جست‌وجوی استاد و پژوهشگر

Valerie Speirsمشاهده پروفایل

استاد

• Molecular Oncology
• Breast Cancer
• Glioblastoma
• +۷ مورد دیگر

Professor Valerie Speirs is Chair in Molecular Oncology at the University of Aberdeen, with a visiting professorship at the University of Leeds. She holds a BSc in Zoology (1986, University of Aberdeen) and a PhD in Cancer Biology (1990, University of Glasgow). Her research focuses on breast cancer biology, tumor-stroma interactions, oestrogen receptor dynamics, and environmental endocrine disruptors. She employs advanced techniques including 3D cell culture, digital pathology, and Raman spectroscopy. Established Aberdeen Cancer Centre for translational research Co-PI for Breast Cancer Now Tissue Bank Founder of SEARCHBreast initiative Current funding includes grants from Breast Cancer Now, NC3Rs, and Animal Free Research UK. She supervises PhD students in biomedical sciences and has led international collaborations with institutions in London, Leeds, and Nottingham. Her awards include the Breast Cancer Now Outstanding Contribution award (2011) and multiple conference prizes.

Jing Yangمشاهده پروفایل

استادیار

• Biostatistics
• Computational Biology
• Genomics
• +۶ مورد دیگر

Jing Yang is an Assistant Professor of Biostatistics at Vanderbilt University Medical Center. With a PhD in Molecular Biology and Biochemistry from East China University of Science and Technology & Shanghai Institutes for Biological Sciences of Chinese Academy of Sciences, Yang specializes in computational genomics, immunogenomics, and pharmacogenetics. Education: PhD in Molecular Biology and Biochemistry Research interests include: Genomic determinants of immunotherapy response Pharmacogenetic mechanisms in infectious diseases Dysfunctional regulatory networks in cancer and autoimmune diseases Development of bioinformatics tools for disease similarity and gene coexpression analysis Recent publications span topics such as tumor-immune feedback loops, pharmacogenomics of tuberculosis treatment, and computational modeling of disease networks. Yang's work integrates biostatistical methods with multi-omics data to address complex biological questions. Email: jing.yang@vumc.org

Jianhua Ruanمشاهده پروفایل

استاد

• Bioinformatics
• Computational Systems Biology
• Machine Learning for Life Sciences
• +۷ مورد دیگر

Dr. Jianhua Ruan is a Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Washington University in St. Louis and has been a faculty member at UTSA since 2007. His research focuses on bioinformatics, computational systems biology, and machine learning applied to life sciences. Specific areas include modeling cis-regulatory networks, analyzing biological networks via graph algorithms, and biomarker discovery using omics data. Dr. Ruan collaborates extensively with biologists at UTSA and the UT Health Science Center on genomic, transcriptomic, proteomic, and epigenomic studies. Key research interests include: Machine learning for cis-regulatory network modeling Graph algorithms for biological network analysis Integrated omics data analysis for disease prediction Epigenetic regulation and cancer biology His work has been supported by NIH, NSF, and the San Antonio Life Sciences Institute. Notable achievements include a Best Performer award in the DREAM Reverse Engineering Challenges (2008) and the UTSA Faculty Research Award (2007). Dr. Ruan organizes the UTSA Computer Science Graduate Research Seminar, fostering interdisciplinary collaboration. He has developed software tools like TSCAN for single-cell RNA-seq analysis and contributes to computational methods for biomarker discovery and network-based classification.

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

Lani Wuمشاهده پروفایل

استاد

• Systems Biology
• Cell Signaling
• Cancer Biology
• +۷ مورد دیگر

Dr. Lani Wu is a Professor in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF), affiliated with the School of Pharmacy. She leads the Altschuler & Wu Laboratories , focusing on systems biology, cell signaling, and drug discovery. Her work integrates experimental biology, mathematics, and computational methods to study cellular individuality, collective behaviors, and drug resistance mechanisms. Key projects include high-throughput screening platforms, cancer persister cell mechanisms, and mathematical modeling of biological networks. Education: PhD in Mathematics, University of California, San Diego (1990) Masters in Mathematics, University of California, San Diego (1987) Bachelor of Arts in Mathematics, Taiwan University (1985) Research Interests: Dr. Wu’s research spans systems biology, cell polarity, and computational approaches to drug discovery. She investigates how biological networks rewire during disease, leveraging machine learning and image analysis to uncover design principles of biological systems. Current projects include modeling cancer drug resistance, identifying compounds for acute hypoxic stress, and developing tools for spatial transcriptomics. Publications: Her work emphasizes drug discovery, cancer biology, and cell signaling. Recent efforts highlight techniques like phenopushing platforms, high-content screening, and spatial analysis of cell morphologies. These studies aim to accelerate therapeutic development and understand cellular adaptation under stress. Affiliations: She is part of the Biological and Medical Informatics Graduate Program and the Quantitative Biosciences Institute (QBI) at UCSF, reflecting her interdisciplinary approach. Labs/Teams: The Altschuler & Wu Laboratories combine experimental and computational expertise to address complex biological questions, with particular emphasis on translational research in oncology and systems biology.

Evan Biederstedtمشاهده پروفایل

پژوهشگر

• Computational Biology
• Cancer Genomics
• Bioinformatics
• +۳ مورد دیگر

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.

• Evolutionary Biology
• Statistical Genetics
• Computational Biology
• +۳ مورد دیگر

Paul Marjoram is a Research Professor at the University of Southern California in the Department of Population and Public Health Sciences. His research focuses on probabilistic and statistical methods for modeling evolutionary processes, including population dynamics and cancer progression. He is a pioneer in Approximate Bayesian Computation (ABC) and computational genetics. Research Interests Development of Bayesian statistical methods for evolutionary and cancer genomics Computational modeling of gene regulatory networks Epigenetic conservation and mutational signatures analysis Publications His work spans hierarchical Bayesian models, MCMC algorithms, and ABC frameworks applied to diverse biological systems, from Drosophila development to human cancer genetics. Software Contributions Methcon5 for DNA methylation analysis fmcmc (Friendly MCMC Framework) slurmR for HPC workflows

Claes Lundströmمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Artificial Intelligence
• Digital Pathology
• +۴ مورد دیگر

Claes Lundström is an Adjunct Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) school. His primary research focuses on medical imaging, integrating machine learning, visualization, and human-computer interaction in clinical settings. He leads the technical side of digital pathology research at the Center for Medical Image Science and Visualization (CMIV), directing national-scale grants and serving as Arena Director for the Analytic Imaging Diagnostics Arena (AIDA). Since 2010, he has held dual roles as Research Director at Sectra AB and academic researcher, driving innovations that translate into commercial healthcare solutions. Lundström's work spans AI-driven diagnostics, uncertainty visualization, and precision orthopedics, with contributions recognized through leadership in major projects like the €70M BIGPICTURE initiative. His academic credentials include a PhD (2007) and Docent degree (2014), alongside extensive industry collaboration. Education: PhD in 2007 and Docent degree in 2014 from Linköping University. PhD: 2007 Docent: 2014 Research interests emphasize AI integration in diagnostics, particularly digital pathology and medical imaging workflows. His work addresses challenges like domain adaptation in AI models, uncertainty quantification in segmentation tasks, and interactive visualization tools for clinicians. Recent projects include the VAI-B platform for AI validation in breast imaging and the BigPICTURE collaboration for global pathology data sharing. Key grants include leadership roles in national-scale projects, such as the AIDA arena and SCAPIS study-based AI lab. His contributions to AI ethics and clinical adoption include studies on human-AI collaboration and quality assurance frameworks. Lundström also oversees labs like CMIV and AIDA, fostering cross-disciplinary research in medical imaging and AI.

Pouya Faridiمشاهده پروفایل

پژوهشگر ارشد

• Proteomics
• Cancer Immunotherapy
• Mass Spectrometry
• +۳ مورد دیگر

Dr. Pouya Faridi is a Senior Research Fellow at Monash University's School of Clinical Sciences and heads the Translational Antigen Discovery Laboratory. He holds a pharmacy doctorate and PhD from Shiraz University of Medical Sciences, Iran, followed by postdoctoral research at ETH Zurich and Monash University. His research focuses on developing mass-spectrometry and proteomics technologies for identifying cancer immunotherapy targets, with clinical applications in cancer vaccines. He co-directs the Monash Clinical Proteomics Facility and leads projects funded by NHMRC, Cancer Australia, and the Victorian Cancer Agency. **Education**: PharmD, Shiraz University of Medical Sciences, Iran PhD, Shiraz University of Medical Sciences, Iran **Research Interests**: Biomarker discovery via mass spectrometry Immunopeptidome analysis for precision oncology Development of novel cancer vaccines Proteomics-based theranostics **Awards & Grants**: Victorian Cancer Agency mid-career fellowship (2021) Australian Melanoma Research Foundation Early Career Award (2021) Lead investigator on NHMRC grants targeting tumor antigens Supported by Isabella & Marcus Foundation for pediatric brain tumor research **Labs & Teams**: Translational Antigen Discovery Laboratory (Monash) Co-director of Monash Clinical Proteomics Facility Collaborations with Hudson Institute of Medical Research

Marco Baiesiمشاهده پروفایل

دانشیار

• Nonequilibrium systems
• Polymers
• Biopolymers
• +۵ مورد دیگر

Marco Baiesi is an Associate Professor in the Department of Physics and Astronomy at the University of Padua. His research focuses on nonequilibrium systems, polymers, biopolymers, topology, and machine learning applications in physics and biophysics. He has contributed to understanding the statistical mechanics of complex systems, including polymer dynamics, topological effects, and non-equilibrium thermodynamics. His work spans interdisciplinary areas such as biophysics, soft condensed matter, and machine learning for medical diagnostics. Notable contributions include studies on knotted polymer behavior, entropy production in non-equilibrium systems, and the application of AI to EEG-based dementia classification. Baiesi’s publications frequently explore topics like fluctuation theorems, stochastic processes, and the interplay between topology and material properties. His research has been published in high-impact journals such as Science , Physical Review Letters , and New Journal of Physics .

Matthieu Defranceمشاهده پروفایل

دانشیار

• Gene Regulation
• Epigenetics
• Epigenomics
• +۶ مورد دیگر

Matthieu Defrance is an Associate Professor in the Computer Science Department at Université Libre de Bruxelles (ULB), Belgium, a position he has held since October 2016. Prior to this, he worked as a PostDoc at ULB's Laboratory of Cancer Epigenetics from May 2010 to September 2016. Dr. Defrance's research bridges computer science with biological sciences, focusing on computational methods for analyzing complex biological data. His work spans epigenetics, genomics, and algorithm development for biological applications. His primary research interests include: Gene Regulation and Chromatin Biology Epigenetics and Epigenomics DNA Methylation analysis Computational Statistics for biological data Algorithm Development for genomic applications Next Generation Sequencing data analysis Dr. Defrance's publication record demonstrates a strong interdisciplinary approach, with recent work focusing on improving DNA methylation analysis techniques, developing novel bioinformatics tools like RedRibbon for gene expression signature comparison, and applying these methods to understand diseases like diabetes and cancer. His research shows consistent innovation in computational biology, with publications spanning from method development to biological applications across diverse fields including diabetes genetics, cancer epigenetics, and evolutionary adaptations in desert ants. With 61 publications and nearly 5,000 citations, Dr. Defrance has established himself as a significant contributor to computational biology and epigenetics research.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

Pedro Abranches De Carvalhoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bioinformatics
• Neural Engineering
• +۳ مورد دیگر

Pedro Abranches De Carvalho is a Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with Prof. Courtine's research group and the Laboratory for Information and Inference Systems (LIONS). He is also enrolled in the Doctoral Program in Electrical Engineering at EPFL. His work spans interdisciplinary research areas, including machine learning, bioinformatics, and computational biology. He holds multiple roles at EPFL: Doctoral Assistant at the UPCOURTINE group (School of Computer and Communication Sciences) Doctoral Assistant at LIONS lab (School of Engineering) Doctoral Student in Electrical Engineering (EDOC program) His research interests focus on applying computational methods to biological systems, such as geometric graph generation using GANs, redox signaling mechanisms, and noncoding RNA roles in leukemia. While no specific grants or awards are mentioned, his work bridges neural engineering and biochemistry. He is part of the Courtine Lab and LIONS, which likely involve collaborations in neural signal processing and machine learning applications in life sciences.

Raphael D. Levineمشاهده پروفایل

استاد

• Physical Chemistry
• Theoretical Chemistry
• Quantum Computing in Chemistry
• +۴ مورد دیگر

Raphael D. Levine is a Professor at the Department of Chemistry, University of California, Los Angeles (UCLA). He holds affiliations in the Chemistry and Biochemistry departments, Molecular & Medical Pharmacology, and is a member of the California NanoSystems Institute and the JCCC Signal Transduction and Therapeutics Program Area. Primary Affiliation: Department of Chemistry, UCLA Secondary Affiliations: Chemistry and Biochemistry, Molecular & Medical Pharmacology Research Institutes: California NanoSystems Institute, JCCC Program Area Levine's research spans interdisciplinary domains such as: Information-theoretic approaches to gene networks and carcinogenesis Quantum and molecular computing via quantum dots, DNAzymes, and redox systems Entropy analysis in biochemical signaling and cellular dynamics Ultrafast spectroscopy for molecular logic devices Isotope effects in planetary and nebular chemistry His work emphasizes the convergence of physical chemistry, computational biology, and nanotechnology. Key methodologies include surprisal analysis, maximal entropy inference, and electrochemical spectroscopy. Levine's publications demonstrate expertise in translating quantum phenomena into practical computing frameworks, with applications in cancer biology, molecular electronics, and astrochemical modeling. Notable collaborations include James R. Heath and Françoise Remacle. Contact: rafi@chem.ucla.edu | Office: Geology 3608A | Mailing: Department of Chemistry, UCLA

• Computational Logic
• Artificial Intelligence
• Knowledge Representation
• +۵ مورد دیگر

Roles and Affiliations: Professor of Computational Logic at the Department of Computing, Imperial College London. Part of the Faculty of Engineering. Active in research, teaching, and publication in computational logic, artificial intelligence, and formal systems. Research Interests: Focuses on computational logic with applications to knowledge representation, action and agency theories, temporal reasoning, normative systems, legal reasoning, and formal organization models. Recent work also intersects with bioinformatics and proteomics analysis. Publications: Over 50 publications since 1986, including foundational work on the Event Calculus, normative systems, and formal methods in multi-agent systems. Notable contributions span deontic logic, agent coordination, and applications in legal reasoning and bioinformatics. Recent work emphasizes logic-based approaches to activity recognition and proteomic profiling. Teaching: Taught advanced courses on modal and temporal logic (2008–2009) and knowledge representation (2017–2018). Course materials emphasize formal semantics, nonmonotonic reasoning, and logic programming. Labs/Teams: Involved in interdisciplinary projects on computational systems, including collaborations with bioinformatics researchers and multi-agent system developers.