Events for November 30, 2022
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Virtual Efficient Estimation of Treatment Effect in Online Experiments
Wed, Nov 30, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Congshan Zhang, Meta , Core Data Science Team at Meta
Talk Title: Efficient Estimation of Treatment Effect in Online Experiments
Abstract: Randomized controlled trials are commonly used by tech companies to draw causal conclusions on various product changes. The confidence intervals from these experiments, however, are usually too large due to reasons such as limited number of users, heavy-tailed outcome variables and small treatment effects. Improving estimation efficiency for randomized controlled trials is not only a scientifically interesting but also a practically relevant area of research. In this talk, I will go over a few prominent techniques in statistics to improve estimation efficiency. Basic techniques such as CUPED and more advanced methodologies based on ML and synthetic controls will be introduced.
Biography: Congshan Zhang is a research scientist on Core Data Science Team at Meta. Congshan is interested in various topics in statistics and econometrics including causal inference, machine learning and time series. Congshan holds Ph.D. in economics from Duke University. Before joining Meta, Congshan did research on financial econometrics, with a focus on nonparametric and semi-parametric inference using high-frequency data and on testing models of financial markets. His work appears in top journals of econometrics such as Journal of Econometrics and Annals of Applied Probability.
Host: Urbashi Mitra
More Info: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 Passcode: 586135
More Information: ECE Seminar Announcement_Nov21.docx
Location: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 P
Audiences: Everyone Is Invited
Contact: Susan Wiedem
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 30, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Vikas Sindhwani, Google Brain
Talk Title: Foundation Models for Robotics
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Trained on internet-scale datasets, large language and vision models demonstrate breakthrough capabilities which until recently were thought to still be decades away in technological feasibility. Does this imply a paradigm shift in Robotics as well? If so, what is the bridge from symbols and tokens on the internet to actions in the physical world? Through a few illustrative vignettes of robotic manipulation and navigation research at Google, I will propose speculative paths towards making robots useful in human-centric spaces.
Biography: Vikas Sindhwani is Senior Staff Research Scientist in the Google Brain team in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical learning, and in end-to-end design aspects of building large-scale, robust machine intelligence systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI) 2013, the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowedge Discovery and Data Mining (KDD). He previously led a team of researchers in the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
