Graph Based Machine Learning: Enhancing Artificial Intelligence to Better Understand Relations of Concepts
Announcing the Final Examination of Sijie Zhu for the degree of Doctor of Philosophy in Computer Science
Towards Secure, Privacy-preserving and Fair Artificial Intelligence
Dr. Murat Kantarcioglu The University of Texas at Dallas Wednesday, September 6, 2023 10:00AM – 11:00AM HEC 101A | Zoom Abstract In the age of big data and Artificial Intelligence (AI), protecting the security and privacy of stored data is paramount for maintaining public trust, accountability and getting the full value from the collected data. […]
Towards Efficient Learning under Label Noise: From Dawid-Skene to Deep Neural Networks
Ms. Shahana Ibrahim Oregon State University Friday, June 30, 2023 2:00PM – 3:00PM CREOL 102/103 | Zoom Abstract With the rise of the AI revolution, there is an exceptionally high demand for data labeling. However, most data labeling is done through crowdsourcing techniques, where non-expert annotators often provide labels. This results in considerably noisy labels, […]
Towards Optimal and Adaptive Algorithms for Optimization in Games
Mr. Junchi Yang ETH Zurich Thursday, June 29, 2023 10:00AM – 11:00AM TC2 222 | Zoom Abstract Recent advancements in Artificial Intelligence (AI), such as AlphaZero and ChatGPT, have significantly impacted various fields. Reinforcement learning (RL) plays a crucial role in these achievements. However, deploying RL in real-world applications, including robotics, finance, and healthcare, presents […]
Deployable Reinforcement Learning: Addressing Real-World Challenges of Artificial Intelligence
Dr. Amrit Singh Bedi University of Maryland Monday, June 26, 2023 10:00AM – 11:00AM Research I 101 | Zoom Abstract Recent advancements in Artificial Intelligence (AI), such as AlphaZero and ChatGPT, have significantly impacted various fields. Reinforcement learning (RL) plays a crucial role in these achievements. However, deploying RL in real-world applications, including robotics, finance, […]
Advanced Machine Learning Techniques for Processing Complex Data
Dr. Dixon Vimalajeewa Texas A&M University Thursday, May 25, 2023 10:00AM – 11:00AM Technology Commons II 222 | Zoom Abstract The recent advancements in Internet of Things (IoT), Internet of Nano-Things (IoNT), and Information and Communication Technologies have made it possible to collect large amounts of data from previously inaccessible locations, such as the human […]
A Tale of Two Worlds: The Variational Approach to Machine Learning
Dr. Alexander Alemi Google Wednesday, May 24, 2023 10:00AM – 11:00AM Research I 101 | Zoom Abstract We are in the era of deep learning. The alchemy that is deep learning enables us to tractably search in the space of functions. Our challenge then is to provide meaningful objectives to guide that search. I will […]
Interactive Decision Making in Multi-agent Intelligent Systems
Mr. Chuanhao Li University of Virginia Monday, May 22, 2023 11:30AM – 11:30PM HEC 101B | Zoom Abstract Interactive decision making (e.g., bandit and reinforcement learning) is a promising paradigm for developing intelligent systems capable of efficiently exploring unknown environments. It has achieved remarkable success in recommender systems, clinical decision systems, robotics and cyber-physical systems, […]
Trustworthy and Explainable Artificial Intelligence
Dr. Anh Nguyen Auburn University Wednesday, May 17, 2023 11:00AM – 12:00PM HEC 101A | Zoom Abstract Artificial Intelligence (AI) is becoming ubiquitous and a key that enables humankind to reach unprecedented horizons (e.g. in protein folding). However, in high-stake applications (e.g. cancer diagnosis, automated target detection, or face identification), a single incorrect decision of […]
Enabling Urban Intelligence by Harnessing Human-Generated Spatial-Temporal Data
Ms. Xin Zhang Worcester Polytechnic Institute Tuesday, May 16, 2023 2:00PM – 3:00PM HEC 101A | Zoom Abstract Technology advancement in mobile sensing and communication has enabled a massive amount of mobility data to be generated from human decision-makers, which we call human-generated spatial-temporal data (HSTD). Applying the HSTD to extract the unique decision-making strategies […]