The FODS-2020 Conference will offer a virtual format. Program times are US Eastern Time (EDT). 

The Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS) have come together to launch a conference series on the Foundations of Data Science. Our inaugural event, the ACM-IMS Interdisciplinary Summit on the Foundations of Data Science, took place in San Francisco in 2019. Starting in 2020 we will have an annual conference with refereed conference proceedings. The 2020 event takes place virtually October 19-20, 2020 and the submission deadline was May 15, 2020. This interdisciplinary event will bring together researchers and practitioners to address foundational data science challenges in prediction, inference, fairness, ethics and the future of data science.

Jeannette Wing and David Madigan
FODS-2020 Conference Co-chairs
[email protected]

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Click here for an interactive version of the program.

Program times are US Eastern Time (EDT).

Monday, October 19th

09:00-12:00 EDT Session 1: Tutorial - Causal Reasoning Tutorial

09:00 EDT - David Blei - Causal Reasoning Tutorial

12:30-13:30 EDT Session 2: Keynote 

12:30 EDT - Mihaela van der Schaar - AutoML and interpretability: powering the machine learning revolution in healthcare

14:00-15:30 EDT Session 3: Plenary Session - Methodology

14:00 EDT - Yu Gui - ADAGES: adaptive aggregation with stability for distributed feature selection

14:20 EDT - Chenglin Fan and Ping Li - Classification Acceleration via Merging Decision Trees

14:40 EDT - Sarah Tan, Matvey Soloviev, Giles Hooker and Martin T. Wells - Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

15:00 EDT - Miguel Carreira-Perpinan and Arman Zharmagambetov - Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting

15:45-17:15 EDT Session 4: Plenary Session - Fairness, Privacy, Interpretability

15:45 EDT - Collin Burns, Jesse Thomason and Wesley Tansey - Interpreting Black Box Models via Hypothesis Testing

16:05 EDT - Ruobin Gong and Xiao-Li Meng - Congenial Differential Privacy under Mandated Disclosure

16:25 EDT - Roland Maio and Augustin Chaintreau - Incentives Needed for Low-Cost Fair Data Reuse

16:45 EDT - Isabella Grasso, David Russell, Jeanna Matthews, Abigail Matthews and Nick Record - Applying Algorithmic Accountability Principles and Frameworks to Ecosystem Forecasting: A Case Study in Forecasting Shellfish Toxicity in the Gulf of Maine

Tuesday, October 20th

09:00-12:00 EDT Session 5: Tutorial - Fairness, Privacy, and Ethics in Data Science Tutorial

09:00 EDT - Michael Kearns - Fairness, Privacy, and Ethics in Data Science Tutorial

12:30-13:30 EDT Session 6: Keynote

12:30 EDT - Oren Etzioni - Semantic Scholar, NLP, and the Fight Against COVID-19

14:00-15:50 EDT Session 7: Plenary Session - Data Science Theory

14:00 EDT - Alex Fout, Bailey Fosdick and Matthew Hitt - Non-Uniform Sampling of Fixed Margin Binary Matrices

14:20 EDT - Claire Mathieu and Michel De Rougemont - Large very dense subgraphs in a stream of edges

14:40 EDT - Xiangyi Chen, Xiaoyun Li and Ping Li - Toward Communication Efficient Adaptive Gradient Method

15:00 EDT - Tianyu Wang, Weicheng Ye, Dawei Geng and Cynthia Rudin - Towards Practical Lipschitz Bandits

15:20 EDT - Devavrat Shah, Varun Somani, Qiaomin Xie and Zhi Xu - On Reinforcement Learning for Turn-based Zero-sum Markov Games

16:00-18:00 EDT Session 8: Plenary Session - Foundations in Practice

16:00 EDT - Ryan Bernstein, Matthijs Vákár and Jeannette Wing - Transforming Probabilistic Programs for Model Checking

16:20 EDT - Haitian Chen, Bai Jiang and Hao Chen - StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks

16:40 EDT - Lina Lin, Mathias Drton and Ali Shojaie - Statistical significance in high-dimensional linear mixed models

17:00 EDT - Thanh Le and Vasant Honavar - Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data