Computer Science Department

Seminar Series

Fall 2020

  • 9/24 Enes Bilgin (1:20pm) -- Real-World Reinforcement Learning: Challenges and Opportunities

    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.

  • 10/8 Fatih Camci (1:20pm) -- Prognostics and Health Management for Computer Scientists

    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.

  • 10/15 Darren Strash (1:20pm) -- Engineering Fast Graph Algorithms with Data Reduction Rules

    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.

  • 10/22 Nick Seaver (1:20pm) -- Algorithms and/as Culture

    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.

  • 11/5 Dan Sheldon (1:20pm) -- Bayesian Forecasting of COVID-19

    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 phenoemena (and now epidemiology).

  • 11/12 CSC 498 Poster Session (1:20pm)

    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)