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Department Events & Announcements

Events

• Mar

  2

  CS Colloquium - Hoda Hedari - "Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness"

  Department of Computer Science

  12:00 PM Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  Title: Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness

  Abstract

  Automated decision-making tools are increasingly in charge of making high-stakes decisions for people—in areas such as education, credit lending, criminal justice, and beyond. These tools can exhibit and exacerbate existing undesirable biases and adversely impact already disadvantaged and marginalized social groups and individuals. In this talk, I will illustrate how we can bring together tools and methods from computer science, economics, and political philosophy to define, measure, and mitigate algorithmic unfairness. In particular, I will address two key questions:

  ▪ Given the decision-making context, how should we define fairness as the equality of some notion ofbenefit or harm across socially salient groups? First, I will offer a framework to think about this question normatively. I map the recently proposed notions of group-fairness to models of equality of opportunity. This mapping provides a unifying framework for understanding these notions, and importantly, allows us to spell out the moral assumptions underlying each one of them. Second, I give a descriptive answer to the question of “fairness as equality of what?”. I mention a series of adaptive human-subject experiments we conducted to understand which existing notion best captures laypeople’s perception of fairness.

  ▪ How can we measure unfairness (both at the individual and group level) and bound it in a computationally efficient manner? Existing notions of fairness focus on defining conditions of fairness, but they do not offer a proper measure of unfairness. In practice, however, designers often need to select the least unfair model among a feasible set of unfair alternatives. I present (income) inequality indices from economics as a unifying framework for measuring unfairness, both at the individual- and group-level. I propose the use of cardinal social welfare functions as an alternative measure of fairness beyond equality and an effective method for bounding inequality.

  Biography

  Hoda Heidari is currently a Postdoctoral Associate at the Department of Computer Science at Cornell University, where she collaborates with Professors Jon Kleinberg, Karen Levy, and Solon Barocas through the AIPP (Artificial Intelligence, Policy, and Practice) initiative. Hoda’s research is broadly concerned with the societal and economic aspects of Artificial Intelligence, and in particular, the issues of fairness and explainability for Machine Learning. She utilizes tools and methods from Computer Science (Algorithms, AI, and ML) and Social Sciences (Economics and Political Philosophy) to quantify and mitigate the inequalities that arise when socially consequential decisions are automated. Her work has appeared in top-tier Computer Science venues, such as ICML, NeurIPS, KDD, AAAI, IJCAI, and EC. Before coming to Cornell, Hoda was a Postdoctoral Fellow at the Machine Learning Institute of ETH Zürich, working under the supervision of Professor Andreas Krause. Hoda completed her doctoral studies in Computer and Information Science at the University of Pennsylvania, where she was advised by Professors Michael Kearns and Ali Jadbabaie. Hoda has organized multiple academic events on the topic of her research, including a tutorial at the Web Conference (WWW) and a workshop at the Neural and Information Processing Systems (NeurIPS) conference. Beyond computer science venues, she has been invited to and participated in numerous interdisciplinary panels and discussions addressing the implications of AI for society.

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  TIME Monday, March 2, 2020 at 12:00 PM - 1:00 PM

  LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Pam Villalovoz    pmv@northwestern.edu EMAIL

  CALENDAR Department of Computer Science

• Apr

  24

  CS Seminar: AI Powered Movement Analysis, Big Rehabilitation Data and a Path to Precision Rehabilitation (R. James Cotton)

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  

  Wednesday / CS Seminar
  April 24th / 12:00 PM
  In Person / Mudd 3514

  Speaker
  R. James Cotton, Northwestern University & Shirley Ryan AbilityLab

  Talk Title
  AI Powered Movement Analysis, Big Rehabilitation Data and a Path to Precision Rehabilitation

  Abstract
  This talk will discuss multiple methodological lines of work making movement and gait analysis more clinically accessible and biomechanically grounded. This includes reconstruction from synchronized multiview videos, smartphone videos, and wearable sensors. We will also discuss how implicit functions provide a powerful representation to map from time to joint angles, and GPU accelerated methods that enable end-to-end biomechanical fits from these different modalities. It will discuss some of the opportunities that large movement data enables, including the use of self-supervised learning to discover gait representations that can function as both diagnostic and response biomarkers. Finally, we will outline a vision for a Causal Framework for Precision Rehabilitation that can model this data to link from impairment to function and identify the optimal dynamic treatment policies to improve rehabilitation outcomes.

  Biography
  I am an electrical engineer, neuroscientist, and physiatrist working as a physician-scientist at Shirley Ryan AbilityLab and Assistant Professor in the Northwestern University Department of Physical Medicine and Rehabilitation. I completed my residency in PM&R at Shirley Ryan AbilityLab (formers Rehabilitation Institute of Chicago) where I remained as faculty. Prior to that I obtained a B.S. in Electrical Engineering from Rice University followed by an MD and PhD in systems neuroscience from Baylor College of Medicine. My lab works at the intersection of artificial intelligence, wearable sensors, computer vision, causal and biomechanical modeling, and novel technologies to more precisely monitor and improve rehabilitation outcomes.

  Research Area/Interests
  AI, computer vision, gait analysis, wearable sensors, rehabilitation

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  TIME Wednesday, April 24, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• Apr

  25

  TGI Bagel Thursday

  Department of Computer Science (CS)

  9:00 AM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  

  Spring is finally here. Enjoy the good vibes to come with some free bagels and coffee courtesy the Computer Science Department. Mix and mingle with fellow CS students and faculty every last Thursday of the month.

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  TIME Thursday, April 25, 2024 at 9:00 AM - 11:00 AM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• Apr

  29

  CS Seminar: Data-Efficient Graph Learning (Kaize Ding)

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  

  Monday / CS Seminar
  April 29th / 12:00 PM
  Hybrid / Mudd 3514

  Speaker
  Kaize Ding, Northwestern University

  Talk Title
  Data-Efficient Graph Learning
  
  Abstract
  The world around us -- and our understanding of it -- is rich in relational structure: from atoms and their interactions to objects and entities in our environments. Graphs, with nodes representing entities and edges representing relationships between entities, serve as a common language to model complex, relational, and heterogeneous systems. Despite the success of recent deep graph learning, the efficacy of existing efforts heavily depends on the ideal data quality of the observed graphs and the sufficiency of the supervision signals provided by the human-annotated labels, leading to the fact that those carefully designed models easily fail in resource-constrained scenarios.

  In this talk, I will present my recent research contributions centered around data-efficient learning for relational and heterogeneous graph-structured data. First, I will introduce what data-efficient graph learning is and my contributions to different research problems under its umbrella, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning. Based on my work, I will elucidate how to push forward the performance boundary of graph learning models especially graph neural networks with low-cost human supervision signals. I will also touch upon the real-world applications of data-efficient graph learning to different domains and finally conclude my talk with a brief overview of my future research agenda.

  Biography
  Kaize Ding is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University. Before joining Northwestern, he obtained his Ph.D. degree in Computer Science at Arizona State University in 2023 under the supervision of Prof. Huan Liu. His research interests are generally in data mining, machine learning, and natural language processing, with a particular focus on graph machine learning, data-efficient learning, and reliable AI. His work has been published in top-tier conferences and journals (e.g., AAAI, EMNLP, IJCAI, KDD, NeurIPS, TheWebConf, and TNNLS), and has been recognized with several prestigious awards and honors, including the AAAI New Faculty Highlights, SDM Best Posters Award, Best Paper Award at the Trustworthy Learning on Graphs workshop, etc.

  Research Area/Interests
  Data Mining; Machine Learning; Natural Language Processing

  Zoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09

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  TIME Monday, April 29, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  1

  CS Seminar: Tong Zhang

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  3

  CS Seminar: Shafi Goldwasser

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Friday, May 3, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  8

  Extending Care: A Conversation about Conservation and Futurity

  Block Museum of Art

  6:00 PM Block Museum of Art, Mary and Leigh

  EVENT DETAILSmore info

  The Center for Scientific Studies in the Arts is a collaboration between the Art Institute of Chicago and materials science-related departments at Northwestern University to pursue objects-based and objects-inspired scientific research. Materials research benefits ongoing work in conservation, archaeology, art history, and curatorial scholarship.

  Learn how the Center uses materials research to care for art objects in sustainable, innovative ways with Maria Kokkori, Senior Scientist in the Center for Scientific Studies in the Arts. She will be in conversation with Corey Byrnes, Northwestern Associate Professor of Chinese Culture and co-founder/co-director of the Environmental Humanities Workshop in Kaplan Humanities Center.

  This event is presented by the McCormick School of Engineering and Applied Science in conjunction with exhibition Actions for the Earth: Art, Care & Ecology.

  Register Now

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  TIME Wednesday, May 8, 2024 at 6:00 PM - 7:30 PM

  LOCATION Block Museum of Art, Mary and Leigh    map it

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  CONTACT Block Museum of Art    block-museum@northwestern.edu EMAIL

  CALENDAR Block Museum of Art

• May

  30

  TGI Bagel Thursday

  Department of Computer Science (CS)

  9:00 AM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Thursday, May 30, 2024 at 9:00 AM - 11:00 AM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  30

  End of Year Awards

  Department of Computer Science (CS)

  3:00 PM TBA, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Thursday, May 30, 2024 at 3:00 PM - 5:00 PM

  LOCATION TBA, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• Jun

  10

  Northwestern Engineering PhD Hooding and Master's Degree Recognition Ceremony

  McCormick School of Engineering and Applied Science

  9:00 AM Welsh-Ryan Arena

  EVENT DETAILSmore info

  McCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony

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  TIME Monday, June 10, 2024 at 9:00 AM - 11:00 AM

  LOCATION Welsh-Ryan Arena   

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  CONTACT Amy Pokrass    amy.pokrass@northwestern.edu EMAIL

  CALENDAR McCormick School of Engineering and Applied Science

• Jun

  10

  Northwestern Engineering Undergraduate Convocation

  McCormick School of Engineering and Applied Science

  2:00 PM Welsh-Ryan Arena

  EVENT DETAILSmore info

  McCormick School of Engineering Undergraduate Convocation

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  TIME Monday, June 10, 2024 at 2:00 PM - 4:00 PM

  LOCATION Welsh-Ryan Arena   

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  CONTACT Amy Pokrass    amy.pokrass@northwestern.edu EMAIL

  CALENDAR McCormick School of Engineering and Applied Science