Find Academic Supervisors

• Immunology
• Genomics
• Multiomics Analysis
• +3 more

Calliope Dendrou is an Associate Professor in Clinical Pathology and Inflammation at the Kennedy Institute of Rheumatology (KIR), University of Oxford, leading the Immune Disease Multiomics Laboratory. She previously held a Wellcome & Royal Society Sir Henry Dale Fellowship at the University of Oxford’s Centre for Human Genetics before joining KIR in 2023. Her research focuses on immune disease mechanisms using multiomics approaches, including genomic profiling to identify therapeutic targets across tissues and immune-mediated diseases. She co-leads large-scale projects like the Oxford-J&J Cartography Consortium and the Chan Zuckerberg Initiative’s LEGACY Network, and teaches on the MSc in Genomic Medicine program. Educational Background: BSc (Biology, Imperial College London, 2005; Forbes Memorial Medal Winner); PhD in Infection & Immunity (University of Cambridge, 2010). Postdoctoral training at the Weatherall Institute of Molecular Medicine under Prof. Lars Fugger. Research interests include immunogenetics, cytokine signaling pathways, drug repositioning, and cross-disease pathophysiology. Her work integrates single-cell and spatial transcriptomics to dissect immune-cell interactions in diseases like rheumatoid arthritis, inflammatory bowel disease, and celiac disease. Recent articles highlight her contributions to understanding vaccine adjuvant responses, Th17 cell roles in spondyloarthritis, and immune-epithelial networks in celiac disease. Collaborations emphasize multi-omic data analysis (e.g., Panpipes pipeline) and translational studies toward precision medicine. Awards: Forbes Memorial Medal (BSc), Wellcome & Royal Society Sir Henry Dale Fellowship. Leadership roles include Equality, Diversity, and Inclusion Champion and 'Single-Cell & Spatial Omics for Precision Medicine' Module Lead. Lab & Teams: Immune Disease Multiomics Lab at KIR. Active in collaborative initiatives such as the LEGACY Network, focusing on large-scale immune profiling in ancestrally diverse populations.

Matthias MannView profile

Professor

• Mass Spectrometry
• Proteomics
• Systems Biology
• +6 more

Professor Matthias Mann is a world-leading scientist serving as Director of the Proteomics and Signal Transduction department at the Max Planck Institute of Biochemistry in Martinsried, Germany, and Director of the Proteomics department at the Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Denmark. With an h-index exceeding 277 and over 350,000 citations, he is recognized as the highest cited German researcher and one of the most influential scientists globally in proteomics. His educational background includes: Ph.D. in Chemical Engineering from Yale University (1988) Master's Degree in Physics from Georg August University Göttingen (1984) Bachelor's of Arts in Mathematics from Georg August University Göttingen (1982) Professor Mann's research focuses on advancing mass spectrometry-based proteomics to understand biological systems at the protein level. His work spans technological developments in mass spectrometry, bioinformatics and computational analysis, signal transduction and posttranslational modifications, and clinical proteomics applications for disease diagnosis and treatment. The Mann lab has pioneered groundbreaking methods like SILAC for quantitative proteomics and MaxQuant for proteome data analysis. Their vision is to translate proteomics knowledge into clinical practice for predictive, diagnostic, and preventive medicine, with recent work focusing on AI-guided platforms for analyzing proteomes from minimal tissue samples. Analysis of Professor Mann's recent publications reveals a strong trend toward clinical applications of proteomics, particularly in cancer research, metabolic diseases, and neurodegenerative disorders. His work increasingly integrates spatial proteomics, single-cell resolution techniques, and artificial intelligence approaches to uncover disease mechanisms and identify potential biomarkers, with a clear shift from basic technology development toward direct clinical applications and personalized medicine. Professor Mann has received numerous prestigious awards throughout his career: 2025: Elected member of the American National Academy of Sciences 2024: Dr. H.P. Heineken Award for Biochemistry and Biophysics 2023: Otto Warburg Medal 2019: Nominated member of the Bavarian Academy of Sciences 2013: Elected member of Leopoldina German National Academy of Sciences 2012: Körber European Science Award, Louis-Jeantet Foundation Prize for Medicine, Ernst Schering Prize, and Leibniz Prize Professor Mann leads a highly collaborative research team involved in multiple international networks including the Bill & Melinda Gates Foundation, Michael J. Fox Foundation for Parkinson's Research, CLINSPECT-M, and Munich Heart Alliance. His lab has mentored numerous successful researchers, with several former postdocs receiving prestigious ERC Starting Grants. The Mann group has developed innovative clinical proteomics pipelines for analyzing archived tissue specimens and body fluids, aiming to identify protein markers for early detection of diseases such as diabetes and cancer. The Mann lab operates across two major research centers with state-of-the-art mass spectrometry facilities. Their Clinical Knowledge Graph platform integrates multi-omics data with extensive metadata, creating an ecosystem for machine learning applications in proteomics. Current research focuses on developing highly sensitive methods that can profile thousands of proteins from minimal cell samples, enabling the identification of critical disease-related proteins and supporting the development of individualized therapies.

Long CaiView profile

Professor

• Spatial Genomics
• Cellular Biology
• Developmental Biology
• +4 more

Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.

Xiuwei ZhangView profile

Assistant Professor

• Computational Biology
• Bioinformatics
• Single-Cell Data Analysis
• +5 more

Xiuwei Zhang is the J.Z. Liang Early-Career Assistant Professor in the School of Computational Science and Engineering (SCoSE) at Georgia Institute of Technology, part of the College of Computing. Her research focuses on computational biology and bioinformatics, particularly in developing machine learning methods for analyzing single-cell omics data, including multi-modal, temporal, and spatial data integration. She leads a lab that designs tools like scDART , scMoMaT , and scMultiSim , which address challenges in multi-omics integration, lineage reconstruction, and simulation. Before joining Georgia Tech, she held postdoctoral positions at UC Berkeley (Nir Yosef’s group), the European Bioinformatics Institute (EBI), and École Polytechnique Fédérale de Lausanne (EPFL). She earned her PhD in computer science from EPFL under Bernard Moret. Her Erdős number is 3, reflecting her collaborative work across computational fields. Her research spans four key areas: multi-batch/single-cell data integration, temporal analysis of cell differentiation, spatial-temporal omics dynamics, and simulation tools for benchmarking methods. She has received prestigious awards, including the NSF CAREER Award (2022) and NIH MIRA (2021). She actively participates in conferences (RECOMB, ISMB) and serves on editorial boards (Journal of Computational Biology). Her group’s recent work includes the scMultiSim simulator (2025), which generates multi-omics spatial data, and LinRace (2023), reconstructing cell lineage histories. She mentors over 15 students and collaborates internationally on projects like the InQuBATE Workshop on Single-Cell Transcriptomics.

• Cancer Biology
• Mathematical and Computational Modeling
• Bioinformatics
• +7 more

Dana Pe'er is a Professor and Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) of Memorial Sloan Kettering Cancer Center. She is also an Investigator of the Howard Hughes Medical Institute and holds the Alan and Sandra Gerry Endowed Chair. Dr. Pe'er leads an interdisciplinary research group that combines advanced genomics approaches with machine learning to address fundamental questions in biomedical science, with particular focus on cancer biology, developmental biology, and immunology. Dr. Pe'er earned her PhD from Hebrew University in Jerusalem, Israel. Her academic journey includes a postdoctoral fellowship with George Church at Harvard Medical School. Before joining Memorial Sloan Kettering Cancer Center in 2016, she held faculty positions at Columbia University. Dr. Pe'er's research focuses on understanding cellular plasticity, the consequences of intra-tumor heterogeneity, cancer evolution and metastasis, and the mechanisms by which regulatory circuits go awry in disease. Her lab combines single-cell and spatial profiling technologies with machine learning approaches to investigate gene regulation, cellular plasticity, and cell-cell communication in the contexts of cancer, immunity, and development. They are particularly interested in how organisms develop from a single cell to generate diverse cell types, how epigenetic control rewires during development, and how cells communicate to execute multicellular responses. Analysis of Dr. Pe'er's recent publications reveals a strong focus on developing computational methods for single-cell and spatial genomics data analysis. Her work spans cancer types including pancreatic, prostate, colorectal, and breast cancer, with emphasis on tumor heterogeneity, metastasis mechanisms, and cellular plasticity. A significant portion of her research involves creating novel algorithms and tools like CellRank, REUNION, and SEACells that enable researchers to extract meaningful biological insights from complex genomic datasets. 2023 Class of 2023 Inductee - American Academy of Cancer Research (AACR) Academy 2023 Innovator Award - International Society for Computational Biology (ISCB) 2021 Fellow - International Society for Computational Biology (ISCB) Howard Hughes Medical Institute Investigator (2021) 2019 Ernst W. Bertner Memorial Award - University of Texas MD Anderson Cancer Center 2016 Lenfest Distinguished Faculty Award - Columbia University 2014 Director's Pioneer Award - National Institutes of Health 2014 Overton Prize - International Society for Computational Biology (ISCB) Dr. Pe'er is known for her dedicated mentorship approach, describing herself as "a mama bear" who cares deeply about her trainees while expecting independence, innovation, and hard work. She mentors numerous PhD students and postdocs in her lab. Her HHMI Investigator award provides approximately $9 million over seven years, enabling ambitious research directions. She also collaborates extensively with the Single-cell Analytics and Innovation Lab (SAIL) at MSK to generate new data from emerging technologies, working closely with wet-lab collaborators at MSK and beyond to apply computational methods to cutting-edge datasets across multiple disease areas. The Pe'er Lab is an interdisciplinary group of computational biologists with diverse backgrounds ranging from pure mathematics to clinical medicine. They work closely with wet-lab collaborators to apply their computational methods to cutting-edge datasets across cancer, immunology, and developmental biology. The lab is described as open, supportive, collaborative, and fun, with access to world-class facilities at the Sloan Kettering Institute. Dr. Pe'er's work continues to push the boundaries of computational biology and cancer research, with the ultimate goal of developing more effective, personalized therapies for cancer patients.

Rong FanView profile

Professor

• Single-cell Omics
• Spatial Omics
• Immunology
• +5 more

Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.

Vitaly KheyfetsView profile

Associate Professor

• Pulmonary Arterial Hypertension
• Critical Care Medicine
• Pediatric Cardiology
• +4 more

Vitaly Kheyfets, PhD, serves as Associate Professor in the Department of Pediatrics-Critical Care Medicine at the University of Colorado Anschutz Medical Campus School of Medicine, where he directs research at the intersection of pediatric critical care and cardiopulmonary pathophysiology with emphasis on pulmonary arterial hypertension (PAH). His primary research focuses on right ventricular adaptation to pulmonary hypertension, utilizing machine learning-driven multi-omics analysis to identify disease biomarkers and molecular networks. He pioneers computational fluid dynamics approaches for hemodynamic modeling in congenital heart conditions like Glenn physiology, while also investigating sleep oscillatory patterns as neurodegenerative biomarkers. His methodology integrates proteomics, spatial transcriptomics, and pressure waveform analysis to dissect vascular remodeling mechanisms. Publication trends reveal a strong emphasis on translating computational models into clinical applications for PAH prognostication, with recent work developing AI-cooperative diagnostic platforms and characterizing microvascular changes in the right ventricle. Cross-disciplinary collaborations span proteomics, imaging, and sleep neuroscience, demonstrating consistent innovation in both pulmonary hypertension and neurodegenerative disease biomarker discovery.

Maizie ZhouView profile

Assistant Professor

• Computational Genomics
• Bioinformatics
• Computational Neuroscience
• +6 more

Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.

Franz Lennard RicklefsView profile

Professor

• Neurosurgery
• Neuro-oncology
• Molecular Neurobiology
• +5 more

Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.

Thomas WalterView profile

Professor

• Biomedical Image Analysis
• Machine Learning
• Computational Pathology
• +4 more

Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.

Facundo M. FernandezView profile

Professor

• Analytical Chemistry
• Bioanalytical Mass Spectrometry
• Ambient Sampling/Ionization
• +4 more

Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.

Chad J BrennerView profile

Associate Professor

• Head and Neck Cancer
• HPV-Driven Oncogenesis
• Liquid Biopsy Technologies
• +5 more

Dr. Chad J. Brenner is an Associate Professor in the Department of Otolaryngology-Head and Neck Surgery and Pharmacology at the University of Michigan Medical School. He also directs the U-M Program in Cellular and Molecular Biology, the Otolaryngology Clinical Laboratory (CLIA), and the Head & Neck Oncology Program. His research focuses on developing liquid biopsy tools for cancer detection and precision therapy, particularly in HPV-driven head and neck cancers. Key roles include membership in the Rogel Cancer Center, Kresge Hearing Research Institute, and Center for Computational Medicine & Bioinformatics. Education: B.S. in Biomedical Engineering, M.S. in Bioelectrical Engineering, and Ph.D. in Cellular and Molecular Biology from the University of Michigan, with doctoral work on prostate cancer mechanisms. Research Interests: HPV integration and cancer heterogeneity Urine- and blood-based liquid biopsies for real-time cancer monitoring Combination immunotherapy strategies for improving checkpoint inhibitor responses Genetic engineering to identify cancer vulnerabilities Clinical trial innovation for adaptive therapies Recent Work Highlights: Development of the MyHPVscore blood test for HPV-related head and neck cancer detection, and exploration of tumor-immune interactions through PD-L1 and T-cell profiling. Ongoing projects include PET-guided radiotherapy optimization and multi-omics analyses of tumor heterogeneity. Labs & Teams: Leads the Michigan Otolaryngology and Translational Oncology (MiOTO) lab and collaborates with interdisciplinary teams across computational medicine, immunology, and clinical oncology.

Ali ShojaieView profile

Professor

• Biostatistics
• Statistics
• Machine Learning
• +4 more

Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.

Ben RaphaelView profile

Professor

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +6 more

Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.

• Systems Biology
• Cancer Research
• Drug Resistance
• +7 more

Sampsa Hautaniemi is a Professor at the Department of Biochemistry and Developmental Biology within the Faculty of Medicine at the University of Helsinki. He serves as Principal Investigator of the Systems Biology of Drug Resistance in Cancer research group and holds docentship in the Faculty of Medicine. Professor Hautaniemi actively supervises doctoral students across multiple programs including the Doctoral Programme in Biomedicine, Doctoral Programme in Clinical Research, and Doctoral Programme in Integrative Life Science. His research focuses on systems biology approaches to understand drug resistance mechanisms in cancer, particularly ovarian cancer. His work integrates computational biology, genomics, epigenetics, and bioinformatics to develop precision medicine approaches for cancer treatment. His laboratory develops innovative computational methods and experimental models including patient-derived organoids to study tumor evolution, identify therapeutic targets, and predict treatment responses. Analysis of his recent publications reveals a strong emphasis on understanding tumor heterogeneity, clonal evolution during therapy, and the development of computational tools for cancer genomics. His work spans multiple disciplines including cancer biology, computational biology, immunology, and precision medicine, with a particular focus on high-grade serous ovarian cancer as a model system. Scientific Awards: The Anders Jahre Medical Prize to young medical scientists (2014) for outstanding research on systems biology and cancer The Finnish Medical Foundation 50-years jubileum award (2010) Professor Hautaniemi has supervised numerous doctoral students and early-career researchers, contributing significantly to cancer research education. His research is supported by multiple active projects including the DECIDER project (Clinical Decision via Integrating Multiple Data Levels to Overcome Chemotherapy Resistance in High-Grade Serous Ovarian Cancer) funded until 2026, as well as projects from the Academy of Finland, Cancer Foundation, and industry partners like Orion Corporation. His laboratory maintains a patient-derived organoid biobank for high-grade serous ovarian cancer and develops computational tools like Jellyfish for visualizing tumor evolution.