April 10, 2023

Guest Speakers: Machine Learning

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School or Program:
Electrical and Computer Engineering
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Guest Speakers: Machine Learning
Announcement from Dr. Inouye's Machine Learning Reading Group
 
 
April 12th, 6pm - 7:30 pm | Tutorial on Weights and Biases | Andrea Parker is a Machine Learning engineer who has worked extensively with large-scale graphs. She will cover a tutorial on Weights and Biases.
 
Event Title: Weights & Biases workshop: Track, Visualize, and Improve Your Experiments
Time & Location: April 12th, 6pm - 7:30 pm
Presenter: Andrea Parker
Presenter Bio: Andrea is a Machine Learning engineer who has worked extensively with large-scale graphs. She has a degree in Information Retrieval from the University of Michigan School of Information and has been working in the industry for over a decade. Andrea is passionate about data-driven problem solving and enjoys working on NLP and IR challenges across varied domains: health data, networks/linked data, and more.
Event format: Andrea will take you through a W&B introduction and product walkthrough, including Experiment tracking, W&B Tables, Sweeps, Artifacts, Dashboards/Reports, and Integrations! Followed by a Colab classification competition in Kaggle with swag for top submissions.
 
April 13th, 3 - 4 pm | Senior Research from Microsoft on GPT-4 | Dr. Yi Zhang is a senior researcher from Microsoft Research talking about his recent work on Sparks of Artificial General Intelligence: Early experiments with GPT-4.
 
Time & Location: April 13 (Thursday) 3-4 PM EST, MSEE239. Zoom link (tinyurl.com/gauenk)
Presenter: Yi Zhang
Presenter's Bio: Yi Zhang is a senior researcher at Microsoft Research. He obtained his Ph.D. in Computer Science in 2021 from Princeton University, where he received the Wallace Memorial Fellowship in Engineering, one of the highest honors awarded to Ph.D. students. Prior to Princeton, he received one Bachelor's degree from Shanghai Jiao Tong University and another one from the University of Michigan, Ann Arbor. His research concentrates on Theoretical Machine Learning. He is broadly interested in theory and empirics for machine learning, focusing on understanding the intrinsic mechanism of deep learning from a theoretical perspective.
 
 
 
 
Kent Gauen
College of Electrical and Computer Engineering
Purdue University, Doctoral Student