from Cornell University in 2019 where his thesis investigated how people build insights around policy concern through a series of studies that involved crowd workers in online discussions related to the AMT participation agreement. Prior to joining Cornell, Brian worked at the RAND Corporation, where he studied a range of public policy issues—from the design of youth summer learning programs to predictive policing techniques. Brian earned his Masters of Public Policy from Vanderbilt University's Peabody College of Education as well as a dual Bachelors in Economics and History from the University of California at Davis.

Recommender System Models for Online Retail and Subscription Platforms
Uzma Mushtaque
RPI

When: Thu, 5/17, 1:50-2:50pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Recommender Systems are used on all online platforms to provide personalized assortments to users. Well-known consideration-set theory from cognitive science identified information overload impacting user choice. In this research, a new family of discrete choice models called MNL-CE models is devised to establish a direct connection between size of the assortment offered and the probability of user selection or rejection. When used as inputs to assortment optimization problem, these models present an optimal assortment that should be offered to minimize no-choice (rejection) probability for a given user. The notion of incorporating diversity in assortments is also addressed by these models. These models find direct application in online retail (Amazon) and/or subscription platforms (Netflix) where customers are inundated with large assortments in the form of personalized recommendations. Algorithms devised to solve for optimal assortments are analyzed in-depth. Data collection and Machine Learning (ML) approaches to parameter estimation is an existing open research question which is discussed in detail. The models developed have their roots in random-utility theory, therefore there are three logical extensions of this research: 1) To devise explainable models for Recommender Systems in specific and for other applications in general, 2) Discrete choice models are naturally represented as Neural Networks, therefore an improved overarching technique for estimation can be developed using AI 3) Improve peer-to-peer systems by utilizing these models.

Bio:
Dr. Uzma Mushtaque is a Lecturer at the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). She has a PhD in Decision Sciences and Engineering Systems from RPI along with a Postdoc experience on developing predictive choice models for various online applications. Her research interests include recommender systems, machine learning, AI, statistical learning, optimization, data analytics and operations research. Additionally, she has a master’s from Penn State's Business School and 3 years of professional experience in the information technology (IT) field working as a consultant in Oracle ERP implementation projects. One of her most recent industry experiences includes working as a Senior Data Scientist at one of the top consulting firms on credit and risk analytics. Her PhD thesis and published work includes an in-depth, iterative, and methodical exploration of some of the Big-Datasets available for consumer preference ratings. Her future research is aimed at estimating choice models using machine learning approaches by collecting relevant data. Currently she is also working on creating XAI (Explainable AI) models for recommender systems and models for personalization in education. Dr. Mushtaque's background in Engineering and Computer Science, work experience in Data Science consulting, a master's in supply chain management and her current research motivates the inter-disciplinary nature of her teaching and research interests.

Career Panel
Featuring Laura Marlin '19, Akshay Kashyap '18, Julian Jocque '15, Eric Rizzi '09

When: Thu, 1/21, 5-6pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Come to this informal panel session to learn about careers in computer science. Four alums will be there to talk about their career paths in CS, the job hunting process and how to prepare for it, and the transition from college to work. Bring your questions!

Machine Learning in the Open World
Murat Dundar
Purdue University

When: Thu, 2/4, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Machine learning algorithms are now routinely used to build predictive models from data in wide range of applications. However, these algorithms are developed under highly controlled settings with a closed world assumption that do not reflect the dynamic nature of the open world surrounding us. For example object categories in the real world exhibits power law property; hence, in a randomly sampled data set collected for training purposes at a given time, no training examples are expected to be available for most of the object categories. A machine learning algorithm trained only with classes observed in the training set will misclassify all samples of unobserved classes into observed ones. This outcome creates a two-sided problem. First, the unknown class, which could potentially represent a significant abnormality such as a residual population of cancer cells in bone marrow or an emerging pathogenic bacteria strain present in food products, cannot be appropriately detected leading to potentially catastrophic consequences. Second, even if the unknown classes do not have any significance, misclassifying irrelevant samples into classes of practical importance raises doubts about the overall stability of the machine-learning systems, and will make these models highly vulnerable to adversarial attacks as has recently been the case with some well-established deep learning models. This talk will discuss machine learning algorithms that produce self-adjusting models that can accommodate new classes observed in data in offline as well as online learning scenarios. We categorize unobserved classes into two as known unknowns and unknown unknowns and discuss open-world machine learning in the context of zero-shot learning and open-set classification.

Bio:
Murat Dundar is an Associate Professor of Computer and Information Science at Indiana University Purdue University – Indianapolis. He received his BS degree from Bogazici University, Istanbul, Turkey, in 1997 and MS and PhD degrees from Purdue University, West Lafayette, IN, USA, in 1999 and 2003 respectively, all in Electrical Engineering. Between 2003 and 2008 He was with the CAD and Knowledge Solutions group of Siemens Health. At Siemens Health, he was involved in the development of a broad spectrum of computer aided diagnosis/detection applications including FDA-approved Lung and Colon CAD products. He has joined IUPUI Computer and Information Science Department as a tenure-track assistant professor in 2008, where he became an associate professor in 2014. His area of expertise is in machine learning with a special focus on open-world machine learning, where the goal is to replace the traditional brute-force approach of fitting a fixed model onto the data with more flexible models that can account for the non-stationary nature of real-world machine learning problems. His research is mainly driven by real-world problems in computer aided diagnosis/detection, hyper-spectral data analysis and remote sensing, and information technology. He was the recipient of the Data Mining Practice Price by ACM SIGKDD in 2009, Best Paper Award by International Association of Pattern Recognition in 2010, and Early Faculty CAREER Award by NSF in 2013. His research has been funded by NSF, NIH, and NASA.

The Network Structure of Online Amplification
Ryan Gallagher
Northeastern University

When: Thu, 2/18, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Social media relies on amplification. It is at the heart of how marginalized communities voice injustices, how elected officials communicate public health guidance, and how misinformation proliferates through vulnerable populations. Each instance of amplification is a networked process emerging from many separate interpersonal interactions around an event, news story, or hashtag. Using the case study of #MeToo, I demonstrate how interactions between those who disclosed early in the hashtag campaign likely reduced the stigma of disclosure, allowing for further amplification of the hashtag. As it continued to be used, these disclosures transcended any single disclosure and coalesced into a larger network, composed of the core participants whose experiences of sexual violence were amplified by a larger periphery of bystanders and other survivors. I argue that this core-periphery structure is a fundamental signature of online amplification, and propose statistical models for how it can be identified empirically in networks. Finally, by applying these models back to the #MeToo case study, I demonstrate their effect on our ability to measure the reach of a hashtag activism event, highlighting the importance of accounting for the core-periphery network structure of amplification.

Bio:
Ryan Gallagher is a network science PhD candidate at Northeastern University. As a member of the Communication Media and Marginalization (CoMM) Lab at Northeastern's Network Science Institute, he studies how individuals use online communication networks to amplify their voices, and how that amplification resonates through online media ecologies. To do so, his research makes advances in network science and text-as-data methodology to develop new approaches for measuring the complexities of polarization, misinformation, and the networked public sphere. Ryan interned with Facebook Core Data Science and their Political Organizations & Society team, where he developed methods for identifying inauthentic coordinated information operations, and spent two summers as a visiting research assistant at the University of Southern California's Information Sciences Institute. He holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in mathematics from the University of Connecticut.

Recognizing and Responding to Bias and Microaggressions
Colleen Lewis
University of Illinois Urbana-Champaign

When: Thu, 10/22, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Have you ever frozen - not knowing what to say - when you heard a comment or question about diversity in CS? We will play a research-based game to practice recognizing and responding to bias. The game invites players to respond to challenging scenarios related to subtle and not-so-subtle bias. For example, what might you say if your colleague said, "Women just don't like CS" or "There are so few Black and Latinx students in CS, it is a lot easier for them to get CS jobs." We're all responsible for learning to recognize and respond to bias -- and the game can provide opportunities to practice! We'll use breakout rooms to have players discuss how they would respond and you can access a copy of the game at www.csteachingtips.org/cards.

Bio:
Colleen Lewis is an Assistant Professor of computer science (CS) at the University of Illinois Urbana-Champaign. Lewis was previously the McGregor-Girand Associate Professor of CS at Harvey Mudd College. At the University of California, Berkeley, Lewis completed a PhD in science and mathematics education, an MS in computer science, and a BS in electrical engineering and computer science. Her research seeks to identify and remove barriers to CS learning and understand and optimize CS learning. Lewis curates CSTeachingTips.org, a NSF-sponsored project for disseminating effective CS teaching practices. Lewis has received the NCWIT.org Undergraduate Mentoring Award and the AnitaB.org Emerging Leader Award for her efforts to broaden participation in computing.

Social Robotics A New Generation of Robots Built for People
Kerstin Dautenhahn
University of Waterloo

When: Mon, 3/8, 5pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Compared to how people interact with computer and other electronic devices, something 'special' happens when humans come in contact and interact with physical robots. Human-Robot Interaction is a growing area of research where researchers try to understand how to design robotic systems that can interact with people. The talk will discuss the recent development of companion robots that can provide useful assistance to users in a socially acceptable manner. Such research focusses on the one hand on fundamental issues of human-robot interaction, learning and adaptation, but on the other hand is deeply inspired by concrete application areas and their requirements. The first part of the talk will introduce concepts and methodologies of developing social robots and discuss challenges.

In the second part of the talk I will illustrate social robotics research in a particular application area that the speaker has studied since 2004, as part of the European projects Cogniron, LIREC and ACCOMPANY. Here the goal is to develop social robots as companions who can provide useful assistance (cognitive, physical and social) to people living in their own homes. One group of target users are elderly people who might benefit from a robot in their home in order to help them live independently. Such assistance is hoped to delay the move into a special care home, which is often associated with a loss of autonomy and a decline in wellbeing. Developing home assistance robots is a topic that is currently studied worldwide, and the talk will give examples from research in our own group. Our human-robot interaction research is primarily conducted in the University of Hertfordshire Robot House where we use the narrative framing technique in order to immerse study participants in our experimental scenarios. Such an approach bridges the gap between experimental laboratory environments and real homes, allowing for controlled studies in an ecologically valid environment. The talk will illustrate the scientific challenges and touch upon ethical and societal issues of such robot companion technology development.

Bio:
Since August 2018 Kerstin Dautenhahn has been Canada 150 Research Chair in Intelligent Robotics at University of Waterloo in Ontario, Canada. She has a joint appointment with the Departments of Electrical and Computer Engineering and Systems Design Engineering and is cross-appointed with the David R. Cheriton School of Computer Science at University of Waterloo. She is Visiting Professor at the University of Hertfordshire, UK. In Waterloo she is director of Social and Intelligent Robotics Research Laboratory (SIRRL). The main areas of her research are Human-Robot Interaction, Social Robotics, Assistive Technology and Artificial Life. She is Editor in Chief (jointly with Prof. Angelo Cangelosi - University of Manchester, UK) of the Journal Interaction Studies-Social Behaviour and Communication in Biological and Artificial Systems published by John Benjamins Publishing Company, Editorial Board Member of Adaptive Behavior, Sage Publications, Associate Editor of the International Journal of Social Robotics, published by Springer, and Associate Editor of IEEE Transactions on Cognitive and Developmental Systems (previously IEEE Transactions on Autonomous Mental Development). She is an Editor of the book series Advances in Interaction Studies, published by John Benjamins Publishing Company. Prof. Dautenhahn is on the Advisory Board of the journal AI and Society (Springer). She is a IEEE Fellow, member of ACM, and a Lifelong Fellow of AISB, as well as a member of the Executive Board of the International Foundation for Responsible Robotics. Since 2006 she has been part of the Standing Steering Committee of the IEEE conference RO-MAN (Human and Robot Interactive Communication).

This seminar presentation had been postponed to fall 2021.

Infrastructure Engineering: Large Scale, Fast Search
Ibrahim Abdul Rahmin
Salesforce

When: Thu, 3/11, 1:20-2:15pm EST
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Infrastructure Engineering is the behind the scenes programming that makes internet scale companies like Microsoft, Google, Facebook able to serve billions of customers. This talk discusses Infrastructure Engineering for search: How do we take search from a single document, to many documents, to so many documents you need thousands of machines to host it all, and how do we manage search at that scale reliably?

Bio:
Ibrahim Abdul Rahim, is an Engineering Manager for Search at Salesforce. Under his leadership, his team builds systems that make search scalable, fast and relevant. We build upon Solr, an open source search solution. Last year the team shipped a search stack capable of indexing at a rate of 11M documents per minute. He was previously at Microsoft doing consumer search at Bing. He is an avid library book reader.

Real-World Reinforcement Learning: Challenges and Opportunities
Enes Bilgin
Microsoft

When: Thu, 9/24, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Reinforcement Learning (RL) is becoming increasingly popular among the machine learning community, thanks to the super-human level performance demonstrated in games like Atari 57, Go, Dota 2, and StarCraft. Many also see RL as a path to Artificial General Intelligence as it promises to tackle complex sequential decision-making problems under uncertainty, similar to animal learning and behavior. On the other hand, we are yet to see the hockey-stick growth in the value RL has the potential to deliver for real-world applications outside of game settings, such as in robotics, manufacturing, supply chain, and more. This talk focuses on what the obstacles in applying RL to real-world problems are, how the recent developments in literature are proposing solutions to these challenges, and how the big tech companies are incorporating RL into their AI-as-a-service offerings for their customers in the industry.

Bio:
Dr. Enes Bilgin is a Sr. AI Engineer in Microsoft’s Autonomous Systems organization, with expertise in Reinforcement Learning (RL). His work focuses on democratizing RL for a broad array of industries as part of Microsoft’s Project Bonsai. To this end, he develops “Machine Teaching” methods to enable subject matter experts to transfer their know-how to AI models in an intuitive and effective manner. Prior to Microsoft, Dr. Bilgin worked at MathWorks, AMD, and Amazon as a researcher and engineer. He was also an adjunct faculty at Texas State University and at the McCombs School of Business at the University of Texas at Austin. Dr. Bilgin holds a Ph.D. in Systems Engineering from Boston University.

Prognostics and Health Management for Computer Scientists
Fatih Camci
Amazon

When: Thu, 10/8, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Prognostics and Health Management (PHM) aims to increase safety and availability of engineering systems and reduce their ownership costs by predicting failures and avoiding unnecessary maintenance. PHM has been attracting industry and researchers from various fields in recent decade with its potential benefits. This presentation introduces the PHM field to computer science students and academics. The presentation will start with general introduction to the field with potential benefits. Then the challenges of PHM will be discussed as problems within computer science field.

Bio:
Dr. Fatih Camci works at Amazon Prime-Air as senior research scientist. He has worked in academia and industry since he received his PhD in 2005 from Industrial Engineering department in Wayne State University. He has MSc and BSc degrees in computer engineering. His research includes development of machine learning algorithms for failure forecasting in electro-mechanical systems. Today’s industry deals with many high-values assets that play critical role in human safety such as airplanes, helicopters, drones, nuclear power plants etc. Maintenance planning based using failure prediction algorithms plays a critical role in safety, reliability and ownership cost of these assets.

Engineering Fast Graph Algorithms with Data Reduction Rules
Darren Strash
Hamilton College

When: Thu, 10/15, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
Algorithm Engineering is a powerful methodology that pays homage to all aspects of algorithms, giving careful treatment to the interplay between theory, practice, and applications. An emerging trend in Algorithm Engineering is the discovery and application of data reduction rules, which can accelerate algorithms by up to multiple orders of magnitude (e.g., 100-10,000 times faster) by transforming an instance of a problem to a smaller equivalent instance. In this talk, I will introduce these critical tools in the context of my recent research in solving classical graph problems using data reduction rules. I will introduce the concept itself, simple data reductions, and their significant impact on several canonical graph problems. These include NP-hard problems (maximum independent set, maximum cut), as well as "easy" problems (minimum cut).

Bio:
Darren Strash is an assistant professor of computer science at Hamilton College, specializing in algorithms for large graphs. Before arriving at Hamilton, Strash worked at Intel, performed postdoctoral research at KIT in Germany, and taught at Colgate University. He and colleagues most recently won the Parameterized Algorithms and Computational Experiments (PACE 2019) challenge, a programming and research competition with participation from teams around the globe. He has co-authored over 40 peer-reviewed publications and received two best paper awards.

Algorithms and/as Culture
Nick Seaver
Tufts University

When: Thu, 10/22, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:
The algorithms that shape culture, filtering what we see and hear, are often understood as inhuman forces. This talk describes how these systems are full of people who impart their own points of view on the software they build. Drawing on anthropological fieldwork with the developers of music recommender systems, it explores the consequences of thinking about algorithms in broader cultural contexts and as cultural objects in their own right.

Bio:
Nick Seaver is an assistant professor in the Department of Anthropology and the Program on Science, Technology, and Society at Tufts University in Medford, MA. He studies how technologists make sense of cultural concerns such as taste and attention.

Bayesian Forecasting of COVID-19
Dan Sheldon
University of Massachusetts Amherst

When: Thu, 11/5, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)

Abstract:

COVID-19 has infected more than 41 million people worldwide, with more cases in the US than any other country. COVID-19 models can help provide “situational awareness” of disease prevalence and make short-term forecasts to provide actionable information to public health planners.

In early March 2019, I was on sabbatical catching up on research projects. In late March, I connected with my friend Nick Reich, who leads a team of infectious disease forecasters at UMass. Since then I have led a small team developing the “MechBayes” (Mechanistic Bayesian) COVID-19 forecast model using tools from computer science and statistics. We submit weekly forecasts to the COVID-19 Forecast Hub, which are then provided to the CDC and used in an ensemble forecast. Our model is one of several featured on the FiveThirtyEight website.

In this talk, I will provide an overview of how we combined classical epidemiology models with modern probabilistic programming to create these forecasts. I will also describe the broader efforts of the COVID-19 Forecast Hub to standardize, collect, and aggregate forecasts by building ensemble models.

Bio:
Dan Sheldon is an associate professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst and the Department of Computer Science at Mount Holyoke College. His research investigates fundamental problems in machine learning and applied algorithms motived by large-scale environmental data, dynamic ecological processes, and real-world network phenomena (and now epidemiology).

CSC 498 Poster Session
CSC 498 students
Union College

When: Thu, 11/12, 1:20-2:15pm EDT
Where: the Zoom link will be sent out by email
(If you would like to be added to the distribution list, please contact Kristina Striegnitz at striegnk@union.edu)