جست‌وجوی استاد و پژوهشگر

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.

Mike Domaratzkiمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Theoretical Computer Science
• +۳ مورد دیگر

Mike Domaratzki is an Associate Professor and Chair of the Department of Computer Science at Western University. His research focuses on bioinformatics, genomics, and theoretical computer science, with recent emphasis on machine learning tools for genomic prediction in crops. He has held academic roles at Western University and previously at the University of Manitoba and Acadia University. Education: (No explicit details provided in texts) Research Interests: Domaratzki’s work bridges computational methods and biological systems, including algorithm design for genomic analysis, machine learning applications in agriculture, and theoretical foundations of computing. His recent projects address challenges in imbalanced data classification and crop yield prediction using advanced neural networks. Teaching: He has taught a wide range of courses, including introductory programming, data structures, algorithms, bioinformatics, and automata theory across multiple institutions. Notable courses include COMPSCI 1026/1027 at Western, and COMP courses at the University of Manitoba/Acadia covering foundational CS topics and specialized areas like bionformatics algorithms. Publications: His work spans machine learning, genomics, and theoretical computer science, with recent trends emphasizing agricultural genomic applications and data-driven health analytics. Key themes include imbalanced data solutions, neural network architectures for genomics, and computational tools for biological data interpretation. Grants/Advising: No specific grants or student advising details are listed, though his teaching and research imply active involvement in mentoring. His lab focuses on interdisciplinary projects combining computational methods with biological datasets.

Khalique Newazمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Computational Biology
• Network Science
• +۳ مورد دیگر

Dr. Khalique Newaz is a Postdoc researcher at Cosy.Bio and a member of the Computational Systems Biology (CDL3) group at the Center for Data Science and Computing, University of Hamburg. His work focuses on developing data and network science approaches to understand biological systems, particularly the impact of alternative splicing on protein interactions. He holds a Ph.D. in Computer Science from the University of Notre Dame, USA, where he specialized in network-based computational methods for protein 3D structure analysis. Education: Ph.D. in Computer Science , University of Notre Dame, USA (Thesis: Network-based computational methods for comparing protein 3D structures) Research Interests: Dr. Newaz’s research integrates computational biology, network science, and machine learning to address complex biological questions. Key areas include: - Protein structure prediction and classification - Systems biology approaches to aging and cancer - Network analysis of genetic and epigenetic mechanisms - Development of alignment-free computational methods for biological data. Advising & Grants: No formal advisees or grants explicitly mentioned in the provided text. Labs/Teams: He collaborates closely with the Computational Systems Biology group and is affiliated with Cosy.Bio, leveraging interdisciplinary approaches to advance systems biology.

Abolfazl Safikhaniمشاهده پروفایل

استادیار

• Network Modeling
• High-Dimensional Statistics
• Spatiotemporal Models
• +۵ مورد دیگر

Abolfazl Safikhani is an Assistant Professor in the Department of Statistics at George Mason University. He holds a PhD in Statistics and Probability from Michigan State University and has held prior positions at Columbia University and the University of Florida. His research focuses on network modeling, high-dimensional statistics, spatiotemporal models, and applications in urban planning, neuroscience, and smart cities. He is an Associate Editor for Technometrics , Statistica Sinica , and Data Science in Science . He has contributed to advancements in statistical methodologies for time series analysis, including change point detection, transfer learning, and spatiotemporal modeling. His work bridges theoretical statistics with practical applications in urban growth prediction, healthcare (e.g., cancer drug response modeling), and transportation systems. Recent research trends include leveraging explainable AI for land use modeling, longitudinal omics data analysis, and structural break detection in high-dimensional systems. His publications span theoretical developments and real-world case studies, such as subway ridership during the pandemic and New York City’s taxi demand dynamics. Despite no listed awards, his editorial roles highlight his influence in statistical science. He actively mentors students and collaborates on interdisciplinary projects, emphasizing data-driven solutions for complex societal challenges.

Jared E. Deckerمشاهده پروفایل

دانشیار

• Animal Genomics
• Beef Production
• Genetics & Breeding
• +۹ مورد دیگر

Jared E. Decker is an Associate Professor of Animal Sciences and the Wurdack Chair of Animal Genomics at the University of Missouri's College of Agriculture, Food and Natural Resources. He earned his Ph.D. in Genetics from the same university in 2012, with a minor in Statistics. His research focuses on computational genomics, integrating quantitative genomics, population genomics, and evolutionary biology to address challenges in livestock production and biology. Key areas include genomic prediction, selection mapping, and genotype-by-environment interactions, with a focus on cattle and swine genetics. Decker leads projects on identifying loci responding to selection using his Generation-Proxy method, studying metabolic efficiency in beef cattle, and analyzing population structure of cattle breeds. He has secured significant funding, including a $500,000 USDA grant for puberty and fertility genomics and a $236,750 grant for veterinary education in cattle reproduction and genomics. He teaches AN_SCI 4323: Applied Livestock Genetics, expanded to 3 credits to enhance quantitative genetics education. His lab collaborates across disciplines, including bioinformatics and informatics, and contributes to national beef cattle genetics outreach. Research outputs span cattle, swine, and even non-livestock species like cats and Brassica, reflecting a broad interest in evolutionary and applied genomics.

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.

• Biology and ecology of marine mammals
• Anthropogenic impacts on marine megafauna
• Population genetics
• +۱ مورد دیگر

Daniele Cagnazzi is a Research Fellow at Southern Cross University's Faculty of Science and Engineering. He holds a PhD from the same institution (2011), following undergraduate studies in Italy. His research focuses on marine mammal ecology, conservation, and the impacts of human activities on marine megafauna. Key interests include population genetics, population dynamics, and the effects of contaminants on marine species. Education: PhD in Marine Biology, Southern Cross University (2011) Undergraduate studies in Italy (specific degree not stated) Research Interests: Marine mammal ecology and conservation Anthropogenic impacts on marine megafauna Population genetics and dynamics Contaminant assessment in marine species Advising & Grants: Supervises Honours, Master's, and PhD students in marine mammal research Involved in projects such as the Reef 2050 Integrated Monitoring Program and contaminant studies in Port Curtis/Port Alma Labs & Teams: Collaborates with the Environmental Analysis Laboratory at Southern Cross University Participates in interdisciplinary teams focusing on marine megafauna conservation

Jenny Koebernickمشاهده پروفایل

دانشیار

• Plant Breeding
• Genetics
• Agricultural Science
• +۳ مورد دیگر

Jenny Koebernick is an Associate Professor in Cotton & Soybean Breeding at Auburn University’s College of Agriculture, within the Department of Crop, Soil & Environmental Sciences. Her work focuses on genetic improvement of cotton and soybean crops, with particular emphasis on disease resistance, seed quality traits, and sustainable agricultural practices. Her research integrates genomic technologies like genome-wide association studies (GWAS) and molecular diagnostics to address challenges such as Cercospora leaf blight, Corynespora cassiicola, and Cotton Leafroll Dwarf Virus (CLRDV). She collaborates on strategic plans for soybean genomics research and develops germplasm lines with enhanced agronomic traits. Recent studies explore interactions between genotype, environment, and management practices, including nutrient efficiency and pest/disease dynamics. Her work spans crop physiology, precision agriculture technologies, and extension efforts to improve Alabama’s agricultural productivity. Key contributions include optimizing cottonseed oil content, refining soybean test weight metrics, and advancing understanding of 65-year yield trends in U.S. cotton production. She leads interdisciplinary efforts to address CLRDV transmission and blueberry stem blight in Alabama’s agroecosystems.

Michael O'Neillمشاهده پروفایل

دانشیار

• Epigenetics
• Genomic Imprinting
• Sex Chromosome Biology
• +۳ مورد دیگر

Michael O'Neill is an Associate Professor at the University of Connecticut , affiliated with the Department of Molecular and Cell Biology / Genetics and Genomics . His research focuses on epigenetic mechanisms in neurodevelopmental disorders and sex chromosome biology, particularly the role of X-linked imprinted genes in transgenerational effects and meiotic sex chromosome inactivation (MSCI). PhD: University of Texas at Austin Post-doctoral study: University of Melbourne, Princeton University Research highlights include: Elucidating the X Chromosome Imprinting Hypothesis through Turner Syndrome mouse models Discovering the role of Xlr3 in MSCI and its transgenerational consequences Investigating epigenetic transmission across generations Characterizing retroelement dynamics in speciation and genome evolution His recent publications emphasize X-linked gene regulation , retrotransposon activity , and transgenerational epigenetic effects across mammals, fish, and oceanic species. Contact: michael.oneill@uconn.edu | Phone: 860-486-6856

Sanjay Mishraمشاهده پروفایل

استاد

• Consumer Preference and Choice Modeling
• Choice Inconsistencies
• Advertising Processing
• +۶ مورد دیگر

Sanjay Mishra serves as Professor in the Department of Marketing and Business Law at the University of Kansas School of Business, where he integrates quantitative methodologies with consumer behavior research. His academic foundation includes a Ph.D. and MBA from Washington State University, an M.S. from Ohio State University, and a B.S. from the Indian Institute of Technology, establishing technical rigor across his career spanning four decades. Ph.D., Washington State University MBA, Washington State University M.S., Ohio State University B.S., Indian Institute of Technology Professor Mishra's research centers on consumer preference modeling, choice inconsistencies, advertising processing, and conjoint analysis applications, with significant contributions to new product development and innovation management. His work bridges theoretical marketing frameworks with practical business strategy, particularly examining cognitive processes in consumer decision-making and methodological validation in survey research. Recent expansions demonstrate interdisciplinary reach while maintaining core marketing science principles. Analysis of his 2022-2025 publications reveals strategic diversification into medical research (ophthalmology, immunology), environmental science (sustainable materials, biomimicry), and sociopolitical studies, yet consistently applies his expertise in choice architecture and quantitative modeling. Key trends include repulsion/attraction effect investigations, cross-cultural consumer behavior, and translational applications of marketing science to health and sustainability challenges. His scholarly impact is evidenced through publications in premier outlets including the Journal of Marketing Research, Journal of Business Research, and Marketing Letters, though specific awards remain unlisted in source materials. Professor Mishra's teaching portfolio spans Business in India, Marketing Management, New Product Development, Categorical Data Analysis, and multiple entrepreneurship courses, reflecting his commitment to bridging academic theory with real-world business applications. While student names and grant details are unspecified, his extensive publication record across marketing, engineering, and medical journals suggests active research mentorship and cross-disciplinary collaboration. Current work indicates growing engagement with health sciences and environmental sustainability through methodological innovations in consumer behavior analysis.

Pei Fen Kuanمشاهده پروفایل

استاد

• Statistical Genomics
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Pei Fen Kuan is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. She holds a Ph.D. in Statistical Genomics from the University of Wisconsin-Madison. Her research focuses on developing statistical methodologies for analyzing high-throughput omics data, particularly in genome biology and psychiatric/cancer-related studies. She leads projects on ChIP-Seq, RNA-Seq, and DNA methylation analysis, with significant contributions to the WTC Wellness Program studying trauma-related disorders. Education: Ph.D. in Statistics (2009), University of Wisconsin-Madison; B.S. (details not specified). Research interests include computational biology, epigenetics, and integrative omics, with a focus on PTSD, cognitive aging, and melanoma genetics. She has developed R packages like methylGSA and RNAAgeCalc for bioinformatics analysis. Current grants include NIH/NIA and CDC/NIOSH funding for PTSD and aging studies. Lab involvement: Directs the WTC Wellness Program research team, focusing on gene-environment interactions in responders' health. Collaborates internationally via the InterMEL consortium for melanoma studies. Teaching: Courses in computational statistics and statistical genomics (details inferred from departmental role).

Kadir C. Akdemirمشاهده پروفایل

استادیار

• Cancer Genomics
• Computational Biology
• Chromatin Organization
• +۴ مورد دیگر

Dr. Kadir C. Akdemir is an Assistant Professor in the Department of Neurosurgery at The University of Texas MD Anderson Cancer Center. His research focuses on integrating computational analyses and multi-omics data to understand chromatin organization and genomic alterations in brain tumors. He leads the Akdemir Lab, which collaborates with global initiatives like the NIH’s 4Dnucleome project and the Break Through Cancer Foundation’s GBM program. Education: Ph.D. in Genetics and Epigenetics from the UTHealth Houston Graduate School (2013), postdoctoral training at MD Anderson’s Genomic Medicine Department. His work combines computer science and genetics to develop tools for analyzing cancer genomes. Research interests include chromatin folding dynamics, somatic rearrangements, and computational methods for genomic data integration. His lab studies glioblastoma and chordoma, focusing on tumor evolution and microenvironment interactions. Notable achievements include identifying ERBB2 alterations in esophageal adenocarcinoma brain metastases and elucidating the role of KDM5D in colon cancer sex differences. He holds grants from NCI, Break Through Cancer, and the UT Rising Stars Award. Lab membership includes PhD/Master’s students and postdocs working on projects like spatial transcriptomics and ecDNA dynamics. Collaborations span institutions like the Brain Cancer SPORE team and the GBM Moonshot initiative.

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.