Events

Final Oral Examination for Doctor of Philosophy (Computer Science) Rohit Gupta Thursday, October 30, 2025 3:00PM – 4:00PM Global 229 Dissertation Video is now a key medium for learning, communication, and autonomy, so perception systems must recognize fine-grained activities, adapt to new concepts, stay robust under change, and support multiple capabilities. Current methods fall...

Final Oral Examination for Doctor of Philosophy (Computer Science) Jyoti Kini Friday, October 31, 2025 2:00PM – 3:00PM Research I, 101A Dissertation Autonomous agents rely on robust segmentation, detection, and tracking to perceive, reason about, and act in dynamic environments. These perception capabilities form the core of intelligent systems, from self-driving vehicles navigating urban...

Final Oral Examination for Doctor of Philosophy (Computer Science) Akash Kumar Monday, October 13, 2025 2:00PM – 3:00PM Dissertation Deep learning has significantly advanced visual understanding tasks like object detection, tracking, and spatio-temporal grounding, benefiting fields such as autonomous vehicles, surveillance systems, and robotics. While large-scale labeled datasets have been crucial to this success...

Dr. Vibhav Vineet Microsoft Research Thursday, September 25, 2025 2:00PM – 3:00PM Global 229 | Zoom Abstract As AI shifts from passive models to active agents, the key bottleneck has become data—its quality, coverage, and controllability. Constructing labeled datasets, whether for per-pixel segmentation or multi-step user interaction, is slow, expensive, and privacy-sensitive. At the same...

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

Dr. Curtis Lisle KnowledgeVis, LLC Thursday, July 24, 2025 3:00PM – 4:00PM ENG1 224 | Zoom Abstract In this presentation, I will cover how I have used AI and machine learning solutions in several healthcare-related contexts. I will start by describing lung cancer research I performed along with radiation oncologists at Orlando Health. We studied...

Mr. Song Wang University of Virginia Thursday, July 17, 2025 2:00PM – 3:00PM R1 101A | Zoom Abstract As machine learning systems are increasingly deployed in dynamic and real-world environments, it is crucial to ensure their ability to generalize beyond the conditions seen during training, such as new data distributions and novel tasks. In this...

Mr. Haoteng Yin Purdue University Thursday, July 10, 2025 1:00PM – 2:00PM TCII 222 | Zoom Abstract Graph learning is a critical driver of advances in scientific discovery, business modeling, and AI-assisted decision-making. However, two fundamental roadblocks hinder its broader adoption: the scalability of powerful, subgraph-based learning methods, and the critical need to protect sensitive...

Dr. Junyuan “Jason” Hong UT Austin Wednesday, July 9, 2025 1:00PM – 2:00PM R1 101A | Zoom Abstract Artificial Intelligence (AI) has demonstrated remarkable potential for tackling grand challenges in human society. Yet, building an integrative Responsible AI system that is comprehensively aligned with multifaceted human values —rather than a single one —remains a major...

Mr. Mudit Gaur Purdue University Thursday, July 3, 2025 11:30AM – 12:30PM R1 101 | Zoom Abstract Artificial Intelligence has become central to innovation in medical diagnostics, offering In this talk, I will present our recent work on bilevel reinforcement learning (BRL), a powerful framework used in tasks such as reinforcement learning from human feedback...

Dr. Pegah Khosravi City University of New York Monday, June 30, 2025 2:00PM – 3:00PM R1 101 | Zoom Abstract Artificial Intelligence has become central to innovation in medical diagnostics, offering unprecedented potential for precision, scalability, and clinical relevance. In this seminar, I will share my research journey at the intersection of deep learning, biomedical...

Dr. Wanhua Li Harvard University Thursday, June 26, 2025 11:30AM – 12:30PM R1 101 | Zoom Abstract Vision Foundation Models (VFMs) have revolutionized computer vision, achieving remarkable generalization across diverse 2D image tasks. However, building general-purpose intelligent agents requires perception that goes beyond static 2D pixels—integrating language, 3D spatial reasoning, and temporal dynamics. In this...

Mr. Yue Bai Northeastern University Monday, June 23, 2025 11:00AM – 12:00PM R1 101 | Zoom Abstract Modern intelligence systems are increasingly designed for multiple modalities to solve real-world tasks. On the one hand, these multi-modal models are expected to deliver strong performance across diverse and complex inputs for practice. On the other hand, as...

Dr. Xinyu Chen MIT Thursday, May 22, 2025 1:30PM – 2:30PM R1 101 | Zoom Abstract In spatiotemporal systems, large amounts of time series data—such as urban mobility and climate variables—are readily available for implementing downstream machine learning tasks and supporting decision-making. Since these data are characterized by spatiotemporal dimensions and reflect underlying system patterns...

Mr. Shashank Sonkar Rice University Thursday, May 8, 2025 11:00AM – 12:00PM R1 101 | Zoom Abstract The rapid advancement of artificial intelligence, particularly large language models, is fundamentally reshaping how we learn and interact with knowledge, offering unprecedented opportunities to develop intelligent systems that enhance human learning at scale. However, realizing this potential requires...

Dr. Xiaomin Lin Johns Hopkins University Monday, May 5, 2025 10:00AM – 11:00AM HEC 101A | Zoom Abstract Autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are transforming marine research, defense, and industrial applications. However, significant challenges persist in enabling robust perception, energy-efficient operation, and adaptive autonomy in dynamic underwater environments. This talk explores...

Dr. Zhenyi Wang University of Maryland Wednesday, April 16, 2025 2:00PM – 3:00PM HEC 101A | Zoom Abstract Deep learning has driven remarkable progress across diverse domains. However, most methods assume stationary data distributions, which rarely hold in real-world applications. In practice, data distributions evolve over time, creating significant challenges such as catastrophic forgetting and...

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...