Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱۶-۳۰ از ۴۶۸ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
- machine learning
- robotics
- machine vision
- +۹ مورد دیگر
Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.
Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.
Boris Gutmanمشاهده پروفایل
استادیار
Boris Gutman is an Assistant Professor of Biomedical Engineering at Illinois Institute of Technology , affiliated with the Armour College of Engineering . He holds a Ph.D. and B.S. in Biomedical Engineering and Applied Mathematics respectively from the University of California, Los Angeles (UCLA) .
Ewa Szczurek is a Professor at the University of Warsaw's Faculty of Mathematics, Informatics and Mechanics and Co-director of the Institute for AI for Health, leading joint labs at Helmholtz Munich and the University of Warsaw. Education: Master degree in Computer Science from Uppsala University, Sweden (2005) Master degree in Computer Science from University of Warsaw, Poland (2006) Doctoral degree from Max Planck Institute for Molecular Genetics, Berlin (2011) Postdoctoral fellowship at ETH Zurich, Switzerland (2011) Habilitation at University of Warsaw, Poland (2020) Visiting associate professor at Northwestern University, USA (2023) Research Focus: Her work centers on probabilistic graphical models and deep generative models applied to computational medicine, with emphasis on oncology (tumor microenvironment modeling and evolution), pulmonology, and AI-driven antimicrobial peptide design to combat antibiotic resistance. She develops specialized deep learning frameworks for spatial transcriptomics analysis, tumor evolution reconstruction, and synthetic antimicrobial compound generation. Publication Trends: Recent publications (2022-2023) demonstrate consistent innovation in AI-driven biomedical solutions, bridging machine learning with oncology and infectious disease research. Her work shows increasing focus on generative models for drug design and spatial data analysis, with strong industry-academia collaborations (e.g., Merck, IMMUcan consortium). Awards: ERC Consolidator Grant (2023) for DOG-AMP project Scientific and didactic award from Rector of University of Warsaw ETH Zurich and IMPRS fellowship awards Grants & Leadership: Leads the ERC Consolidator Grant project DOG-AMP for antimicrobial peptide design. Serves as Associate Editor for Genome Biology and participates in program committees for ISMB and RECOMB-CCB conferences. Collaborates with international consortia including IMMUcan and Merck's Oncology Bioinformatics department. Labs & Networks: Co-directs AI for Health Institute with joint labs at Helmholtz Munich and University of Warsaw. Active in ELLIS (pan-European AI network) and Polish Bioinformatics Society, driving interdisciplinary research at the AI-medicine interface.
- human genetics
- medical genetics
- cancer genetics
- +۷ مورد دیگر
Xiaolin Zhu, MD, PhD is an Adjunct Lecturer in the School of Medicine at the University of California, San Francisco (UCSF), specializing in the Genitourinary Medical Oncology Program. His clinical focus is on prostate, bladder, and kidney cancers, while his research explores cancer evolution mechanisms through patient biospecimen analysis. He holds roles as Principal Investigator on grants from the Department of Defense, Prostate Cancer Foundation, and Conquer Cancer Foundation. Education: Bachelor of Medicine (MD equivalent) in Clinical Medicine, Shanghai Jiao Tong University School of Medicine (2011) PhD in Genetics and Genomics, Duke University (2017) Internal Medicine Residency, UT Southwestern Medical Center (2020) Hematology/Oncology Fellowship, UCSF (2024) Research Interests: Focuses on precision oncology, genomic profiling of treatment resistance, and translational research in prostate and urothelial cancers. Key areas include epigenetic evolution in cancer progression, biomarker discovery, and developing novel therapeutic strategies. Awards: ASCO Young Investigator Award (2022-2023) PCF Young Investigator Award (2022-2025) Department of Defense Physician Research Award (2024-2028) Grants: Investigating SSTR1 as a novel biomarker for advanced prostate cancer (DOD, 2024-2028) Resistance mechanisms to androgen receptor inhibitors (PCF, 2022-2025) Genomic profiling of metastatic castration-resistant prostate cancer (ASCO, 2022-2023) Labs/Teams: Active in UCSF's Genitourinary Oncology research teams and collaborates with the West Coast Prostate Cancer Dream Team.
Xu Shiمشاهده پروفایل
دانشیار
Xu Shi is an Associate Professor in the Department of Biostatistics at the University of Michigan. Previously, they held a postdoctoral fellowship at Harvard’s Data Science Initiative (2017) under Tianxi Cai and Eric Tchetgen Tchetgen. Their research focuses on statistical methods for administrative healthcare data, electronic health records (EHR), and causal inference. Key projects include developing scalable pipelines for EHR curation, causal inference methods for comparative effectiveness studies, and co-leading the FDA’s Sentinel Initiative Innovation Center’s Causal Inference Core. Education: Ph.D. in Biostatistics from University of Washington (201?), B.S. in Mathematics & Applied Mathematics from Zhejiang University, China. Research emphasizes harmonizing distributed EHR data, addressing unmeasured confounding in air pollution studies, and rare adverse event analysis through flexible propensity score methods. They also explore medical knowledge extraction from ICD code patterns and natural indirect effect estimation robust to measurement errors. Awards: None explicitly listed. Grants & Advising: No specific grants or students listed, but their work involves collaborative initiatives like the FDA Sentinel Program. Labs/Teams: Active in the Causal Inference Core and distributed healthcare data partnerships with institutions like Henry Ford Health System and Kaiser Permanente.
Kristen Brennand is the Elizabeth Mears and House Jameson Professor of Psychiatry and Professor of Genetics at Yale School of Medicine. She leads research at the intersection of genomic engineering, neuroscience, and stem cell biology to decode mechanisms underlying brain diseases. Her work focuses on translating genetic insights into medically actionable information for schizophrenia, autism, PTSD, and Alzheimer’s disease. Education: PhD in Molecular and Cellular Biology from Harvard (2008), Postdoc at Salk Institute Key research areas: Gene-environment interactions , neuronal circuit dysfunction , precision psychiatry , and reversible disease mechanisms Recent publications span single-cell transcriptomics , neurogenetic atlases , and computational psychiatry methods . Awards: Executive Leadership in Academic Medicine (ELAM) Fellowship (2025) Funded by NIH, New York Stem Cell Foundation, Brain Research Foundation, and Brain and Behavior Research Foundation Laboratory: Brennand Lab at Yale School of Medicine
Perminder Sachdev is a Scientia Professor of Neuropsychiatry at UNSW Sydney and Co-Director of the Centre for Healthy Brain Ageing (CHeBA). He holds roles as Clinical Director of the Neuropsychiatric Institute and has led major longitudinal studies on cognitive aging, including the Sydney Memory and Ageing Study. His research focuses on dementia, neuroimaging, biomarkers, and brain stimulation techniques. Education: MBBS and MD from All-India Institute of Medical Sciences, New Delhi (1978–1983), PhD in Neuropsychiatry from UNSW (1991). Clinical training in Australia, with expertise in movement disorders and later dementia research. Research interests span dementia prevention, neuroimaging biomarkers, and psychiatric disorders in aging. He leads international consortia like COSMIC and STROKOG, and has authored over 600 peer-reviewed papers with an H-index of 90. Awards include NSW Scientist of the Year (2010) and Fellowship of the Australian Academy of Health and Medical Sciences (2015). Key Projects: Sydney Centenarian Study, Older Australian Twins Study. Grants: Extensive funding for aging and dementia research. Labs/Teams: CHeBA, Neuropsychiatric Institute.
Li-San Wangمشاهده پروفایل
دانشیار
Li-San Wang, PhD, serves as Associate Professor of Pathology and Laboratory Medicine and Associate Dean for Computing at his institution, while chairing the Genomics and Computational Biology Graduate Group to direct academic programs and student mentorship. His research spans bioinformatics, computational biology, genomics, and data integration , with concentrated focus on Alzheimer's disease genetics. Wang leverages whole-genome sequencing , multi-ancestry analyses , and structural variation detection to identify risk loci across diverse populations, particularly advancing South Asian and African ancestry genomic studies. Analysis of his 15 most recent publications reveals dominant themes in polygenic risk score transferability across ancestries, genomic data infrastructure development (NIAGADS, ADSP), and functional validation of GWAS findings through tools like BTS and CHARMER. His work consistently addresses health disparities in dementia research through population-specific genetic architectures. As Graduate Group Chair, Wang oversees student training in computational genomics while leading NIH-funded consortia that generate critical genomic resources. His administrative role as Associate Dean for Computing indicates strategic oversight of institutional computational infrastructure supporting large-scale genomic research.
Qiang Weiمشاهده پروفایل
استاد مدعو
- Cancer Genomics
- Epigenetics
- Computational Biology
- +۳ مورد دیگر
Qiang Wei, Ph.D., is an Adjunct Research Instructor in the Department of Molecular Physiology and Biophysics at Vanderbilt University School of Medicine. His research focuses on computational and systems biology approaches to study genomic and epigenomic mechanisms in cancer, drug response prediction, and disease risk gene prioritization. He has pioneered integrative frameworks combining multi-omics data (genomics, epigenomics, transcriptomics) with clinical information for precision medicine applications. Key research areas include: Developing computational tools for analyzing non-coding variants and DNA methylation patterns Characterizing tumor heterogeneity through circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) Identifying genomic drivers of therapy resistance in metastatic cancers Integrating GWAS data with functional genomics for disease mechanism discovery His work spans multiple cancer types including breast, prostate, and hepatocellular carcinoma, with a particular emphasis on translating genomic insights into clinical diagnostics and therapeutic strategies. Ongoing projects include optimizing liquid biopsy approaches for early cancer detection and leveraging single-cell technologies to understand tumor evolution. In recent years, his lab has developed novel algorithms like TVAR for functional variant analysis and Bayesian frameworks for multi-omics integration. These methods have been applied to study schizophrenia genetics, autism risk genes, and platelet reactivity regulation.
- Genomics
- Bioinformatics
- Computational Biology
- +۴ مورد دیگر
Peter Park is a Professor of Biomedical Informatics at Harvard Medical School and Director of its Bioinformatics and Integrative Genomics (BIG) PhD program. His research focuses on computational analysis of genomic data, particularly in cancer genomics, somatic mosaicism, and epigenetic mechanisms. He leads major projects like the Somatic Mosaicism across Human Tissues (SMaHT) network and contributes to consortia such as The Cancer Genome Atlas (TCGA) and ENCODE. His work integrates advanced computational methods with clinical applications, aiming to advance precision medicine and understand genetic drivers of disease. Education: B.A. in Applied Mathematics, Harvard University Ph.D. in Applied Mathematics, California Institute of Technology Postdoctoral studies in biostatistics, focusing on molecular biology and genetics Research Interests: His lab develops algorithms for genomic data analysis, including methods for detecting copy number variations, mutational signatures, and structural variations. Key areas include cancer genomics, somatic mosaicism in neurodegenerative diseases, and 4D nucleome structure-function relationships. He emphasizes translational research, applying computational tools to clinical problems like tumor evolution and drug response prediction. Funding & Grants: NIH grants (e.g., R01HG012573, R01CA269805) Funding from Mark Foundation, Simons Foundation, and Chan Zuckerberg Initiative Principal Investigator for multiple collaborative projects Labs & Teams: The Park Lab collaborates with interdisciplinary teams in computational biology, oncology, and neuroscience. Current projects include developing tools for single-cell DNA sequencing analysis and exploring mutational signatures in cancer and neurodegenerative disorders.
Professor Nicholas G. Martin is a distinguished researcher at the Queensland Brain Institute, University of Queensland, with extensive contributions to behavioral genetics and psychiatric research. His work primarily focuses on twin studies, genetic epidemiology, and the genetic basis of mental health disorders. His research interests span behavioral genetics, particularly twin studies methodology, psychiatric genetics focusing on depression and substance use disorders, neurogenetics examining brain structure and function, and genome-wide association studies of complex traits. Martin has pioneered methodologies for analyzing genetic and environmental contributions to complex human behaviors and disorders. His publication record demonstrates consistent productivity in high-impact journals, with recent work emphasizing polygenic risk scores, gene-environment interactions, and cross-ancestry genetic studies. His research often involves large-scale collaborative efforts examining the genetic architecture of psychiatric conditions and related phenotypes. Multiple publications in Nature Genetics, Molecular Psychiatry, and other top-tier journals Extensive work on depression genetics and related phenotypes Significant contributions to twin study methodology Research spanning psychiatric disorders, substance use, and cognitive traits Professor Martin maintains active collaborations across international research networks and has contributed significantly to advancing our understanding of the genetic underpinnings of human behavior and mental health conditions through rigorous quantitative genetic approaches.
Jennifer Gülerمشاهده پروفایل
دانشیار
Jennifer Güler is an Associate Professor in the Department of Biology at the University of Virginia. Her research focuses on understanding mechanisms of antimalarial drug resistance in Plasmodium falciparum, combining molecular, biochemical, and computational approaches. She leads the Güler Malaria Lab, which collaborates with global researchers to study parasite adaptation and clinical implications of resistance. Education: B.S. in Biology from the University of California, Santa Barbara (2000); Ph.D. in Molecular Microbiology & Immunology from Johns Hopkins Medical Institute (2007). Completed postdoctoral training at Johns Hopkins (2007-2008) and the University of Washington (2008-2013). Research interests include genetic changes driving drug resistance, metabolic adaptation pathways, and translational studies with patient-derived parasites. Her lab develops diagnostic tools like the Malaria TaqMan Array Card for simultaneous drug resistance and speciation analysis. Ongoing work integrates metabolomics and systems biology to uncover survival strategies of resistant parasites. Grants and collaborations involve partnerships with institutions in malaria-endemic countries to bridge lab-based models with clinical realities. Her team's findings contribute to understanding disease severity and informing therapeutic strategies.
Jessica Chiangمشاهده پروفایل
استادیار
Dr. Jessica Chiang is an Assistant Professor in the Department of Psychology at Georgetown University. She holds a BA in Psychology and Economics from Smith College, an MA and PhD in Psychology from UCLA, and completed a postdoctoral fellowship at Northwestern University. Her research focuses on how stress experiences, particularly during early development, influence health across the lifespan, emphasizing links between socioeconomic status, inflammation, and physiological resilience. Key areas include the interplay of stress, sleep, and mental/physical health outcomes in adolescents and young adults. Her work employs longitudinal designs and integrates psychosocial, behavioral, and biological data to understand how adversity and social environments shape health trajectories. She has explored topics such as family socioeconomic status, inflammatory biomarkers, and the moderating roles of sleep and emotional regulation in health disparities. Her research bridges developmental psychology, psychoneuroimmunology, and public health, with implications for interventions targeting vulnerable populations. Dr. Chiang’s studies frequently involve large-scale datasets and collaborations, including pooled analyses of multiple cohorts to examine lifecourse health patterns. She has published extensively on topics such as how early adversity influences immune function, the bidirectional relationships between stress and sleep, and the role of cultural factors in health behaviors. Her findings highlight the need for early interventions to mitigate the long-term impacts of psychosocial stressors.