جست‌وجوی استاد و پژوهشگر

Marcy K. Uyenoyamaمشاهده پروفایل

استاد

• Evolutionary Biology
• Population Genetics
• Molecular Evolution
• +۳ مورد دیگر

Marcy K. Uyenoyama is a Professor of Biology at Duke University, affiliated with the Trinity College of Arts & Sciences. Her research focuses on mechanisms of evolutionary change at molecular and population levels, including the development of statistical methods for inferring evolutionary processes. She holds a PhD (1978) and BS (1974) from Stanford University. Research Interests: Dr. Uyenoyama studies evolutionary genetics, population structure, and the application of Bayesian and maximum-likelihood methods to analyze genetic variation. Her work addresses topics like genetic diversity in mating systems, coalescent theory, and FST statistics. Grants & Awards: Notable grants include NIH support for Mathematical Population Genetics (1986–2014) and a Fogarty International Center grant (1996). Awards include the Karlin Prize (1991) and Sloan Foundation Sabbatical Award (1992). Teaching & Service: Teaches courses like BIOLOGY 460 (Population Genetics) and BIOLOGY 660 (Evolution from a Coalescence Perspective). She has held leadership roles such as Director of Graduate Studies (1997–1999) and chairs committees like the Biology Computing Committee (2005–2007). Active in editorial roles for journals like Theoretical Population Biology and Molecular Biology and Evolution . Labs & Teams: Directs the Marcy Lab ( marcylab.duke.edu ), focusing on theoretical and computational approaches to population genetics.

José N. Onuchicمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Chemistry
• Protein Folding
• +۳ مورد دیگر

José N. Onuchic is the Harry C. and Olga K. Wiess Chair of Physics and Professor of Chemistry and BioSciences at Rice University. His research focuses on integrating theoretical and computational approaches to understand biological systems, including protein folding, cancer metabolism, and chromatin architecture. He leads the Center for Theoretical Biological Physics and has pioneered concepts like protein folding funnels and energy landscape theory. His work spans scales from molecular interactions to cellular systems, with applications in cancer biology, viral mechanisms, and epigenetics. Education: B.S. in Electrical Engineering and Physics (1980, 1981), Universidade de São Paulo M.S. in Applied Physics (1982), Universidade de São Paulo Ph.D. in Chemistry (1987), California Institute of Technology Research Interests: Protein folding dynamics and energy landscapes Chromatin structure and transcription regulation Cancer cell metabolism and metastasis Quantum effects in biological systems Machine learning in biomolecular modeling Key Contributions: Developed the folding funnel theory to explain protein stability Investigated magnesium ion effects on RNA conformational switches Advanced computational models of interphase chromosome organization Explored SARS-CoV-2 spike protein mechanics for antiviral strategies Labs & Affiliations: Directs the Center for Theoretical Biological Physics at Rice University, fostering interdisciplinary research in theoretical biological physics.

• Bioinformatics
• Protein Structure Prediction
• Machine Learning
• +۳ مورد دیگر

Gianluca Pollastri is an Associate Professor in the School of Computer Science at University College Dublin (UCD). He leads a research group focused on machine learning applications in bioinformatics, particularly protein structure prediction and analysis. His academic roles include Associate Professor since 2016, Senior Lecturer from 2008, and Lecturer from 2003. He earned an MSc from the University of Florence and a PhD from the University of California, Irvine. His research integrates deep learning and neural networks to address challenges in protein subcellular localization, secondary structure prediction, and intrinsically disordered regions. Key tools developed include SCLpred, PaleAle, Porter, and PUNCH2. He has secured grants from Science Foundation Ireland, the Health Research Board, and UCD. Pollastri’s work emphasizes rigorous validation of machine learning methods in biology, as outlined in the DOME framework. His lab has produced over 100 peer-reviewed articles, with recent focus on leveraging pre-trained language models (PLMs) for protein analysis. Education: MSc, University of Florence PhD, University of California, Irvine Awards: Best M.Sc. Thesis in Artificial Intelligence (1999) Best Student Project in Artificial Intelligence (1997) His teaching includes modules on Bioinformatics, Connectionist Computing, and Programming. He coordinates research collaborations and maintains a lab with postdoctoral fellows and graduate students.

Brian Rossمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Intelligence
• Genetic Programming
• +۱۰ مورد دیگر

Brian Ross is a Professor at the Department of Computer Science, Brock University. He holds a BSc (Hon) from the University of Manitoba, MSc from the University of British Columbia, and PhD in Artificial Intelligence from the University of Edinburgh. His research focuses on evolutionary computation , particularly genetic programming , with applications in computational biology , evolutionary design , and computational aesthetics . Education: BSc Computer Science (Hon), University of Manitoba MSc Computer Science, University of British Columbia PhD Artificial Intelligence, University of Edinburgh Research Interests: Computational Intelligence: genetic programming, evolutionary algorithms, multi-objective optimization Evolutionary Design: computational aesthetics, 2D/3D modeling, energy-efficient architecture Applications: computational biology, stochastic process algebra, GPU-based image analysis Article Trends: His recent work combines deep learning with evolutionary algorithms to enhance diversity in agent behavior and design optimization. Earlier studies focused on stochastic modeling of biological networks and procedural texture generation . Collaborations span computational aesthetics, passive solar architecture, and non-photorealistic rendering techniques. Students & Collaborations: He has supervised diverse projects including evolved intelligent agents , passive solar building design , and evolutionary art systems . Notable students include Marshall Joseph (2023), Tyler Cowan (2021), and Sheikh Faishal Basher (2021).

Xuefeng Nick Pengمشاهده پروفایل

استادیار

• Biogeochemistry
• Geochemistry
• Climate Change
• +۴ مورد دیگر

Xuefeng Nick Peng is an Assistant Professor at the School of the Earth, Ocean & Environment within the College of Arts and Sciences at the University of South Carolina. His research bridges microbial ecology and geochemistry to study complex interactions between microorganisms and their environments in marine systems. Biogeochemistry Geochemistry Climate Change Coastal Processes Earth Systems Marine Ecology Oceanography Peng employs laboratory techniques (e.g., mass spectrometry, microbial cultivation), field sampling (e.g., oxygen minimum zones, salt marshes), and computational methods (e.g., bioinformatics, numerical modeling) to investigate microbial roles in nutrient cycling and environmental dynamics. His recent work focuses on nitrogen cycling, fungal adaptations in extreme marine environments, and microbial contributions to biogeochemical processes. The 15 most recent publications highlight his contributions to understanding nitrogen transformations in marine sediments, fungal diversity in oxygen minimum zones, and genomic adaptations of anaerobic microbes. Key themes include climate change impacts, biogeochemical feedbacks, and microbial community responses to environmental stressors. Peng’s lab has mentored multiple students, including Margaret Bernish (Master’s thesis), Sydney Staines (Honors thesis), Dylan Lane (Poster Presentation Award), and others who have received scholarships and fellowships. His outreach efforts include collaborations with the Riverbanks Zoo & Garden and field trips at the Baruch Marine Field Lab.

Andrew Eckfordمشاهده پروفایل

دانشیار

• Molecular Communication
• Information Theory
• Computational Biology
• +۳ مورد دیگر

Andrew Eckford is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, Toronto. His research bridges information theory with biological systems, focusing on unconventional communication channels. Education: B.Eng. from Royal Military College of Canada (1996) M.A.Sc. from University of Toronto (1999) Ph.D. from University of Toronto (2004) His research explores molecular communication, computational biology, and information theory applications in biological systems. Key areas include nanoscale networking, kinetic proofreading in immune responses, and energy-efficient molecular transmitters. The lab investigates how living systems process information, with implications for nanotechnology and synthetic biology. Recent publications reveal a strong trend toward interdisciplinary work connecting physics, biology, and communication theory. Articles examine viral communication mechanisms, T-cell activation models, and fundamental limits of biological information processing, demonstrating increasing convergence between information theory and life sciences. Scientific Recognition: 2015 IET Communications Innovation Award 2014 Bell Labs Prize Finalist Co-author of Molecular Communication textbook (Cambridge University Press) Eckford leads the Molecular Communication and Information Theory Laboratory, advising graduate students while collaborating with international researchers. His work has received significant media coverage including The Economist and The Wall Street Journal. Current projects include organizing the 3rd Workshop on Molecular, Biological, and Multi-Scale Communications (Glasgow, 2026) and exploring fundamental principles of information in living systems. The EckfordLab is affiliated with York University's Centre for Innovation in Computing @ Lassonde, maintaining active collaborations across engineering and biological disciplines to advance molecular-scale communication technologies.

Ozlem Ozmen Garibayمشاهده پروفایل

استادیار

• Big data
• social media analysis
• social cybersecurity
• +۹ مورد دیگر

Ozlem Ozmen Garibay serves as an Assistant Professor at the University of Central Florida with office location in ENG2 403 and contactable via phone (407) 882-1122 or email. Her academic foundation includes: Ph.D. in Computer Science from the University of Central Florida (2008) M.S. in Computer Science from the University of Central Florida (2001) B.S. in Electrical and Electronics Engineering from Middle East Technical University (1998) Dr. Ozmen Garibay's research portfolio demonstrates exceptional breadth across computational and social domains. Her work critically examines Big data applications in social media analysis and social cybersecurity , while pioneering investigations into artificial social intelligence and collaborative human-machine teams . She integrates network science methodologies to study social and economic networks , extending her impact to educational spheres through STEM education analytics and assessments of higher education economic impact . Her technical contributions span artificial intelligence , evolutionary computation , and modeling of complex systems , establishing interdisciplinary connections between computational theory and societal applications.

Vincenzo Carnevaleمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Protein Structure-Function Relations
• +۳ مورد دیگر

Vincenzo Carnevale is an Associate Professor in the Department of Biology at Temple University's College of Science and Technology. He holds a B.Sc. and M.Sc. in Physics from the University of Pisa and a Ph.D. from SISSA in Trieste, Italy. His research group focuses on statistical physics and machine learning to study sequence-structure-function relations in proteins, epistasis in molecular evolution, and self-organization in biomolecules. Key themes include how interactions drive collective phenomena and emergent behaviors in biological systems, with a long-term goal of understanding electrical signaling in excitable cells. Recent work spans computational approaches in medical applications (e.g., AI in obstetrics-gynecology diagnostics), lipid-ion channel interactions, and genotype imputation algorithms. His methods include molecular dynamics simulations, Monte Carlo techniques, and deep learning frameworks. Carnevale has no listed scientific awards, but his work emphasizes interdisciplinary collaborations at the interface of physics, biology, and computer science. He contributes to programs like Molecular and Cellular Biology and maintains a lab focused on theoretical/computational biology.

Lingling Jinمشاهده پروفایل

دانشیار

• Bioinformatics
• Genome Evolution
• Comparative Genomics
• +۲ مورد دیگر

Dr. Lingling Jin is an Associate Professor in the Department of Computer Science at the University of Saskatchewan. Her research focuses on Bioinformatics, Genome Evolution, Comparative Genomics, Natural Computing, and Mathematical Genomics. She holds a Ph.D. in Computer Science with a specialization in Bioinformatics from the University of Saskatchewan (2017), preceded by an M.Sc. (2010) and B.Eng. (2006) in Computer Science from the same university and Beijing University of Technology, respectively. Dr. Jin’s work integrates computational methods with genomic data analysis, addressing challenges in plant genomics, structural variant detection, and evolutionary biology. Her research explores topics such as polyploid subgenome inference, anti-CRISPR activity modeling, and environmental stress response in organisms like mites and legumes. She also applies machine learning techniques to problems in agriculture, such as wheat kernel defect detection and phenotype prediction from genomic markers. Recent projects include developing tools like SV-JIM and SVPS for structural variant identification, sequencing the Camelina neglecta genome, and advancing self-supervised learning for video object segmentation and dense-pattern analysis. Her interdisciplinary approach bridges computational science with biological applications, contributing to both foundational and applied research in genomics. No scientific awards or grants are explicitly mentioned in the provided text. She currently advises no listed students, though her research likely involves graduate students and collaborators in bioinformatics and computational biology.

• Heuristic search
• Program synthesis
• Deep learning for sound

Vadim Bulitko is a Professor in the Faculty of Science at the University of Alberta, affiliated with the Department of Computing Science. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. Research Interests: His work spans heuristic search, program synthesis, and deep learning applications for sound. Recent projects focus on AI-driven puzzle generation, pathfinding in dynamic game environments, and neural network classification. Publications: His 15 most recent articles explore AI advancements in puzzle design, video-game navigation, and bioacoustic classification, emphasizing heuristic optimization and explainable AI techniques.

• Microbial Genetics
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Dr. Madhusudan Choudhary is a Professor in the Department of Biological Sciences at Sam Houston State University. He holds a Ph.D. from McMaster University and completed post-doctoral training at Duke University. His research laboratory focuses on microbial genetics, genomics, and bioinformatics using Rhodobacter sphaeroides as a model organism to study bacterial cell-cycle regulation, metabolic innovations through gene duplication, and the evolution of genomic complexity in prokaryotes. Key investigations include chromosome replication origins, duplicate gene expression patterns, and the functional specialization of multipartite genomes. Dr. Choudhary's research spans diverse areas including bacterial responses to microgravity, CRISPR-Cas systems, heavy metal resistance mechanisms, and nanoparticle interactions. He directs studies on transcriptomics under stress conditions and develops computational methods for genome analysis. His work has significant implications for understanding microbial evolution and environmental adaptation.

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

Will Pearseمشاهده پروفایل

استاد

• Evolutionary Ecology
• Biodiversity Conservation
• Statistical Modeling
• +۵ مورد دیگر

Will Pearse is a Professor in Evolutionary Ecology at Imperial College London's Department of Life Sciences (Silwood Park). He leads the Pearse Lab, which focuses on using evolutionary history to predict ecological processes, develop computational tools, and address biodiversity, health, and AI challenges. His work spans academia, government, and industry collaborations. Education: PhD in Ecology, Evolution & Conservation, Imperial College London (2013) MSc in Ecology, Evolution & Conservation, Imperial College London (2009) BSc Zoology, University of Cambridge (2008) Research Interests: The lab integrates phylogenetics, AI, and statistical modeling to study biodiversity conservation, climate impacts on species, and ecosystem services. Key areas include: - Phylogenetic diversity and conservation prioritization (EDGE program) - Fractal sampling designs for ecological monitoring - AI-driven phenology and disease forecasting (e.g., SARS-CoV-2) - Biodiversity-ecosystem service linkages Recent Articles Trends: His 2024-2025 work emphasizes AI applications in ecology, climate-driven pathogen evolution, and rewilding assessments. Earlier publications focused on phylogenetic methods, phenology modeling, and biodiversity policy. Awards: Robert May Prize (2014) for phylogenetic tools in ecology NERC funding leadership ($1.48M+) Co-lead on EDGE 2.0 conservation framework Advising & Grants: Supervised 19+ students/postdocs. Key grants include: - NERC/UKRI projects on biodiversity and health - NSF funding for macroecological studies (e.g., $299k for NSF MacroSystems Biology) - Imperial College collaborations with Hitachi and USDA Forest Service Labs/Teams: Maintains the Ecological Fractal Network (EFN) with >40 global collaborators and leads Imperial's Alan Turing Institute partnership for data-driven ecology.

• Scientific Visualization
• Data Visualization
• Data Science
• +۵ مورد دیگر

Min Chen is a Professor of Scientific Visualization at the University of Oxford, affiliated with the Department of Engineering Science and Pembroke College. He holds fellowships from the British Computer Society, European Computer Graphics Association, and Learned Society of Wales. His career spans over three decades, with previous roles at Swansea University (1984–2011) and current leadership in visualization research. His research focuses on visualization theory, video visualization, visual analytics, and interdisciplinary applications in fields like epidemiology and cybersecurity. He has authored over 200 publications and led projects such as RAMPVIS during the COVID-19 pandemic. Key roles include editor-in-chief of Computer Graphics Forum and associate editor of IEEE Transactions on Visualization and Computer Graphics. Education: BSc and PhD in relevant fields (details not explicitly stated in texts). Awards include the VGTC Visualization Lifetime Achievement Award (2024). His work emphasizes the theoretical underpinnings of visualization and practical tools for data intelligence.

• Plant Genomics
• Machine Learning
• Computational Biology
• +۵ مورد دیگر

Philipp Bayer is an Adjunct Research Fellow at the University of Western Australia (UWA) and a Principal in the OceanOmics project at the Minderoo Foundation. His roles include computational biology research focused on plant genomics, machine learning applications in genomics, and environmental DNA (eDNA) methods for ocean biodiversity monitoring. He holds a PhD from the University of Queensland (UQ) and has held positions such as a Forrest Fellowship and DECRA fellowship, focusing on wheat breeding and evolutionary genomics. Bayer is a member of the UWA Data Institute's Research Committee and the Scientific Advisory Committee for the ARDC Machine Learning CoP. Education and Career Highlights: PhD in Plant Genomics, UQ (2016) DECRA Fellowship at UWA (2021–2023), studying plant gene evolution Forrest Fellowship (2018–2020) on wheat genomics and machine learning Principal, Computational Biology, Minderoo Foundation (2022–present) Research Interests: Plant pangenomics and genome evolution Machine learning for crop improvement and biodiversity analysis eDNA-based marine biodiversity monitoring Genetic mechanisms underlying crop domestication Grants and Collaborations: GRDC: Machine learning for canola disease resistance ARC DECRA: Evolutionary genomics of plant genes Collaborations with BASF, Intergrain, and the OceanOmics initiative Awards: Finalist in the 2019 WA Premier’s Science Award. Labs/Teams: Leads the OceanOmics project and organizes Hacky Hour (code/data support sessions).