جست‌وجوی استاد و پژوهشگر

• Autism Spectrum Disorder
• Biostatistical Methods
• Chronic disease
• +۴ مورد دیگر

Xiaoyu Che is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health, where he serves as the principal biostatistician in the Center for Infection and Immunity (CII) at Columbia University Irving Medical Center. His work bridges statistical methodology with biomedical research, focusing on complex disease mechanisms through advanced data analysis approaches. Dr. Che received his academic training at prestigious institutions: BS in Mathematics from Zhejiang University (2006) PhD in Mathematics from Claremont Graduate University (2013) Dr. Che's research program centers on the development and application of statistical methods for multi-omics analyses, with particular focus on understanding the pathogenesis of chronic and neurodevelopmental conditions. His work spans multiple domains including Autism Spectrum Disorder (ASD), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Gulf War Illness (GWI). He employs sophisticated biostatistical approaches to integrate diverse biological data types, revealing novel insights into disease mechanisms. His methodological expertise includes Bayesian statistics, metabolomic analysis, immune signature identification, and microbiome characterization, all aimed at translating complex biological data into meaningful clinical insights. Analysis of Dr. Che's publication record reveals a strong thematic focus on applying advanced statistical methods to understand complex disease mechanisms. His work consistently bridges biostatistical innovation with biomedical discovery, particularly in the areas of neurodevelopmental disorders and chronic fatigue conditions. The publications demonstrate progression from foundational methodological work to increasingly sophisticated multi-omics integration approaches, reflecting both technical growth and expanding research impact. His collaborative approach is evident through numerous high-impact publications with interdisciplinary teams across Columbia University and beyond. Dr. Che teaches BIST P8104: Probability in the Biostatistics MS degree program at Columbia, demonstrating his commitment to training the next generation of biostatisticians. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact journals suggests successful grant funding supporting his research program. As principal biostatistician in the Center for Infection and Immunity, Dr. Che plays a critical role in the analytical framework of the center's research initiatives. His work supports the center's mission to understand the relationship between infectious agents and human health through rigorous quantitative analysis. The collaborative nature of his research is evident in the diverse range of co-authors spanning immunology, virology, microbiology, and clinical medicine.

Miaoyan Wangمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Matrix/Tensor Data Analysis
• High-Dimensional Statistics
• +۳ مورد دیگر

Miaoyan Wang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. She holds early tenure and is a faculty affiliate in the Mathematical Foundations of Machine Learning, Institute for Foundations of Data Science (IFDS), and Center for Demography of Health and Aging (CDHA). She is currently on sabbatical as a visiting associate professor at Stanford University and Lawrence Livermore National Laboratory. Education: PhD in Statistics from the University of Chicago (2015), BS in Mathematics from Fudan University (2010). Postdoctoral training included positions at UC Berkeley (Computer Science) and the University of Pennsylvania (Math+X). Research focuses on statistical machine learning, with emphasis on matrix/tensor data analysis, high-dimensional statistics, nonparametric learning, and applications in genetics. Her work bridges theory and practice, addressing challenges in computational efficiency and statistical optimality for complex data structures. Awards include the prestigious NSF CAREER Award (2022), multiple best paper awards (ASA, IMS, NEURIPS), and recognition from ASHJ and IGES. Her group has secured grants totaling $3.4 million, including NSF funding for foundational machine learning research and collaborative projects in population genomics. Advising includes PhD students Chanwoo Lee and Jiaxin Hu, with former students Yuchen Zeng and Zhuoyan Xu. She teaches advanced statistical methods and computational courses, emphasizing rigorous theoretical foundations and practical applications. Key collaborations include work on tensor decomposition algorithms, statistical genetics, and interdisciplinary projects with biology and computer science departments. Her lab contributes open-source software tools for data analysis, including packages for tensor block models and multiway clustering.

Jonathan Hugginsمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Computation
• Statistical Genetics
• +۳ مورد دیگر

Jonathan Huggins is an Assistant Professor at Boston University, affiliated with the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. He holds a Ph.D. in Computer Science from MIT (2018) and a B.A. in Mathematics from Columbia University (2012). His research focuses on developing fast, trustworthy machine learning and Bayesian methods that balance computational efficiency and statistical optimality, with applications in ecological forecasting and genomic data analysis. Education: Ph.D. in Computer Science, Massachusetts Institute of Technology (2018) B.A. in Mathematics, Columbia University (2012) Research Interests: Large-scale machine learning and Bayesian computation Robust statistical inference Applications in genomics and ecological modeling Algorithmic development for scalable inference Key Projects: Stochastic Methods for Data Science: A book on stochastic processes and algorithms VIABEL: A Python package for variational inference and diagnostics ShorTeX: A LaTeX package for mathematical writing Recent Articles: Focus on scalable Bayesian methods, error bounds for iterative algorithms, and mutational signature discovery. His work emphasizes reproducibility and robustness in statistical inference. Awards: Blackwell–Rosenbluth Award (Outstanding Junior Bayesian Researcher) Grants & Funding: Supported by NIH, NSF, and the Department of Defense. Active in advising students across multiple BU programs. Labs/Teams: Affiliated with the BU URBAN Program, Program in Bioinformatics, and Department of Computer Science.

Romdhane Rekayaمشاهده پروفایل

استاد

• Animal Breeding and Genetics
• Quantitative Genetics
• Genomics
• +۴ مورد دیگر

Romdhane Rekaya serves as a Professor in the Department of Animal and Dairy Science within the College of Agricultural & Environmental Sciences at the University of Georgia. He also holds courtesy faculty positions in the Department of Statistics and is an associate faculty member with the Institute of Bioinformatics at UGA. His research program focuses on developing statistical and computational tools for analyzing large genetic and genomic datasets with applications in livestock, poultry, and human health. Dr. Rekaya's educational background includes: Agriculture Engineer from the High Institute of Agriculture, Tunisia Master of Science from the International Center for Advanced Studies in Mediterranean Agriculture of Zaragoza, Spain Ph.D. from the Polytechnic University of Madrid, Spain Dr. Rekaya's research interests span quantitative genetics, genomics, biostatistics, and bioinformatics. His work centers on developing statistical and computational methodologies for analyzing big genetic and genomic data sets with practical applications in livestock, poultry, and human health. His research has been particularly focused on addressing critical challenges in animal agriculture including horn fly resistance in beef cattle, water utilization efficiency in poultry, and greenhouse gas emissions in dairy production. His methodological approaches often integrate machine learning, Bayesian statistics, and genomic technologies to solve complex biological problems. Dr. Rekaya's publication record demonstrates a consistent focus on statistical methodology development for genetic analysis, with recent work increasingly emphasizing practical applications of genomic technologies in animal agriculture. His research spans both theoretical statistical development and practical implementation in livestock improvement programs, with particular emphasis on innovative approaches to traditional breeding challenges. Among his professional recognitions: Student Career Success Influencer Award 2024 Mini-Sabbatical Award Student Career Success Influencer Award 2022 Carnegie fellowship Carnegie African Diaspora Fellow Faculty with significant positive impact of at least one graduate student Gamma Sigma Delta outstanding Research Achievements Award Dr. Rekaya has successfully mentored numerous graduate students including PhD candidates Amanda Warner, Mahsa Zare, Koushik Das, and Evan Hartono, along with undergraduate researchers. His research has been consistently supported by major funding agencies including USDA NIFA, USDA ARS, Georgia Agricultural Commodity Commission for Beef, National Academy of Sciences, and industry partners including Tyson Foods and Cobb-Vantress. Current projects include developing genetic solutions to the horn fly problem in beef cattle, improving water utilization efficiency in poultry, and examining associations between greenhouse gas emissions and feed efficiency in dairy cattle. Dr. Rekaya leads an active research laboratory that collaborates with multiple departments and institutions. His lab focuses on applying advanced statistical and computational methods to solve pressing problems in animal agriculture, with particular emphasis on integrating genomic information into practical breeding programs. The lab maintains strong industry connections and international collaborations, particularly with researchers in Africa through the African Animal Breeding Network.

Pavel P. Kuksaمشاهده پروفایل

عضو هیئت علمی

• Bioinformatics
• Computer Science
• Functional Genomics
• +۵ مورد دیگر

Pavel P. Kuksa is a Research Assistant Professor in the Department of Pathology and Laboratory Medicine, specializing in bioinformatics, computer science, and functional genomics. His work focuses on high-throughput sequencing analysis, chromatin interaction data, and developing scalable software platforms for genomics research.

Torsten Schwedeمشاهده پروفایل

استاد

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Structural Biology
• +۳ مورد دیگر

Torsten Schwede is a Professor for Structural Bioinformatics at the Biozentrum, University of Basel, and serves as Vice President for Research at the same institution. He leads the SPHN Data Coordination Center at SIB Swiss Institute of Bioinformatics. His research focuses on computational structural biology, protein structure prediction, and structural bioinformatics, with contributions to tools like SWISS-MODEL. He has been honored as a Highly Cited Researcher in Biology and Biochemistry (2019–2021). Education: PhD in Protein X-ray Crystallography from Albert-Ludwigs-Universität Freiburg (Germany). Positions include leadership roles in academia and industry (e.g., GlaxoSmithKline). His work emphasizes protein modeling, data management, and integrative structural methods. Collaborations span computational drug design, benchmarking initiatives (CASP), and open-source software development. Scientific contributions include advancements in protein-ligand interactions, homology modeling, and the development of ModelCIF and QMEANDisCo frameworks. He actively participates in global initiatives like the Swiss Personalized Health Network (SPHN) and precision medicine.

• Computer-aided drug discovery
• Docking
• Virtual screening
• +۸ مورد دیگر

Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.

Nick Heardمشاهده پروفایل

استاد

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +۶ مورد دیگر

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

• Biotechnology
• Systems Engineering in Bioprocessing
• Model-Based Tools
• +۴ مورد دیگر

Professor Cleo Kontoravdi is a Professor of Biological Systems Engineering at the Department of Chemical Engineering, Imperial College London, within the Faculty of Engineering. Her roles include Director of Postgraduate Studies (2021–present) and Postgraduate Admissions Tutor (2018–2021). She holds affiliations with key research centers such as the Centre for Process Systems Engineering, Centre for Synthetic Biology, and Future Vaccine Manufacturing Research Hub. Her research focuses on applying systems engineering principles to bioprocessing, integrating model-based tools like sensitivity analysis and optimization with experimental work on mammalian cell cultures and vaccine production. Key areas include metabolic flux analysis, media optimization, and multiscale modeling. Her recent publications highlight advancements in hybrid modeling frameworks, mRNA vaccine process design, and metabolic engineering of CHO cells. She actively contributes to vaccine manufacturing strategies and sustainable biopharmaceutical supply chains. Education: PhD (2007) and MEng (2002) in Chemical Engineering from Imperial College London. Professional experience spans academic roles (since 2007) and industry R&D at Lonza Biologics (2006–2007). Her work bridges computational biology, bioprocess engineering, and systems biology, addressing challenges in vaccine development and production efficiency. Awards: Not explicitly listed in the provided text. Research grants and collaborations are implied through her involvement in high-impact projects like the Future Vaccine Manufacturing Hub. She advises on bioprocess optimization, glycoengineering, and biomanufacturing sustainability.

Mohsen Pourahmadiمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +۷ مورد دیگر

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.

Sudhir Kumarمشاهده پروفایل

استاد

• Molecular Evolution
• Phylomedicine
• Functional Genomics
• +۵ مورد دیگر

Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.

Qi Yuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

Özlem İlk Dağمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Multilevel Modeling
• Interactive Graphics
• +۵ مورد دیگر

Prof. Dr. Özlem İlk Dağ is a Professor of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds the position of Department Head of Statistics and serves on advisory boards for TUBITAK and the Journal of Biostatistics. Her academic journey includes roles from Research Assistant (1997) to Professor (2018), with affiliations in Actuarial Sciences and Biostatistics. Education: Ph.D. in Statistics (Iowa State University, 2004), M.S. and B.S. from METU. Research focuses on longitudinal data analysis, multilevel modeling, Bayesian methods, and biostatistics. She authored three editions of R Yazilimina Giris and a foundational book on multivariate longitudinal data analysis. Key contributions include developing marginalized transition random effects models (MTREM) and R packages like 'mmm'. Awards include METU's 20-year service recognition and teaching excellence. She has supervised numerous research projects and co-authored over 50 peer-reviewed articles across biostatistics, genomics, and statistical computing. Her work bridges statistical theory and application, with notable contributions to gene expression clustering, maternal antibody studies in veterinary science, and computational methods for longitudinal data. She maintains active collaborations in genomics and biostatistics, and her lab focuses on integrating statistical computing with biomedical research.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +۸ مورد دیگر

Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.