Past events

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During each session, the graduate staff will review:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

Students considering the following programs should attend:

• Computer Science M.S.
• Computer Science M.C.S.
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate
  
   

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Rosanna Bellini (Cornell Tech), will be giving a talk titled "Decoding Abusive Adversaries for Safer Digital Systems."

Abstract

People today face threats to their digital safety that most computing systems were never designed to protect them from: those closest to them. Abusive adversaries take ample advantage of standard user interfaces and ineffective anti-abuse mechanisms, leveraging their close social and physical proximity to their target to stalk, harass, and control. In this talk, I describe my research focused on intimate partner violence where I: (1) pioneer approaches to engaging with abusive adversaries first hand across online and in-person contexts, (2) design and deploy bespoke systems to challenge abusive behaviors via community-based interventions; and (3) develop new frameworks for building abuse-resilient technologies. I outline my research vision to achieve digital safety for all people across critical domains, including finance, healthcare, and research.

Biography

Rosanna Bellini is a Postdoctoral Associate at Cornell Tech in New York City. Her research develops data-driven and engaged research methods to tackle complex societal challenges, such as technology-enabled harms. Her work has been published in top-tier human-computer interaction (HCI) and computer security venues, including USENIX Security, IEEE S&P, CHI, and CSCW, and featured on the BBC World Service. She has received multiple Best Paper awards from CHI and CSCW, as well as Distinguished Paper awards from USENIX Security. Her research has helped to prompt legislative changes and improvements to consumer-facing financial applications, benefiting tens of millions of customers. She also helps to lead the Clinic to End Tech Abuse, a frontline service for survivors of technology-facilitated abuse, and has personally helped over 150 survivors reclaim their privacy, security, and financial freedom.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Anshuman Chhabra (UC-Davis), will be giving a talk titled "Towards a Future Powered By Safe and Human-Centered AI and ML."

Abstract

With the growing adoption of ML and AI in society, there is an increasing need to ensure that models (and systems employing these models) are centered around human safety so as to minimize harm to end users. In this talk, I will motivate this problem through multiple examples, and delve into my works on the safety risks associated with security, robustness, fairness, and accuracy of model performance. I will describe my efforts along two thrusts: (1) advancing the science behind safe ML/AI, and (2) improving real-world systems to minimize harm. These efforts offer both theoretical guarantees and empirical efficacy, illustrating the synergy of theory and pragmatic application. To conclude, I will discuss two exciting directions for future work in this domain: (1) accelerating Generative AI alignment efforts, and (2) utilizing Generative AI for social good.

Biography

Anshuman Chhabra is a recent Ph.D graduate (September 2023) at the University of California, Davis advised by Prof. Prasant Mohapatra. His research seeks to safeguard users from harm by curbing the negative behavior of foundational ML/AI models as well as real-world systems employing these models. He received the UC Davis Graduate Student Fellowship in 2018, and has held research positions at Lawrence Berkeley National Laboratory (2017), the Max Planck Institute for Software Systems, Germany (2020), and the University of Amsterdam, Netherlands (2022). His research has been funded by the NSF, Army Research Laboratory, and Robert N. Noyce Foundation.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Yogatheesan Varatharajah (University of Minnesota), will be giving a talk titled "Domain-guided Machine Learning for Healthcare."

Abstract

Recent advances in wearables, brain implants, and sensing technology have enabled us to design systems that continuously monitor patients' brain health and ascertain individualized treatments for neurological diseases. However, there is a lack of efficient methods that translate continuous physiological data streams into meaningful biological models of underlying diseases, relate them to existing clinical knowledge and biomarkers, and provide actionable treatment parameters. Machine learning (ML) holds great promise in tackling these challenges; however, the mainstream black-box-ML approaches have proven to be untrustworthy because of label inconsistencies, spurious correlations, and the lack of deployment robustness. My goal is to ensure trustworthiness in ML for healthcare, particularly neurology, via a novel framework known as “Domain-guided Machine Learning” or “DGML” that merges machine learning with clinical domain expertise. In this talk, I will discuss the need for trustworthy ML in healthcare, how to leverage clinical domain knowledge to engineer trustworthy ML models, and several real-world applications of DGML in neurological care and decision making.

Biography

Dr. Yoga Varatharajah is an Assistant Professor in Computer Science & Engineering at the University of Minnesota and a Visiting Scientist at the Mayo Clinic, Rochester. He leads the Health Intelligence Laboratory, where an interdisciplinary team of researchers work closely to develop novel ML-based solutions to improve disease diagnosis, clinical decision making, review of patient data, and discovery of new clinical knowledge. He obtained his Ph.D. in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. Over the past seven years, he has been working closely with domain experts at Mayo Clinic and Cleveland Clinic to develop, evaluate, and deploy domain-guided ML models to inform clinical decisions related to neurological diseases. His research has been published at reputed engineering conferences (e.g., Neurips, ML4H, BIBM, ISBI, EMBC, NER) and medical journals (e.g., Scientific Reports, Journal of Neural Engineering, Brain Communications, Epilepsia, Neuroimage), has contributed to an ongoing clinical trial in neuromodulation for epilepsy, and has resulted in a joint patent between Mayo and Illinois. He also received several honors, including a CSL Ph.D. Thesis Award, a Mayo-Clinic-Illinois Alliance Fellowship, an American Epilepsy Society Young Investigator Award, an NSF CRII Research Initiation Award, NCSA Faculty Fellowship, and several best paper awards and nominations.

Computer Science Ph.D. application deadline for fall.

We offer fall admission only and do not admit for the spring semester.

Computer Science Ph.D. application deadline to be considered for fellowships for fall. Applications must be submitted by 11:59 p.m. CST.

We offer fall admission only and do not admit for the spring semester.

Interested in learning about the post-PhD job application process? We have a diverse group of faculty joining for a panel on various elements of the academic and industry job markets. Students who plan to graduate in the next couple of years will get a chance to learn more about writing the application materials, the interview process, and how search committees make hiring decisions. Early career students will get a chance to start thinking about their options post-graduation and ask questions about these. We look forward to an engaging discussion with you all! 

CSE DSI Machine Learning seminars will be held Tuesdays 11a.m. - 12 p.m. Central Time in hybrid mode. We hope facilitate face-to-face interactions among faculty, students, and partners from industry, government, and NGOs by hosting some of the seminars in-person. See individual dates for more information.

This week's speaker, Albert Berahas (University of Michigan), will be giving a talk titled, "Next Generation Algorithms for Stochastic Optimization with Constraints".

Abstract

Stochastic gradient and related methods for solving stochastic optimization problems have been studied extensively in recent years. It has been shown that such algorithms and much of their convergence and complexity guarantees extend in straightforward ways when one considers problems involving simple constraints, such as when one can perform projections onto the feasible region of the problem. However, settings with general nonlinear constraints have received less attention, and many of the approaches that have been proposed for solving such problems resort to using penalty or (augmented) Lagrangian methods, which are often not the most effective strategies. In this work, we propose and analyze stochastic optimization algorithms for deterministically constrained problems based on the sequential quadratic optimization (commonly known as SQP) methodology. We discuss the rationale behind our proposed techniques, convergence in expectation and complexity guarantees for our algorithms, and present numerical experiments that we have performed. This is joint work with Raghu Bollapragada, Frank E. Curtis, Michael O'Neill, Daniel P. Robinson, Jiahao Shi and Baoyu Zhou.