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Diffusion Schrödinger Bridges with Applications to Score-Based Generative Modelling

November 25, 2021

Speakers:

Arnaud Doucet, University of Oxford

Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian.

Designing Creative Courses with Students in Mind

November 22, 2021

Speakers:

Sarah Mayes-Tang

The students that we teach have far different needs than they did when most of our traditional math and statistics courses were initially added to

Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier

November 15, 2021

Speakers:

Munmun De Choudhury

Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations.

PDA: A New Framework of Privacy-Preserving Distributed Algorithms and Inference

November 11, 2021

Speakers:

Yong Chen, University of Pennsylvania

With the increasing availability of electronic health records (EHR) data, it is important to effectively integrate evidence from multiple data sour

Delphi’s COVIDcast Project: Lessons from Building a Digital Ecosystem for Tracking and Forecasting the Pandemic

November 1, 2021

Speakers:

Ryan Tibshirani

In March 2020, the Delphi group at CMU launched an effort called COVIDcast, which has many parts: 1.

Precise High-Dimensional Asymptotics for AdaBoost via Max-Margins and Min-Norm Interpolants

November 4, 2021

Speakers:

Pragya Sur, Harvard University

This talk will introduce a precise high-dimensional asymptotic theory for AdaBoost on separable data, taking both statistical and computational per

Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice

October 25, 2021

Speakers:

Tiffany Timbers

In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible an

Teaching with Computation and Data Science Across the Social Science

October 21, 2021

Speakers:

Michael Widener

Ayobami Laniyonu

Fedor Dokshin

In the first session of the new Computation and Data Science Education Community of Practice (CDS Education CoP), Professors Michael Widener (Depar

A Top-Down Approach Toward Understanding Deep Learning

October 21, 2021

Speakers:

Weijie Su, University of Pennsylvania

The remarkable development of deep learning over the past decade relies heavily on sophisticated heuristics and tricks.

What I Learned Applying for Faculty Positions

October 14, 2021

Speakers:

Jessica Gronsbell, University of Toronto

I completed my academic job search a couple of years ago. It was an overwhelming, exhausting, and amazing experience.

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