جست‌وجوی استاد و پژوهشگر

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.

Peter Parkمشاهده پروفایل

استاد

• 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.

Matthias Oliver Wilhelmمشاهده پروفایل

دانشیار

• Quantum Field Theory
• Mathematical Physics
• Special Functions
• +۳ مورد دیگر

Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.

Michael Fergusonمشاهده پروفایل

استاد

• Biochemistry
• Glycobiology
• Mass Spectrometry
• +۷ مورد دیگر

Professor Sir Michael Ferguson is the Regius Professor of Life Sciences at the University of Dundee and an Interim Dean of the School of Life Sciences. He is a Fellow of the Royal Society (FRS), Royal Society of Edinburgh (FRSE), Academy of Medical Sciences (FMedSci), and Royal Society of Biology (FRSB). Education : BSc in Biochemistry, University of Manchester (1976-1979) PhD in Biochemistry, University of London (1979-1982) His research focuses on the biochemistry of protozoan parasites causing tropical diseases such as human African Sleeping Sickness, Chagas' disease, and leishmaniasis. He emphasizes the Biology/Chemistry interface and translational research, co-founding the Drug Discovery Unit and directing the Dundee Proteomics Facility . His work on glycosylphosphatidylinositol (GPI) membrane anchors has transformed understanding of eukaryotic biology. Recent publications highlight advancements in antimicrobial resistance , serology standards for SARS-CoV-2 , and mechanochemical drug formulation . He contributes to UN Sustainable Development Goals in Health and Life Sciences . Scientific Awards : Royal Society Leeuwenhoek Medal (2025) Biochemical Society Morton Lecture (2024) Knight Bachelor (2019) Colworth Medal (1991) Howard Hughes International Scholar (1993) He serves on the boards of UK Biobank , Medicines Discovery Catapult , and chairs PhaSER Bio . During the pandemic, he advised the UK government on antibody testing and national core studies.

• Plant Phenotyping
• Precision Agriculture
• Machine Vision
• +۶ مورد دیگر

Dr. Huajian Liu is a Grant-Funded Researcher (B) at the University of Adelaide's School of Agriculture, Food and Wine, within the Faculty of Sciences, Engineering and Technology. He is affiliated with The Plant Accelerator, the Adelaide node of the Australian Plant Phenomics Facility. His research focuses on machine vision and machine learning for plant phenotyping and precision agriculture, particularly using hyperspectral imaging for plant nutrient estimation, disease detection, stress tolerance analysis, and pest monitoring. Dr. Liu holds a PhD in machine vision for invertebrate detection from the University of South Australia (2018) and has been a post-doctoral researcher at The Plant Accelerator since then. His expertise spans hyperspectral imaging, bio-inspired machine vision systems, 3D model reconstruction, and optical sensing system design. He is eligible to supervise Masters and PhD students in plant phenotyping and precision agriculture. Key research areas include crop stress detection (drought, salt, nutrient deficiencies), invertebrate pest identification, and automation in agricultural monitoring. Affiliations: The Plant Accelerator, School of Agriculture, Food and Wine Research Themes: Plant phenotyping, precision agriculture, machine learning applications Grants: Ongoing grant-funded research in plant phenomics and hyperspectral technologies Publications highlight innovations in hyperspectral imaging for crop yield prediction, disease diagnostics, and nitrogen content analysis. His work bridges computational methods with agricultural challenges, emphasizing sustainable farming solutions.

Luca Tosoniمشاهده پروفایل

پژوهشگر ارشد

• Cybercrime
• Cybersecurity
• Data Protection
• +۳ مورد دیگر

Luca Tosoni is a Doctoral Research Fellow in Cybercrime and Cybersecurity Law at the Norwegian Research Center for Computers and Law (NRCCL), Department of Private Law, University of Oslo. His academic background includes a combined J.D./LL.M. (magna cum laude) from the University of Milan, specializing in EU and International Law, a Diplôme d'Université in Common Law and Comparative Law from Paris Descartes University, and the China Intellectual Property Summer Institute (CHIPSI) Certificate from Tsinghua University. Before his doctoral studies, Tosoni practiced law at Covington & Burling LLP in Brussels, advising multinational companies on cybersecurity, data protection, and life sciences regulations. He also served as counsel in EU Courts and as an Assistant Policy Officer at the Fair Trade Advocacy Office. His research interests focus on Cybercrime, Cybersecurity, Data Protection, Internet Governance, and Life Sciences. His publications include extensive commentary on GDPR provisions, such as interpretations of key definitions and cooperation mechanisms between supervisory authorities. He has co-authored analyses on topics like automated decision-making rights under Article 22 and the legal implications of pseudonymisation. Awards: China Intellectual Property Summer Institute (CHIPSI) Certificate Tosoni’s work bridges legal frameworks and technological challenges, contributing to EU regulatory compliance and privacy standards in digital environments.

• Cloud Computing
• Distributed Computing
• Parallel Computing
• +۱۰ مورد دیگر

Professor Javid Taheri is a leading academic at Karlstad University (2019–present), previously serving as Associate Professor (2015–2019) and Senior Lecturer (2015). His research focuses on cloud computing, edge computing, distributed systems, and AI-driven networking. He holds a Ph.D. in Information Technologies from The University of Sydney (2007) and an M.Sc./B.Sc. in Electrical Engineering from Sharif University of Technology (2000/1998). Research interests include cloud-edge continuum systems , resource optimization , 5G/6G networking , and AI for IoT . Notable contributions include frameworks like PerfSim (microservice performance simulation) and MultiScaler (auto-scaling for cloud applications). Publications highlight innovations in edge computing optimization, security for distributed systems, and machine learning for resource management. He has co-authored over 150 papers across top venues like IEEE Transactions and ACM conferences. Academic leadership includes roles as conference chair (IC2E 2023) and editorial work for journals on cloud and edge computing.

Xan Chackoمشاهده پروفایل

استادیار

• Science and Technology Studies
• Feminist Science Studies
• History of Science
• +۷ مورد دیگر

Xan Chacko is an Assistant Teaching Professor and Interim Director of the Program in Science, Technology, and Society (STS) at Brown University, where they also serve as Director of Undergraduate Studies. A feminist science studies scholar, Chacko focuses on natural history, ecology, agriculture, and conservation through the lens of colonial extraction and climate catastrophe, with seed banking as a central case study in their research on biodiversity conservation. Chacko's educational background includes: PhD in Science and Technology Studies, University of California at Davis (2018) MSc, Imperial College (2008) BA, Wellesley College (2005) Research examines how data practices in seed banks ignore complexities of colonial histories while claiming to secure humanity's future, revealing how classification systems erase local and indigenous knowledge. Chacko analyzes the relationship between physical seeds and their metadata, demonstrating how data storage choices reflect scientists' expectations for the future. Their feminist STS approach extends to teaching data, algorithms, and code's societal impacts. Analysis of recent publications shows consistent focus on seed banking's colonial legacies, invisible labor in science, and data's role in biodiversity narratives. The work spans history of science, feminist theory, and political ecology, highlighting gaps in data systems that perpetuate colonial power structures while proposing alternative epistemic frameworks. Chacko supervises undergraduate students from data and computer science pursuing STS as an additional concentration. They participated in Brown's Data Science Institute (DSI) faculty fellowship and are developing a new course on data and algorithms with a DSI fellow, bridging computational science and critical STS perspectives. As co-editor of 'Invisible Labour in Modern Science' and contributor to collaborative projects like the DSI fellowship, Chacko engages with institutional initiatives while directing the STS program's undergraduate curriculum and research directions.

• Natural Language Processing
• Sociophonetics
• Social Meaning Analysis
• +۵ مورد دیگر

Rob Voigt Assistant Professor of Linguistics and courtesy faculty in Computer Science at Northwestern University, affiliated with the Institute for Policy Research and Cognitive Science Program. Specializes in computational linguistics, focusing on natural language processing (NLP) applied to social science questions including policing, mental health, and immigration. Director of the Linguistic Mechanisms Lab, exploring how language reflects and shapes societal structures. Education PhD in Linguistics (2019), Stanford University Postdoctoral Scholar (2019-2020), Stanford University M.A. in East Asian Studies (2013), Stanford University B.A. in Chinese (2008), Vassar College Research Interests Combines computational methods with sociolinguistics to study: Police-community interaction through body-worn camera data Linguistic markers of procedural justice and racial disparities Language in marginalized communities and social movements LLM applications to clinical language analysis (autism spectrum disorders) Historical discourse analysis of immigration policies Recent Work Recent projects include analyzing 140 years of political immigration rhetoric (PNAS 2022), evaluating officer communication training via bodycam NLP (PNAS Nexus 2024), and developing stereotype analysis pipelines (EMNLP 2024). Active in policy-relevant research through grants on police training (NIJ) and mental health language analysis (NIH). Awards & Recognition Holds Cozzarelli Prize (2017) for groundbreaking policing research and Cialdini Prize for field methods innovation. Recipient of interdisciplinary fellowships from Stanford and NIH grants totaling $8M+. Teaching Current courses include Text Processing for Linguists . Known for ungrading pedagogy emphasizing intrinsic motivation and collaborative learning. Develops open-access computational linguistics curricula using Unix/Python. Labs & Collaborations Linguistic Mechanisms Lab focuses on socially responsible NLP. Collaborates with criminologists, cognitive scientists, and sociologists on projects like the Media Accountability Project (racial disparities in gun violence reporting).

Anatoly B. Kolomeiskyمشاهده پروفایل

استاد

• Statistical Mechanics
• Biophysics
• Chemical Biology
• +۷ مورد دیگر

Anatoly B. Kolomeisky is a Professor of Chemistry and Chemical and Biomolecular Engineering at Rice University, serving as Chair of the Department of Chemistry. He earned his BS/MS from Moscow State University and PhD from Cornell University (1998), followed by postdoctoral research at the University of Maryland. His research focuses on statistical mechanics applied to biological systems, including molecular motors, cellular transport mechanisms, error correction processes, and cancer dynamics. Over 20 PhD students and postdocs have graduated from his lab, now contributing to academia, industry, and national labs. Key research areas include theoretical biophysics, stochastic processes in biological systems, and nanocatalysis. His work has been recognized through awards like the NSF CAREER Award, Sloan Fellowship, and Humboldt Research Fellowship. He is a Fellow of the American Physical Society. Recent publications explore transcriptional bursting, antimicrobial peptide mechanisms, and plasmon-driven molecular machines. His lab develops coarse-graining methods for chemical networks and analyzes protein-DNA interactions using discrete-state stochastic models. Grants and collaborations span cancer initiation dynamics, evolutionary network theory, and nanoparticle-based therapies. His research bridges physics, chemistry, and biology, with applications in drug design, cancer therapy, and nanotechnology.

• Database Systems
• Healthcare Information Systems
• Artificial Intelligence
• +۵ مورد دیگر

Carlo Combi is a Full Professor at the Department of Computer Science, University of Verona. He serves as Department Director and holds roles in various academic committees, including the Faculty Board of PhD in Computer Science and the Computer Science Teaching Committee. His research focuses on databases, healthcare information systems, temporal data mining, and business process management. He leads the STARS laboratory and has contributed to projects like PREPARE (prostate cancer evaluation) and EDIPO (neuroimaging genetics). His teaching spans courses on biomedical databases, software engineering, and Python programming. Combi's work integrates ethical considerations into process models and addresses challenges in healthcare workflows, temporal analytics, and AI applications in medicine. Education: Not explicitly listed in provided texts. Research Projects: Includes 30+ projects since 2008, emphasizing healthcare informatics, temporal systems, and data warehousing. Labs/Teams: STARS (Semistructured Temporal clinical Geographical Systems) and collaborations with CBMC (Computational Biomedicine Center). Grants: Multiple national/international grants, including funding for temporal data mining and healthcare process modeling. Awards: None explicitly mentioned, though his leadership roles and extensive publications highlight academic recognition. His research bridges clinical processes, temporal databases, and AI, with applications in acute kidney injury prediction, BPMN extensions for healthcare, and explainable AI in medical systems. Combi actively contributes to open-source solutions for clinical data analysis and integrates ethical frameworks into process management.

Anna Goldenbergمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence in Healthcare
• Biomedical Informatics
• +۳ مورد دیگر

Anna Goldenberg is an Associate Professor in Computer Science at the University of Toronto and Senior Scientist in the Genetics & Genome Biology Program at The Hospital for Sick Children (SickKids). She holds the Varma Family Chair in Biomedical Informatics & AI and CIFAR AI Chair. Her roles include Co-chair of SickKids' AI in Medicine Initiative and Associate Research Director, Health at Vector Institute. Goldenberg's research focuses on developing machine learning methods for healthcare applications, including disease mechanisms, risk prediction systems, and responsible AI deployment. Affiliations: SickKids Research Institute, University of Toronto, Vector Institute Education: PhD from Carnegie Mellon University, Postdoc in Computational Biology Research Interests: Machine learning for healthcare, biomedical informatics, precision medicine, and overcoming AI challenges like rare event prediction and model explainability. Her lab emphasizes ethical AI integration into clinical workflows. Publications: Over 119 peer-reviewed articles spanning AI in medicine, genomics, and clinical prediction systems. Recent work includes early cancer detection via cell-free DNA and AI-driven pediatric urology management. Awards: Early Researcher Award, CIFAR AI Chair, Canadian Research Chair in Computational Medicine Grants: Multiple NIH, CIHR, and industry-funded projects totaling ~$20M, including grants for AI in cardiac arrest prediction, pediatric asthma modeling, and cancer detection via federated learning. Labs/Teams: Leads Goldenberg Lab at SickKids focusing on clinical AI systems and responsible innovation in healthcare technology.

• Animal Breeding
• Livestock Genetics
• Beef Cattle Management
• +۳ مورد دیگر

Mark Johnson is a Professor in the Department of Animal & Food Sciences at Oklahoma State University (OSU), holding dual roles as an Extension Specialist for Livestock Evaluation and academic faculty. He has been with OSU since 1992, serving as Associate Professor from 1997 to 2020 before attaining his current rank. He earned his Ph.D. in Animal Sciences from Kansas State University in 1992. His research focuses on animal breeding, livestock genetics, and beef cattle management, with particular emphasis on genetic parameters and livestock evaluation techniques. His work integrates ultrasound imaging for measuring carcass traits in Brangus cattle and develops methodologies for livestock scoring systems. Teaching responsibilities include courses on animal breeding, beef seedstock management, and peer-led learning strategies in animal science. Dr. Johnson’s publications span genetic analysis in livestock to pedagogical methods in music education, reflecting his interdisciplinary approach. He actively contributes to OSU Extension, providing expertise in livestock evaluation and agricultural outreach. His teaching portfolio includes 67 documented courses across three decades, emphasizing practical application of genetic principles and cattle enterprise management.

• Catalysis
• Environmental Chemistry
• Materials Science
• +۵ مورد دیگر

Dr. Harsh Vardhan is a Postdoctoral Research Associate and Rice Academy Junior Fellow at Rice University's Department of Chemical & Biomolecular Engineering, working under Dr. Rafael Verduzco. His research focuses on covalent organic frameworks (COFs) for applications in PFAS degradation and selective metal ion removal from water. Previously, he held roles as a postdoctoral fellow at Northwestern-Argonne Institute of Science and Engineering and conducted doctoral research on COFs for heterogeneous catalysis at the University of South Florida. He has been recognized with multiple awards, including the Alexiou Award in Environmental Chemistry and Rice Academy Fellowship. Education: Ph.D. in Chemistry (University of South Florida, 2021), M.S. in Chemistry (Indian Institute of Technology-Kharagpur). His work bridges materials science, environmental engineering, and nanotechnology. Key projects include developing COF-based materials for ion rejection and catalytic processes, alongside pioneering CRISPR-Cas9 genome editing tools for therapeutic applications in sickle cell disease. Research Interests: Catalytic materials design Environmental remediation technologies Nanomaterials synthesis CRISPR-based gene editing Water purification systems Publications Trends: Over 15 peer-reviewed articles highlight his contributions to genome editing precision, CRISPR off-target mitigation, and COF material applications. Recent work emphasizes spatial control of gene editing and AAV vector engineering. Awards & Grants: Recipient of departmental research grants, travel awards, and student-nominated teaching accolades. Current funding includes the Rice Academy Fellowship supporting his PFAS degradation studies. Labs & Affiliations: Member of the Verduzco Polymer Engineering Laboratory at Rice. Collaborates with interdisciplinary teams on environmental and biomedical material innovations.

Megan Kenمشاهده پروفایل

پژوهشگر ارشد

• RNA Biology
• Structural Biology
• Computational Biology
• +۳ مورد دیگر

Megan Ken, MD, PhD is a Research Fellow in the Department of Integrative Structural and Computational Biology at Scripps Research, with joint appointments in Immunology & Microbiology and the Skaggs Graduate School of Chemical and Biological Sciences. Her work bridges RNA structural biology, computational modeling, and drug discovery, focusing on understanding RNA dynamics in cellular contexts. Dr. Ken holds a B.A. in Biology and Chemistry from Washington University in St. Louis (2013), an M.D. and Ph.D. in Biochemistry from Duke University School of Medicine (2022–2023). Her research emphasizes RNA conformational propensities, ensemble behavior, and their implications for therapeutic targeting. Her notable achievements include the 2025 Andy Kaplan Prize and the NIH Director’s Early Independence Award (2024). Her lab develops predictive models of RNA function and explores nonspecific drug-RNA interactions. She has pioneered methods for high-performance virtual screening targeting RNA dynamic ensembles. Dr. Ken advocates for inclusive excellence in STEM, co-developing curricula on health disparities and cultural determinants of health. Grants include NIH/NIAID funding for MD/PhD training and the Ellen Browning Scripps Foundation Award.