Current Mentors

Mark Gerstein, PhD Williams Professor of Biomedical Informatics Assoc w/ MBB & SDS Mark Gerstein began working at Yale University in 1997 as an assistant professor. He holds appointments in the Department of Molecular Biophysics and Biochemistry and the Department of Statistics & Data Science. He was later named an associate professor in 2001 and became co-director of the Yale Computational Biology and Bioinformatics Program in 2003. He is currently the Albert L Williams Professor of Biomedical Informatics. Dr. Gerstein’s research is engaged in biomedical data science for the past ~25 years – before the field had a defined name. He initially focused on macromolecular structure and physical simulation due to the availability of data and a well-developed calculational formalism. While he continues to work in these areas, the excitement surrounding the human genome has led us to increasingly focus on genomics. Overall, his lab serves as a connector, bridging the vast data generation in the biomedical sciences with analytic approaches from statistics and computer science, particularly AI-driven methods. Much of our work takes place within large consortia, such as ENCODE and 1000 Genomes. He has chaired the analysis groups of numerous national and international projects, including ENCODE, modENCODE, PsychENCODE, 1000 Genomes, PCAWG, ERCC, and SCORCH. Prof. Gerstein completed his PhD training in Computational Chemistry and Biophysics at Cambridge University, followed by postdoctoral training at Stanford. Since then, he has published >600 manuscripts in total, including several in prominent venues, such as Science, Nature, and Cell, with an H-index of >200. He has also written popular science pieces for venues such as Scientific American and the Wall Street Journal. He is a specialist in bioinformatics with a particular interest in large-scale data analysis, especially as it pertains to personal genome analyses. Current research foci in his lab include disease genomics (particularly neurogenomics and cancer genomics), human genome annotation, genomic privacy, network science, wearable and molecular image data analysis, text mining of the biological science literature and macromolecular simulation. Prof. Gerstein has received awards such as being elected as a fellow of the AAAS and ISCB. His lab currently comprises >35 trainees and he has placed >35 of his past alumni/ae in academic faculty positions and an equivalent number in industry positions. He has mentored >200 Yale undergraduates and has taught undergraduate and graduate courses in bioinformatics at Yale for >20 years. He has also consulted for many companies and currently serves on several corporate advisory boards.

Dr. Hua Xu is Robert T. McCluskey Professor and Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science at Yale School of Medicine (YSM), as well as Assistant Dean for Biomedical Informatics at YSM. He received his Ph.D. in Biomedical Informatics from Columbia University. His primary research interests include biomedical natural language processing (NLP) and data mining, as well as their applications in secondary use of electronic health records data for clinical and translational research. His research is funded by multiple agencies (i.e., NLM, NCI, NIGMS, NIA, AHA, and CPRIT), and methods/tools developed in his lab have been widely used to support diverse biomedical applications. Dr. Xu is a fellow of both the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

Dr. David van Dijk is an Assistant Professor in the departments of Computer Science and Internal Medicine at Yale University, where he leads research in developing advanced machine learning (ML) and artificial intelligence (AI) algorithms for large-scale biomedical data. His work integrates foundation models, large language models (LLMs), graph representation learning, and neural operator learning to analyze spatiotemporal biological systems. Dr. van Dijk earned his PhD in Computer Science from the University of Amsterdam and the Weizmann Institute of Science, focusing on ML techniques to understand the link between DNA sequence and gene activity. He completed postdoctoral training at Columbia University and Yale, specializing in manifold learning and single-cell genomic data analysis. Currently, Dr. van Dijk’s lab develops innovative AI methods for diverse biomedical applications, including single-cell RNA sequencing, electronic health records, medical imaging, and brain activity data. His research aims to uncover novel biological patterns, predict clinical outcomes, and drive advances in precision medicine. Dr. van Dijk’s contributions have been recognized with awards such as the Dutch Research Council Rubicon Fellowship, Colton Award, Roberts Innovation Award, NIH R35 MIRA Award, and NSF CAREER Award.

Dr. Hall's research career spans the fields of genetics, genomics, bioinformatics and data science. He received a B.A. in Integrative Biology from the University of California at Berkeley (1998), and worked as a technician for 2 years in Sarah Hake's plant genetics group at the USDA/ARS Plant Gene Expression Center. He received his Ph.D. in genetics from Cold Spring Harbor Laboratory (2003), where his work in Shiv Grewal's laboratory established the first direct link between RNA interference and chromatin-based epigenetic inheritance. As a postdoc with Michael Wigler (2004) and independent Cold Spring Harbor Laboratory Fellow (2004-2007), Dr. Hall used microarray technologies and mouse strain genealogies to conduct the first systematic study of DNA copy number variation hotspots. As a faculty member at the University of Virginia (2007-2014), Washington University (2014-2020) and Yale (2020-present), his work has sought to understand the causes and consequences of genome variation in mammals, with an increasing focus on computational methods development and human genetics. His group has developed bioinformatics tools for variant detection, variant interpretation, sequence alignment, data processing, and data integration. He has led genome-wide studies of human genome variation, heritable gene expression variation, human genetic disorders, tumor evolution, mouse strain variation, genome stability in reprogrammed stem cells, and single-neuron somatic mosaicism in the human brain. Dr. Hall's work has been featured in Science Magazine's Breakthrough of the Year (2003 & 2007), the NIMH Director's "Ten Best of 2013" and The Scientist (2013), and he has received several prestigious awards including the AAAS Newcomb Cleveland Prize (2003), the Burroughs Wellcome Fund Career Award (2006), the NIH Director's New Innovator Award (2009), and the March of Dimes Basil O'Connor Research Award (2010). He has also served as an Associate Editor at Genome Research (2009-2014) and Genes, Genomes and Genetics (2011-2018).

Most recently, Dr. Hall has played a leadership role in several large collaborative projects funded by NIH/NHGRI including the Centers for Common Disease Genomics, the AnVIL cloud-based data repository and analysis platform, and the Human Pangenome Project. His current work is focused on two broad goals: (1) mapping variants and genes that confer risk to human disease, with ongoing projects focused on coronary artery disease and cardiometabolic traits in unique and underrepresented populations, and (2) developing methods for the detection and interpretation of human genome variation, with an emphasis on structural variation and other difficult-to-detect forms, and on comprehensive trait association in human disease studies.

Associate Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine); Director of Data Analysis and Bioinformatics Hub, The Center for Precision Pulmonary Medicine (P2MED); Assistant Professor, Biostatistics

Dr. Yan received doctoral degrees in both applied statistics and computational biology and bioinformatics. She is interested in genetics, genomics, computational biology, biostatistics, system biology and bioinformatics. Her current research topics include (1) understanding disease heterogeneity and pathogenesis using large-scale omics data at both bulk and single cell resolution and (2) developing novel statistical and computational methods for analyses of different types of omics data and the integration of them with drug perturbation data for potential personalized treatment design.

John Tsang is a systems immunologist, computational biologist, and engineer. He is the Anthony N. Brady Professor of Immunobiology and Biomedical Engineering at Yale University, a Chan Zuckerberg Biohub Investigator and the Yale lead of CZ Biohub New York, and the Founding Director of the Yale Center for Systems and Engineering Immunology (CSEI). The CSEI serves as a home and cross-departmental center of research for systems, quantitative, and synthetic immunology at Yale University. Dr. Tsang earned his PhD in biophysics from Harvard University (2008) as an NSERC Postgraduate Scholar, and has Master of Mathematics (MMath) and Bachelor of Applied Science (BASc) degrees in computer science and computer engineering from the University of Waterloo, Canada.

Dr. Tsang's research group investigates the molecular and cellular underpinnings of human immune variations in health and disease: why immune system states and responses to perturbations (e.g., to vaccines, viral infections, and diseases) are highly variable across individuals in the human population. Their approach involves the development and application of machine learning, quantitative modeling, and experimental methods, including high-dimensional, longitudinal immune monitoring of human cohorts throughout the lifespan and around the globe, ex vivo experiments, and animal models.

As a scientific conceiver and the Yale lead of CZ Biohub NY, Dr. Tsang is interested in developing a predictive immune cell engineering toolkit to program immune cells as sensors of tissue statuses (e.g., early detection of pre-clinical disease and inflammation). Towards achieving this vision, he and his colleagues are working on quantitatively dissecting the mechanisms and design principles of tissue-blood communications and immune cell trafficking, including cell-cell interaction and signal integration by immune cells in tissues.

He has won multiple awards for his research, including NIH/NIAID Merit Awards recognizing his scientific leadership in systems immunology, COVID-19, and human immunology research. His work on mapping human immune variations and predicting vaccination responses was selected as a Top NIAID Research Advance of 2014. Dr. Tsang has served as an advisor on systems immunology and computational biology for numerous programs and organizations, including the Allen Institute, World Allergy Organization, National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Snow Medical and the Walter and Eliza Hall Institute (Australia), and the Fred Hutchinson Cancer Center. He currently serves on the Editorial Board of PLOS Biology and the Scientific Advisory Board of NIAID ImmPort, the NIAID Influenza IMPRINT Program, the NIH Common Fund Cellular Senescence Network (SenNet), Vaccine and Immunology Statistical Center of the Gates Foundation, the Human Immunome Project, ImmunoScape Inc., and CytoReason Ltd. He has organized major scientific conferences, including Keystone and Cold Spring Harbor Laboratory meetings on systems and engineering immunology.

Prior to joining Yale, Dr. Tsang was a tenured Senior Investigator in the National Institutes of Health's Intramural Research Program and led a laboratory focusing on systems and quantitative immunology at the National Institute of Allergy and Infectious Diseases (NIAID). He was the Co-Director of the Trans-NIH Center for Human Immunology (2017-2022) and led its research program in systems human immunology (2010-2022) until his move to Yale. He remains an Adjunct Investigator at NIAID.

Dr. Ma received his Ph.D. degree in statistics at University of Wisconsin in 2004. Prior to arriving at Yale, Dr. Ma was a Senior Fellow in Collaborative Health Studies Coordinating Center (CHSCC) and Department of Biostatistics at University of Washington. He has been involved in developing novel statistical and bioinformatics methodologies for analysis of cancer (NHL, breast cancer, melanoma, lung cancer), mental disorders, and cardiovascular diseases. He has also been involved in health economics research, with special interest in health insurance in developing countries.

Sidi Chen joined the Yale Faculty in 2015 in the Department of Genetics, Systems Biology Institute, and Yale Cancer Center. His research focuses on providing a global understanding of biological systems and development of novel breakthrough therapeutics. Chen developed and applied genome editing and high-throughput screening technologies, precision CRISPR-based in vivo models of cancer, global mapping of functional drivers of cancer oncogenesis and metastasis. He is leading a research group to seek global understandings of the molecular and cellular factors controlling disease progression and immunity. His group continuously invents versatile systems that enable rapid identification of novel targets and development of new modalities of cancer immunotherapy, cell therapy and gene therapy. His goal is to uncover novel insights in cancer and various other immunological diseases and develop next generation therapeutics.

Dr. Chen received a number of national and international awards including the Pershing Square Sohn Prize, DoD Era of Hope Scholar, NIH Director’s New Innovator Award, Blavatnik Innovator Award, Yale Cancer Center Basic Science Research Prize, AACR NextGen Award for Transformative Cancer Research, Ludwig Foundation Award, Damon Runyon Cancer Research Fellow, Dale Frey Award for Breakthrough Scientists, TMKF Innovative/Translation Cancer Research Award, BCA Exceptional Research Grant Award, MRA Young Investigator Award, V Scholar, Bohmfalk Scholar, Ludwig Family Foundation Award, St. Baldrick’s Foundation Award, CRI Clinic & Laboratory Integration Program (CLIP), MIT Technology Review Top 35 Innovators (Regional), and Sontag Foundation Distinguished Scientist Award.

Dr. Youli Xia is a Senior Scientist in the Department of Computational Innovation at Boehringer Ingelheim. She joined Boehringer in 2022 and her work focuses on harnessing multi-modal data and advanced analytics to accelerate early drug programs in oncology research. She contributes to multiple cross-functional internal and external initiatives to discover novel cancer therapeutics. Youli earned her Ph.D. in Bioinformatics and Computational Biology from the University of North Carolina at Chapel Hill, where she studied breast cancer genomics and developed a DNA-based predictive framework for known biomarkers. Prior to joining Boehringer, she consulted for a Spanish biotech company, supporting the clinical translation of her Ph.D. biomarker research. Whilst she joined an AI-driven drug discovery startup as a computational biologist, focusing on leveraging single cell sequencing and perturbation modeling for drug discovery.

LinkedIn:

Google Scholar

Jon Hill is a researcher at Boehringer Ingelheim specializing in the discovery and validation of novel therapeutic concepts for cardiorenal metabolic diseases. His work integrates traditional bioinformatics with cutting-edge artificial intelligence approaches to uncover new insights into disease mechanisms. With over two decades of experience in the pharmaceutical industry, Jon has contributed to drug discovery efforts across a range of diseases and development stages. His core research centers on the biological underpinnings of metabolically driven fibrotic diseases, leveraging multi-omic techniques to identify key drivers of disease onset and progression. A long-standing advocate for natural language processing (NLP) in scientific research, Jon is particularly interested in the application of large language models (LLMs) to accelerate the extraction, synthesis, and interpretation of unstructured data from the scientific literature. His current focus is on using these tools to enhance the quality and efficiency of scientific decision-making.

LinkedIn

Google Scholar

Dr. Boris Bartholdy is a principal scientist at Boehringer Ingelheim in Biberach, Germany, where he focuses on genetics to identify and assess potential drug targets, and support target safety assessments. He holds a Ph.D. in Genetics and has over 15 years of experience in translational research. His work has contributed to multiple high-impact publications and collaborative projects across the world.

Prior to his work in the pharmaceutical industry, he held academic appointments at the Albert Einstein College of Medicine in the Bronx, USA, where he focused on translational biostatistics and bioinformatics in the context of cell biology, with particular focus on hematopoietic stem cells and cancer.

Publications

Daniel joined Boehringer Ingelheim at the end of 2021. He is a Principal Scientist and Partner for Obesity research. His focus is on integration and interpretation of human multi-omic data with the aim of better understanding obesity biology and identifying new therapeutic targets. Daniel obtained his PhD in obesity neurobiology from the University of Cambridge. He then pursued postdoctoral studies at the University of Michigan in mouse genetics, and at Stanford University in functional genomics. He then moved to the Helmholtz Centre in Munich, where he did functional genomic research on neurological disorders. Directly before joining BI, he worked at the Nucleic Acid Therapy Accelerator in Harwell, England.

LinkedIn

Google Scholar

Alexandra Popa has joined Boehringer-Ingelheim as a Principal Scientist in the Computational Department since 2020. Her research is focused on the identification of new targets as well as the establishment of strong biomarkers in the field of oncology. Prior to her current position, she worked at the CeMM Institute in Austria (studying the evolution and impact of the SARS-COV-2 pandemic, the Tasmanian Devil transmissible cancer, the virus-induced liver immune-metabolism), the IPMC Institute in France (investigating the translational mechanism of proteins in cells and profiling immune cell populations in cutaneous squamous cell carcinoma), and the LBBE laboratory in France (examining the mechanisms of transcription processing during alternative splicing). Dr. Popa has obtained her PhD from the Universite Claude Bernard Lyon 1 in France on the topic of recombination-induced genome evolution changes.

LinkedIn

Google Scholar

Dr. Katja Koeppen is a Senior Scientist in the Department of Global Computational Biology and Digital Sciences at Boehringer Ingelheim in Ridgefield, Connecticut. Her focus is on research and drug target discovery in immunology and respiratory diseases. She is currently developing a new gene prioritization algorithm to accelerate the identification of novel drug targets. Dr. Koeppen was originally trained as a biochemist and molecular biologist and obtained her PhD from the University of Tuebingen in Germany. Over 10 years ago, she started transitioning from the wet lab to computational biology during her research on Cystic Fibrosis at Dartmouth College. Dr. Koeppen has extensive experience analyzing complex data sets and teaching these skills to others. She has developed and published several web applications that enable data analysis by scientists without computational skills. She is passionate about harnessing high throughput data to gain a better understanding of disease mechanisms and to identify novel drug targets and treatment options.

LinkedIn

‪Google Scholar

Zuojian Tang is a principle scientist of Global Computational Biology and Data Sciences at Boehringer Ingelheim (BI) with extensive experience alongside both computational biology and bioinformatics engineering. Prior to joining BI, she worked as bioinformatics engineer for Memorial Sloan Kettering Cancer Center. She also spent about 10 years with New York University Langone Health as senior research scientist. Zuojian has designed and developed widely recognized and adopted analysis methods and systems for various computational biological applications. She has more than 35 peer reviewed full-length papers published with more than 3000 citations. Zuojian received her Ph.D. of Systems and Computational Biomedicine from New York University, Master of Computer Science from McGill University, Canada, and Bachelor of Engineering in China.

LinkedIn

Google Scholar

Dr. Di Feng is a senior member of GCBDS (Global Computational Biology and Digital Science), working at Boehringer Ingelheim's US headquarters in Ridgefield, CT on computational drug discovery research. He is a Computational Biology professional with substantial multidisciplinary expertise in Computational Immunology, Pathology, and Machine Intelligence applications for drug discovery. Dr. Feng managed the Artificial Intelligence and Machine Learning partnership with The Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western's University Hospital, the Cleveland Medical Center. Dr. Feng has also contributed open source software tools such as Single Cell Explorer, a platform to facilitate the collaboration between computational biologists and experimental scientists. Dr. Feng led computational projects for a small molecule and biological drug program from early research to clinical trials. He has worked with and led teams to solve complex research challenges using computational approaches across multiple therapeutic areas such as Cancer Immunology, Immunomodulation, Immunology and Respiratory, and Cardiometabolic diseases. He received Ph.D. from Rutgers - Graduate School of Biomedical Sciences, studying basic and clinical biology of plasmacytoid dendritic cells, followed by postdoc research on autoimmune and cancer susceptibility genes with integrating bioinformatics with wet lab science. He also earned his medical degree from Shanghai Jiao Tong University School of Medicine. Prior to joining Boehringer Ingelheim, he developed therapeutics supported by Lupus Research Alliance.

LinkedIn