About the event: The April 29th event marks the next milestone in our collaborative journey and builds momentum from our convening last fall. This professionally facilitated event will kickstart interdisciplinary research ideas and pave a path for competitive formal funding submissions. $$ CAPHI will provide research support and funding for follow-up professional grant writing services to teams formed during our Spring event. $$ 

This gathering is about aligning technical strengths and building essential relationships among participants. By connecting on both the capabilities and interpersonal levels, and by engaging in open dialogue, sharing perspectives, and fostering collaboration, we set the stage for meaningful impact and ensure that our collective efforts are both visionary and actionable. We look forward to seeing you there!

• When: April 29th 2026, from 10:00 am to 3:45 pm
• Where: Dalney 180 Conference Room (926 Dalney Street NW, Atlanta GA 30318; Dalney 180 | Office of Sponsored Programs)
• Parking: W22 Dalney Parking Deck (Directions and Parking | Office of Sponsored Programs)
• Registration: Community & Population Health Initiative (CAPHI)  Spring 2026 Convening – Fill out form
• CAPHI Researcher Database: CAPHI Researcher Database – Fill out form

Speaker:  Ilias Diakonikolas, Professor, The University of Wisconsin–Madison

Abstract:

Robustness is a basic requirement for trustworthy machine learning, yet achieving it efficiently in high dimensions has long been a fundamental challenge. For decades, the prevailing view was that learning algorithms with strong robustness guarantees necessarily come with prohibitive computational cost, creating a sharp tension between statistical guarantees and algorithmic tractability. This talk describes a research program aimed at overcoming this barrier through an algorithmic theory of robust learning. 

 I will describe two interconnected threads within this research program. The first develops a unified framework for efficient robust high-dimensional estimation, including the first polynomial-time algorithms for several fundamental unsupervised learning tasks under adversarial corruption. The second studies supervised learning under noisy labels, with an emphasis on learning predictors with low-dimensional latent representations. I will conclude by discussing future directions, including robustness beyond worst-case corruption and the efficient learning of richer nonlinear representations.

Bio:

lias Diakonikolas is the Lubar Professor in the Department of Computer Sciences at UW Madison. He obtained a Diploma in electrical and computer engineering from the National Technical University of Athens and a Ph.D. in computer science from Columbia University where he was advised by Mihalis Yannakakis. Before moving to UW, he was an Andrew and Erna Viterbi Early Career Chair at USC and a faculty member at the University of Edinburgh. Prior to that, he was the Simons postdoctoral fellow in theoretical computer science at the University of California, Berkeley. His research is on the algorithmic foundations of massive data sets, in particular on designing efficient algorithms for fundamental problems in machine learning. He is a recipient of the ACM Grace Murray Hopper award, a Sloan Fellowship, an NSF CAREER Award, a Romnes Faculty Fellowship, a Google Faculty Research Award, a Marie Curie Fellowship, best paper awards at NeurIPS and COLT, the IBM Research Pat Goldberg Best Paper Award, and an honorable mention in the George Nicholson competition from the INFORMS society. Ilias wrote with Daniel Kane the textbook "Algorithmic High-dimensional Robust Statistics" published by Cambridge University Press.

• What makes hearing fundamentally different from vision?
• What computations allow the brain to infer space from sound?
• How does early blindness reorganize these computations, and what does that reveal about brain plasticity? 

Participants will be introduced to generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The workshop will also cover ethical AI use, good and bad uses of generative AI (GenAI), and rapidly emerging use cases. By the end of the session, participants will have a better understanding of how to begin applying AI to enhance supply chain processes and drive success within their organizations.

Event includes networking, presentation, light bites, beverages, and Q&A.

Featuring Chris Gaffney, Managing Director of the Supply Chain and Logistics Institute (SCL), Academic Program Director for Georgia Tech Professional Education, and Edenfield Executive-in-Residence and a Professor of the Practice in the H. Milton Stewart School of Industrial and Systems Engineering.

Register via Eventbrite to attend the event taking place at the Georgia Tech Atrium (147 Technology Parkway Northwest, Peachtree Corners, GA 30092), the first-of-its-kind professional, academic, and research innovation space within Curiosity Lab’s state-of-the-art Innovation Center.

This event will take place on Wednesday, April 29 from 12 - 1 p.m. at the Beth Cabrera Reflection Space and Multicultural Lounge (John Lewis Student Center). If interested in attending, please RSVP on Engage.