Bi-Weekly Seminar Series

Seminars are bi-weekly, on Thursdays at 12pm ET (more time zones) and last 90 minutes.

To receive announcements of upcoming seminars, join our mailing list or subscribe to our YouTube. To participate in Zoom, locate the registration link below (posted a few days before the seminar). If you are interested in giving a talk, contact trustworthyml@gmail.com.

FORMAT and instructions

The seminars will take place in Zoom Webinar; some may be recorded and live-streamed to YouTube.

The first hour typically follows one of these formats:

  • 40-min talk, followed by 20-min Q&A and moderated chat on the speaker's journey in ML and research process.

  • Rising Star Spotlights: Two talks of 20-25 mins each, followed by 5-10 min Q&A.

Then after a 5-min break, we reconvene for discussions with fellow participants. Our hope is that this participant-driven discussion in the second hour allows participants to meet new people working in similar research areas. Each seminar will also have a Twitter thread to continue the conversation.

By participating in the seminar, you agree to abide by the Code of Conduct. Please report any issues to trustworthyml@gmail.com.

Joining Zoom: There is a limit of 100 participants in Zoom, first-come-first-serve; registering does not guarantee you a spot. You can join Zoom by clicking the link in your registration confirmation email shortly before the seminar starts. We recommend downloading and installing Zoom in advance.

Live-stream and recording: If Zoom reaches capacity, please watch the YouTube live-stream and check Zoom again in case spots open up. If you do not wish to appear in the live-stream and recording, please only join Zoom in the second hour. You can find recordings of previous seminars here.

Participate: In Zoom, you will be muted in the first hour however you can ask questions using Zoom's Q&A tool. You can also upvote and leave comments on questions. The moderator will select questions, and may call on you to ask yours. In the second hour, you will be un-muted for a free-form discussion with fellow participants. You can use the Twitter thread to continue the conversation after the seminar.

UPCOMING SEMINARS

Click to see abstract, bio, registration link, Twitter thread.

Dec 3, 2020: Jenn Wortman Vaughan, Microsoft Research

Intelligibility Throughout the Machine Learning Life Cycle

Abstract: People play a central role in the machine learning life cycle. Consequently, building machine learning systems that are reliable, trustworthy, and fair requires that relevant stakeholders—including developers, users, and the people affected by these systems—have at least a basic understanding of how they work. Yet what makes a system “intelligible” is difficult to pin down. Intelligibility is a fundamentally human-centered concept that lacks a one-size-fits-all solution. I will explore the importance of evaluating methods for achieving intelligibility in context with relevant stakeholders, ways of empirically testing whether intelligibility techniques achieve their goals, and why we should expand our concept of intelligibility beyond machine learning models to other aspects of machine learning systems, such as datasets and performance metrics.

Bio: Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.

Zoom registration: https://us02web.zoom.us/webinar/register/WN_tFiL3Oc0S4qUEqd6_g2hng

YouTube live-stream and recording: https://youtu.be/bogHfN-RkaA

Twitter thread to continue the conversation: Check back again after the seminar.

Dec 17, 2020: Pin-Yu Chen, IBM Research

Practical Backdoor Attacks and Defenses in Machine Learning Systems

Abstract: Backdoor attack is a practical adversarial threat to modern machine learning systems, especially for deep neural networks. It is a training-time adversarial attack that embeds Trojan patterns to a well-trained model for gaining the ability to manipulate machine decision-making at the testing phase. In this talk, I will start by providing a comprehensive overview of adversarial robustness in the lifecycle of machine learning systems. Then, I will delve into recent backdoor attacks and practical defenses in different scenarios, including standard training and federated learning. The defenses include methods to detect and repair backdoored models. I will also cover a novel application of transfer learning with access-limited models based on the lessons learned from backdoor attacks.

Bio: Dr. Pin-Yu Chen is a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen’s recent research focuses on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. He has published more than 30 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at CVPR’20, ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award.

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Jan 7, 2021 Rising Star Spotlights

Title TBA

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Jan 21, 2021: Zachary Lipton, Carnegie Mellon University

Title TBA

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Feb 4, 2021: Steven Wu, Carnegie Mellon University

Title TBA

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Feb 18, 2021: Celia Cintas, IBM Research Africa

Title TBA

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Mar 4, 2021 Rising Star Spotlights

Shibani Shanturkar, MIT

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Mar 18, 2021: Katherine Heller, Google / Duke University

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Apr 1, 2021: Gautam Kamath, University of Waterloo

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Apr 15, 2021: Suresh Venkatasubramanian, University of Utah

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Apr 29, 2021 Rising Star Spotlights

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

May 13, 2021: Alexander D'Amour, Google Brain

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

May 27, 2021: Hoda Heidari, Carnegie Mellon University

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

June 24, 2021 Rising Star Spotlights

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

July 8, 2021: Cynthia Rudin, Duke University

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

August 19, 2021 Rising Star Spotlights

Title TBA

Abstract TBA

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YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Sep 16, 2021: Sherri Rose, Stanford University

Identifying Subgroups in Algorithmic Fairness

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.

Oct 14, 2021 Rising Star Spotlights

Title TBA

Abstract TBA

Zoom registration: Check back again a few days before the seminar.

YouTube live-stream and recording: Check back again a few days before the seminar.

Twitter thread to continue the conversation: Check back again after the seminar.