جست‌وجوی استاد و پژوهشگر

Abdulkadir Celikkanatمشاهده پروفایل

استادیار

• Computer Science
• Machine Learning
• Data Science
• +۳ مورد دیگر

Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.

Maria Braoudakiمشاهده پروفایل

دانشیار

• Pediatric Cancer Genomics
• Genetics
• Epigenetics
• +۴ مورد دیگر

Dr. Maria Braoudaki is a Reader in Molecular Medicine at the University of Hertfordshire, UK. She holds a PhD in Molecular Microbiology from Aston University (2004) and an MSc in Molecular Medicine (with distinction) from the National and Kapodistrian University of Athens. Her research focuses on pediatric cancer genomics, epigenetics, and proteomics, with emphasis on microRNA profiling and high-throughput omics strategies. She has authored over 40 peer-reviewed publications and received prestigious awards, including the L’OREAL-UNESCO Award for Women in Science (2016) and recognition from the Cretan Scientists Association (2022). Research Projects: Cancer Management using AI (Co-Investigator, 2022–present) Apafix fixation for tissue analysis (Principal Investigator, 2022) Research Interests: MicroRNA-driven therapeutic strategies in pediatric and adult cancers Epigenetic regulation in oncogenesis Omics-based approaches for cancer diagnosis and prognosis Key Contributions: Pioneered work on microRNA signatures in medulloblastoma and glioblastoma Developed novel biomarker strategies for lung and colorectal cancers Explored extracellular vesicles as therapeutic delivery systems Grants & Collaborations: Lead investigator in Apafix fixation studies Co-Investigator in AI-driven cancer recurrence prediction (CaRPrAI, 2021–2022)

Eliezer M. Van Allenمشاهده پروفایل

دانشیار

• Computational Oncology
• Precision Cancer Medicine
• Genomics
• +۷ مورد دیگر

Dr. Eliezer M. Van Allen is an Associate Professor of Medicine at Harvard Medical School and Chief of the Division of Population Sciences at the Dana-Farber Cancer Institute. He is also an Institute Member at the Broad Institute of MIT and Harvard and a Member Researcher at The Parker Institute for Cancer Immunotherapy. His work bridges clinical oncology and computational biology to advance precision cancer medicine. Dr. Van Allen’s research focuses on computational oncology , single-cell transcriptomics , cancer genomics , and translational research , particularly in prostate, pancreatic, melanoma, and kidney cancers. He pioneered the field of clinical computational oncology, developing AI-driven tools to interpret cancer genomes and uncover resistance mechanisms to therapy. His lab actively explores tumor evolution, immunogenomics, and the role of inherited and environmental factors in cancer progression. The recent publications from his lab reflect a strong trend in applying artificial intelligence , single-cell and spatial multiomics , and deep learning to dissect tumor-immune interactions, identify biomarkers, and improve clinical decision-making in oncology. His work spans from algorithm development to real-world clinical implementation. Featured in Newsweek on 6 Lessons for Harnessing Artificial Intelligence Wall Street Journal feature on the Metastatic Prostate Cancer Project New York Times feature highlighting his work on cancer immunotherapy and genomics Dr. Van Allen mentors a large team of students, postdoctoral fellows, and researchers, with multiple successful PhD and Master’s thesis defenses. His lab is actively involved in patient-partnered research through initiatives like mpcproject.org. He leads a dynamic research group focused on creating the next generation of computational oncologists and advancing the clinical interpretation of cancer genomes. His lab, the Van Allen Lab, is dedicated to driving precision cancer medicine through innovative computational approaches. The team includes computational biologists, postdoctoral fellows, and clinical researchers working at the intersection of AI and oncology. The lab frequently welcomes new members and emphasizes collaborative, interdisciplinary science.

Felipe A. Lisboaمشاهده پروفایل

عضو هیئت علمی

• Surgical Critical Care
• Precision Medicine
• Inflammatory Response
• +۵ مورد دیگر

Felipe A. Lisboa, M.D. is a Research Assistant Professor in the Department of Surgery at the Uniformed Services University of the Health Sciences (USUHS) School of Medicine in Bethesda, MD. He serves as the Surgical Critical Care Initiative (SC2i) Associate Director of Clinical Research and SC2i USU/WR Biorepository Director. With over ten years of experience in both basic and clinical research, Dr. Lisboa has established himself as a prominent researcher in surgical critical care, precision medicine, and inflammatory response mechanisms. Dr. Lisboa's research interests span surgical critical care, precision medicine, inflammatory response mechanisms, trauma and wound healing, angiogenesis, immunology, molecular biology, and the application of machine learning in clinical research. His work particularly focuses on multiomics approaches (genomics, proteomics, and metabolomics) to understand surgical complications including wound failure, local infection, sepsis, pneumonia, ARDS, AKI, and VTE in military and civilian patients. His publication record demonstrates a clear progression from fundamental molecular immunology research to applied clinical trauma medicine, with recent work emphasizing precision medicine approaches to surgical decision-making. His articles reveal a consistent focus on inflammatory biomarkers, wound healing mechanisms, and computational methods to improve outcomes for trauma patients. Dr. Lisboa's scientific contributions include developing clinical decision support tools, expanding the SC2i USU/WR Biorepository inventory, and implementing clinical study protocols for surgical critical care research. His work bridges basic science with clinical applications, particularly in combat casualty care.

Onur Turkogluمشاهده پروفایل

استادیار

• Maternal-Fetal Medicine
• Metabolomics
• Obstetrics and Gynecology
• +۳ مورد دیگر

Onur Turkoglu is an Assistant Professor in the Department of Maternal Fetal Medicine at Baylor College of Medicine. He specializes in high-risk pregnancies, with clinical expertise in specialized ultrasound, obstetric surgery, genetic counseling, and prenatal diagnostics. His translational research focuses on metabolomics and epigenetics to address maternal-fetal conditions. Education: MD from Ankara University School of Medicine Residency at William Beaumont University Hospital (MI) Clinical Fellowship at William Beaumont University Hospital Postdoctoral Fellowship at Beaumont Research Institute Residency at Dr. ZTB Women’s Health Research and Education Hospital (Turkey) His publications highlight applications of AI and metabolomics in predicting pregnancy outcomes, including preeclampsia, fetal growth restriction, and ectopic pregnancy. He has contributed to understanding fetal effects of SARS-CoV-2 infection and epigenetic markers for congenital heart defects. Scientific Affiliations: Society of Maternal Fetal Medicine American College of Obstetricians and Gynecologists (Fellow) Metabolomics Society Turkish Society of Obstetrics and Gynecology His work bridges clinical practice with translational research, aiming to improve maternal-fetal outcomes through precision medicine and novel diagnostic models.

Syed Murtuza Bakerمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Single Cell RNA-seq
• Spatial Transcriptomics
• +۲ مورد دیگر

Syed Murtuza Baker is a Research Fellow in the Division of Informatics, Imaging & Data Sciences at the University of Manchester. He earned his doctorate from Martin-Luther University Halle Wittenberg with a focus on kinetic models of biological systems. His expertise includes single-cell genomics, spatial transcriptomics, and bioinformatics. His research investigates gene regulatory networks, immune cell coordination in inflammatory diseases, and computational methods for analyzing spatial transcriptomic data. He contributes to UN Sustainable Development Goals through his work in health and biomedical research. Baker's publications demonstrate consistent focus on computational biology approaches to disease mechanisms, particularly in liver disease, leukemia, and immune disorders. His recent work develops novel algorithms for spatial transcriptomics data analysis. None reported No information is available regarding student advising or research grants. He collaborates extensively with international teams through the Sustainable Futures and Digital Futures research platforms.

• Cancer Research
• Bioinformatics
• Proteomics
• +۳ مورد دیگر

Dr. Rebecca Poulos is an NHMRC Early Career Fellow and Conjoint Lecturer at the Children’s Medical Research Institute, University of Sydney. Her research focuses on cancer genomics, proteomics, and data science, particularly integrating multiomic data to uncover cancer biomarkers and drug response mechanisms. She holds a BBus, BSc (Hons) with a University Medal, and a PhD from UNSW Sydney. Key research interests include cancer driver mutations, proteogenomics, and machine learning for multi-omics integration. Notable achievements include an NHMRC Early Career Fellowship and the Cancer Institute NSW Rising Star PhD Student Award. Her work spans pediatric cancer proteomics, drug response prediction, and reproducible large-scale proteomics. Recent studies highlight her contributions to cancer pathway modeling (DeePathNet), pediatric cancer molecular signatures, and proteomic stratification of prostate cancer. She leads projects funded by NHMRC and Sydney Cancer Partners, advancing precision medicine through proteomics. Her lab is embedded in the Cancer Data Science Group (ProCan), collaborating on pan-cancer proteomic maps and clinical applications.

Siddharth Deyمشاهده پروفایل

دانشیار

• Single-cell sequencing technologies
• Epigenetic regulation of gene expression
• Cell fate decisions
• +۵ مورد دیگر

Siddharth Dey is an Associate Professor in both the Department of Bioengineering and the Department of Chemical Engineering at the University of California, Santa Barbara. His research focuses on developing novel single-cell sequencing technologies to study epigenetic regulation of gene expression and cell fate decisions. The Dey Lab investigates mechanisms underlying DNA methylation dynamics, cancer cell invasion, and HIV latency using advanced multiomics approaches and 3D organoid models. He leads the Dey Lab , which integrates synthetic biology, optogenetics, and computational modeling to understand cellular heterogeneity and disease processes. Key research areas include: (1) Single-cell multiomics integration, (2) Epigenetic regulation during human development, (3) Mechanotransduction in cancer progression, and (4) Engineering tools for bacterial transcriptomics. Recent work demonstrates breakthroughs in simultaneous profiling of 5mC/5hmC modifications with transcriptomes, enabling unprecedented insights into epigenetic maintenance fidelity and cellular decision-making. His research is supported by grants including the NSF CAREER award (2024) for spatial epigenetic studies in human development. The lab actively collaborates on projects involving photopatterning technologies, stress response signaling, and HIV latency therapies. Experimental approaches combine cutting-edge sequencing methods with microfluidics-based platforms to study cellular responses in physiologically relevant 3D environments. Publications highlight contributions to understanding: (1) DNA methylation erasure in germ cells, (2) YAP-driven cardiac hypertrophy mechanisms, (3) macrophage priming via Fc receptors, and (4) bacterial mRNA sequencing innovations. These studies bridge engineering principles with fundamental biological mechanisms to address critical questions in development, cancer biology, and infectious diseases.

Lara Nonellمشاهده پروفایل

دانشیار

• Bioinformatics
• Cancer Diagnostics
• Machine Learning in Biomedicine
• +۳ مورد دیگر

Lara Nonell is an Associate Professor in the Department of Biosciences at the University of Vic – Central University of Catalonia and Head of the Bioinformatics Unit. She holds a Doctorate in Biomedicine from Pompeu Fabra University (2019) and has expertise in bioinformatics, cancer diagnostics, and multiomics data analysis. Her research focuses on developing computational methods for biomarker identification and cancer progression modeling. Education Doctorate in Biomedicine, Pompeu Fabra University (2019) MSc in Information Technology Management, La Salle Ramon Llull University (2000) Bachelor’s in Mathematics, Autonomous University of Barcelona (1996) Research Interests Dr. Nonell’s work integrates machine learning, bioinformatics pipelines, and multiomics data to drive precision oncology. Key areas include: Biomarker development for cancer diagnostics Epithelial-mesenchymal transition mechanisms in metastasis Gene expression profiling in leukemias and lymphomas Systems biology approaches to comorbidities like diabetes and cancer Publications Her 20+ peer-reviewed articles (2019-2023) span oncology, immunology, and bioinformatics. Recent work includes: Epigenetic drivers of hepatoblastoma progression MYC-driven gene signatures in melanoma prognosis MicroRNA regulation in cutaneous lymphomas Grants & Labs Leads the Bioinformatics Unit supporting VHIO research. Collaborations include multi-institutional projects in Barcelona’s biomedical ecosystem. No specific grants mentioned in the text.

Kevin Heمشاهده پروفایل

دانشیار

• Survival Analysis
• Healthcare Provider Profiling
• Machine Learning
• +۷ مورد دیگر

Kevin He is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as Associate Director of the Kidney Epidemiology and Cost Center (KECC) and leads statistical innovations in survival analysis, healthcare provider profiling, and data integration for biomedical applications. PhD in Biostatistics (University of Michigan, 2012) MS in Biostatistics (University of Michigan, 2008) BS in Statistics (Queen’s University, 2006) MS in Epidemiology (Queen’s University, 2004) BM in Clinical Medicine (Dalian Medical University, 2002) His research focuses on survival analysis for large-scale datasets, machine learning for healthcare provider profiling, and statistical genetics in organ transplantation and chronic disease. He develops data integration frameworks for polygenic risk scores and statistical optimization algorithms for time-varying effects in national registries. The 15 most recent articles highlight his work in survival modeling with time-varying coefficients, federated learning for privacy-preserving data integration, and genomic applications in inflammatory diseases. His methodological contributions span Kronecker product algorithms , proximal optimization , and penalized partial likelihood . He mentors a team including software developers and graduate researchers working on deep learning , frailty models , and distributed computing . His lab maintains the surtvep R package for scalable survival analysis.

Rohit Singhمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Single-Cell Genomics
• +۴ مورد دیگر

Rohit Singh is an Assistant Professor at Duke University with appointments in the Departments of Biostatistics & Bioinformatics, Cell Biology, Computer Science, and Electrical and Computer Engineering. He is a member of the Duke Cancer Institute and the Division of Integrative Genomics. Academic Rank : Assistant Professor Departments : Biostatistics & Bioinformatics, Cell Biology, Computer Science, Electrical and Computer Engineering University Affiliations : Duke Cancer Institute, Division of Integrative Genomics His research focuses on computational biology, machine learning for drug discovery, and decoding disease mechanisms through single-cell genomics and protein language models. Current projects include SAME, Velorama, Raygun, and Allo-Allo. Recent publications span single-cell analysis, protein language models, and causal inference. He develops tools like Schema , D-SCRIPT , and Sceodesic . Grants : NIAID, Chan Zuckerberg Initiative, Foundation for Prader-Willi Research Awards : MIT Sprowls Award, Stanford Stephenson Award, RECOMB Test of Time Award He advises students in computational biology and has contributed to patents in protein interaction modeling and viral detection. The lab welcomes postdocs, rotation students, and collaborators.

Supriyo Bhattacharyaمشاهده پروفایل

عضو هیئت علمی

• Advanced bioinformatics
• Multiomics data integration
• Protein structure and dynamics
• +۵ مورد دیگر

Supriyo Bhattacharya, Ph.D., is an Assistant Research Professor at the Department of Computational and Quantitative Medicine, City of Hope National Medical Center. His work focuses on integrating multiomics data with protein structure and dynamics to advance computational drug discovery, particularly for intrinsically disordered proteins (IDPs). Education: Ph.D. in Chemical Engineering (North Carolina State University, 2006), B.Tech. in Chemical Engineering (IIT Kharagpur, 2000) Key Research Areas: Multiomics-structure integration via AI (e.g., AlphaFold) IDP dynamics and drug targeting GPCR allosteric communication His publications emphasize computational methods for protein dynamics, with trends in Bayesian networks (2024), machine learning for conformational transitions (2025), and IDP interactions in diseases like cancer and diabetes (2021–2024). Recent work (2025) explores paxillin-GPCR dynamics and β-cell homeostasis. Scientific Recognition: NIH R01 grants (2015, 2023) GPCR DOCK 2010 top-3 ranking Editorial roles (Frontiers, Biomolecules MDPI) Anton supercomputer grant Grants & Collaborations: Co-investigator in NIH R01LM013876-02 (2023), key personnel in multi-institutional immunotherapy projects (2019), and computational resource grants. Lab & Teams: Leads integrative genomics core at City of Hope, collaborating with molecular immunology and computational therapeutics teams.

Kathrin Plathمشاهده پروفایل

استاد

• Stem Cell Biology
• Epigenetics
• Gene Regulation
• +۵ مورد دیگر

Kathrin Plath, Ph.D., is a Professor and Vice Chair in the Department of Biological Chemistry at the David Geffen School of Medicine, University of California, Los Angeles (UCLA) . Her research focuses on epigenetic regulation of cell fate transitions, particularly in stem cell biology, reprogramming, and X-chromosome dynamics. Education: Ph.D., Biochemistry, Humboldt University, Germany (1999) Post-doctoral Fellowships: Stem Cell Biology (Whitehead Institute, 2006), Chromatin Biology (UC San Francisco, 2003), Cell Biology (Harvard Medical School, 2000) Research Interests: Plath investigates mechanisms governing pluripotency, X-chromosome inactivation, and nutrient regulation of stem cell differentiation. Her lab develops single-cell omics technologies like SEC-seq to study transcriptomes and secretomes simultaneously, and explores 3D genome organization's role in gene regulation. Scientific Awards: HHMI Faculty Scholar (2016) ISSCR Board of Directors (2011-2017) NIH Director’s New Innovator Award (2007) Kimmel Foundation Scholar (2007) V Foundation Scholar (2007) Advising & Collaborations: Mentors students like Clara Cano and Sasha Barinsky. Collaborates with Jason Ernst (UCLA), Dino Di Carlo (UCLA), and international researchers on projects involving iPSC reprogramming, X-chromosome dosage compensation, and lung disease modeling.

Erik van Nimwegenمشاهده پروفایل

استاد

• Gene Regulatory Networks
• Genome Evolution
• Computational Biology
• +۵ مورد دیگر

Prof. Dr. Erik van Nimwegen is a Professor at the Biozentrum of the University of Basel, where he leads a research group focused on computational and systems biology. His laboratory is housed in Room 08.012 at the Biozentrum, with contact information including phone number +41 61 207 15 76 and email erik.vannimwegen@unibas.ch. His administrative support is provided by Rita Ruppen and Jonila Vladi. Van Nimwegen's research centers on understanding the function and evolution of genome-wide regulatory networks. His group investigates how cells control gene expression using both theoretical and experimental methods, with particular interest in deciphering the genome's regulatory code, analyzing gene expression dynamics from single cells to genome-wide regulatory programs, and uncovering quantitative laws of genome evolution. His interdisciplinary approach combines computational predictions with experimental data to model regulatory networks across organisms from E. coli to humans. His work has significant implications for understanding cellular behavior, developing evolutionary theory based on measurable quantities, and potential applications ranging from taming pathogens to engineering human tissue. Analysis of his recent publications reveals a consistent focus on gene regulatory networks, with increasing emphasis on single-cell analysis, computational modeling of biological systems, and the evolutionary aspects of gene regulation. His work spans bacterial systems (particularly E. coli) to higher eukaryotes, with recent publications showing expansion into visualization techniques for single-cell data, cancer research related to RNA processing, and even virology with work on COVID-19 origins. A notable trend is the integration of multiple data types (multiomics) and the development of sophisticated computational tools for analyzing complex biological systems. Van Nimwegen's group has developed numerous software tools and web services for regulatory and comparative genomics, including ISMARA (Integrated System for Motif Activity Response Analysis), CRUNCH (for ChIP-seq data analysis), SwissRegulon database, Phylogibbs, and others. These resources reflect his commitment to creating practical tools that advance the field of computational biology. His laboratory operates as an interdisciplinary team with researchers from diverse backgrounds including theoretical physics, computer science, and molecular biology. The group actively recruits postdocs and engineers, as evidenced by a 2018 posting seeking researchers for computational analysis of bioimages. His work has established important theoretical frameworks for understanding gene regulatory networks and their evolution, with significant contributions to the field of systems biology.

• Computational Biology
• Systems Biomedicine
• Machine Learning
• +۵ مورد دیگر

Jorge GONCALVES is a Full Professor at the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, and a Principal Research Associate at the University of Cambridge's Department of Plant Sciences. He holds expertise in computational biology, systems biomedicine, and machine learning applications in healthcare. His roles include leading research on gene regulatory networks, epidemic modeling, and deep brain stimulation technologies. Education: Licenciatura from University of Porto (1993), M.S. and Ph.D. from MIT (1995/2000). Postdoctoral work at MIT and Caltech, followed by academic positions at the University of Cambridge (Lecturer, Reader, Principal Research Associate). Visiting Professorships at the University of Luxembourg and Caltech. Research Interests : Dynamical systems modeling, network inference, biomedical applications of AI, and systems biology. His work spans drug repositioning, early warning signals for disease transitions, and multiomics analysis in immunology. Publications : Focused on interdisciplinary approaches combining machine learning with biological systems. Key themes include computational methods for gene network analysis, epidemic monitoring systems, and AI-driven medical diagnostics. Awards : Not explicitly listed in provided texts. Advising and grants: Extensive collaboration with institutions globally, including leadership in EU-funded projects and industry partnerships. Active in mentoring researchers across computational biology and engineering disciplines. Labs/Teams: Leads LCSB teams focused on systems biomedicine and AI-driven health solutions. Collaborates with the University of Cambridge on plant sciences and robotics initiatives.