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Nilah IoannidisView profile

Professor

• Computational Biology
• Genomics
• Machine Learning
• +5 more

Nilah Ioannidis is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) and the Center for Computational Biology (CCB) at University of California, Berkeley. Her research focuses on computational methods for analyzing personal genomes using machine learning and deep learning approaches. Education: Ph.D. in Biophysics, Harvard University (2013) Her work spans predicting the clinical impact of genetic variants, modeling gene expression dynamics, and developing tools like REVEL for variant pathogenicity prediction. Current research emphasizes genomic AI, non-coding variant interpretation, and cell-type-specific modeling. Recent publications highlight advancements in sequence-based gene expression prediction, variant effect benchmarking, and the application of transfer learning to genomic datasets. These works bridge computational methods with clinical and molecular biology applications. Scientific Awards: Chan Zuckerberg Biohub Investigator Award (2022-2027) Computational Transformation Fellow, UC Noyce Initiative (2022-2023) Okawa Foundation Research Grant (2021) NIH K99/R00 Pathway to Independence Award (2017-2022) She has advised PhD students and collaborated with labs such as the Berkeley Artificial Intelligence Research Lab (BAIR) and the Center for Computational Biology (CCB).

• Machine Learning
• Computational Neuroscience
• Probabilistic Modeling
• +5 more

Guido Sanguinetti is an Honorary Professor in the School of Informatics at The University of Edinburgh, a leading institution in computer science and artificial intelligence research. His affiliation with the Institute for Adaptive and Neural Computation highlights his contributions to data-driven modeling and inference in complex systems. His research focuses on developing probabilistic and machine learning methods for modeling biological and neural systems. Key areas include Bayesian inference , computational biology , and neural computation . His work bridges the gap between statistical machine learning and applications in life sciences, particularly in single-cell genomics and systems biology. The Institute for Adaptive and Neural Computation is a major research hub within the School of Informatics, known for pioneering work in deep learning, probabilistic modeling, and computational neuroscience. As an Honorary Professor, Guido Sanguinetti continues to contribute to this vibrant academic community, mentoring researchers and collaborating on interdisciplinary projects. Though no recent publications or awards are listed in the current profile, his sustained recognition by the university underscores his impactful career in computational sciences. He has likely supervised numerous PhD and postdoctoral researchers over the years, particularly in the areas of machine learning for biological data analysis.

Xinxing WuView profile

Assistant Professor

• Machine Learning
• Data Analysis
• Computer Science
• +4 more

Xinxing Wu is an Assistant Professor in the School of Business, Equine, and Sport Management at Midway University, where he contributes to computer science education and applied data science research. He joined the institution in January 2023 after completing a postdoctoral position at the University of Kentucky. Education: B.S. in Mathematics, Huzhou University M.S. in Mathematics, Anhui Normal University Ph.D. in Computer Application Technology, East China Normal University Dr. Wu's research focuses on machine learning and data analysis , with applications spanning artificial intelligence, neuroscience, and biomedical domains. His work integrates computational techniques with real-world data challenges, particularly in complex system modeling and algorithm development. His teaching expertise includes Java, Python, PHP, JSP, Linux, and data structures. His publications appear in leading venues such as NeurIPS, AAAI, IJCAI, TNNLS, and Alzheimer's & Dementia, reflecting a strong interdisciplinary trend between computer science and health informatics. The research combines theoretical machine learning with practical data analysis, especially in cognitive and neurodegenerative disease modeling. Scientific Recognition: Publications in top-tier AI and neuroscience conferences/journals (NeurIPS, AAAI, IJCAI, TNNLS, Alzheimer's & Dementia) Dr. Wu mentors students in computer science and data analytics within the School of Business, Equine, and Sport Management. While specific grants are not listed, his research output suggests active engagement in funded or collaborative academic projects. He brings five years of teaching experience and a strong technical background to his academic role. There is no mention of specific labs or research teams, but his work indicates collaboration in interdisciplinary research environments, particularly at the intersection of machine learning and biomedical data science.

• Scientific AI/ML
• Optimal Experimental Design
• Objective-Based Uncertainty Quantification
• +4 more

Byung-Jun Yoon is a Professor at the Department of Electrical and Computer Engineering , Texas A&M University , with a joint appointment as a Scientist in the Computational Science Initiative at Brookhaven National Laboratory . His academic journey includes a B.S. in Electrical Engineering from Seoul National University (1998), and M.S. & Ph.D. in Electrical Engineering from Caltech (2002 & 2007). Key Research Areas: Scientific AI/ML, Optimal Experimental Design (OED), Objective-Based Uncertainty Quantification (UQ), Computational Network Biology & Bioinformatics. Notable Contributions: Development of uncertainty-aware machine learning frameworks for materials discovery, computational biology applications, and optimization of virtual screening pipelines. Scientific Awards & Recognitions: NSF CAREER Award (2012) Best Paper Award at APBC (2011) Best Paper Award at MCBIOS (2015) SLATE Teaching Excellence Award (2009) Microsoft Research Ph.D. Fellowship (2004) Killgore Fellowship (2001) KFAS Doctoral Scholarship (2001) Current Appointments: Professor at Texas A&M University (2023–present), Scientist at Brookhaven National Laboratory (2018–present). Former roles include Associate Professor (2014–2023) and Assistant Professor (2008–2014) at Texas A&M.

Claudia AngeliniView profile

Lecturer

• Computational Biology
• Bioinformatics
• Statistical Genomics
• +3 more

Claudia Angelini is a CNR Research Director at the Istituto per le Applicazioni del Calcolo 'Mauro Picone' and a Contract Lecturer at the Department of Chemistry and Biology, University of Salerno. She holds a PhD in Applied Mathematics and Computer Science from the University of Naples 'Federico II' (2002) and has been part of the Italian National Research Council (CNR) since 2001. Education : PhD in Applied Mathematics and Computer Science (2002), Advanced Degree in Advanced Information and Communication Technologies (1996), Degree in Mathematics (1994). Her research interests span computational biology , statistical genomics , and multi-omics data integration , with a focus on Bayesian methods and machine learning . Her recent work includes analysis of single-cell RNA-seq data , CMV replication in CVID patients , and ADViSELipidomics pipeline development . She has led major projects like PRIN PNRR 2022 , Media , and OliveHealth , and served on international conference committees including ISMB 2018 and ECCB2022 . Her lab, BioinfoLab , trains researchers in advanced computational techniques.

• Theoretical Physics
• Statistical Mechanics
• Computational Biology
• +2 more

Anna Paola Muntoni is a Fixed-term Assistant Professor at the Department of Applied Science and Technology (DISAT) within Politecnico di Torino. She is affiliated with the Institute of Condensed Matter Physics and Complex Systems (DISAT) and contributes to the College of Electronic, Telecommunications, and Physics Engineering. Her research bridges theoretical physics, computational biology, and machine learning. Her research focuses on applying statistical mechanics and computational methods to biological sequences, epidemic modeling, metabolic strategies, and complex systems. Key projects include epidemic contact tracing, co-evolutionary sequence alignment, and Boltzmann machine applications in bioinformatics. Selected publications highlight her interdisciplinary work across 2024: Probabilistic contact tracing in epidemic containment 2023: Biological sequence alignment and metabolic optimization 2021: Boltzmann machine modeling of protein sequences 2019: Statistical mechanics in optimization problems

Cavan S. ReillyView profile

Professor

• Biostatistics
• Infectious disease epidemiology
• HIV/AIDS
• +7 more

Cavan S. Reilly is Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota School of Public Health , and concurrently serves as Director of the Coordinating Centers for Biometric Research (CCBR) . His expertise lies in the design, conduct, and analysis of clinical trials and observational studies for infectious diseases, with major programmatic emphases on HIV/AIDS and Ebola virus disease (EVD). Education PhD, Statistics, Columbia University (2000) MS, Statistics, Columbia University (1997) MA, Economics, New School for Social Research (1995) BS, Sociology, University of Minnesota (1993) Research Interests Reilly’s methodological work spans Bayesian modeling , high-dimensional longitudinal analysis , and statistical genomics . Application-wise, he presently directs large-scale trials and cohort studies of Ebola survivors, HIV prevention and treatment strategies, and chronic conditions such as COPD. His lab has produced open-source software for microarray and mass-spectrometry data processing, as well as platforms supporting multi-site clinical data management. Scientific Awards & Recognition Elected Member, Delta Omega Honorary Society in Public Health Teaching & Mentorship At Minnesota, Reilly regularly teaches PubH 8401 Linear Models and has previously offered courses in statistical genetics, Bayesian decision theory, and non-parametric methods. Graduate students working at CCBR routinely receive methodological and domain mentorship under his guidance. Laboratories & Centers He leads the Coordinating Centers for Biometric Research , a major academic contract research organization that provides statistical, data-management, and informatics support to multi-center NIH and international trials in infectious disease and chronic illness.

• Reinforcement Learning
• Simulated Annealing
• Computational Genomics
• +5 more

Bernard Manderick serves as Professor in the Department of Informatics and Applied Informatics at Vrije Universiteit Brussel (VUB), Belgium, with research activity spanning from 1988 to 2023. His work encompasses 116 research outputs across 40 projects, including fundamental AI research, international collaborations with institutions in Kenya and France, and interdisciplinary ethics initiatives. Manderick's research fingerprint reveals dominant expertise in Reinforcement Learning (100% match), Simulated Annealing (99%), and Computational Genomics (95%), with significant contributions to Support Vector Machines, Machine Learning, Sampling Methods, and Combinatorial Optimization. His methodology integrates engineering principles with materials science applications, particularly in optimization algorithms and bioinformatics. Recent publication trends (2016-2023) demonstrate sustained focus on adaptive sampling techniques, evolutionary algorithms, and AI ethics. His 2023 MCMC study builds upon 2017 work on CMA-based samplers, while 2016 publications bridge genomic variant classification and game-theoretic analysis of signaling dynamics in adaptive populations. No scientific awards were explicitly documented in the source material. Manderick has supervised three PhD candidates: David Catteeuw (2015, signaling evolution), Feng Liu (2010, gene normalization), and Yifei Chen (2010, protein interaction extraction). His grant portfolio includes the €2.7M SRP66 project on AI ethics (2019-2026), bilateral PhD programs with INSA Rouen, and Kenya-focused development initiatives through Moi University partnerships. Research leadership spans the Existential Ethics of AI platform and international networks including the PPSN conference series (active since 1991), with ongoing collaborations establishing VUB as a hub for adaptive systems research and global AI capacity building.

Qingyuan ZhaoView profile

Assistant Professor

• Causal Inference
• Biostatistics
• Social Sciences
• +2 more

Professor Qingyuan Zhao is a University Assistant Professor in Statistics at the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. He previously held a postdoctoral fellowship at the Wharton School, University of Pennsylvania, and is currently affiliated with the Statistical Laboratory at Cambridge. Born in Wuhan, China, Zhao earned his BSc in Mathematics from the University of Science and Technology of China and a PhD in Statistics from Stanford University. Research Focus: His work centers on causal inference, particularly using Mendelian randomization and graphical models to analyze complex relationships in biomedical and social sciences. He develops statistical methodologies for observational studies, adaptive experiments, and high-dimensional data analysis. Publications: Zhao's recent research explores causal mediation analysis, off-policy evaluation, confounder selection, and sensitivity analysis in Mendelian randomization. His methodological contributions include matrix algebra for graphical models and iterative graph expansion techniques. Academic Roles: He serves as a Fellow and Director of Studies in Mathematics at his college, contributing to education and academic governance in mathematics and statistics.

Yong ChenView profile

Assistant Professor

• Bioinformatics
• Cancer Epigenetics
• Chromatin Interaction
• +5 more

Dr. Yong Chen is an Assistant Professor at Rowan University's College of Science & Mathematics, Department of Biological & Biomedical Sciences. He is a computational biologist with expertise in bioinformatics, cancer epigenetics, and chromatin interaction analysis, focusing on integrating omics data to understand cancer biology and discover clinical biomarkers. BS in Mathematics, Shandong University Joint PhD in Bioinformatics and Mathematics, University of Georgia and Shandong University Postdoctoral research in Bioinformatics and computational system biology, Tsinghua University Postdoctoral research in Cancer Epigenetics, University of Texas at Dallas His research develops mathematical models and computational pipelines using Bayesian statistics, differential equations, network optimization, and machine learning to analyze next-generation sequencing data and unravel biological mechanisms. Current work includes 3D chromatin interaction studies and epigenetic disorder-cancer associations. Recent publications highlight his contributions to single-cell RNA-seq analysis, Hi-C data modeling, gene regulatory network inference, and machine learning applications in cancer research. He also pioneered methods for chromatin interaction data analysis and omics integration. Dr. Chen is member of the International Society for Computational Biology (ISCB), Society of Biological Engineering (SBE), American Mathematical Society (AMS), and IEEE. His work bridges mathematical modeling, bioinformatics, and biomedical applications.

Russell L. Finley, Jr.View profile

Professor

• Protein-Protein Interaction Networks
• Gene Regulatory Networks
• Cell Cycle Regulation
• +3 more

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 AgrawalView profile

Assistant Professor

• Biostatistics
• Bioinformatics
• Machine Learning for Health
• +11 more

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 MyersView profile

Lecturer

• Mathematics education and curriculum development
• Informal learning opportunities
• Formal derivation of algorithms
• +4 more

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.

Stefan GräfView profile

Research Fellow

• Computational Genomics
• Genomic Medicine
• Vascular Biology
• +7 more

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 RestifView profile

Lecturer

• Infectious Diseases
• Ecology
• Evolution
• +6 more

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.