Our lab fosters an inclusive, judgment-free environment, emphasizing respect for all members and ascribing value to diverse perspectives and all questions. We embrace the richness of multidisciplinary approaches in scholarship and innovation. We support students in cross-disciplinary coursework, encourage collaborative relationships with aligned labs and departments, and celebrate their interdisciplinary achievements.
Graduate Students
Yang Li [LinkedIn]
Former software engineer and UX designer turned researcher, now focused on studying Alzheimer’s progression through topological analysis of brain networks. Passionate about uncovering insights into neurodegeneration using advanced computational methods. Outside of research, I enjoy playing guitar, photography, and finding joy in life’s simple moments.
Undergraduate Students
Yutong Wu [LinkedIn]
My research focuses on applying machine learning in a biomedical context. I adopt brain-inspired techniques to study topological structures in artificial neural networks. I also analyze clinical data on Alzheimer’s disease to gain insights into disease mechanisms and progression.
Former Lab Mentees
Boqian Shi (MS in CS, 2024, First Position: Software Engineer, Google) [LinkedIn]
Research Focus in the Lab: I once delved into high-performance and parallel computing, now inspired by a passion for the analysis of the human brain in Alzheimer’s progression. Outside of research, I am an enthusiastic board game collector and strategist, and enjoy strategic challenges both in and out of academia.
Yingrui Zhang (MEng in FinTech, 2024, First Position: Consultant, FRG) [LinkedIn]
Research Focus in the Lab: My research centers on leveraging data science and machine learning to address practical challenges. I analyze brain network topology to understand the intricacies of synaptic plasticity disruption in the brain circuitry of human dystonia. Beyond academia, I enjoy watching movies, playing sports (soccer, badminton), going fishing and traveling to explore new experiences.
Nirvan Silswal (BS in CS & Stats, 2024, First Position: Software Engineer, GlossGenius) [LinkedIn]
Research Focus in the Lab: My research developed an interpretable image recognition approach that focuses on global shapes and patterns instead of individual pixels, enabling adaptability to changes in image size and providing clearer insights into model decisions.