Description:

Wednesday / CS Seminar
September 25th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Miklos Z. Racz (Miki Racz)

Talk Title
Recent Advances in Learning in Networks: The Interplay Between Community Recovery and Graph Matching

Abstract
Community recovery and graph matching are two fundamental inference tasks on networks. I will discuss recent work that studies the interplay between them, focusing on the natural setting of edge-correlated stochastic block models. We determine the information-theoretic thresholds for both graph matching and community recovery. In particular, this uncovers and characterizes a region of the parameter space where exact community recovery is possible using multiple correlated graphs, even though this is impossible using a single graph and exact graph matching is also impossible. Furthermore, we develop an efficient algorithm for these tasks under a condition on the correlation strength that is conjectured to be necessary. This is based on joint works with Shuwen Chai, Julia Gaudio, Anirudh Sridhar, and Jifan Zhang.

Biography
Miklos Z. Racz is an assistant professor at Northwestern University, jointly in the Department of Statistics and Data Science and the Department of Computer Science. Before joining Northwestern, he received his PhD in Statistics from UC Berkeley, he was a postdoc in the Theory Group at Microsoft Research, Redmond, and was then an assistant professor at Princeton University. Miki's research lies at the interface of probability, statistics, computer science, and information theory, with a focus on combinatorial statistics, discrete probability, and applied probability. Miki's research and teaching has been recognized by Princeton's Howard B. Wentz, Jr. Junior Faculty Award, a Princeton SEAS Innovation Award, and an Excellence in Teaching Award.

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Zoom: https://northwestern.zoom.us/j/94751868037
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=82943682-aad1-43d3-85a6-b1ef00ee3f53

Description:

Wednesday / CS Seminar
October 2nd / 12:00 PM
Hybrid / Mudd 3514

Speaker
Yan-Ran (Joyce) Wang, Stanford University

Talk Title
Advancing Healthcare Through AI and Machine Learning Innovations

Abstract
Healthcare is approaching one-fifth of the U.S. economy, roughly 10% of the global economy, yet it remains riddled with challenges such as inequitable access, an unprecedented demographic shift toward older populations, and unsustainably rising per capita costs. This convergence of forces signals an urgent need for innovative solutions. In my talk, I will explore how AI innovations, driven by the pressing needs of healthcare, are advancing both clinical and technological frontiers. I will present the impactful work of our team in developing safer medical imaging, automated diagnosis for cardiovascular diseases, and AI-powered precision oncology. Central to this work are specialized deep learning algorithms specifically designed for healthcare, with careful consideration of the underlying biological complexities. These advancements not only represent breakthroughs in medical diagnosis and treatment but have also expanded the possibilities for AI, particularly in computer vision, making a broader contribution to the AI landscape as a whole.

Biography
Dr. Yan-Ran (Joyce) Wang is a trailblazer in the intersection of artificial intelligence and healthcare. After completing her Ph.D. in Computer Science at Northwestern University in 2019, she joined Stanford's Biomedical Data Science and the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI). Dr. Wang’s research is at the intersection of artificial intelligence and healthcare, where she focuses on developing advanced machine learning methods inspired by healthcare challenges. Dr. Wang is committed to driving AI-enabled discoveries in complex human diseases, particularly in the fields of digital cardiology and precision oncology. As both first and corresponding author, Dr. Wang has published groundbreaking research in leading journals including Nature Medicine (2024), Cancer Cell (2024), European Journal of Nuclear Medicine and Molecular Imaging (2023), and Radiology: Artificial Intelligence (2023). Her work was notably highlighted in the July 2024 issue of Cancer Cell, where it was selected as a preview article, with an accompanying editorial from SickKids Hospital, the world's leading pediatric care institution. In addition to her impact in healthcare, Dr. Wang’s contributions to computer vision and algorithm development have been showcased at premier conferences like CVPR, ICIP, AAAI, and ACM Multimedia.

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Zoom: https://northwestern.zoom.us/j/95736395513
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=978e7148-20f5-4379-bda4-b1ef01233708
DEI Minute: TBA

Description:

Wednesday / CS Seminar
October 09th / 12:00 PM
Hybrid / Mudd 3514

Speaker
David Radke, Chicago Blackhawks

Talk Title
Presenting Multiagent Challenges in Team Sports Analytics

Abstract
This talk will present several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, where players invade the opposing team's territory and can interact anywhere on a playing surface (ice hockey or soccer). We discuss how MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. We highlight topics along the axes of coaching (i.e., short-term strategy) and management (i.e., long-term team planning) and present high level findings at the intersection of multiagent reinforcement learning and ice hockey.

Biography
Dr. David Radke is a Senior Research Scientist with the Chicago Blackhawks of the National Hockey League (NHL). He holds a PhD from the University of Waterloo where he was advised by Dr. Kate Larson and Dr. Tim Brecht. His research areas include artificial intelligence (AI) and ice hockey analytics, specifically focusing on the areas of multiagent systems and reinforcement learning. He has published several papers at several top AI conferences, including IJCAI, AAAI, and AAMAS. He has also published papers at sports analytics workshops and LINHAC, a hockey analytics conference, where he received a best paper award in 2022 for his work with NHL tracking data.

Research/Interest Areas:
Artificial Intelligence, Multiagent Systems, Reinforcement Learning

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Zoom: https://northwestern.zoom.us/j/96174601253
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3fb25d27-4daa-4eaa-8084-b1ef00f60a80 
DEI Minute: TBA

Description:

Monday / CS Seminar
October 14th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Xiao Wang, Northwestern University

Talk Title
Tackling Challenges in Deploying Modern Cryptography

Abstract
Modern cryptographic protocols play a critical role in securing information at various stages. Despite significant advancements in their design, challenges remain in practical deployment. These challenges often arise from concerns related to efficiency, security, and user awareness. My research addresses these issues by exploring the theoretical foundations, protocol design, and real-world applications of cryptography. In this talk, I will present key efforts to tackle these obstacles, the impact of these solutions, and lessons learned along the way.

Biography
Xiao Wang is an assistant professor of Computer Science at Northwestern University. His research focuses on practical secure multi-party computation, zero-knowledge proofs, post-quantum cryptography, and their applications to artificial intelligence, formal methods, and health. He was a postdoctoral researcher at MIT and Boston University between 2018 and 2019. He obtained a Ph.D. from the University of Maryland in 2018.

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Zoom: https://northwestern.zoom.us/j/93028426865
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=1980ec4e-09e2-4d1c-a16e-b1fd014104fe
DEI Minute:  Domestic Violence Awareness Month http://tinyurl.com/cspac-dei-minute

Description:

Wednesday / CS Seminar
October 16th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Christos Dimoulas, Northwestern University

Talk Title
The Rational Programmer, A Method for Investigating Programming Language Pragmatics

Abstract
"The work of software developers directly depends on decisions that
language creators make for them. Therefore, when language creators choose
from a number of design alternatives, they should consider how their
choices affect the developers. Specifically, language creators should
consider the use of a language feature in particular work contexts, an
idea analogous to what linguists call ``pragmatics.'' However, so far,
there are only a few instruments for investigating Programming Language
(PL) pragmatics.

To address this gap, I have developed a new scientific instrument called
the Rational Programmer. At the technical level, the Rational Programmer
method puts the idea of simulation, a technique with a long history in
Computer Science, to new use in PL research. The heart of a
rational-programmer simulation is an algorithmic abstraction of using a
language feature for information gathering, interpretation and action in a
work context. Typically, a rational-programmer simulation produces a
recommendation for a use strategy that a developer can employ while
working in the given context. It may also identify a problematic aspect of
a feature's design with concrete evidence, which then the creators and
developers can leverage to address the problem.  Finally, a
rational-programmer simulation can inform instructors how to teach
students the effective use of a feature. In this talk, I will demonstrate
the workings of the Rational Programmer method with examples."

Biography
Christos is an assistant professor of Computer Science at Northwestern
University. The goal of his research is to understand how Programming
Language techniques can improve the work life of software developers.

Research/Interest Areas:
Programming Languages

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Zoom: https://northwestern.zoom.us/j/99788665335
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=44a7f2e8-bcaf-4f82-beba-b1fd014323d7
DEI Minute: TBA