Past events

The computer science colloquium mainly takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Harmanpreet Kaur (University of Michigan), will be giving a talk titled "Leveraging Social Theories to Enhance Human-AI Interaction".

Abstract

Human-AI partnerships are increasingly commonplace. Yet, systems that rely on these partnerships are unable to effectively capture the dynamic needs of people, or explain complex AI reasoning and outputs. The resulting socio-technical gap has led to harmful outcomes such as propagation of biases against marginalized populations and missed edge cases in sensitive domains. My work follows the belief that for human-AI interaction to be effective and safe, technical development in AI must come in concert with an understanding of human-centric cognitive, social, and organizational phenomena. Using human-AI interaction in the context of ML-based decision-support systems as a case study, in this talk, I will discuss my work that explains why interpretability tools do not work in practice. Interpretability tools exacerbate the bounded nature of human rationality, encouraging people to apply cognitive and social heuristics. These heuristics serve as mental shortcuts that make people's decision-making faster by not having to carefully reason about the information being presented. Looking ahead, I will share my research agenda that incorporates social theories to design human-AI systems that not only take advantage of the complementarity between people and AI, but also account for the incompatibilities in how (much) they understand each other.

  
Biography

Harman Kaur is a PhD candidate in both the department of Computer Science and the School of Information at the University of Michigan, where she is advised by Eric Gilbert and Cliff Lampe. Her research interests lie in human-AI collaboration and interpretable ML. Specifically, she designs and evaluates human-AI systems such that they effectively incorporate what people and AI are each good at, but also mitigate harms by accounting for the incompatibilities between the two. She has published several papers at top-tier human-computer interaction venues, such as CHI, CSCW, IUI, UIST, and FAccT. She has also completed several internships at Microsoft Research and the Allen Institute for AI, and is a recipient of the Google PhD fellowship. Prior to Michigan, Harman received a BS in Computer Science from the University of Minnesota.

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. More details about the spring 2023 series will be provided at the beginning of the semester. This week's speaker, Vedant Das Swain (Georgia Institute of Technology), will be giving a talk titled "Passive Sensing Frameworks for the Future of Information Work".

Abstract

We live in a time when our conception of a thriving worker is in flux. These changing definitions are especially affecting information workers, who are increasingly unsatisfied with the care they get at work. Organizations are failing to identify these trends and promote positive behaviors. We need applications that provide precise and actionable insight to help information workers strive for better wellbeing. I believe that sensing day-level behaviors in a passive (automatic, unobtrusive, and continuous) way can offer unique insights into worker success. My research investigates approaches to leverage everyday digital technologies as sensors that enable algorithmic insights for information worker behaviors. In this talk, I will show how passive sensing can reveal personal and social behaviors linked to better performance and mental wellbeing. I will also demonstrate the methodological and societal challenges in predictive applications for work wellbeing with passive sensing. Finally, I will describe my vision to design passive sensing applications as tools to empower information workers towards holistic success.

Biography

Vedant Das Swain is a Ph.D. Candidate in the School of Interactive Computing at the Georgia Institute of Technology, advised by Munmun De Choudhury and Gregory Abowd. His research contributes to the future of work and behavioral wellbeing in general. He identifies, develops, and critiques opportunities to leverage ubiquitous technologies for algorithmic inference of performance and mental wellbeing. He consistently works with organizational psychologists to inform his investigations and also collaborates with Microsoft Research to develop better tools for worker wellbeing. His research has been published at top-tier computing venues like CHI, CSCW, UbiComp/IMWUT, ACII, and IEEE CogMI. His paper at CHI 2022 won a Best Paper Honorable Mention award. He is the winner of the Gaetano Borriello Outstanding Student Award at UbiComp 2022 and the GVU Foley Scholar Award 2022. His research has been supported by IARPA, NSF, CDC, ORNL, and Semiconductor Research Corporation.

RSVP today!.

During each session, the MSSE staff will review:

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

RSVP today!.

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 and Fridays from 11:15 a.m. - 12:15 p.m. More details about the spring 2023 series will be provided at the beginning of the semester. This week's speaker, Tianshi Li (Carnegie Mellon University), will be giving a talk titled "Protecting User Privacy by Helping Developers".

Abstract

Data has driven many technological advancements, while the ubiquitous collection and sharing of data has caused a privacy trust crisis in our society. Developers play a critical role in making apps that respect user privacy, yet many lack the necessary awareness, knowledge, and time to ensure their apps meet privacy requirements. How can we support average developers (who are oftentimes not privacy experts) in building privacy-friendly apps? In this talk, I will discuss how my research at the intersection of Privacy, HCI, and Software Engineering is engaging developers to better protect user privacy. I will talk about two main threads of my work: (1) empirical HCI studies to identify the challenges developers face in handling privacy requirements, and (2) system building work to tackle the identified challenges by building IDE plugins and breaking down privacy responsibilities into lightweight code annotating tasks. In the final remarks, I will discuss my future research agenda of creating a safe and trustworthy world by helping developers.

Biography

Tianshi Li is a Ph.D. Candidate at the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Prof. Jason Hong. Her main research interest lies at the intersection of Human-Computer Interaction, Security and Privacy, and Software Engineering. Before coming to CMU, she received a bachelor's degree in Computer Science from Peking University. She interned at Google during her Ph.D. study, working on research about novel mobile text entry techniques and intelligent notification management systems. Her work has been published at top-tier venues (CHI, CSCW, IMWUT, TOCHI) and has won a best paper honorable mention award at ACM CHI 2022. She was awarded a CMU CyLab Presidential Fellowship in 2021 and named an EECS Rising Star in 2022.

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. More details about the spring 2023 series will be provided at the beginning of the semester. This week's speaker, Devansh Saxena (Marquette University), will be giving a talk titled "Designing Human-Centered Algorithms for the Public Sector: A Case Study of the Child-Welfare System". 

Abstract

Public sector agencies in the United States are increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithmic systems. These data-driven systems purportedly improve decision-making; however, the public sector poses its own unique challenges where all decisions are mediated by policies, practices, and organizational constraints. Drawing upon a case study of the child-welfare system, I highlight how algorithms that do not account for these pertinent aspects of professional practice frustrate caseworkers and diminish the quality of human discretionary work. Why haven’t these algorithms lived up to expectations? And how might we be able to improve them? A human-centered research agenda can help us develop algorithms centered in social-ecological theories that support the decision-making processes of caseworkers, incorporate novel sources of data, as well as offer a means to evaluate algorithms in their real-world contexts.

Biography

Devansh Saxena is a doctoral candidate in the Department of Computer Science at Marquette University and a member of the Social and Ethical Computing Research Lab where he is co-advised by Dr. Shion Guha and Dr. Michael Zimmer. His research interests include investigating and developing algorithmic systems employed in the public sector, especially the Child-Welfare System. His current research examines collaborative child-welfare practices where decisions are mediated by policies, practices, and algorithms. His work is driven by Human-Centered Data Science and sits at the intersection of Human-Computer Interaction, Machine Learning, and FAccT (Fairness, Accountability, and Transparency in Sociotechnical Systems).

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the spring 2023 semester.

This week's speaker, Professor Haizhao Yang (University of Maryland), will be giving a talk titled "Finite Expression Method: A Symbolic Approach for Scientific Machine Learning".

Abstract

Machine learning has revolutionized computational science and engineering with impressive breakthroughs, e.g., making the efficient solution of high-dimensional computational tasks feasible and advancing domain knowledge via scientific data mining. This leads to an emerging field called scientific machine learning. In this talk, we introduce a new method for a symbolic approach to solve scientific machine learning problems. This method seeks interpretable learning outcomes in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can avoid the curse of dimensionality in discovering high-dimensional complex systems. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for learning the solution of high-dimensional PDEs and learning the governing equations of raw data.

Haizhao Yang's personal website

Register now for the 15th Annual Bioinformatics and Computational Biology (BICB) Research Symposium on January 12, 2023 at the University of Minnesota, Rochester Campus.

This year’s program will include distinguished faculty speakers, presentations given by BICB graduate students, updates on the BICB program and a poster session.

Some of our upcoming speakers include:

Christopher Tignanelli - Associate Professor Division of Critical Care/Acute Care Surgery, Department of Surgery

Nuri Ince - Associate Professor of Biomedical Engineering

Dr. Ince will be joining the faculty at the Mayo Clinic in 2023 he is currently a faculty member at the University of Houston 

Gasper Kitange - Associate Professor of Cancer Therapy Resistance and Drug Target Discovery

Student Speakers include:

Nancy Scott

Sharada Sridhar

Josh Fry

Oral and poster abstract submissions by the University of Minnesota and BICB-affiliated graduate students are due December 29, 2022.