Events

Dr. Wu Lin Vector Institute for Artificial Intelligence Monday, April 14, 2025 11:00AM – 12:00PM TC2 222 | Zoom Abstract Mathematical structures play an essential role in classical machine learning. Yet, many such structures remain hidden or underexplored in modern large-scale machine learning systems. Identifying and leveraging these structures not only deepens our understanding of...

Dr. Ali Siahkoohi Rice University Monday, April 7, 2025 10:30AM – 11:00AM HEC 101A | Zoom Abstract Recent advances in artificial intelligence (AI) have shifted computational science and engineering from first-principle methods to data-driven approaches. Such approaches, by leveraging insights from large datasets and machine learning, promise enhanced predictive accuracy and reduced computational costs. However...

Dr. Yue Wang UCF Department of Electrical & Computer Engineering Monday, April 7, 2025 12:00PM – 1:00PM HEC 101 Abstract Offline reinforcement learning (RL) focuses on learning good decision-making strategies from pre-collected datasets, without further interaction with the environment. This is especially important in high-stakes areas like healthcare and transportation, where active exploration can be...

Dr. Takeo Kanade Carnegie Mellon University Wednesday March 26, 2025 12:00PM – 1:30PM HEC 101 | Zoom Artificial Intelligence (AI) is revolutionizing our world, offering unprecedented advancements across various sectors. However, with its immense power comes the responsibility to navigate its ethical implications and societal impacts. Join us for an enlightening session with Professor Takeo...

Mr. Yuzhang Shang Illinois Institute of Technology Monday, March 10, 2025 11:00AM – 12:00PM HEC 101A | Zoom Abstract Artificial Intelligence (AI) models are growing increasingly complex and resource-intensive, posing significant challenges for widespread deployment. This research focuses on efficient AI as a crucial pathway towards democratizing large AI models, making their benefits accessible to...

Dr. Jaehong Yoon University of North Carolina at Chapel Hill Thursday, April 10, 2025 11:00AM – 12:00PM HEC 101A | Zoom Abstract As our world advances, AI systems must evolve beyond static models to become more adaptive and continuously improving. These systems should seamlessly integrate new knowledge and capabilities throughout their operational lifespan while ensuring...

Dr. Peng Wang University of Michigan Thursday, March 13, 2025 11:00AM – 12:00PM TC2 222 | Zoom Abstract Recent empirical studies have demonstrated that diffusion models can effectively learn the image distribution and generate new samples. Remarkably, these models can achieve this even with a small number of training samples despite a large image dimension, circumventing the curse...

Ms. Akshita Gupta TU Darmstadt Friday, February 7, 2025 2:00PM – 3:00PM R1 101 | Zoom Abstract In this talk, I will be discussing advancements in Temporal Action Localization (TAL) with a focus on two key innovations: Efficient Large Model Adaptation and Open-Vocabulary Recognition in Videos.  The first part of the talk introduces the Long-Short-range...

Dr. Ser-Nam Lim UCF Department of Computer Science Monday, February 3, 2025 12:00PM – 1:00PM HEC 101 Abstract I will first go through the work on generative AI that my students and collaborators have been working on. These works answer in general the question “What can Generative AI do for you?”. I will then follow...

Dr. Christian Kümmerle University of North Carolina Monday, January 13, 2025 11:00AM – 12:00PM MSB 318 | Zoom Abstract For a machine learning model’s ability to generalize well to unseen data, it has been understood that it needs to be able to capture a hidden, low-dimensional data distribution in the high-dimensional feature space. In this...

Dr. Kevin Bello Soroco Thursday, November 21, 2024 11:00AM – 12:00PM HEC 101A | Zoom Abstract Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships. However, the task of estimating DAG...

Dr. Yu Tian University of Pennsylvania Thursday, November 14, 2024 11:00AM – 12:00PM HEC 101A | Zoom Abstract In today’s world, deep neural networks drive machine learning systems that pervade every aspect of our daily lives. Yet, their deployment often raises concerns about trustworthiness, including safety, fairness, and other issues. This is particularly important and...

Announcing the Final Examination of Ishan Dave for the degree of Doctor of Philosophy in Computer Science...

Dr. Pingkun Yan Rensselaer Polytechnic University Tuesday, October 22, 2024 2:00PM – 2:45PM HEC 101B | Zoom Abstract Despite advancements in AI models for medical image analysis, ensuring their trustworthiness remains a challenge. Accuracy, interpretability, robustness, and generalizability are critical for gaining the confi-dence of healthcare professionals and other stakeholders. This talk focuses on AI...

Dr. Aditya Desai UC Berkeley Thursday, August 29, 2024 10:00AM – 11:00AM HEC 101A | Zoom Abstract The recent advancements in the capabilities of AI models have been extraordinary. However, training and deploying these models is prohibitively costly. The primary reason for increasing costs is the exponential increase in model sizes, which requires commensurate computing and...

Dr. Qi Zhang University of South Carolina Thursday, August 22, 2024 10:00AM – 11:00AM HEC 101A | Zoom Abstract Cooperative artificial intelligence equips a team of autonomous agents with the capability of planning and learning to maximize their joint utility, which finds a wide range of applications. Current solutions to cooperative AI, instantiated as cooperative...

Announcing the Final Examination of Matias Mendieta for the degree of Doctor of Philosophy in Computer Science...

Announcing the Final Examination of Matias Mendieta for the degree of Doctor of Philosophy in Computer Science...

Announcing the Final Examination of Praveen Tirupattur for the degree of Doctor of Philosophy in Computer Science...

Dr. Nicole Yang Emory University Tuesday, May 21, 2024 11:00AM – 12:00PM R1 101A | Zoom Abstract Dynamical systems are widely used to model complex real-world phenomena. They are models based on physical principles, which are highly interpretable but have limits in expressive capabilities. Furthermore, the already high dimensional, and complicated behavior make them difficult...