جست‌وجوی استاد و پژوهشگر

Evelyn Tangمشاهده پروفایل

استادیار

• Theoretical Biology
• Condensed Matter Theory
• Computational Neuroscience

Evelyn Tang is an Assistant Professor in the Department of Physics and Astronomy at Rice University, where she joined in 2021. Her research focuses on theoretical physics applied to living and active matter, exploring how robust dynamics emerge from stochastic and heterogeneous components. She has held prior positions as a group leader at the Max Planck Institute for Dynamics and Self-Organization and as an Africk Postdoctoral Fellow at the University of Pennsylvania. Education: PhD in Physics, Massachusetts Institute of Technology (2015) MPhil in Physics, University of Cambridge (2008) BS in Physics, Yale University (2007) Research Interests: Tang’s work integrates statistical mechanics, topology, and information theory to study emergent phenomena in biological and out-of-equilibrium systems. She investigates topics such as topological protection of long-timescale dynamics (e.g., circadian rhythms), nonreciprocal systems, and robust oscillations in biological networks. Her interdisciplinary approach bridges theoretical biology, condensed matter physics, and computational neuroscience. Awards: NSF CAREER Award IUPAP Interdisciplinary Early Career Scientist Prize Scialog Award Simons-Berkeley Research Fellowship Gates Cambridge Scholarship Advising & Grants: Supported by funding from the NSF, Kavli Foundation, and Chan-Zuckerberg Initiative. Her lab actively seeks students and postdocs to explore theoretical frameworks for understanding biological systems and stochastic topological systems. Labs/Teams: The Evelyn Tang Group develops physical theories for emergent phenomena in biological and out-of-equilibrium systems, emphasizing topology’s role in robust dynamics across scales—from quantum to biological contexts.

Madhavi Ganapathirajuمشاهده پروفایل

دانشیار

• Computational Biology
• Proteomics
• Genomics
• +۴ مورد دیگر

Madhavi Ganapathiraju is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where she conducts research at the intersection of computational biology and medicine. She also serves as Adjunct Faculty at the Language Technologies Institute (LTI) at Carnegie Mellon University, leveraging her expertise in natural language processing and machine learning for biomedical applications. Dr. Ganapathiraju received her Ph.D. in Language and Information Technologies from the School of Computer Science at Carnegie Mellon University in 2007. Prior to that, she earned an M.Eng. in Electrical Communications Engineering from the Indian Institute of Science (1993) and a B.Sc. in Electronics, Physics, and Mathematics from Delhi University (1990). Her research focuses on large-scale discovery of protein-protein interactions and their application to understanding disease mechanisms. She leads projects predicting mental health and inflammation (MHAIN) interactomes, funded by the NIMH BRAINS award. Using machine learning approaches including active learning and transfer learning, her lab develops methods to analyze interaction networks for biologically relevant insights. Her work bridges computational biology with clinical applications, particularly in schizophrenia, congenital heart disease, and inflammation-related disorders. Analysis of Dr. Ganapathiraju's publications reveals consistent focus on protein-protein interaction networks across multiple disease contexts. Her work spans computational method development for predicting interactions, application to specific disease areas (particularly schizophrenia and congenital heart conditions), and translation of network findings into potential therapeutic insights. The research demonstrates increasing sophistication in integrating multi-omics data with network analysis. Notable achievements include the NIMH BRAINS award supporting her work on mental health interactomes. Her publications in high-impact journals like Nature Genetics, Cell Stem Cell, and npj Schizophrenia demonstrate the significance of her contributions to understanding the genetic and molecular basis of complex diseases. Dr. Ganapathiraju mentors students through rotation projects focusing on genetic variant to function studies for complex diseases using interactome network analysis and prioritizing coding variants using protein structure, function and interaction studies. Her lab employs machine learning and bioinformatics approaches to address challenging problems in biomedical research.

Nicola Armstrongمشاهده پروفایل

استاد

• Applied Statistics
• Data Science
• Genetics
• +۷ مورد دیگر

Nicola Armstrong is a Professor at Curtin University in Western Australia, where she serves in the School of Electrical Engineering, Computing and Mathematical Sciences within the Faculty of Science and Engineering. She is currently the Discipline Lead for Mathematics and Statistics (2024-25) and serves as president of the Western Australian branch of the Statistical Society of Australia. Dr. Armstrong received her PhD from the Department of Statistics at UC Berkeley. Her academic journey has taken her from postdoctoral work in the Netherlands to positions at the Netherlands Cancer Institute as a senior statistician and the Garvan Institute as a senior bioinformatics officer before joining Curtin University. Her research spans multiple disciplines at the intersection of statistics, genomics, and biomedical science. Dr. Armstrong has made significant contributions to: Development of statistical methodology for genomic analysis Application of statistics to cancer research Studies of healthy ageing and dementia Genome-wide association studies (GWAS) Epigenetics and biomarker research Analysis of her recent publications reveals a strong focus on the genetic and epigenetic factors influencing brain structure, cognitive function, and age-related diseases. Her work often involves large-scale collaborative efforts through international consortia, particularly in the areas of neuroimaging genetics and dementia research. Many of her recent studies employ multi-omics approaches to understand the complex interplay between genetic variation, environmental factors, and disease outcomes. Dr. Armstrong actively contributes to the academic community through her teaching of statistics and data science courses at both undergraduate and postgraduate levels, including STAT1006 Regression and Non-parametric Statistics, STAT3001 Statistical Modelling, STAT4002 Introduction to Applied Statistics, STAT5009 Decision Methods and Predictive Analytics, and ACTL4003 Predictive Analytics Principles.

Suman Rakshitمشاهده پروفایل

مدرس ارشد

• Spatial Statistics
• Agricultural Statistics
• Genome-wide Association Studies
• +۳ مورد دیگر

Dr. Suman Rakshit is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, with a dual role as a Research Fellow at SAGI-West. His primary research focuses on statistical methodologies for agricultural trials, including spatial variogram modeling and linear mixed models. He has also contributed to genome-wide association studies and developed an R-package for analyzing point patterns on linear networks. Dr. Rakshit holds a PhD in Statistics from Monash University and a Master's from IIT Kanpur. His professional experience includes roles as a Data Scientist at Horizon Power and Prima Consulting. His teaching spans multiple disciplines including Science and Engineering, and he is affiliated with the Office of the Provost. Research interests emphasize spatial statistics, experimental design for on-farm trials, and computational methods. Recent work explores team playing styles in Australian Football using clustering frameworks and addresses spatial dependency in plant pathogens. His publications span agricultural, ecological, and computational topics, with a focus on methodological advancements in spatial analysis.

• Computer Vision
• High Performance Computing
• Data Science
• +۱ مورد دیگر

Kannappan Palaniappan is a Curators' Distinguished Professor in Electrical Engineering and Computer Science at the University of Missouri. His pioneering work spans computer vision, high-performance computing, and biomedical image analysis, with applications from molecular microscopy to satellite remote sensing. Received prestigious honors including NASA Public Service Medal for scientific visualization innovations and National Academies Jefferson Science Fellowship. Research interests include large-scale image analysis, geospatial visualization, biomedical imaging, and parallel computing algorithms. Developed foundational techniques for the Digital Earth visualization system and co-founded NASA's Visualization and Analysis Lab. Publications demonstrate extensive applications across scales from cellular neuroscience to Earth observation. Recent works focus on neural circuit modeling, high-performance computing frameworks, and interdisciplinary applications in neuroscience and geospatial analysis. NASA Public Service Medal National Academies Jefferson Science Fellowship Air Force Summer Faculty Fellowship Boeing Welliver Faculty Fellowship William T. Kemper Teaching Fellowship Secured research funding from NIH, NSF, NASA, and DoD laboratories. Holds patents for moving object detection and camera pose estimation technologies.

Liangxiu Hanمشاهده پروفایل

استاد

• Big Data Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Liangxiu Han is Professor of Computational Science at Manchester Metropolitan University's Department of Computing and Mathematics, where she also serves as co-Director of the Centre for Advanced Computational Science and Deputy Director of the ManMet Crime and Well-Being Big Data Centre. Her research focuses on developing novel approaches to big data analytics, machine learning, and AI, with applications in precision agriculture, healthcare, smart cities, and cybersecurity. Professor Han's research integrates fundamental and applied work in large-scale data processing, intelligent management of distributed systems, and mathematical modeling. Her current activities aim to provide intelligent ICT-based solutions for societal challenges in food security, healthcare, and sustainable urban development. Her recent publications demonstrate leadership in applying AI to medical challenges like Alzheimer's drug discovery and glaucoma diagnosis. The research combines Bayesian deep learning, computational chemistry, and neuropharmacology to develop innovative therapeutic approaches and diagnostic tools.

Trevor Cickovskiمشاهده پروفایل

استادیار

• Bioinformatics
• Microbiome Analysis
• GPU Computing
• +۶ مورد دیگر

Trevor Cickovski is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences, with a focus on bioinformatics and computational biology. Holding Graduate Faculty status, he specializes in microbiome analysis, GPU computing, network analysis, and Unix systems. His academic journey began with a Ph.D. in Computer Science and Engineering from the University of Notre Dame (2008). Current roles: Associate Director, Assistant Teaching Professor Research affiliations: Bioinformatics Research Group (BioRG), ACM, IEEE Dr. Cickovski's research spans microbiome analysis, software engineering, and GPU-accelerated computing. His work explores the microbiome's role in neuropsychiatric disorders (ADHD), genetic conditions (A1AD), respiratory diseases (COPD), and environmental phenomena (red tides) through multi-omics integration. His publications in Journal of Medical Microbiology , Bioinformatics , and IEEE/ACM TCBB reflect strengths in plugin-based software frameworks (PluMA), network analysis, and computational pipeline optimization. Awards include multiple teaching excellence honors and industry funding from NVIDIA. 2020 Graduate Faculty Status 2019 FIU CAT Fellow 2019 FIU Faculty Teaching Award 2017 Teaching Excellence Award 2014 Promotion to Associate Professor As lead developer of PluMA, he enables cross-language plugin development for bioinformatics workflows. Funded by NIJ and NSF, his projects apply machine learning to epigenetics and computational approaches to vaccine discovery.

Aniket Bhattacharyaمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Soft Matter Systems
• Polymer Physics
• +۵ مورد دیگر

Aniket Bhattacharya is a Professor in the Department of Physics at the University of Central Florida (UCF), specializing in theoretical and computational studies of soft condensed matter physics and biophysics. His work spans DNA translocation through nanopores, intrinsically disordered proteins, and confined biopolymers, with applications in biomedical materials. PhD in Theoretical Condensed Matter Physics (University of Maryland, 1992) BS – Physics Honors (Presidency College, Kolkata) MS in Physics (University College of Science, Kolkata) Research interests: Scaling theory and simulations of soft matter systems Polymer physics in crowded environments Biologically inspired physics, particularly DNA transport through nanopores Physics of intrinsically disordered proteins linked to disease Development of soft materials for biomedical applications Recent publications focus on machine learning strategies for protein characterization, nanopore DNA barcoding, and polymer dynamics in confined spaces. His work has been supported by grants from the NSF, NASA, and NIH. He holds multiple patents related to nanopore-based DNA analysis and mutation prediction in disordered proteins. United States Patent 12,049,663B2 (2024) Non-Provisional Patent Application 19/232,158 (2025) His lab collaborates on interdisciplinary projects involving computational modeling, nonequilibrium transport phenomena, and biomedical applications.

Jason Warnerمشاهده پروفایل

مدرس

• Membrane Fusion
• Biophysical Chemistry
• Cell Membrane Dynamics
• +۳ مورد دیگر

Jason Warner is a Lecturer in the Department of Chemical and Biological Engineering at Rensselaer Polytechnic Institute (RPI), located in Troy, New York. His work focuses on membrane fusion mechanisms, particularly the role of SNARE proteins and cholesterol in mediating cellular processes such as viral entry and lipid bilayer interactions. He is actively involved in teaching courses like Introduction to Engineering Analysis and Biotechnology and Bioprocessing Laboratory, contributing to foundational education in engineering and biochemical systems. Research interests span biophysical chemistry, cell membrane dynamics, and the interplay between proteins and lipid environments. His studies investigate how SNARE complexes generate forces for membrane fusion, the influence of cholesterol on pore stability, and the structural evolution of hemifusion intermediates. These efforts combine experimental techniques like pTIRFM microscopy with computational modeling to understand molecular-scale fusion processes. Jason Warner has contributed significantly to understanding fusion pathways through persistent investigations since 2007. His published works highlight advancements in quantifying pore dynamics, exploring cationic peptide membrane traversal, and modeling viral acidification mechanisms. While no scientific awards are listed, his research demonstrates a deep commitment to advancing membrane biophysics and its applications in biotechnology. No formal advisees or grant information are provided in the profile. He maintains a presence in the Ricketts Building at RPI, indicating active participation in departmental activities and access to research facilities associated with that location.

Yunfeng Shiمشاهده پروفایل

استاد

• Materials Science
• Nanomaterials
• Energy
• +۷ مورد دیگر

Dr. Yunfeng Shi is a Professor of Materials Science and Engineering at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. from the University of Michigan (2006) and completed postdoctoral research at North Carolina State University. His research focuses on computational modeling of material behaviors, including metallic glasses, polymers, and nanomaterials, with applications in energy, nanotechnology, and structural materials. Notably, he was recognized as an inaugural Gordon S. Fulcher Distinguished Researcher by Corning Inc. in 2015. Dr. Shi's research interests encompass: Fracture mechanics and deformation mechanisms in amorphous solids Self-assembly of biomimetic polymers Nanostructured carbon and graphene-based composites Computational design of materials for energy and thermal applications Interface engineering in van der Waals materials His recent publications highlight advancements in atomic-scale material characterization, including cooling-free infrared detection via epitaxial membranes and enhanced fracture toughness in graphene-epoxy nanocomposites. He is affiliated with RPI's Center for Materials, Devices, and Integrated Systems (CMDIS), leveraging interdisciplinary collaboration for cutting-edge research. Scientific awards include the Gordon S. Fulcher Distinguished Researcher Award (2015). His work spans experimental and theoretical methods, with a focus on bridging atomistic simulations and macroscopic material behavior.

Kevin Keenمشاهده پروفایل

استاد

• Biostatistics
• Multivariate Statistics
• Clinical Epidemiology
• +۶ مورد دیگر

Dr. Kevin Keen is a Professor in the Department of Mathematics and Statistics at the University of Northern British Columbia (UNBC). He joined UNBC in July 2004 and was promoted to full Professor in July 2015. Dr. Keen holds dual accreditation as a Professional Statistician (P.Stat.) from both the Statistical Society of Canada and the American Statistical Association, and is a Fellow of the Royal Statistical Society. His academic career includes visiting appointments at the Netherlands Institute for Health Sciences (Erasmus University, Rotterdam) and the Faculty of Medicine at Leiden University. Dr. Keen's research focuses on the development of novel statistical techniques and cutting-edge computer software for the analysis of multivariate data in the study of complex diseases. His expertise spans clinical epidemiology, data science, genetic epidemiology, molecular epidemiology, and multivariate statistics. He has made significant contributions to statistical graphics methodology, particularly through his widely used textbook "Graphics for Statistics and Data Analysis with R." His work bridges statistical theory with practical applications in biomedical research, especially in rheumatology and autoimmune diseases. Analysis of Dr. Keen's publication record reveals a strong focus on statistical methodology development applied to complex biomedical problems, particularly in rheumatology and autoimmune diseases. His work demonstrates consistent integration of advanced statistical techniques with clinical research questions, with particular emphasis on multivariate analysis of complex disease patterns. The trajectory of his research shows increasing specialization in statistical applications for systemic sclerosis, lupus, and related conditions, while maintaining strong contributions to fundamental statistical methodology and educational resources. Professional Statistician (P.Stat.) by Statistical Society of Canada Professional Statistician (PStat) by American Statistical Association Fellow of the Royal Statistical Society Pen and Scroll Award of the American Academy of Otolaryngic Allergy (2005) Certificate of Teaching Excellence from the University of Manitoba (1996) Commemorative Medal for the 125th Anniversary of the Confederation of Canada (1993) Dr. Keen actively supervises graduate students in PhD Health Sciences, MSc Mathematics, and BSc Mathematics programs at UNBC. He serves as a statistical consultant for clinical trials and public health intervention studies, applying his expertise in research design and analysis. His consulting work spans multiple domains including rheumatology, pulmonary medicine, and genetic epidemiology. Dr. Keen is available as a media expert on statistical matters and has contributed to developing standardized health assessment tools through his involvement with World Health Organization classification systems.

Roland Kaysمشاهده پروفایل

استاد پژوهشی

• Mammal Ecology
• Conservation Biology
• Animal Movement Ecology
• +۵ مورد دیگر

Roland Kays is Research Professor in the Department of Forestry and Environmental Resources at North Carolina State University and Director of the Biodiversity & Earth Observation Lab at the North Carolina Museum of Natural Sciences. His research investigates mammal ecology and conservation using advanced tracking technologies. Education includes: Ph.D., Zoology, University of Tennessee, 1999 B.S., Biology, Cornell University, 1993 Research combines camera trapping, GPS tracking, and community science to study animal movement, species distributions, and human-wildlife interactions. Current projects examine range expansion dynamics, impacts of human recreation on wildlife, and conservation of carnivores in human-dominated landscapes. Recent publications (2022-2025) reflect leadership in large-scale biodiversity monitoring (Snapshot USA), movement ecology databases (Movebank, MoveTraits), and conservation applications of wildlife tracking. Studies frequently integrate continental-scale data from collaborative networks. Directs global initiatives including the Movebank animal tracking database and eMammal camera trapping platform. Teaches mammalogy and mentors students in field ecology methods.

Torsten Schenkelمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Biomedical Engineering
• Computational Fluid Dynamics
• +۷ مورد دیگر

Torsten Schenkel is an Associate Professor of Continuum Mechanics at the Department of Engineering and Mathematics, Sheffield Hallam University. He holds a Dr.-Ing. habil. (Habilitation) from the Karlsruhe Institute of Technology (KIT) in Germany. His research focuses on physiological flows, fluid-structure interaction (FSI), and optical measurement methods in cardiovascular systems. Education: PhD and Habilitation in Fluid Mechanics from KIT Mechanical Engineering studies at the University of Karlsruhe Research interests include multi-scale hemodynamic modeling, turbulence interactions, boundary layer dynamics, and biomedical applications of computational fluid dynamics (CFD). Collaborations span institutions like the University of Sheffield, Brunel University, and NHS Trusts. Scientific awards include Chartered Engineer (CEng) and Fellow of the Institute of Mechanical Engineers (FIMechE). He has supervised numerous PhD students in biomechanics and numerical methods. Labs/teams involved include the Materials and Engineering Research Institute and the Insigneo Institute for in silico medicine. His research integrates experimental and numerical approaches to address cardiovascular challenges such as atherosclerosis and fluid-structure interactions in heart mechanics.

Charles Guمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• Statistical Genetics
• Machine Learning in Biomedical Data
• +۵ مورد دیگر

Charles Gu is an Associate Professor of Biostatistics and Genetics at Washington University in St. Louis, affiliated with the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute for Informatics, and the Center for Biostatistics and Data Science (CBDS). His research focuses on genetic epidemiology and statistical genetics, particularly in developing computational methods for analyzing high-dimensional genetic data and studying gene-environment interactions in complex diseases like hypertension, cardiovascular disorders, and metabolic syndromes. Key affiliations include the DBBS programs in Biomedical Informatics, Computational and Systems Biology, and Human and Statistical Genetics. He mentors PhD/MSTP students and collaborates internationally on large-scale genomic studies. Recent work includes studies on clonal hematopoiesis, lipid metabolism, and blood pressure prediction using machine learning. His 164+ publications span genetic association studies, epigenetic research, and multi-omics approaches, with a focus on translating statistical methods to clinical insights. Notable projects involve genomic analyses of atrial fibrillation, coagulation factors, and the impact of lifestyle on genetic traits.

Thomas Marklandمشاهده پروفایل

دانشیار

• Quantum Mechanics
• Statistical Mechanics
• Hydrogen Bonding
• +۶ مورد دیگر

Thomas Markland is an Associate Professor in the Department of Chemistry at Stanford University's School of Humanities and Sciences. His research bridges quantum and statistical mechanics, with applications in chemistry, biology, geology, and materials science. He has developed path integral methods for simulating quantum effects in condensed-phase systems at near-classical computational costs. Education: MChem, University of Oxford (2006) DPhil, University of Oxford (2009) Postdoctoral Fellow, Columbia University (2010) Current research focuses on quantum effects in chemical systems, particularly proton/electron transfer in materials and enzymes, atmospheric isotope separation, and catalytic reactivity in heterogeneous environments. His group combines theoretical methods with molecular mechanics and ab initio simulations to study multi-scale systems. Scientific Awards: ACS Early Career Award in Theoretical Chemistry NSF CAREER award Camille Dreyfus Teacher-Scholar Alfred P. Sloan Fellowship The Markland Group develops quantum-classical hybrid methods and master equation approaches for non-equilibrium dynamics. Their work has advanced understanding of reaction mechanisms in liquids and enzymes, including quantum liquid-glass transitions.