Frontier of Artificial Network
A Series of Invited Talks @ FAN Group, CityU

Biography: Lisha Chen’s research develops mathematical and algorithmic foundations for large-scale signal processing and machine learning, with a focus on multi-objective and multi-level optimization and learning theory. Her work addresses structured learning problems involving competing objectives, hierarchical decision-making, and constraints arising from real-world scientific and engineering systems. Bridging theory and practice, her research has contributed to IBM products and patents and provides foundational tools for building machine learning systems that can reason over multiple objectives, and adapt to new deployment conditions. Dr. Chen has organized and delivered tutorials on multi-objective learning at ICASSP and AAAI, and served as a reviewer or program committee member for leading conferences and journals in machine learning, signal processing, and optimization. Her achievements have been recognized through the IEEE Signal Processing Society Scholarship, and the IBM AIRC Fellowship.

Abstract: Many real-world learning systems must balance multiple competing objectives while respecting user-specified preferences. This talk presents recent work on preference-guided multi-objective learning, with a focus on how to efficiently navigate and explore trade-offs along the Pareto front. I will first discuss a framework for navigating the Pareto set toward user-preferred solutions, allowing preferences to directly guide optimization. I will then introduce SURF, a complementary method that steers scalarization weights to uniformly traverse the Pareto front, improving coverage of diverse trade-off solutions. The methods have been applied to multi-lingual speech recognition, multi-objective reinforcement learning, and multi-objective LLM alignment. Together, these works provide principled and computationally tractable tools for incorporating user preferences into modern multi-objective learning systems.

Biography: Jinwei Zhang received his B.S. in Optics and Optical Engineering (2016) and a dual B.S. in Computational Mathematics (2017) from Sun Yat-sen University, China, and his Ph.D. in Biomedical Engineering from Cornell University (2023). He is currently a Postdoctoral Fellow in Electrical and Computer Engineering at Johns Hopkins University and will join the University of Massachusetts Amherst as a tenure-track Assistant Professor in Biomedical Engineering in Fall 2026. His research focuses on accelerated quantitative magnetic resonance imaging, quantitative susceptibility mapping, and lesion-centric imaging analysis in multiple sclerosis and related neurological disorders. He is a recipient of a U.S. Department of Defense Early Investigator Research Award and a Johns Hopkins Data Science and AI Demonstration Award, supporting his efforts to develop and translate lesion-centric imaging biomarkers for neurological diseases.

Abstract: Quantitative magnetic resonance imaging (qMRI) enables noninvasive characterization of tissue microstructure for studying neurodegeneration, but its clinical utility is limited by long scan times and the lack of methods for identifying and tracking individual white matter lesions (WMLs) over time—a hallmark of many neurological disorders. This work develops accelerated qMRI acquisition and reconstruction methods, robust quantitative susceptibility mapping (QSM), and lesion-wise segmentation and tracking algorithms to enable fine-grained analysis of lesion evolution. By integrating pulse sequence programming, physics-informed modeling, and domain-generalizable deep learning, this framework aims to translate lesion-level imaging biomarkers into scalable tools for neurodegenerative disease research and clinical studies.