Upcoming

Past

• Towards Efficient and Practical Privacy-Preserving Computing and AI

  Speaker: Qian Lou Date: Nov 22, 2:05pm – 3:15pm Abstract: With growing reliance on cloud-based Machine Learning as a Service (MLaaS), privacy concerns are escalating, particularly in fields like healthcare and finance. For instance, clinicians and researchers use AI models…

• Incredible Yet Limited Large Language Models in the Wild

  Speaker: Hanjie Chen Date: Nov 8, 2:05pm – 3:15pm Abstract: Large language models (LLMs) demonstrate remarkable capabilities in handling a wide range of tasks, from generating human-like text to answering complex questions. Despite achieving remarkable performance on existing benchmarks, the…

• Responsible Graph Machine Learning Under a Fairness Lens

  Speaker: Yushun Dong Date: Nov 1, 2:15 – 3:05 pm Abstract: Graph learning algorithms have been increasingly deployed in a plethora of real-world applications, such as epidemic analysis, healthcare, and financial analysis. Nevertheless, there has been a rise in societal…

• The Age of Creative Machines: Their Rise, Impact, and Vision for the Future

  Speaker: Maya Ackerman Date: Oct 25, 2:15 – 3:05 pm Abstract: What happens when machines become creative? How did creative machines evolve from their beginnings in academia to their industry proliferation? While this shift has sparked excitement, it has also…

• Harnessing Explainable, Equitable, and Actionable Informatics and AI to Improve Health

  Speaker: Zhe He Date: Oct 18, 2:15 – 3:05 pm Abstract: Over the past two decades, data science and artificial intelligence (AI) have significantly transformed biomedical research and healthcare. The growing availability of large-scale data, such as electronic health records…

• Graph Representation Learning for Network Generation, Optimization, and Verbalization

  Speaker: Liang Zhao Date: Oct 4, 2:05pm – 3:15pm Abstract: Graphs are ubiquitous data structure that denotes entities and their relations, such as social networks, citation graphs, and neural networks. The topology of graphs is discrete data which prevents it…

• New Efficient Machine Learning Methods: from Convolutional Neural Networks to Large Language Models

  Speaker: Shangqian Gao Date: Apr 3, 11:45am–12:45pm Abstract: Recently, Machine Learning and Artificial Intelligence have achieved significant success in various domains, such as playing GO, ChatGPT, recommendation systems, autonomous driving, etc. However, the model architectures behind these accomplishments have grown…

• Physics-Motivated and Inspired Probabilistic Learning

  Speaker: Shibo Li Date: Apr 1, 11:45am–12:45pm Abstract: AI has emerged as the most transformative and revolutionary technique, reshaping many aspects of our lives. Its intersection with science, particularly physics, has opened new avenues for understanding our world and universe.…

• The Power of Opening the Black Box of Deep Learning

  Speaker: Chaoyue Liu Date: Mar 29, 11:45am–12:45pm Abstract: We are now observing an ongoing “AI spring” powered by the emergence and successful implementation of deep neural network models. However, neural networks are often considered as black box models lacking a…

• Data-Efficiency and Robustness in Machine Learning

  Speaker: Shiwei Zeng Date: Mar 27, 11:45am–12:45pm Abstract: Machine learning has been a powerful tool in the modern world. In the past decades, due to the explosion of unverified data sources, and the increasing interaction between human and computer, it…