جست‌وجوی استاد و پژوهشگر

James Murphyمشاهده پروفایل

دانشیار

• Machine Learning
• Harmonic Analysis
• Statistical Learning
• +۵ مورد دیگر

James Murphy is an Associate Professor of Mathematics at Tufts University, with affiliate appointments in Computer Science and Electrical and Computer Engineering. His research focuses on the mathematics of artificial intelligence, statistical and machine learning, high-dimensional probability, and their applications to image/signal processing, molecular dynamics, and computational biology. He holds grants from the NSF, NGA, Camille & Henry Dreyfus Foundation, and others. His work integrates theoretical mathematics with computational tools, emphasizing open-source software development. Education: PhD in Mathematics (2015) and MS (2013) from University of Maryland, BS in Mathematics (2011) from University of Chicago. Research interests include machine learning theory (e.g., spectral clustering, unsupervised learning), harmonic analysis, graph theory, and applications to hyperspectral imaging, medical data analysis, and remote sensing. Notable projects involve Wasserstein-based methods for data clustering, diffusion geometry for image processing, and algorithms for anomaly detection in high-dimensional datasets. His research group, HILDA, collaborates on projects like GLIDE (biological network prediction), Fermat distances for clustering, and entropy-regularized optimal transport. He teaches courses in functional analysis and has contributed to open-access educational materials for calculus and algebra.

Samantha Pettiمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

Samantha Petti is an Assistant Professor in both the Department of Mathematics (School of Arts and Sciences) and the Department of Computer Science (School of Engineering) at Tufts University. She is based at 177 College Avenue, Medford, MA. Her research focuses on computational biology, bioinformatics, and machine learning, with particular emphasis on protein structure analysis, genotype-phenotype mapping, and algorithm design for biological sequence analysis. She teaches courses such as Master's Thesis supervision, PhD Thesis guidance, and specialized topics in mathematics and computer science. Her work integrates interdisciplinary approaches, combining statistical methods, deep learning, and probabilistic models to address challenges in genomics, structural biology, and network science. Recent projects include developing end-to-end protein alignment tools and exploring sparse graph models for biological systems. Teaching: Supervises graduate thesis work (Master’s/PhD) and advanced courses in mathematics and computer science at Tufts. Lab/Team: Engages in collaborative research at the intersection of computational methods and biological systems.

Yen-Yi Hoمشاهده پروفایل

دانشیار

• Computational Biology
• Statistical Genetics
• Genetic Data Analysis
• +۶ مورد دیگر

Yen-Yi Ho is an Associate Professor in the Department of Statistics at the University of South Carolina, with a joint appointment in Biological Sciences. She holds a Ph.D. in Biostatistics from Johns Hopkins University. Her research focuses on computational biology, statistical genetics, and the development of methods for analyzing high-throughput genetic data. Key interests include gene pathway enrichment analysis and applications in cancer, frailty mechanisms, and Hirschsprung disease genetics. Her work bridges statistical methodology and biological discovery, with notable contributions to liquid association modeling and modular network construction using eQTL data. She has collaborated on studies involving chemopreventive cancer strategies, frailty biomarkers, and genomic analyses of pancreatic adenocarcinoma. Dr. Ho has pioneered software tools like the LiquidAssociation R package and contributed to the fastLiquidAssociation package. Her publications span topics from Bayesian hierarchical modeling in medical research to single-cell RNA-seq analysis of cellular heterogeneity. Beyond academia, she has applied statistical methods to public health challenges, such as optimizing disability applicant prioritization systems.

Emile Rugamika Chimusaمشاهده پروفایل

استاد

• Computational Population Genomics
• Bioinformatics
• Genome-Wide Association Studies
• +۵ مورد دیگر

Prof. Emile Rugamika Chimusa is a Professor at Northumbria University , College of Engineering and Science, specializing in Computational Population Genomics and Bioinformatics . Previously, he served as Associate Professor and Programme Director at the University of Cape Town for postgraduate programs in Computational Health Informatics and Human Genetics. His research focuses on genomic methodologies for analyzing large-scale genomic data, including Genome-wide association studies Admixture mapping in mixed-ancestry populations Pharmacogenetics of disease treatments Multi-omics integration for disease-drug associations Development of bioinformatics tools Population-specific disease risk prediction He has received over £17.7 million in research funding from organizations like the NIH , Wellcome Trust , and National Research Foundation . His work spans translational genomics in both non-communicable and infectious diseases .

Istvan Rajcanمشاهده پروفایل

استاد

• Soybean Breeding
• Plant Genetics
• Seed Quality Traits
• +۶ مورد دیگر

Dr. Istvan Rajcan is a Professor and Graduate Coordinator in the Department of Plant Agriculture at the University of Guelph, Ontario Agricultural College. His research focuses on soybean breeding and genetics, particularly developing high-yielding, disease-resistant cultivars for short- and medium-season environments. He leads efforts to enhance seed quality traits, such as protein and isoflavones, using genomic tools like GWAS and genomic selection. Education: B.Sc. (Agr.) from the University of Novi Sad and Ph.D. in Plant Agriculture from the University of Guelph. Research Interests: Soybean seed quality (nutraceuticals), disease resistance (Sclerotinia, soybean cyst nematode), genomic technologies, and breeding innovation. His work integrates molecular markers, quantitative genetics, and phenotyping to address challenges in soybean production. Collaborative projects include the SoyaGen initiative, advancing genomic resources for breeders. Publications highlight advancements in soybean genetics, remote sensing applications, and sustainable breeding strategies. His contributions span trait dissection, disease management, and the application of machine learning in genomics. Advising and Grants: While specific grants are not detailed, his extensive publication record indicates sustained research funding. He mentors graduate students and collaborates widely with institutions like the Ontario Oil & Protein Seed Crop Committee. Labs/Teams: Research activities are based in the Crop Science Building, with involvement in the SoyaGen project and collaborations on soybean diversity panels.

Giovanni Dalmassoمشاهده پروفایل

دانشیار

• Computational Biology
• Developmental Biology
• Biomedical Image Analysis
• +۴ مورد دیگر

Giovanni Dalmasso is an Associate Professor in the Department of Mathematics and Data Analytics at the IQS School of Engineering, Ramon Llull University. His research focuses on computational modeling of biological systems, including morphogenesis, cellular homeostasis, and biomedical image analysis. He specializes in developing tools for 3D/4D visualization of developmental processes and agent-based models of cellular dynamics. Key research areas include: 3D/4D reconstruction of embryonic development (e.g., limb bud morphogenesis) Biological shape correspondence algorithms (μMatch) Systems biology approaches to mitochondrial function and stress responses Mathematical modeling of calcium signaling dynamics His work contributes to UN Sustainable Development Goals related to innovation and infrastructure through advances in bioimaging and computational tools. Collaborations span institutions globally, including projects with the University of Edinburgh and University Pompeu Fabra. He has developed open-source software like LimbLab and veda for scientific visualization. Research outputs emphasize interdisciplinary methods combining engineering, mathematics, and biology to address fundamental questions in developmental biology and cellular systems. Current efforts focus on integrating moral decision frameworks into AI agents through contractualist models.

Donald Bairdمشاهده پروفایل

عضو هیئت علمی

• Wetland Ecology
• Stress Ecology
• Global Change Processes
• +۳ مورد دیگر

Dr. Donald Baird is a Visiting Research Professor in Biology at the University of New Brunswick and Senior Research Scientist at Environment and Climate Change Canada. As a Fellow of the Canadian Rivers Institute, his research focuses on ecological risk assessment and developing innovative methods for biodiversity observation in freshwater ecosystems using ecogenomics approaches. He leads a national initiative applying DNA-based techniques for monitoring Canada's threatened ecosystems, particularly river floodplains and boreal wetlands. His work addresses the complex stressor regimes affecting aquatic ecosystems in the Anthropocene through integrated assessment approaches that combine traditional ecology with molecular tools. With over 150 publications and 4 books, Dr. Baird has conducted research across five continents and serves as a science advisor for international organizations including WHO-Water, Ramsar, and WWF-Canada.

Virendra Bhavsarمشاهده پروفایل

استاد پژوهشی

• Natural Language Processing
• Cognitive Informatics
• Distributed Systems
• +۳ مورد دیگر

Virendra Bhavsar is an Honorary Research Professor and Professor Emeritus in the Department of Computer Science at the University of New Brunswick (UNB). He holds a PhD in Computer Science and is affiliated with UNB's Fredericton campus, located in Gillin Hall GE109A. His research focuses on natural language processing (especially for Hindi and other Indian languages), cognitive informatics, distributed systems, and bioinformatics. Bhavsar has also contributed to projects involving virtual reality in education, parallel computing, and machine translation systems. Education: PhD in Computer Science Research Interests: Development of NLP tools for free-word-order languages Optimization of in-memory data processing systems on NUMA architectures Cognitive learning environments using virtual reality Tree-based parsing algorithms for linguistic analysis High-performance spatial and spatio-temporal data systems Recent Work Trends: His articles from 2017–2022 highlight advancements in Hindi language processing, distributed computing optimizations, and cognitive informatics applications. Notable areas include improving data skew resilience in hash joins and developing visualization systems for language learning. Awards: No specific scientific awards listed in available materials. Advising & Grants: No formal advisee list or grant details provided in current data. His collaborations include work on grid computing frameworks like GARUDA and HPGC initiatives. Labs/Teams: Involved in projects related to distributed in-memory systems (e.g., DISTIL) and cognitive informatics research groups.

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.

• Biomedical Informatics
• Cancer Genomics
• Computational Biology
• +۳ مورد دیگر

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

Francesco Iorioمشاهده پروفایل

پژوهشگر

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +۳ مورد دیگر

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

Erin Heinzenمشاهده پروفایل

دانشیار

• Epilepsy Genetics
• Neurodevelopmental Disorders
• Genomics
• +۳ مورد دیگر

Erin Heinzen is an Associate Professor at the UNC Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, with a joint appointment in the UNC Department of Genetics. She holds a Pharm.D. and Ph.D. from the University of North Carolina at Chapel Hill, followed by postdoctoral training at Duke University. Her research focuses on the genetic and genomic basis of epilepsy disorders, particularly somatic mutations and transcriptomic regulation in neurological diseases. Education: Pharm.D., University of North Carolina at Chapel Hill (2001) Ph.D., Pharmaceutical Sciences, University of North Carolina Chapel Hill (2004) Research Interests: Dr. Heinzen’s lab investigates somatic mutations in epilepsy and brain malformations, using induced pluripotent stem cells and advanced sequencing technologies. Key projects include studying SLC35A2 variants in intractable epilepsy and analyzing transcriptomic changes in brain tissue. Her work has identified 15 epilepsy genes, including the gene for Alternating Hemiplegia of Childhood. Grants & Awards: NIH-funded research, including $7M in collaborative grants to study genetic variants in epilepsy. Active in consortia like Epi4K and ILAE. Labs & Teams: The Heinzen Lab collaborates with neurology, neuropathology, and neurosurgery groups globally, advancing neurogenetics and treatment approaches for intractable seizures.

Yehuda Ben-Shaharمشاهده پروفایل

استاد

• Behavioral Genetics
• Neurogenetics
• Insect Behavior
• +۴ مورد دیگر

Yehuda Ben-Shahar is a Professor of Biology at Washington University in St. Louis, where he has served since 2008. His research integrates behavioral, genetic, genomic, and molecular approaches to investigate the genetic architectures underlying animal behavior, primarily using Drosophila melanogaster and honey bees as model systems. He maintains laboratory facilities at the Bayer Laboratory (Room 406) and conducts field research at the Tyson Research Center. Ben-Shahar earned his PhD from the University of Illinois at Urbana-Champaign. His research focuses on how specific genes in neuronal circuits govern behaviors like feeding and mating decisions, with significant contributions to understanding honey bee social behavior evolution. Recent work centers on the gut microbiome's role in nestmate recognition and developing genetic tools for non-model insects. His publication record reveals consistent innovation in behavioral genetics, with recent emphasis shifting toward microbial influences on social behavior and neural circuit analysis. Early work established foundational insights into gene-behavior relationships, while current research explores chemosensory mechanisms across insect and mammalian systems. Scientific recognition includes: Multiple Faculty of 1000 'Must read' designations (2002, 2007, 2009) Cover features in Science (2009) and Journal of Experimental Biology (2002) 'Mutant of the Month' in Nature Genetics (2006) Extensive media coverage including NPR, CBC Radio, and major newspapers Ben-Shahar mentors graduate students including Cassie Vernier (whose 2020 Science Advances paper defined gut microbiome roles in hive membership) and has supervised numerous undergraduates. His lab operates through collaborative grants supporting research at the intersection of genetics, neuroscience, and social behavior. The Ben-Shahar Lab maintains active research teams at the Bayer Laboratory for molecular work and the Tyson Research Center for honey bee studies, with recent projects involving cyborg locust development and odor-guided navigation research.

Matt Wrightمشاهده پروفایل

مدرس

• Computer Music
• Acoustics
• Music Technology
• +۷ مورد دیگر

Matt Wright is a Lecturer at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA), specializing in media systems design and computer music research. He previously served as Musical Systems Designer at UC Berkeley's CNMAT (1993-2008) and Research Director at UCSB's CREATE center for eight years. His work focuses on real-time music synthesis, sound standards (SDIF/OSC), and computational ethnomusicology. He holds a PhD from Stanford CCRMA on musical rhythm perception, and contributed to the AlloSphere immersive audiovisual instrument. Education: PhD in Computer-Based Music Theory from Stanford University (completed at CCRMA). Earlier studies include training in traditional lutes and Afro-Brazilian percussion. Research interests bridge technology and music: developing tools for musical gesture analysis, sound synthesis, and cross-cultural music analysis. His recent work also involves genomic data platforms (ClinGen) for clinical variant curation, reflecting a dual focus on computational music and bioinformatics. Publications emphasize both music technology (e.g., EEG studies of piano duets) and genomics (e.g., ClinGen variant curation tools). He has advised students in electronic arts and contributed to genetic disease research through collaborative frameworks like ClinVar. Labs/Teams: Lead the CREATE Ensemble (technology-driven live performance) and contributed to the AlloSphere research instrument at UCSB's CREATE center.

LIU Boxiangمشاهده پروفایل

استادیار

• Genomics
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Asst Prof LIU Boxiang holds the position of Assistant Professor and NUS Presidential Young Professorship at the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore (NUS), within the Faculty of Science. His research focuses on integrating multi-omics approaches with computational methods to study complex diseases such as coronary artery disease and age-related macular degeneration. He specializes in developing statistical and machine learning tools for genomic analysis, including eQTL mapping and deep learning architectures for gene expression regulation. Education: BA in Biophysics (Illinois Wesleyan University), MS and PhD in Bioinformatics (Stanford University). He contributed to the GTEx consortium and is part of the Asian Immune Diversity Atlas (AIDA) initiative. His lab develops methods like ANTseq for ancestry determination and scPrediXcan for cell-type-specific transcriptome studies. Research Interests: Functional genomics, eQTL analysis, deep learning in biomedicine, and computational tools for omics data integration. His work bridges disciplines such as natural language processing and computer vision with biological questions. Scientific Awards: NUS Presidential Young Professorship (2021). His lab's innovations include ParaMed, a biomedical translation dataset, and LinearDesign for optimized mRNA stability. Advising and Grants: Leads the Liu Lab (boxiangliulab.com), focusing on single-cell genomics, mitochondrial dynamics, and computational biomedicine. Collaborates on projects like the RESET cohort study for cardiovascular disease prevention.