Dr. Mingyao Li joined the Biostatistics faculty in 2006. She is also a faculty member of the Genomics and Computational Biology graduate program. Her main research area is statistical genetics and genomics. The central theme of her current research is to use statistical and machine learning approaches to understand cellular heterogeneity in human disease relevant tissues, to characterize gene expression diversity across cell types, and to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell transcriptomics studies. In addition to methods development, Dr. Li is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes. Her collaborative research includes cardiometabolic diseases, age-related macular degeneration, Alzheimer's disease, chronic kidney disease, type 1 diabetes, and cancer. Findings from her research will seed cell-specific functional studies, in vivo modeling, and precision therapeutic targeting of human diseases. Dr. Li actively serves in the scientific community. She is an Associate Editor of Statistics in Biosciences, and was a regular member of the Genomics, Computational Biology and Technology study section and a member of the review committee of the Center for Inherited Disease Research of the NIH.

We are a computational systems immunology lab. Our research focuses on the development and use of novel systems approaches to analyze high-dimensional immunological datasets, and elucidate molecular mechanisms of immunological disorders. Our past work has utilized systems approaches to analyze Mendelian mutations in the context of three-dimensional protein-protein interaction networks, to understand molecular mechanisms of corresponding disorders. We have also developed network analyses frameworks to characterize the evolutionary dynamics of these protein networks. Another key dimension of our past work has been the use of statistical and machine-learning approaches for the analyses of high-dimensional antibody-omic to elucidate correlates of vaccine-mediated and natural immunity in HIV, tuberculosis and malaria.

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We are currently working on using network systems and functional genomic approaches to perform multi-scale integration of genomic and epigenomic datasets with biological networks to identify molecular phenotypes underlying these immunological disorders, with an emphasis on autoimmune and alloimmune diseases. We also use high-dimensional statistical and machine-learning techniques to integrate multi-omic datasets (genomic, transcriptomic, proteomic, metabolomic and antibody-omic) and elucidate molecular mechanisms of immune regulation and dysregulation.

​My research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology (Nature Medicine'23 cover article), human-AI collaboration (Nature BME'24 cover article), neurodegenerative diseases (Nature Communications'23), optimizing LLMs (Nature'25), etc. My research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, my postdoc mentors and I co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology.

Dr. Gunderson received his bachelor’s in science from the University of Wisconsin-River Falls and his Ph.D. in Immunology and Infectious disease from Penn St. University where he studied the role of innate and adaptive inflammation during RAS-initiated skin carcinogenesis. He then moved to a postdoctoral fellowship in the laboratory of Lisa Coussens at Oregon Health and Science University. Here he published a study describing the preclinical efficacy of BTK inhibitors in combination with chemotherapy for the treatment of pancreatic cancer. Dr. Gunderson continued his fellowship training in the lab of Kristina Young at the Earle A. Chiles Research Institute - Providence Cancer Center. Their work reported on the mechanism of action for TGFR1 antagonists for colorectal cancer patients, revealing that TGF-signaling directly represses CXCR3-dependent recruitment of CD8+ T cells critical for radiation induced immune responses. Dr. Gunderson also described on the survival benefit of pancreatic cancer patients who spontaneously from tertiary lymphoid structures (TLS) associated with superior humoral immune memory and neoantigen expression. Dr. Gunderson’s research lab in the Pelotonia Institute of Immuno-Oncology at OSUCCC focuses on the mechanisms of how TLS form and the function and fate of TLS resident T and B cells. Recently, his lab reported on how cancer associated fibroblast phenotypes are determinative for TLS formation in PDAC. Currently the Gunderson lab is exploring the clonotypes, phenotypes, and activity of T and B cells that are present in TLS and their relationship to systemic immunity.

​Ivan Raimondi, PhD, is Research Innovation Director in the Landau Lab at the New York Genome Center and Weill Cornell Medicine. His work focuses on developing innovative genomic technologies to study gene regulation, cellular identity, and somatic evolution at single-cell resolution. He has led the development of multiple experimental platforms integrating genomic, epigenomic, and transcriptomic measurements, with a particular emphasis on transforming complex biological questions into scalable molecular assays. Most recently, he led the development of D&D-seq, a technology that enables direct mapping of transcription-factor binding in individual cells through targeted DNA deamination, published in Cell. His broader research interests include multimodal single-cell genomics, chromatin biology, spatial profiling, genome amplification, and technologies for detecting and characterizing somatic mutations in human tissues. His work bridges molecular biology, genomic engineering, and translational research, to create new tools to understand, and ultimately anticipate, human disease.