IACS Seminar Series

During Fall Semester 2021, the IACS seminar series will be held virtually.  Seminars are free and open to the public but registration is required.

The IACS seminar series is a forum for thought leaders from academia, industry, and government to share their research on innovative computational and data science topics and methodologies. Past topics include smart city design, data science for social good, data privacy and security, socially assistive robotics, big data software, machine learning for small business lending, and AI technology development, and data-driven algorithmics.

Harvard University welcomes individuals with disabilities to participate in its programs and activities. If you would like to request accommodations or have questions about the physical access provided, please contact Jackie Strom at jstrom@seas.harvard.edu

Fall 2021 Seminars

2021 Sep 10

Machine Learning in Science: Applications, Algorithms and Architectures | Katherine Yelick, UC Berkeley

1:00pm to 2:00pm

Location: 

Virtual

Kathy Yelick Photo
Katherine Yelick
Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences
Executive Associate Dean, Division of Computing, Data Science, and Society, UC Berkeley
Senior Faculty Scientist, Lawrence Berkeley National Laboratory
 

Click here to access the seminar recording.
 

ABSTRACT: Machine learning is being used in nearly every discipline in science, from biology and environmental science to chemistry, cosmology and particle physics.... Read more about Machine Learning in Science: Applications, Algorithms and Architectures | Katherine Yelick, UC Berkeley

2021 Oct 06

Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference | Emmanuel Candés, Stanford University

1:00pm to 2:00pm

Location: 

Virtual

Emmanuel Candès Photo
Emmanuel Candès
Barnum-Simons Chair in Mathematics and Statistics
Professor of Electrical Engineering
Stanford University
 

Click here to access the seminar slides. 
Click here to access the seminar recording.

ABSTRACT: Recent progress in machine learning provides us with many potentially effective tools to learn from datasets of ever increasing sizes and make useful predictions.... Read more about Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference | Emmanuel Candés, Stanford University

2021 Oct 22

Generative Flow Networks | Yoshua Bengio, Université de Montréal

1:00pm to 2:00pm

Location: 

Virtual
Yoshua Bengio Photo

Photo by Camille Gladu-Douin

Yoshua Bengio
Full Professor, Université de Montréal
Founder and Scientific Director, Mila
Scientific Director, IVADO


Registration is required for this event. Click here to register.

ABSTRACT: Generative Flow Networks (or GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function.... Read more about Generative Flow Networks | Yoshua Bengio, Université de Montréal

2021 Nov 12

We used RL; but did it work? | Susan Murphy, Harvard University

1:00pm to 2:00pm

Location: 

Virtual

Susan Murphy Picture
Susan Murphy
Professor of Statistics, 
Radcliffe Alumnae Professor, Radcliffe Institute, Harvard University
Professor of Computer Science,  
Harvard John A. Paulson School of Engineering and Applied Sciences


Registration is required for this event. Click here to register.

ABSTRACT: Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in a Digital Health.... Read more about We used RL; but did it work? | Susan Murphy, Harvard University

2021 Nov 19

A large-scale analysis of racial disparities in police stops across the United States | Ravi Shroff, NYU

1:00pm to 2:00pm

Location: 

Virtual

Ravi Shroff Photo
Assistant Professor, Applied Statistics
NYU Steinhardt School of Culture, Education, and Human Development


Registration is required for the event. Click here to register. This seminar will be cohosted with the Office of Diversity, Inclusion, and Belonging. 

ABSTRACT: To assess racial disparities in police interactions with the public, we compiled and analyzed a dataset detailing nearly 100 million municipal and state patrol traffic stops conducted in dozens of jurisdictions across the country---the largest such effort to date.... Read more about A large-scale analysis of racial disparities in police stops across the United States | Ravi Shroff, NYU

2021 Dec 10

Enabling Zero-Shot Generalization in AI4Science | Anima Anandkumar, Caltech

1:00pm to 2:00pm

Location: 

Virtual

Anima Anandkumar Photo
Anima Anandkumar
Bren Professor, Caltech
Director of ML Research, NVIDIA


Registration is required for this event. Click here to register

ABSTRACT: Many scientific applications heavily rely on the use of brute-force numerical methods performed on high-performance computing (HPC) infrastructure.... Read more about Enabling Zero-Shot Generalization in AI4Science | Anima Anandkumar, Caltech