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Jason SchweinsbergView profile

Professor

• Probability Theory
• Stochastic Processes
• Mathematical Biology
• +5 more

Jason Schweinsberg is a Professor in the Department of Mathematics at the University of California, San Diego (UCSD), where he has been a faculty member since Fall 2004. His academic journey began with a Ph.D. in Statistics from the University of California, Berkeley in 2001, followed by a three-year NSF Postdoctoral Fellowship at Cornell University. His research focuses on probability theory with applications to evolutionary biology and population genetics. Schweinsberg's work centers on stochastic processes involving coalescence, branching Brownian motion, and their connections to biological phenomena. He has made significant contributions to understanding population models undergoing selection, cancer evolution, and spatial mutation processes. Recent publications reveal a strong emphasis on coalescent theory (particularly Λ-coalescents and nested coalescents), branching processes with absorption, and spatial evolutionary models. His work often bridges rigorous mathematical analysis with biological applications, especially in population genetics and cancer modeling. The 15 most recent articles demonstrate consistent focus on asymptotic analysis of stochastic processes, genealogical structures, and mutation dynamics in evolving populations. Scientific Awards: Fellow of the Institute of Mathematical Statistics NSF Postdoctoral Research Fellowship While specific student names aren't listed in the source material, Schweinsberg has delivered numerous lecture series at international institutions including the Indian Institute of Science (Bangalore), Centre de Recherches Mathématiques (Montreal), and the Isaac Newton Institute (Cambridge), indicating active mentorship and academic leadership. His collaborations span multiple institutions, with frequent co-authorship with researchers like Julien Berestycki, Nathanaël Berestycki, and Rick Durrett. Though no formal lab structure is mentioned, Schweinsberg participates in interdisciplinary research communities through workshops at institutions like BIRS (Banff International Research Station), where he presented on mutation patterns in spatially structured populations in May 2025. His work connects probability theory with biological applications through sustained collaborations across mathematics, statistics, and computational biology fields.

Simon CrouchView profile

Research Fellow

• Statistical Modeling
• Epidemiology
• Cancer Research
• +6 more

Simon Crouch is a Senior Research Fellow in Biostatistics at the University of York's Health Sciences department. With a strong mathematical background from Cambridge and Warwick, he leads the analytics team within the Epidemiology and Cancer Statistics Group and works closely with the Haematological Malignancy Research Network (HMRN) and Cardiovascular Health team. His work focuses on statistical modeling of complex epidemiological data related to hematological malignancies. University of Cambridge: MA, MMath in Mathematics University of Warwick: PhD in Mathematics University of Lancaster: MSc in Medical Statistics Dr. Crouch specializes in the statistical modeling of complex epidemiological data, with particular focus on hematological malignancies. His research encompasses predictive modeling, event history analysis, and machine learning applications in cancer epidemiology. He has made significant contributions to understanding myelodysplastic syndromes, lymphoma classification, and survival analysis in blood cancers through population-based studies. His work often involves collaboration with international registries including the European Myelodysplastic Syndromes Registry (EUMDS) and the Haematological Malignancy Research Network. Analysis of his recent publications reveals a strong focus on myelodysplastic syndromes (MDS), with particular attention to risk stratification, survival analysis, and treatment outcomes. His work increasingly incorporates genomic and molecular data to refine disease classification and prediction models. The trend shows progression from purely statistical methodology development toward integrated translational research that combines clinical, genomic, and epidemiological data to improve patient outcomes. Extensive publication record with 113 research outputs including 66 articles, 21 patents, and numerous meeting abstracts Active participation in major international research consortia including MDS-RIGHT and ImmunAID Significant contributions to the development of statistical methodologies for cancer epidemiology Dr. Crouch actively supervises PhD students in mathematical and statistical modeling applied to cancer epidemiology, with particular interest in time-to-event models, complex longitudinal models, and simulation techniques. His research has been supported through multiple projects, including the European Myelodysplastic Syndromes Registry and the MDS-RIGHT project focused on facilitating informed decision-making in hemato-oncology. He contributes to the Advanced Health and Social Statistics module for postgraduate students at the University of York.

• Artificial Intelligence
• Biomedical Informatics
• Machine Learning
• +5 more

Mehmet Eren Ahsen is an Assistant Professor at the University of Illinois at Urbana-Champaign, holding dual appointments in Business Administration and Biomedical and Translational Sciences . He is also the Deloitte Scholar in Accountancy and an affiliate at the Carl R. Woese Institute for Genomic Biology. His research bridges artificial intelligence, healthcare analytics, and biomedical informatics, focusing on applications in medical decision-making, disease diagnosis, and drug development. Key research interests include: AI-driven healthcare workflows, particularly in mammography screening and radiology Machine learning for biomarker discovery and genomic analysis Economics of AI adoption in clinical settings Unsupervised ensemble learning for biomedical problems His work has been recognized by the CHITA Young Researcher Award (2024) . Notable collaborations involve interdisciplinary teams addressing challenges in cancer genomics, supply chain resilience, and pandemic response. Recent studies highlight: Economic impact of AI-human task sharing in mammography Optimizing screening mammography recall strategies Analysis of social media's role in pandemic information dissemination Development of ensemble models for disease prediction Dr. Ahsen's research also explores: Extracellular vesicle RNA signatures for early cancer detection Correlated drug action models for combination therapies Algorithmic fairness in healthcare AI systems His lab integrates computational methods with clinical and genomic data to advance precision medicine and healthcare efficiency.

Carla CasuloView profile

Associate Professor

• Lymphoma Research
• Follicular Lymphoma
• Hodgkin Lymphoma
• +5 more

Carla Casulo, M.D. serves as Associate Professor of Medicine and Assistant Director of Cancer Research Training and Education at the Wilmot Cancer Institute, University of Rochester Medical Center. She holds dual board certification in Hematology and Medical Oncology from the American Board of Internal Medicine. Education and Training: Medical Degree: SUNY Downstate Health Sciences University (2004) Internship & Residency: Yale New Haven Hospital, Internal Medicine (2004-2007) Chief Residency: Yale University School of Medicine Fellowship: Memorial Sloan-Kettering Cancer Center, Hematology & Oncology (2008-2011) Dr. Casulo is an internationally recognized clinician-researcher specializing in all forms of lymphoma, with particular expertise in follicular lymphoma, Hodgkin lymphoma, and young adult lymphoma. Her research focuses on precision oncology approaches using real-world datasets and clinical trials to identify risk factors, optimize treatment sequencing, and improve survival outcomes. She has pioneered work in follicular lymphoma prognostication and transformation patterns. Her publication portfolio reveals strong emphasis on lymphoma subtyping, treatment resistance mechanisms, and novel therapeutic combinations. Recent work includes investigations into venetoclax combinations, T-cell lymphoma diagnostics, and outcomes in young follicular lymphoma patients. She actively leads multiple clinical trials including phase 3 studies in Hodgkin lymphoma and novel approaches for peripheral T-cell lymphoma. Major Awards: NIH K12 Career Development Award Lymphoma Research Foundation Clinical Research Mentoring Program Scholar Arthur W. Bauman Teaching Award Multiple patient care excellence awards including ICARE Patient Centered Bronze Star Dr. Casulo serves as Chair of lymphoma sessions at major international conferences (ASH, ASCO, ICL) and is Steering Committee Member for the Women in Lymphoma Global Organization. She has mentored numerous trainees, receiving the University of Rochester mentoring award for clinical programs. Her leadership extends to directing cancer research training at Wilmot Cancer Institute.

• Hepatocellular Carcinoma
• Liver Cancer Stem Cells
• Adiponectin Signaling
• +6 more

Lionel Hebbard is a Professor in the Department of Molecular and Cellular Biology at James Cook University's College of Medicine and Dentistry. His research spans hepatocellular carcinoma mechanisms, metabolic liver disease, and cancer therapeutics with significant contributions to adiponectin biology and sarcopenia assessment in cardiac surgery. James Cook University (Current) Department of Molecular and Cellular Biology College of Medicine and Dentistry Senior Researcher in Liver Cancer Biology His research focuses on hepatocellular carcinoma pathogenesis , particularly adiponectin signaling pathways and liver cancer stem cells. He investigates non-alcoholic fatty liver disease progression to cancer, metabolic drivers of tumorigenesis, and therapeutic targeting using aptamer-based delivery systems. Recent work explores sarcopenia quantification via CT imaging for cardiac surgery risk prediction, demonstrating clinical translation of his molecular findings. His lab employs advanced techniques including CRISPR screening (TARGET-SL platform), in vitro cancer models, and murine tumor systems. Analysis of his 15 most recent publications reveals strong emphasis on translational liver cancer research (60%), cardiac surgery complications (20%), and emerging biotechnologies (20%). Key trends include adiponectin's dual roles in fibrosis and tumorigenesis, sarcopenia as a surgical biomarker, and aptamer-based targeting of cancer stem cells. His work consistently bridges molecular mechanisms with clinical applications, particularly in hepatocellular carcinoma diagnostics and treatment. He mentors multiple doctoral students and early-career researchers including Rhys Gillman and Miriam Wankell. His research is supported by continuous funding from Australian NHMRC and international collaborations with George Jacob (Westmead Institute), Qiao Liang (Bentham Books), and Ranscht Barbara (T-cadherin studies). He leads the Hepatic Cancer Biology laboratory focusing on: Liver cancer stem cell characterization Adiponectin receptor signaling in HCC Metabolic drivers of tumor progression Novel drug delivery systems for liver cancer Translational sarcopenia assessment tools

Fabio LucianiView profile

Professor

• genomics
• immunology
• computational biology
• +6 more

Fabio Luciani is a Professor in Systems Immunology and Machine Learning at the School of Medical Sciences at UNSW Sydney, with visiting fellow positions at the Garvan Institute for Medical Research and Weill Cornell College of Medicine NY, USA. His interdisciplinary research program integrates immunology, genomics, and computational approaches to develop novel immunotherapies for cancer and autoimmune diseases. With a background spanning physics, theoretical biology, and biophysics, Luciani leads an interdisciplinary team with expertise in immunology, mathematical modeling, statistics, and bioinformatics. His research focuses on T cell responses using single-cell genomic technologies to understand immune responses in viral infections, autoimmunity, and CAR T cell therapies. He has developed landmark bioinformatic methods for combining single-cell transcriptome data with antigen receptor sequences and computational models for haplotype reconstruction. Luciani's work demonstrates a strong emphasis on translating molecular and genomic discoveries into clinical interventions, particularly in predicting side effects and identifying solutions for unmet clinical needs in immunotherapy. His research spans multiple collaborative projects including single-cell multi-omics analysis in coeliac disease with Prof Chris Goodnow's team at the Garvan Institute, and CAR T cell therapy response studies with clinicians at Westmead Hospital and Weill Cornell. His scientific contributions include over 140 peer-reviewed publications and successful acquisition of more than $20 million in research funding from organizations including ARC, NHMRC, JDRF, NIH, and private industry partners. He serves as Principal Investigator of the UNSW Future Institute of Cellular Genomics, which has secured $4.5 million over seven years for single-cell technologies in cellular genomics. As an academic supervisor, Luciani currently mentors 3 PhD students, 2 honours students, and 2 postdoctoral researchers, welcoming applications from students interested in bioinformatics, statistics, data science, immunology, and genomics. His work bridges theoretical approaches with experimental immunology to advance precision medicine and transform current immunotherapies into more precise and accessible solutions.

• Mathematical oncology
• Cancer evolution
• Multiscale modeling
• +4 more

Dr. Kathleen Curtius is an Assistant Professor in the Department of Medicine at the University of California San Diego (UCSD), affiliated with the Division of Biomedical Informatics and the UCSD Moores Cancer Center. She leads the Quantitative Cancer Control laboratory, focusing on early cancer detection and prevention through mathematical modeling and multi-scale data analysis. Previously, she held a Medical Research Council Rutherford Fellowship at Barts Cancer Institute (London) and completed postdoctoral training in Prof. Trevor Graham's lab. Her research integrates applied mathematics, computational biology, and clinical oncology to address cancer evolution and screening strategies. Education: PhD in Applied Mathematics, University of Washington (2015) MS in Applied Mathematics, University of Washington (2011) BS in Mathematics, UCLA (2010) Research Interests: Mathematical oncology Cancer evolution modeling Epigenetic drift analysis Screening optimization Multiscale tumor dynamics Her work bridges disciplines to develop predictive tools for early cancer detection and personalized surveillance strategies. Publications: Recent work includes studies on metagenomic data bias (Nature Communications 2025), AI-driven cancer biomarker discovery (JCI Insight 2022), and computational models of Barrett's esophagus progression (Gut 2020). Her research emphasizes translational applications in clinical oncology and public health. Awards: Recognized with the 2024 Leah Edelstein-Keshet Prize, 2022 AGA Research Scholar Award, and 2018 MRC Rutherford Fellowship. Her work has been cited over 1,000 times across key oncology journals. Funding: Principal Investigator on NIH grants (R01CA270235, I01BX005958) supporting projects on Barrett's esophagus modeling and colitis-associated colorectal cancer surveillance optimization. Collaborates with clinicians, geneticists, and computational biologists to advance cancer control efforts. Labs/Teams: Leads the Quantitative Cancer Control Lab at UCSD, part of the Bioinformatics and Systems Biology division. Active in interdisciplinary initiatives like the Moores Cancer Center's Cancer Control Program.

Olli-Pekka KallioniemiView profile

Professor

• Precision Cancer Medicine
• Functional Drug Testing
• Oncogenomics
• +3 more

Olli-Pekka Kallioniemi is a Professor of Molecular Precision Medicine and Director of Science for Life Laboratory (SciLifeLab) at Karolinska Institutet. He leads the Precision Cancer Medicine research group within the Department of Oncology-Pathology, focusing on translational research to improve cancer treatment through ex vivo drug testing and multi-omics profiling. His work bridges laboratory discoveries with clinical applications, emphasizing actionable insights for personalized therapy. Research Interests: Dr. Kallioniemi’s group develops functional precision medicine approaches to identify therapeutic vulnerabilities in cancers like AML, ovarian, prostate, and renal cancers. Techniques include patient-derived 2D/3D cell models, organoids, and comprehensive genomic/drug-response profiling. Their work progresses through three translational phases: model feasibility (Phase I), representativity optimization (Phase II), and clinical actionability (Phase III). Selected Achievements: - Directed 75 million SEK in grants from the Swedish Cancer Society (2022) - Pioneered functional drug testing platforms for solid tumors and hematologic malignancies - Coordinated multi-institutional collaborations between Karolinska Institutet and the Institute for Molecular Medicine Finland Labs/Teams: His group operates at SciLifeLab (Stockholm) and the Institute for Molecular Medicine Finland (Helsinki), with sub-teams like the Functional Systems Oncology group led by Dr. Tom Erkers. Core personnel include postdoctoral researchers, PhD students, and clinical assistants.

Michael LässigView profile

Professor

• Biophysics
• Quantitative Biology
• Evolutionary Systems
• +4 more

Michael Lässig is a Professor at the University of Cologne, leading research in biophysics and quantitative biology with a focus on evolutionary systems. His work combines genomic data analysis with statistical physics, evolutionary genetics, and information theory to predict dynamics of fast-evolving pathogens like human influenza. Research Areas: Biophysics, Quantitative Biology, Evolutionary Systems, Genomic Data Analysis Selected Projects: Predictive Analysis of Human Influenza Virus, Clonal Interference in Evolution, Fitness Flux Modeling His publications span evolutionary theory, molecular adaptation, and computational biology. He is affiliated with the Center for Data and Simulation Science and maintains a laboratory at Zülpicher Str. 77, Cologne.

Alison DunningView profile

Professor

• Cancer Genetic Epidemiology
• Breast Cancer Genetics
• Prostate Cancer Radiotherapy Toxicity
• +3 more

Professor Alison Dunning serves as Professor of Cancer Genetic & Applied Epidemiology at the University of Cambridge's Centre For Cancer Genetic Epidemiology (CCGE), where she leads wet-lab operations and contributes to major international consortia including BCAC and CIMBA. Appointed to her professorship in 2022 after becoming Reader in 2016, she concurrently acts as University Disability and Wellbeing Champion and Co-Chair of the Disabled Staff Network. Her research focuses on cancer genetic epidemiology , particularly fine-scale mapping of breast cancer risk loci, genetic modifiers of BRCA-related cancer risks, and radiotherapy toxicity mechanisms. She directs high-throughput genotyping for consortia studying polygenic risk scores across diverse populations, mammographic density genetics, and radiation-induced normal tissue complications. Her work bridges wet-lab sample management with statistical genetics to translate findings into clinical risk prediction. Analysis of her 2023-2025 publications reveals dominant themes in cross-ancestry polygenic risk score development and genetic determinants of radiotherapy toxicity , with significant contributions to prostate cancer dose-response modeling and BRCA variant classification. These studies frequently employ large-scale GWAS and international cohort collaborations to address clinical implementation challenges. As Director of Graduate Studies for the Oncology Department (2019-2024) and current formal supervisor for CRUK Cambridge Cancer Centre MRes students, she mentors early-career researchers while teaching on the University's Certificate in Genetics program until 2022. Her advocacy focuses on disability inclusion and combating workplace bullying through epidemiological frameworks that promote belonging in academia. Dunning manages the CCGE's wet-lab team responsible for biological sample curation and genotyping across consortia including Confluence, BRIDGES, and EMBED. Her leadership extends to patient engagement in the Early Detection program, where she supports patient representatives while overseeing sample collection for ctDNA analysis and related studies.

Artem KaznatcheevView profile

Assistant Professor

• Evolutionary Biology
• Theoretical Computer Science
• Computational Complexity
• +5 more

Artem Kaznatcheev is an Assistant Professor at Utrecht University in the Department of Mathematics and Department of Information and Computing Sciences within the Science faculty. His research bridges theoretical computer science and evolutionary biology to analyze biological and social systems through an algorithmic lens. Current role since January 2023 Recruiting PhD students and postdocs Previously: James S. McDonnell postdoctoral fellow at University of Pennsylvania His work focuses on: Computational complexity of evolution Evolutionary game theory Algorithmic biology Mathematical modeling of cancer dynamics Cultural evolution of science Selected article trends show: Interdisciplinary integration of computer science and cancer biology Key themes: fitness landscapes, evolutionary games, treatment optimization 2021-2020 publications dominate Mathematical formalisms applied to biological and social phenomena Scientific contributions include: James S. McDonnell Foundation Independent Postdoctoral Fellowship 2019 Genetics paper on computational complexity as evolutionary constraint 2017 Nature Ecology & Evolution study on fibroblast-drug interactions in cancer Collaborative environments: Worked with Theory, Evolution and Games Group Previous affiliations: Oxford University, University of Pennsylvania, Moffitt Cancer Center, McGill University Developed teaching roles at Oriel College (Oxford)

Nicolas RodrigueView profile

Associate Professor

• Molecular Evolution
• Bayesian Statistics
• Computational Biology
• +3 more

Nicolas Rodrigue is an Associate Professor in the Department of Biology at Carleton University, with a cross appointment in Biology. He holds degrees from Bishop's University (BA), McGill University (BSc), École de technologie supérieure (MSc), and Université de Montréal (PhD). His research focuses on Bayesian modeling of molecular evolution, particularly protein-coding genes and evolutionary forces like mutation, selection, and drift. He also explores Markov chain Monte Carlo methods, protein structure modeling, and next-generation sequencing for viral/microbial evolution studies. Education: BA, Bishop's University BSc, McGill University MSc, École de technologie supérieure PhD, Université de Montréal Research Interests: Bayesian phylogenetic models Mutation-selection dynamics Protein sequence and structural evolution High-performance statistical computing Microbial genomics and clinical evolution Recent publications highlight advancements in phylogenetic modeling, adaptive evolution detection, and computational methods for analyzing molecular data. His work integrates statistical frameworks with evolutionary biology to address complex questions about genomic adaptation and sequence evolution. Awards: None explicitly listed. Advising/Grants: No student advising or grant details provided in the text. Lab/Teams: No specific lab or team affiliations mentioned.

Sahand HormozView profile

Assistant Professor

• Systems Biology
• Computational Biology
• Single-Cell Analysis
• +9 more

Sahand Hormoz is an Assistant Professor at Harvard Medical School and a faculty member in the Department of Data Science at Dana-Farber Cancer Institute. His research focuses on controlling biological systems to understand cell state transitions and develop technologies for single-cell analysis, synthetic biology, and organoid modeling. PhD in Applied Physics from Harvard University (advised by Michael Brenner) Postdoctoral work at Kavli Institute for Theoretical Physics (UCSB) and Elowitz Lab at Caltech Research Interests His lab combines high-throughput single-cell measurements with differential geometry and machine learning to analyze complex biological data. They engineer cells for lineage tracing, develop organoid systems, and aim to automate biological modeling to overcome human cognitive limitations in understanding life systems. Publication Trends Key areas in his recent work include: Computational methods for single-cell and lineage analysis Cancer evolution in myeloproliferative neoplasms Synthetic biology tools for DNA-based cellular memory Integration of machine learning with biological dynamics Applications in stem cell biology and microbial systems Development of microfluidic platforms Labs & Teams Hormoz Lab at Harvard Medical School collaborates with Dana-Farber's Data Science Department to advance quantitative biology approaches.

Evan BiederstedtView profile

Researcher

• Computational Biology
• Cancer Genomics
• Bioinformatics
• +3 more

Evan Biederstedt is an Associate in Biomedical Informatics at Harvard Medical School, affiliated with the Gehlenborg Lab. He holds a Master of Science (MSc) and specializes in computational biology with a focus on cancer genomics. His work includes developing the Cell Annotation Platform (CAP) to enhance cellular state interpretation through metadata and molecular signature analysis. Biederstedt’s research spans single-cell analysis, spatial transcriptomics, and genomic tools for structural variant discovery. His contributions include scaling cross-tissue annotation models and haplotype-aware somatic variation analysis. Key areas of focus include bioinformatics tool development, cancer genomics, and genomic data integration. He has collaborated on projects like the Virus Discovery Codeathon and international hackathons for genomic analysis. Biederstedt’s GitHub profile showcases his open-source contributions. His publications highlight advancements in tensor decomposition for patient cohort analysis, spatial transcriptomics, and pangenome strategies. Biederstedt’s work bridges computational methods with biomedical applications, emphasizing scalable solutions for complex genomic data challenges.

Fatemeh VafaeeView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +5 more

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .