Maddie Burbage ’22 Wins 2019-2020 Ward Prize

Yesterday, the faculty had the distinct pleasure of watching Ward prize presentations.  The Ward Prize, given in honor of the generous contributions of alum Rich Ward ’89, recognizes the best student class project of the year.  The faculty were extremely impressed with the quality of the work and presentations done… Continue reading »

Senior CS Students Awarded Honors

The following Computer Science majors successfully defended their theses on Monday, May 18, and will be awarded honors upon graduation.   Tongyu Zhou: “Confusion Detection on Annotator Affect” (Advisor: Iris Howley) Josh Kang: “SOAR: a Self-Optimizing Adaptive SoC on FPGAs” (Advisor: Duane Bailey) Adly Templeton: “Inherently Interpretable Sparse Word Embeddings… Continue reading »

CS dept response to COVID-19

Hello CS friends, I hope you all are doing as well as can be expected during these unpredictable times.  I wanted to send you an update from our department regarding how we are planning to support you remotely.  Note that we will be reaching out to students in… Continue reading »

CS Colloquium – Faculty Research

Friday, February 07 2:35pm in Wege Auditorium (TCL 123) Computer Science Faculty will present their current research and discuss student research opportunities. Receive colloquium credit for attending and enjoy light snacks! Summer Science Research students, faculty, and staff on the annual canoe trip. Continue reading »

Colloquium – Melanie Subbiah ’17

Friday, January 10, 2020 2:35pm, Wege Auditorium (TCL 123) AI Research at OpenAI: Language Models and Unsupervised Learning Are you interested in working in ML or AI after Williams? In this talk, I will cover my path from Williams to AI research in the tech industry, a… Continue reading »

Colloquium Speaker Iris Bahar, Brown University

Computer Science Colloquium Friday, December 06, 2019 @ 2:35pm Wege Auditorium, TCL 123 “Combined Discriminative-Generative AI Techniques for Robust Scene Perception in Adversarial Environments” Despite the strengths of deep learning using convolutional neural networks (CNNs), they have several vulnerabilities, such as their opacity in understanding how its… Continue reading »