May 17, 2022 Newsletter

May 17, 2022 Newsletter

May 17

Take a look at our May 17, 2022 Newsletter with updates from the Ken Kennedy Institute.

Meet the May Member of the Month, Dr. Eric Chi, Associate Professor of Statistics. Dr. Chi's research areas include statistical learning, numerical optimization, and their application to analyzing large and complicated modern data in biological science and engineering applications

Dr. Chi received BA and PhD degrees from Rice University and an MS from the University of California, Berkeley. His PhD studies were funded through a Department of Energy Computational Sciences Graduate Fellowship (DOE CSGF). As part of this fellowship, he completed research practica at Sandia National Laboratories and Lawrence Berkeley National Laboratory. After his PhD, he completed postdoctoral positions in the Human Genetics department at UCLA and the Digital Signal Processing group at Rice University. Before joining Rice, he was an Assistant Professor in the Department of Statistics at North Carolina State University (2015-2021).

How do you explain your research in one sentence?
I develop computational algorithms finding low dimensional and interpretable latent structure in data.

What challenges do you see in your work that you didn't expect?
One challenge that was hard to anticipate until I became a PI was writing compelling (and fundable!) grants. I have certainly not figured this all out, but I have to say that I have learned to enjoy (in some part) the process of grant writing. It forces me to take a step back and visualize my research efforts over a longer scale and to take a hard assessment of the impact and value of the work I do. I wouldn’t say what goes on between start and finish is all fun (it’s not), but it can also be energizing to go through the exercise of convincing yourself (and hopefully a panel of reviewers!) that you’re doing something that’s worth supporting.
Read More

2022 Machine Learning Boot Camp: Executives & Beginners

May 19-20, 2022 | 8:00am - 5:00pm CST | BRC at Rice University

This two-day boot camp will provide a broad introduction to Machine Learning (ML) for executives and beginners. Our goal is to give a manager or a CEO in a field such as medicine, finance, manufacturing, or energy an introduction to the essential concepts behind Machine Learning. Additionally, this boot camp is great for non-practicing beginners looking to learn. Taught by a team of Rice professors in the computational sciences, the Machine Learning Boot Camp will integrate lectures describing the basic concepts to state-of-the-art Machine Learning.

The boot camp assumes no specific prerequisites and will highlight a broad set of concepts, ranging from classical supervised (e.g., linear regression, logistic regression and decision trees) and unsupervised methods (e.g., clustering algorithms, principal components analysis) to modern machine learning approaches (e.g., different neural network architectures, min-max games and generative models, deep reinforcement learning), with numerous case studies and applications. Throughout its duration, the workshop will provide practical suggestions to executives with DOs and DON’Ts when using Machine Learning in practice.

Prerequisites: A general level of familiarity with mathematics is assumed, including college-level calculus, though those without this background will still find the boot camp a useful introduction to modern ML.

Click here to see the full 2022 curriculum.

Register Here

ICYMI: Machine Question Answering in the Wild

Machine Question Answering in the Wild presented by Eunsol Choi is available to watch on the Ken Kennedy Institute YouTube channel.

GCC AI in Healthcare Symposium

May 18, 2022 | 9:00am-3:45pm CST | Virtual

Please visit here to view full agenda and speaker line up. Register here.

META Request for Research Proposals

Meta Research has RFP requests for university researcher consideration.
  • 2022 Networking RFPs: Network for AI
    • Focus Areas: Hardware computational offloading for AI workload, End-to-end novel transport designs for distributed AI training, Scheduling, resource allocation, communication collectives and network joint optimization, New network interconnect architectures for AI training and inference
    • Award Opportunity: Up to 6 awards, each in the $50,000 USD range
    • Deadline: May 23
    • > Learn More
  • 2022 Systems Research
    • Focus Areas: Fundamental challenges that arise in distributed systems operating at a large scale. Ex. Infrastructure control planes, Service abstractions and efficiency, Global service management
    • Award Opportunity: Up to 6 awards, each in the $50,000 USD range
    • Deadline: June 12
    • > Learn More
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