TAD Year-End Event: "Use Interpretable Machine Learning Models for High Stakes Decisions"
Prof. Cynthia Rudin gave the keynote lecture at the center's year-end event, earlier this month. More highlights: film screening and panel discussion, awards, lectures and posters
TAD Year-End Event took place on June 8, 2022 at the Steinhardt Museum of Natural History. The event started with a brunch and a Poster Session of projects funded by TAD, including several posters by members of the Fundamentals of AI and DS community (See link to pictures gallery below).
Prof. Meir Feder opened the event and presented the awardees of the High Impact Research grants: Prof. Haim Suchowski (Physics) with Dr. Nadav Cohen (Computer Science) and Dr. Ro’ee Levy (Economics) received full grants of $300K each. In addition, two projects received seed grants of $50K each: Dr. Maayan Gal and Dr. Daniel Bar (Dental Medicine) with Prof. Haim Wolfson (Computer Science) and Prof. Yossi Yovel (Zoology), Prof. Lilach Hadany (Plant Science) and Prof. Amir Globerson (Computer Science).
A screening of the documentary film "CODED BIAS" was followed by an interesting panel discussion on bias, fairness and regulation of AI. The panel, moderated by Prof. Meir Feder, Head of TAD, included Prof. Yishay Mansour (Computer Science), Prof. Niva Elkin-Koren (Law) and Josef Gedalyahu, Adv., from the Ministry of Justice. Each of the panel members shared his view on regulation of AI.
Lectures in the field of responsible AI
Later on that day a session of lectures was held by TAD members (moderated by Prof. Assaf Hamdani, Law) on projects in the field of responsible AI:
- Dr. Ro'ee Levy (Economics), "The Demand for and Supply of Biased News: Determining Article-Level Slant"
- Prof. Ran Gilad - Bachrach (Biomedical Engineering), "Enabling Markets for Data and Data Driven Goods"
- Prof. Eran Toch (Industrial Engineering), "Privacy Engineering in the Wild: Addressing Real-World Challenges when Providing Scientists with Access to Data"
- Dr. Roi Livni (Electrical Engineering), "Fair Use of Synthetic Data"
Interpretable machine learning models
Prof. Cynthia Rudin (Duke University) gave a fascinating keynote lecture on "The Extreme of Interpretability in Machine Learning" (moderated by Prof. Amir Globerson).
Prof. Rudin’s lecture emphasized the importance of interpretability of machine learning models which is critical in high stakes decisions.
The TAD Center would like to thank all the speakers and participants, and wish everyone a great Summer!
List of Posters:
- Eitam Arnon, School of Zoology (Advisors: Orr Spiegel & Sivan Toledo)
"Advanced localization and filtering algorithms for The ATLAS animal tracking system"
- Noa Ecker, The Shmunis School of Biomedicine and Cancer Research (Advisor: Tal Pupko)
"A LASSO-based approach to sample sites for phylogenetic tree search"
- Avia Efrat and Or Honovich, School of Computer Science (Advisor: Omer Levy)
"Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language"
- Shilo Horev, Department of Statistics and Operational Research (Advisor: Malka Gorfine)
"Predicting chronic PTSD based on early fMRI measures"
- Uri Kapustin Meir, School of Electrical Engineering (Advisors: Gili Bisker & Dan Raviv)
"Estimating Entropy Production Rate of nonequilibrium systems using Machine Learning"
- Efrat Muller, School of Computer Science (Advisor: Elhanan Borenstein)
"A meta-analysis study of the universality of gut microbiome-metabolome associations"
- Chen Xing, School of Zoology (Advisor: Yossi Yovel)
"How do bats use echoes to recognize places - a computational approach?"
- Matan Schliserman, School of Computer Science (Advisor: Tomer Koren)
"Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond"
- Tal Lancewicki, School of Computer Science (Advisor: Yishay Mansour)
"Cooperative Online Learning in Stochastic and Adversarial MDPs"
- Noam Razin, School of Computer Science (Advisor: Nadav Cohen)
"Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks"
- Edo Cohen, Itamar Menuchin, Raja Giryes, Nadav Cohen, Amir Globerson
"Implicit Bias in RNNs"