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ACM-IMS Interdisciplinary Summit on the Foundations of Data Science

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On June 15, 2019, this interdisciplinary event in San Francisco brought together researchers and practitioners to address deep learning, reinforcement learning, robustness, fairness, ethics and the future of data science.

Lectures and panels with corresponding video timestamps follow:

– (0:00) Introduction
(Jeannette Wing, Columbia University)

– (5:41) Keynote Talk: "Making the Black Box Effective: What Statistics Can Offer"
(Emmanuel Candès, Stanford University, with Introduction by David Madigan, Columbia University)

– (40:22) Panel: Deep Learning, Reinforcement Learning, and Role of Methods in Data Science
(Moderator: Joseph Gonzalez, University of California Berkeley; Panelists: Shirley Ho, Flatiron Institute, Sham Kakade, University of Washington, Suchi Saria, Johns Hopkins University, Manuela Veloso, J.P. Morgan AI Research, Carnegie Mellon University)

– (1:34:50) Panel: Robustness and Stability in Data Science (part 1)
(Moderator: Ryan Tibshirani, Carnegie Mellon University; Panelists:
Aleksander Madry, Massachusetts Institute of Technology,
Xiao-Li Meng, Harvard University, Richard J. Samworth, University of Cambridge & The Alan Turing Institute, Bin Yu, University of California, Berkeley)

– (1:56:58) Panel: Robustness and Stability in Data Science (part 2)

– (2:29:20) Panel: Fairness and Ethics in Data Science (Moderator: Yannis Ioannidis, National and Kapodistrian University of Athens
Panelists: Joaquin Quiñonero Candela, Facebook, Alexandra Chouldechova, Carnegie Mellon University, Andrew Gelman, Columbia University, Kristian Lum, Human Rights Data Analysis Group (HRDAG)

– (4:04:19) Keynote Talk: "Deep Learning for Tackling Real-World Problems"
(Jeffrey Dean, Google, with introduction by Suchi Saria, Johns Hopkins University)

– (4:36:00) Keynote Talk: "Machine Learning: A New Approach to Drug Discovery" (Daphne Koller, insitro, with introduction by Kristian Lum, Human Rights Data Analysis Group)

– (5:23:57) Panel: Future of Data Science (Moderator: David Madigan, Columbia University; Panelists: Michael I. Jordan, University of California, Berkeley, Jeannette Wing, Columbia University)

– Closing Remarks: David Madigan and Jeannette Wing, Columbia University

ACM-IMS Interdisciplinary Summit on the Foundations of Data Science

Runtime Analysis of Whole-System Provenance

Session 10C - Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal

A Badge, Not a Barrier: Designing for–and Throughout–Digital Badge Implementation

Michael Stonebraker, 2014 ACM Turing Award Recipient

Heterogeneous Acceleration and Challenges for Scientific Computing on The Exascale

Jim Gray, 1998 ACM Turing Award Recipient

Geoffrey Hinton and Yann LeCun, 2018 ACM A.M. Turing Award Lecture "The Deep Learning Revolution"

Databite No. 90: Kristian Lum

Charles W. Bachman, 1973 ACM Turing Award Recipient

MainView Console Management - Creating a CCS Server and Console Sessions

Talk by Nicolas Gillis (University of Mons)

Day 1 - Alloy - A Tool for Relational Modelling - Stanly Samuel

Impact of Final Plated Finish on RF PCB Performance

Measurement of PIM Distortion in Microstrip Transmission Lines

Data mining

OpenNSM (Applied Detection and Flow Analysis - Chris Sanders Jason Smith)

Building a Discovery Engine with Machine Learning by Gary Sieling

VIS 2020: LDAV - Rendering and Displays

Connecting the Arduino to a Raspberry Pi

A Real-World Test-bed for Mobile Adhoc Networks: Methodology, Experimentations, Simulation & Results

2nd Session Turing100 | Life and Work of Ted Codd

Outerz0ne 2010: Pbr90x Social Networking fail

z/OS Container Extensions - Linux on Z docker containers inside z/OS

Introduction to R (part 1, basics)

TIES 2022: Christopher Wikle - TIES working group session

Roadmap Session: Achieve DevOps on the Mainframe for Faster Time to Market

How to interpret high frequency circuit material data sheets

== PostgreSQL for Oracle People ==

24C3: Anonymity for 2015

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