The UVa Artificial Intelligence and Machine Learning (AIML) seminar provides a communication platform for our colleagues and friends who are working on artificial intelligence, machine learning, and their applications.
If you would like to receive the announcement of seminar talks, please subscribe our mailing list: aiml-seminar at virginia.edu
We are looking for speakers to share their work in this AIML seminar. If you are interested in giving a talk or suggest a speaker, please contact the current organizers: {yangfeng, ptf8v, ylkuo} at virginia.edu
If you would like to watch the previous talks, you can find the videos here.
Upcoming Talks
Past Talks
- 2025-04-09 Shenghui (Vivian) Chen : Human-Agent Collaboration under Incomplete Information: Intent, Communication, and Planning
- 2025-03-26 Haolin Liu : Reinforcement Learning in Changing Environments: Statistical Complexity and Efficient Algorithms
- 2025-03-19 Jing Yang : On the Training Dynamics of Transformers for Language Tasks
- 2025-03-05 Roger Chang, Hung-Ru Lin : Leveraging Mental Simulation for Dynamic Experience Retrieval in LLM Agents & Rethinking Self-Enhancement Bias in LLMs
- 2025-01-29 Matthew Korban : Beyond Frames: How Transformer Networks Revolutionize Video Understanding and Analysis
- 2024-12-04 Zezhou Cheng : Recognize Anything in 3D with Minimal Human Supervision
- 2024-11-20 Sheng Li : Generalizing Models to Unseen Domains and Open Environments
- 2024-11-13 Juba Ziani : Towards More Realistic and Fairer Strategic Classification
- 2024-10-30 Ruixuan Liu : Privacy Challenge as Models Scale: Training Efficiency and Amplified Risks
- 2024-10-23 Xiang Yue : Understanding and Improving Reasoning in Large Language Models
- 2024-10-16 Mathews Jacob : Generative AI for Faster and better MRI
- 2024-10-03 Labs in SEAS : AI/ML Resource Fair
- 2024-04-24 Jack Morris : Inverting Language Models
- 2024-04-17 Furong Huang : Invisible Foes: Crafting and Cracking AI in the Shadows of Language - Poison Data and Jailbreak Prompts for LLMs
- 2024-04-10 Tao Tu : How LLMs might help us scale world class healthcare to everyone?
- 2024-03-27 Zhifan Lu : I can still see you: Layer 2 correlation attack in LTE Tor network
- 2024-02-28 Anshuman Suri : Distribution Inference: New Perspectives in Data Privacy
- 2024-02-14 Yushun Dong : Responsible Graph Machine Learning Under a Fairness Lens
- 2023-11-29 Students & Kaizhao Liang : Student Flash Talks & Simulating Disease Progression via Progressive Image Editing
- 2023-11-15 Stephanie Schoch & Hannah Chen : Data Contribution Estimation for Large Language Models & Debiasing Can Be Complementary
- 2023-11-08 Tariq Iqbal : Toward Fluent Collaboration in Human-Robot Teams
- 2023-11-01 Seokhyun Chung : Probabilistic Predictive Analytics for Collaborative Systems
- 2023-10-25 Denis Nekipelov : Identification and privacy guarantees
- 2023-10-18 Miaomiao Zhang : Deep Neural Networks To Analyze Deformable Shapes From Images
- 2023-10-04 Ferdinando Fioretto : Constrained-aware Machine Learning
- 2023-09-20 Tom Hartvigsen : Towards Responsibly Deploying Machine Learning in Healthcare
- 2023-09-06 Chen-Yu Wei : Exploration Bonus for Policy Optimization
- 2022-02-04 Cynthia Rudin : Scoring Systems: At the Extreme of Interpretable Machine Learning
- 2021-12-03 Lirong Xia : The Semi-Random Possibilities of Social Choice
- 2021-11-12 Zhuoran Yang : Demystifying (Deep) Reinforcement Learning: The Pessimist, The Optimist, and Their Provable Efficiency
- 2021-11-05 Guru Guruganesh : Big Bird: Transformers for Longer Sequences
- 2021-10-29 Felix Lin : Transformers on the Edge
- 2021-10-22 Matthew Dwyer : Distribution-aware Validation and Verification of Neural Networks
Sponsors
University of Virginia School of Engineering & Applied Science