جست‌وجوی استاد و پژوهشگر

Russell L. Finley, Jr.مشاهده پروفایل

استاد

• Protein-Protein Interaction Networks
• Gene Regulatory Networks
• Cell Cycle Regulation
• +۳ مورد دیگر

Russell L. Finley, Jr. is a Professor at Wayne State University, affiliated with the departments of Molecular Medicine and Genetics and Biochemistry, Microbiology, and Immunology. He is a member of the Karmanos Cancer Institute and serves as Division Director for Education and Graduate Officer for CMMG. PhD, SUNY Upstate Medical University (1990) Dr. Finley's research focuses on understanding regulatory networks that control biological processes and molecular mechanisms of cell division. His lab employs high-throughput technologies such as yeast two-hybrid systems, RNAi screens, and protein microarrays to map gene/protein interaction networks in Drosophila and bacterial pathogens. His recent publications (2024–2019) highlight work on: CHEK2 genetic variants in breast cancer Protein interactomes of pathogens (Campylobacter jejuni, Streptococcus pneumoniae, dengue virus) Novel cyclin proteins (Cyclin Y, Cyclin J) in cell cycle regulation Meta-interactome approaches to improve functional predictions Integration of Drosophila interactome and transcriptome data Dr. Finley contributes to systems biology and bioinformatics through algorithm development (e.g., TraM for graph matching) and interaction confidence scoring methods. His work bridges fundamental cell biology with translational applications in cancer and infectious disease research. Mentoring: Dr. Finley is accepting new Master's students for 2024/2025 but not new Ph.D. students. Labs and Teams: His research is conducted within the Karmanos Cancer Institute and the College of Medicine Microbiology and Immunology department at Wayne State University.

Monica Agrawalمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Machine Learning for Health
• +۱۱ مورد دیگر

Monica Agrawal is an Assistant Professor at Duke University with joint appointments in the Division of Translational Biomedical (Biostatistics & Bioinformatics), Trinity College of Arts & Sciences (Computer Science), and Pratt School of Engineering (Biomedical Engineering). Holding a Ph.D. from MIT (2023), her work bridges machine learning, clinical data analysis, and health equity through biomedical AI systems. Research Focus: Combines natural language processing, graph networks, and EHR analysis to address medical challenges. Key areas include polypharmacy side effects, health knowledge graphs, and human-AI collaboration in clinical settings. Scientific Contributions: Pioneering applications of large language models in health equity promotion, clinical information extraction, and EHR-based research. Collaborates with Harvard Medical School and Harvard School of Public Health on translational health projects. Teaching: Instructs courses on natural language processing (COMPSCI 572) and research independent study (COMPSCI 393/394), emphasizing hands-on AI development for healthcare. Recent Publications: Explore medical conversational AI, ambient scribing tools, and LLM safety in clinical communication. Her 2025 paper on health equity highlights AI's potential to reduce disparities.

Margaret Myersمشاهده پروفایل

مدرس

• Mathematics education and curriculum development
• Informal learning opportunities
• Formal derivation of algorithms
• +۴ مورد دیگر

Dr. Margaret Myers serves as a Lecturer in the Department of Statistics and Data Sciences at the University of Texas at Austin, having previously held the same position in the Department of Computer Science. She also contributed as a senior research scientist with the Dana Center, teaching undergraduate and graduate courses in discrete mathematics, linear algebra, probability, statistics, and Bayesian Statistics while developing foundational MOOCs for computational mathematics education. Her research spans mathematics education and curriculum development, informal learning opportunities, formal derivation of algorithms, high performance computing, scientific computing, and Bayesian statistical applications. She has pioneered educational resources through collaborations with Prof. Robert van de Geijn, including the Linear Algebra: Foundations to Frontiers series and specialized MOOCs on programming correctness and high-performance computing. Publication analysis reveals sustained focus on linear algebra algorithm derivation and high-performance computing, with recent expansion into software product line optimization and statistical computing applications. Her work consistently bridges theoretical computer science, numerical methods, and educational technology innovation. Awards: 2015 CNS Teaching Award for excellence in instruction Dr. Myers actively contributes to The Science of High Performance Computing Group (shpc.ices.utexas.edu), advancing research in computational methodologies and open educational resources for scientific computing.

Kevin Tianمشاهده پروفایل

استادیار

• Algorithms
• Continuous Optimization
• High-Dimensional Statistics
• +۴ مورد دیگر

Kevin Tian is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. His research focuses on fundamental algorithmic problems in modern data science, particularly in continuous optimization and high-dimensional statistics. He also has broad interests in trustworthy machine learning, including robustness, privacy, and fairness. Dr. Tian completed his Ph.D. in Computer Science at Stanford University from 2016-2022, where he was advised by Aaron Sidford. Prior to that, he earned his B.S. in Mathematics and Computer Science at MIT from 2012-2015. From 2022-2023, he was a Postdoctoral Researcher in the Machine Learning Foundations group at Microsoft Research. Dr. Tian's research spans several areas of theoretical computer science and machine learning. His work primarily focuses on developing efficient algorithms for high-dimensional statistical problems, with particular emphasis on continuous optimization methods. He has made significant contributions to areas such as robust statistics, differential privacy, and graph algorithms. His research often bridges the gap between theoretical guarantees and practical applicability, developing algorithms that are both theoretically sound and practically efficient. His recent publications demonstrate a strong focus on developing algorithms that address challenges in modern data science, including robustness against adversarial contamination, efficient methods for high-dimensional statistics, and privacy-preserving computation. His work frequently appears in top theoretical computer science conferences such as STOC, FOCS, and COLT, as well as leading machine learning venues like NeurIPS and ICML. Dr. Tian has received numerous honors and awards for his research, including: 2021 Simons-Berkeley VMware Research Fellowship 2021 Google Ph.D. Fellowship 2019, 2020, 2021 Oral presentations at Neural Information Processing Systems 2018 SICOMP Special Issue invite, Foundations of Computer Science 2016 NSF Graduate Research Fellowship Arthur Samuel Award for Best Doctoral Thesis in Computer Science (2022) Dr. Tian advises multiple Ph.D., M.S., and B.S. students at UT Austin, with research spanning theoretical computer science and machine learning. His current Ph.D. students include Anming Gu, Syamantak Kumar (co-advised with Purnamrita Sarkar), Chutong Yang, and Yusong Zhu (co-advised with Eric Price). His research has been generously funded by prestigious fellowships including the NSF Graduate Research Fellowship, Google Ph.D. Fellowship, and VMware Research Fellowship. At UT Austin, Dr. Tian teaches courses including CS 331: Algorithms and Complexity and CS 395T: Continuous Algorithms. He is actively involved in the theoretical computer science community, serving on program committees for conferences such as COLT, STOC, and ICML.

Stefan Gräfمشاهده پروفایل

پژوهشگر ارشد

• Computational Genomics
• Genomic Medicine
• Vascular Biology
• +۷ مورد دیگر

Dr. Stefan Gräf serves as Principal Research Associate at the Victor Phillip Dahdaleh Heart & Lung Research Institute within the University of Cambridge's School of Clinical Medicine. His work bridges computational biology and vascular medicine with focus on pulmonary hypertension (PH) and cerebral small vessel disease (CSVD). Gräf's research centers on computational genomics approaches to analyze population-scale datasets, integrating genetic, multi-omic, imaging, and health records. Key interests include sex-aware genomic analysis , deep learning for regulatory element interpretation , and molecular network characterization of vascular microcirculation . His group develops secure data infrastructure for high-dimensional analyses using frameworks like Hadoop. His publication portfolio demonstrates consistent focus on PH/CSVD genomics, with recent work exploring sex-specific genetic determinants of right ventricular function (2024) and Bayesian methods for rare variant association (2020). Collaborations span international consortia including PAH-ICON and NIHR BioResource. Notable research partnerships include: BHF UK National Cohort Study for PAH NIHR Cambridge Biomedical Research Centre BHF Cardiovascular Centre of Research Excellence University of Cambridge Research Computing Services Early career highlights include contributions to METABRIC breast cancer reclassification and foundational work with Ensembl/ENCODE at EMBL-EBI. His trajectory shows deliberate shift from basic genomics to clinical translation in vascular medicine.

Olivier Restifمشاهده پروفایل

مدرس

• Infectious Diseases
• Ecology
• Evolution
• +۶ مورد دیگر

Dr. Olivier Restif is a Lecturer in Epidemiology at the University of Cambridge, Department of Veterinary Medicine, and a Fellow at his college. His research focuses on the intersection of infectious diseases, ecology, and evolution, utilizing mathematical and computational models to understand pathogen dynamics. He teaches Mathematical Biology in Part IA and Dynamics of Infectious Diseases in Part II of the Natural Sciences Tripos. His recent work explores epidemiological modelling in health crises, zoonotic spillover mechanisms, and antimicrobial resistance dynamics. Key projects include viral surveillance in bat populations, optimal Bayesian model design, and genomic analysis of AMR transmission. Scientific honors include recognition as a Fellow, reflecting his contributions to disease dynamics research. His publications emphasize bat-borne pathogens, mathematical frameworks for infection tracking, and computational tools for experimental design.

Jing Hua Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Epidemiology
• Public Health
• Proteogenomics
• +۴ مورد دیگر

Jing Hua Zhao is a Senior Research Associate in the Department of Public Health and Primary Care at the University of Cambridge, affiliated with the Cardiovascular Epidemiology Unit and Cambridge Cardiovascular . With academic appointments at institutions including Harvard University and King's College London, he specializes in genetic epidemiology and proteogenomics . Education: PhD in Genetics (King's College London), MMed (Fudan University), BMed (Shandong University) Research: Focus on pQTL mapping , GWAS meta-analysis , and inflammatory protein networks using INTERVAL cohort data. Software: Maintains open-source tools on CRAN and GitHub including gap , pQTLtools , and SCALLOP-INF packages. His work has produced 15+ major publications since 2018, with a focus on inflammatory disease mechanisms and therapeutic target identification . Notable collaborations include the Host Genetics Initiative and SCALLOP consortium.

• Biostatistics
• Genetics
• Genomics
• +۷ مورد دیگر

Dr. Shuangge Steven Ma is the Department Chair and Professor of Biostatistics at the Yale School of Public Health , with affiliations to the Yale Institute for Global Health , Yale Cancer Center , and Yale-BI Biomedical Data Science Fellowship . His research bridges statistical methodology development and biomedical applications in cancer, mental disorders, and cardiovascular diseases. PhD in Statistics (University of Wisconsin, 2004) Postdoctoral Associate (University of Washington, 2006) Dr. Ma's research interests focus on statistical methods for complex biomedical data , including: gene-environment interactions, network analysis of omics datasets, high-dimensional regression, and integrative modeling of multi-source data. Recent work applies these methods to cancer genomics, tumor microenvironment characterization, and spatial transcriptomics. His 2024-2025 publications highlight innovations in Bayesian regression , neural network modeling , and network-based biomarker discovery , with applications spanning pancreatic cancer , melanoma , and hepatocellular carcinoma . Methodological contributions include robust variable selection, heterogeneous data integration, and spatial gene expression analysis. Fellow, American Statistical Association (2013) Elected Member, International Statistical Institute (2007) Dr. Ma provides statistical leadership for biomedical studies and health policy research , including surveys in mainland China and Taiwan examining health insurance utilization. He collaborates with institutions like the SPORE in Lung Cancer and SPORE in Skin Cancer programs.

David Shulmanمشاهده پروفایل

مدرس

• Pediatric Oncology
• Bone Tumors
• Brain Tumors
• +۶ مورد دیگر

David Shulman, MD, is an Attending Physician at Dana-Farber/Boston Children's Cancer and Blood Disorders Center and a Lecturer in Pediatrics at Harvard Medical School. His clinical expertise encompasses pediatric bone tumors, brain tumors, leukemia, neuroblastoma, and soft tissue sarcomas. He actively contributes to the Dana-Farber Experimental Therapeutics Program, focusing on early-phase clinical trials and biomarker research. Dr. Shulman received his medical degree from Harvard Medical School (2013), completed pediatric residency at Boston Combined Residency Program, and a fellowship in Pediatric Hematology/Oncology at Boston Children's Hospital/Dana-Farber Cancer Institute. His research integrates novel therapies and biomarkers for pediatric solid tumors, with emphasis on circulating tumor DNA (ctDNA) as a tool for treatment optimization. Recent publications highlight sarcoma biomarker validation, clinical trial innovations, and socioepidemiological factors in cancer outcomes. His work frequently involves collaborations with the Children's Oncology Group. Dr. Shulman's recent articles (2022-2025) predominantly address sarcoma therapeutics, liquid biopsy applications, and statistical methodologies in oncology trials. A consistent theme is optimizing diagnostic and therapeutic strategies for high-risk pediatric cancers through molecular profiling and innovative trial designs.

Simon Prestonمشاهده پروفایل

مدرس

• Statistical inference on manifolds
• Shape analysis
• Spherical data analysis
• +۶ مورد دیگر

Simon Preston is a Lecturer at the School of Mathematical Sciences , University of Nottingham. He obtained his MMath and PhD in Mathematics from Nottingham, supervised by Sarah Waters and Oliver Jensen. His research focuses on statistical inference for manifold-valued data , including shape analysis, spherical distributions, and stochastic differential equations. He has contributed to methods like ESAG distribution for anisotropic spherical data and shapePCA for analyzing shape variation via principal components. Recent publications address network analysis on manifolds , non-parametric regression , and Bayesian model selection in biological and urban contexts. His work spans applications in immunology (T-cell motility), plant architecture, and computational linguistics. Scientific Awards : RCUK Academic Fellowship EPSRC Statistics Mobility Fellowship He has collaborated extensively with Andy Wood, Sarah Waters, and Oliver Jensen, and his technical work includes MATLAB toolboxes for shape analysis and stochastic differential equation inference .

• Machine Learning
• Computational Biology
• Information Theory
• +۶ مورد دیگر

Alfred Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, where he is affiliated with the College of Engineering and the Electrical and Computer Engineering Department. His research spans multiple interdisciplinary domains, connecting theoretical foundations with practical applications in complex biological and physical systems. Professor Hero's research interests focus on the intersection of machine learning, information theory, and computational biology. His work particularly emphasizes developing advanced computational methods to understand complex biological systems, with significant contributions in gut microbiome analysis, protein-protein interaction prediction, and biomedical sensor applications. He has pioneered approaches using recurrent neural networks to model and design synthetic human gut microbiome dynamics, enabling prediction of microbial community behaviors and metabolic profiles. His research also extends to astrophysics applications, where he applies deep learning techniques to predict solar flares. Analysis of his recent publications reveals a strong trend toward developing theoretically grounded machine learning methods with applications across diverse domains. His work consistently bridges statistical theory with practical implementations, focusing on information-theoretic approaches to complex data analysis. Key themes include developing robust algorithms for high-dimensional data, creating secure distributed computing frameworks, and applying graph-based machine learning to biological networks. Professor Hero has led significant collaborative research efforts, particularly evident in his work on gut microbiome modeling which involved partnerships between biologists and engineers from the University of Michigan and the University of Wisconsin. His research has received substantial attention, with multiple publications picked up by numerous news outlets and cited extensively in academic literature. His laboratory work involves sophisticated computational approaches combined with experimental validation, as evidenced by references to the Venturelli Lab's robotic systems for creating microbial communities used to train and test algorithms. This integration of computational modeling with physical experimentation represents a hallmark of his interdisciplinary research approach.

H. M. Zaferمشاهده پروفایل

دانشیار

• Arabian and African Languages
• Historical Linguistics
• Medieval Studies
• +۷ مورد دیگر

H. M. Zafer is an Associate Professor at the University of Washington , specializing in Arabian and African Languages through pre-modern networks of knowledge spanning 6th-16th century Horn of Africa and Western Arabian societies. He also serves as a Senior Lecturer in the Program in African Studies. Education: PhD from Cornell University (2014) Zafer's research focuses on historical linguistics, medieval studies, and Islamic studies, particularly analyzing the Ecumenical Community in the context of Red Sea wars and Early Muslim communitarianism. His current projects include The Comrades of the Ship (Early Muslim historiography) and The Ethiopic Quran (16th-century Ge'ez commentary on the Quran). His scholarly contributions bridge interdisciplinary themes in comparative literature, religious studies, and computational methodologies through his earlier work in machine learning applications for bioinformatics and natural language processing. Scientific Awards: Mellon-Sawyer Fellow, University of Notre Dame Katz Fellow, University of Pennsylvania His publications from 1997-2009 explore hierarchical classification, Bayesian aggregation techniques, and neural networks applied to genomic prediction, linguistic analysis, and automated puzzle generation, demonstrating technical expertise in algorithm development and data modeling.

• Probabilistic Machine Learning
• Genomics
• Algorithms
• +۵ مورد دیگر

Derek Cole Aguiar serves as an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut's School of Engineering. His academic journey includes B.S. degrees in Computer Engineering and Computer Science from the University of Rhode Island, a Ph.D. in Computer Science from Brown University under Professor Sorin Istrail, and postdoctoral research at Princeton University with Professor Barbara Engelhardt. University of Rhode Island: B.S. Computer Engineering & Computer Science Brown University: Ph.D. Computer Science Princeton University: Postdoctoral Research Dr. Aguiar's research focuses on developing probabilistic machine learning models and combinatorial algorithms for analyzing high-dimensional genomic data, with applications to complex diseases. His work bridges theoretical computer science with practical biological applications, particularly in genomics, transcriptomics, population genetics, and immunology. He develops foundational methods for haplotype assembly, isoform discovery, variant calling, and cis-regulatory element analysis. Analysis of his publication record reveals a consistent trajectory in computational genomics, beginning with haplotype assembly algorithms and expanding into RNA-seq analysis, variant detection, and regulatory genomics. His work demonstrates a progression from algorithmic development to increasingly sophisticated probabilistic modeling approaches applied to complex biological problems. The tools he develops (HapCompass, BIISQ, Tractatus, DELISHUS, CYRENE) have become established methods in their respective subfields. Dr. Aguiar actively seeks motivated PhD students interested in foundational research at the intersection of statistics, probabilistic machine learning, and algorithms applied to genomics and related fields. His teaching portfolio includes advanced courses in Bayesian Machine Learning (CSE5825) and Algorithms & Complexity (CSE3500), where he emphasizes the three fundamental components of probabilistic modeling: model specification, inference algorithms, and model checking. His laboratory develops several widely-used bioinformatics tools including HapCompass for haplotype assembly, BIISQ for isoform discovery, Tractatus for identity-by-descent analysis, DELISHUS for variant calling, and CYRENE for cis-regulatory element visualization. These tools represent significant contributions to the computational genomics community and demonstrate his lab's focus on developing practical, scalable solutions to challenging biological problems.

• Machine Learning
• Differential Privacy
• Bayesian Statistics
• +۳ مورد دیگر

Antti Honkela serves as a Visitor at Aalto University's Department of Computer Science and is deeply affiliated with the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki. Within HIIT, he actively contributes to the Myllymäki Petri research group and leads the Probabilistic Machine Learning research group, focusing on advanced statistical methodologies. His research centers on machine learning with rigorous emphasis on differential privacy, Bayesian statistics, and probabilistic modeling. Honkela bridges theoretical innovation with biomedical applications, particularly in drug sensitivity prediction, genomic analysis, and privacy-preserving data sharing. His work addresses critical challenges in maintaining data utility while ensuring mathematical privacy guarantees for sensitive health information. Recent publications reveal a clear trajectory from foundational privacy mechanisms (e.g., FFT-based accounting) toward efficient biomedical implementations. His 10 publications between 2016-2021 demonstrate consistent output in top venues like Nature Communications and NeurIPS, with growing emphasis on transfer learning and distributed frameworks for real-world healthcare data. Scientific recognition includes: 2001 Year-End Thesis Award from CMCM, Center for Mathematical and Computational Modeling, University of Jyväskylä While specific student counts aren't public, his active research profile suggests ongoing supervision opportunities. Funding appears sustained through HIIT's infrastructure and competitive Nordic research grants, enabling work on privacy-preserving algorithms for genomic and clinical datasets. The Probabilistic Machine Learning group provides a collaborative environment leveraging Aalto University's computational resources and HIIT's interdisciplinary network. Lab activities focus on developing theoretically sound privacy mechanisms with practical biomedical impact, particularly in drug response modeling and genomic data analysis where privacy constraints are critical.

Paolo Magniمشاهده پروفایل

استاد

• Biomedical Engineering
• Bioinformatics
• Synthetic Biology
• +۵ مورد دیگر

Paolo Magni is a Full Professor of Biomedical Engineering at the University of Pavia, Italy, where he serves as Director of the Bioinformatics, Mathematical Modelling and Synthetic Biology Lab. His primary appointment is in the Department of Industrial and Information Engineering within the Faculty of Engineering, and he maintains additional affiliation with the Center for Health Technologies (CHT). With a continuous academic career at the University of Pavia since 1999 spanning roles from Researcher to Full Professor, he has established significant leadership in computational biomedical engineering and translational research. His academic qualifications include: Laurea cum laude in Electronics Engineering (Automatics), University of Pavia, 1993 PhD in Bioengineering, University of Pavia, 1998 Professor Magni's research centers on quantitative modeling of dynamic biological systems, with expertise in Bayesian estimation, Markov chain Monte Carlo methods, and probabilistic data analysis techniques. His work spans pharmacometrics (PK/PD modeling), metabolic systems simulation, bioinformatics, and synthetic biology, consistently targeting solutions for complex clinical and biological challenges. He integrates machine learning and AI methodologies to advance intelligent data analysis in healthcare contexts. His scholarly output exceeds 120 peer-reviewed journal publications and 400 conference proceedings, demonstrating sustained contributions to biomedical modeling. Publication trends reveal strong emphasis on pharmacometrics applications in oncology drug development, synthetic biology implementations, and Bayesian computational methods for physiological systems analysis. Key recognitions include the Novartis Oncology Award (2017), multiple iGEM competition medals (2008-2011), and Pavia's Benemerenza di S. Siro award (2009). His spin-off companies Biomeris and enGenome received national excellence recognition. As academic leader, he chairs both Bachelor and Master Bioengineering programs while serving as PhD program Vice-coordinator. His research portfolio features substantial funding including EU's DDMoRe project, MIUR grants across multiple categories (PRIN, FIRB, PNRR), and industry collaborations with Merck, Janssen, and Menarini focused on pharmacometrics and synthetic biology translation. He directs the Bioinformatics, Mathematical Modelling and Synthetic Biology Lab and co-founded three spin-off entities: Biomeris (2012), enGenome (2016), and the DDMoRe Foundation (2016), driving innovation from academic research to healthcare applications.