جست‌وجوی استاد و پژوهشگر

Hasan Jamilمشاهده پروفایل

دانشیار

• Databases
• Knowledge Representation and Reasoning
• Data Science
• +۷ مورد دیگر

Hasan Jamil is an Associate Professor in the Department of Computer Science at the University of Idaho, where he also serves as Director of Graduate Studies. He holds a Ph.D. in Computer Science from Concordia University (Canada, 1996), and earlier degrees from the University of Dhaka, Bangladesh (M.Sc. 1984, B.Sc. 1982). Affiliations: College of Engineering, University of Idaho Roles: Academic Leadership | Research Mentorship | Curriculum Development His research focuses on databases , knowledge representation , bioinformatics , health informatics , and educational technologies . Recent work emphasizes AI-driven systems for medical diagnosis (e.g., Explainable AI for heart disease), large language model integration in scientific workflows, and educational platforms like SQL tutoring systems (ViSQL). Publications span 1993–2025, with notable contributions to computational biology pipelines (VisFlow), health question-answering systems (ClinLearning), and materials design (MatFlow). His NSF/NIH-funded projects bridge data science with healthcare, education, and life sciences. Key Projects: MatFlow (materials design), ClinLearning (medical education), SociaLens (digital journalism) Labs/Teams: Leads interdisciplinary teams in computational biology and educational AI

Jinrui Xuمشاهده پروفایل

استادیار

• Computational Biology
• Genome Evolution
• Gene Regulation
• +۳ مورد دیگر

Jinrui Xu is an Assistant Professor in the Department of Biology at Howard University's College of Arts & Sciences. A computational biologist, their research focuses on genome evolution, gene regulation, and cancer genomics. They develop bioinformatics tools to analyze complex biological data, such as GRAM for predicting non-coding variant effects and CytoSVM for cytokine-receptor interactions. Education: PhD in Bioinformatics from the University of Michigan, Ann Arbor (2015). Research interests include evolutionary mechanisms of genetic variation, regulatory networks in cancer, and protein structure analysis. Their work emphasizes functional genomic data interpretation and experimental methodology improvements, such as optimizing ChIP-seq analysis protocols. Key contributions include advancing understanding of pseudogene functionality, protein fold classification, and regulatory element differentiation between wolf/dog populations. Collaborations span institutions like Yale University and the University of Washington, reflecting interdisciplinary research engagement.

David Juedesمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Approximation Algorithms
• +۴ مورد دیگر

David Juedes is a Professor of Electrical Engineering and Computer Science and Associate Dean for Academics at Ohio University's Russ College of Engineering and Technology. He also directs the Avionics Engineering Center and is part of the Center for Scientific Computing and Immersive Technologies. Juedes holds a Ph.D. from Iowa State University (1994) and has taught courses ranging from introductory computer science to advanced graduate topics like computational complexity and formal languages. His research focuses on algorithms, complexity theory, approximation algorithms, and computational biology. Notable contributions include work on secure embedded systems (e.g., d-GUARD and DAGGER frameworks) and parameterized algorithm analysis. He has advised multiple graduate and undergraduate students in areas like parameterized algorithms and bioinformatics. Juedes has received awards including ACM Senior Membership (2010) and the Russ College Outstanding Research Paper Award (2004). His work spans 30+ years with over 80 publications in top journals/conferences. He actively promotes STEM education through initiatives like cybersecurity workshops for K-12 teachers and curriculum development for real-time systems and CORBA-based resource management. Labs/Teams: Director of Avionics Engineering Center, Core member of Center for Scientific Computing and Immersive Technologies.

Demetris P. Gerogiannisمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +۷ مورد دیگر

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.

• Cloud Computing
• Grid Computing
• Health Informatics
• +۵ مورد دیگر

David Levine serves as an Associate Professor in the Computer Science and Engineering Department within the College of Engineering at The University of Texas at Arlington. His academic career spans decades with continuous teaching and research contributions, evidenced by his ongoing course instruction through Fall 2025 and active grant leadership. Levine's research spans cloud and grid computing, health informatics, bioinformatics, pervasive computing, and secure programming. His work integrates computational techniques with practical applications in healthcare (Smart Hospital for Elderly Care), high-energy physics (Fermilab collaborations), and accessibility infrastructure. He has pioneered curriculum development in emerging fields including Cloud Computing, Secure Programming, Mobile Computing, and Data Mining, with courses consistently reaching maximum enrollment capacity and establishing distance learning records within the engineering college. His publication portfolio demonstrates significant interdisciplinary impact across IEEE Supercomputing, health informatics, genetics, and pervasive computing. Recent work focuses on spatial data visualization for public health (2022), hardware simulation platforms (2017), and scalable notification frameworks for healthcare systems (2011). His research has secured over $5 million in funding from NSF, NIH, DoE, and industry partners including Luminant Power. Computer Science Department Teaching Award recipient Student teams won ATT University Challenge, Nokia Symbian Challenge, and Department of Energy Challenge ($200k+ total) Levine has mentored 4 PhD students as co-supervisor, chaired 6 Master's theses, and advised numerous undergraduate researchers. His laboratory work focuses on grid computing applications for high-energy physics and bioinformatics, with current projects including the GAANN Doctoral Fellowships in IoT and Smart Hospital infrastructure development. He serves as NTT Hiring Committee Chair and Faculty Senate Chair, demonstrating significant institutional leadership.

Dingzhu Duمشاهده پروفایل

استاد

• Computer Science
• Wireless Networks
• Sensor Networks
• +۵ مورد دیگر

Dingzhu Du is a Professor of Computer Science at the University of Texas at Dallas (UTD) and concurrently serves as Dean of Science at Xi'an Jiaotong University. He holds a Ph.D. in Mathematics from the University of California, Santa Barbara (1985) and an M.S. in Operations Research from the Chinese Academy of Sciences (1982). His research focuses on optimization, wireless networks, sensor networks, and computational biology. He has pioneered work in network topology control, fault tolerance, DNA screening algorithms, and combinatorial optimization with applications in bioinformatics. His contributions to the Steiner ratio conjecture and relay sensor placement have been internationally recognized. Awards include the CSTS Prize (1998), 2nd Class National Natural Science Prize (China, 1996), and multiple best paper awards. He has held leadership roles such as NSF Program Director (2002–2005) and editorial board memberships in journals like Theoretical Computer Science and Journal of Combinatorial Optimization . His work bridges theoretical computer science and practical applications in wireless systems, security protocols, and algorithm design. Ongoing research explores secure communication protocols (e.g., GeoSENS) and efficient network design methodologies.

• Bioinformatics
• Genomics
• Multiomics Data Integration
• +۳ مورد دیگر

Dr. Trupti Joshi is an Associate Professor in the Department of Biomedical Informatics, Biostatistics and Medical Epidemiology at the University of Missouri-Columbia's School of Medicine, with joint appointments in Plant Sciences and Technology and affiliations with multiple research institutes. Her research focuses on bioinformatics, genomics, multiomics data integration, and machine learning applications in precision medicine and agriculture. She has over 20 years of experience and 157+ publications. Her work spans computational methodologies for biomarker discovery, deep learning frameworks for phenotype prediction, and translational bioinformatics for COVID-19 genomics. Recent articles emphasize epigenetic profiling, plant stress responses, immunotherapy prediction, and cross-modality genomic data portals, reflecting interdisciplinary innovation.

• Developmental Psychology
• Epidemiology
• Mental Health
• +۷ مورد دیگر

Rebecca M Pearson is a Professor of Developmental Psychology and Epidemiology at Manchester Metropolitan University, affiliated with the School of Psychology and the Institute for Children’s Futures. Previously, she held positions at the University of Bristol and Oxford University, working on global longitudinal cohorts. Education: BSc in Applied Psychology (1st Class), Cardiff University PhD in Psychiatric Epidemiology, University of Bristol (Bristol Medical School) Her research focuses on intergenerational transmission of mental health disorders , combining developmental psychology and epidemiology to study parenting relationships, genetic liability, and environmental influences. She innovates in measurement tools (e.g., wearable cameras, TIDAL software) and intervention design for maternal self-efficacy. Recent publications highlight her work on genetic pathways , breastfeeding-related mental health , and urban environmental impacts on perinatal depression. Her methodological contributions include automated facial coding, thin-slice sampling, and stakeholder-engaged protocol design. Her research has informed national and international policies on maternal mental health, including UK NHS guidelines, WHO recommendations, and Swedish maternity protocols. She actively supervises students and collaborates across 7 international cohorts.

Mun-Kit Choyمشاهده پروفایل

مدرس ارشد

• Cardiovascular Genomics
• Stem Cell Biology
• Epigenetics
• +۲ مورد دیگر

Dr. Mun-Kit Choy is a Senior Lecturer in Cardiovascular Science at Manchester Metropolitan University with honorary appointments as Senior Research Fellow at the University of Manchester and Life Member at Clare Hall, University of Cambridge. His research employs stem cell/differentiation models to investigate cardiovascular development and disease through chromosomal interactomics, epigenomics, and transcriptomics. Research interests focus on: Cardiovascular genomics and congenital heart disease mechanisms Long non-coding RNAs in cardiac development Epigenetic regulation of heart pathogenesis Stem cell-based disease modeling Publication analysis reveals consistent focus on genomic approaches to cardiovascular development, with recent emphasis on cardiac organoids, lncRNA functions, and congenital heart defect epidemiology. Awards include FHEA recognition and Clare Hall Life Membership.

Sebastian Kadenerمشاهده پروفایل

استاد

• Molecular Neurobiology
• RNA Metabolism
• Circadian Rhythms
• +۵ مورد دیگر

Sebastian Kadener is a Professor of Biology at Brandeis University, affiliated with the Rosenstiel Basic Medical Sciences Research Center and the Neuroscience Program. His research focuses on molecular neurobiology, RNA metabolism, and circadian rhythms, particularly investigating the roles of circular RNAs (circRNAs) and circadian clock mechanisms in the brain. Kadener holds a Lic. Cs. Biol. and Ph.D. from the University of Buenos Aires. His work explores circRNA functions in vivo, their roles in aging, neurodegenerative diseases (e.g., Parkinson’s), and circadian robustness. He employs molecular, computational, and genetic approaches to study how circRNAs regulate gene expression and interact with neural systems. Key contributions include developing tools like SRCP for circRNA analysis and uncovering circRNAs’ translational potential and proviral activities in viral infections. Scientific awards include the HFSP Long-Term Fellowship (2003) and Career Development Award (2009). Kadener collaborates widely, with publications addressing circadian networks, temperature adaptation, and RNA splicing. His lab integrates systems biology and neurosciences to address questions about circadian plasticity, circRNA evolution, and their roles in disease. Awards: Long-Term Fellowship for Post-Doctoral Research (HFSP, 2003) Career Development Award (HFSP, 2009) Labs/Teams: Kadener Lab at Brandeis University, focusing on RNA metabolism and circadian biology.

Brian Eamesمشاهده پروفایل

استاد

• Skeletal development
• Molecular genetics
• Synchrotron imaging
• +۶ مورد دیگر

Dr. Brian Eames is a Professor in the Department of Anatomy, Physiology and Pharmacology at the University of Saskatchewan's College of Medicine. His research focuses on skeletal development and evolution, molecular genetics, synchrotron imaging, and 3D bioprinted tissue engineering. He leads an interdisciplinary lab of evolutionary and developmental biologists and tissue engineers. University of Saskatchewan, College of Medicine, Department of Anatomy, Physiology and Pharmacology Research Interests: Skeletal cell differentiation mechanisms External signals directing bone/cartilage cell fate Pathway activation in skeletal development Application to osteoarthritis therapies Evolutionary variation in skeletal genetics Comparative transcriptomics across chordates Publication Trends (2016-2025): His work spans developmental biology, evolutionary genetics, biomedical engineering, and imaging technology. Key themes include proteoglycan signaling, 3D bioprinting for cartilage, comparative skeletal evolution in cartilaginous fishes, and gene network analysis using bioinformatics tools. Scientific Awards: CIHR New Investigator Salary Award Faculty of 1000 'Must Read' recognition Cover image in Tissue Engineering Part C Methods ACS Editors’ Choice feature Laboratory Overview: The Eames Lab integrates molecular genetics, advanced imaging, and tissue engineering to explore skeletal development and evolution. They employ zebrafish and pig models, synchrotron radiation imaging, and computational approaches to analyze gene co-expression networks.

• Clinical Data Exchange Standards
• Clinical Information Models
• Electronic Health Record Systems
• +۳ مورد دیگر

Stanley M. Huff is a Clinical Professor in the Department of Biomedical Informatics at the University of Utah. He holds adjunct appointments in Nursing and Pathology, and has been actively contributing to healthcare informatics since 1987. Dr. Huff is board certified in Clinical Informatics by the American Board of Preventive Medicine. MD: University of Utah School of Medicine BS: Brigham Young University Residency: University of Utah and University of New Mexico Dr. Huff’s research focuses on medical information representation in database systems , healthcare data exchange standards (HL7, LOINC, SNOMED CT), and electronic health record architecture . His work spans interoperability frameworks, clinical decision support systems, and standardized terminologies for global health IT. Recent publications emphasize FHIR implementations, genetic test reporting, and harmonization of clinical quality metrics. Dr. Huff’s scientific contributions include over 30 peer-reviewed articles and election to the American College of Medical Informatics (ACMI) in 1997. He co-chairs the LOINC Committee, serves on the HL7 Board, and contributes to national health IT standards development. His research has been pivotal in advancing interoperable health data models and computable clinical knowledge across institutions.

• Human Evolution
• Exercise Physiology
• Quantitative Genetics
• +۴ مورد دیگر

Melissa A. Ilardo serves as Assistant Professor in Biomedical Informatics at the University of Utah with an adjunct appointment in Human Genetics, leading the Ilardo Lab focused on evolutionary genomics and health phenotypes. Her interdisciplinary work partners with historically excluded populations like Indonesia's Bajau Sea Nomads and Korea's Haenyeo divers to investigate physiological adaptations in extreme environments. Her academic journey includes a PhD in Evolutionary Genomics from the University of Copenhagen, followed by dual postdoctoral fellowships at the University of Utah and UC Berkeley. Prior to returning to academia, she spent three years in industry developing genomics-based drug discovery platforms using large-scale health cohorts. Dr. Ilardo's research centers on Human Evolution, Exercise Physiology, and Quantitative Genetics, specifically examining how indigenous populations' adaptations (e.g., to breath-hold diving) can inform medical insights. She challenges deficit-focused genomics by celebrating human resilience, emphasizing community-based participatory research that honors participant strengths rather than pathology. Her publication record (2010-2025) reveals consistent exploration of human population genetics, evolutionary adaptations to extreme environments, and translational applications in medicine. Key trends include genetic studies of hypoxia tolerance, ancient DNA analyses, and investigations into amino acid evolution, demonstrating an integrated approach spanning computational genomics, physiology, and anthropology. While no specific awards are documented, her work has appeared in premier journals including Cell , Nature , and Science , reflecting significant scholarly impact. The Ilardo Lab actively mentors graduate students in genomic research methodologies and community-engaged science. Current lab initiatives focus on replicating biological mechanisms from adapted populations to improve general wellness, with ongoing fieldwork in Indonesia and Korea. The team employs computational genomics to translate evolutionary advantages into medical applications, particularly for hypoxia-related conditions, while maintaining strong ethical partnerships with participating communities.

• Clinical Decision Support Systems
• Knowledge Management
• Personalized Medicine
• +۳ مورد دیگر

Dr. Kensaku Kawamoto is a Professor and Vice Chair of Clinical Informatics at the University of Utah Department of Biomedical Informatics, and Associate Chief Medical Information Officer at University of Utah Health. He leads the ReImagine EHR initiative, developing award-winning EHR add-ons like neonatal bilirubin management and lung cancer screening tools. He chairs the Clinical Decision Support committee and co-leads the HL7 Clinical Decision Support Work Group. His research focuses on clinical decision support systems, personalized medicine, and health IT standards. Education: B.A. in Biochemical Sciences, Harvard University M.D., Ph.D. in Biomedical Engineering (focusing on informatics), Duke University M.H.S. in Clinical Research, Duke University School of Medicine Key Roles: HL7 Interoperability Standards Priorities Task Force Co-Chair Past Board Member of HL7 Fellow of American College of Medical Informatics & AMIA His research interests emphasize clinical decision support , standards development , and healthcare equity . Recent work addresses disparities in genetic testing access via chatbot platforms (BRIDGE trial) and lung cancer screening optimization. He has secured $55M+ in grants from NCI, NIDDK, AHRQ, and industry. Notable achievements include 100+ peer-reviewed articles, OpenCDS initiative leadership, and recognition as a Top 25 Healthcare Innovator (2019). Current projects include the MAINTAIN PRIME weight management trial and lung cancer screening tools integrated into EHR workflows. Dr. Kawamoto's labs focus on interoperable clinical decision support, with collaborations spanning Huntsman Cancer Institute and national standards bodies. He advocates for precision medicine enabled by scalable informatics solutions.

Michael Gilchristمشاهده پروفایل

دانشیار

• evolutionary theory
• mathematical biology
• molecular evolution
• +۳ مورد دیگر

Michael Gilchrist is an Associate Professor in the Department of Ecology & Evolutionary Biology at the University of Tennessee, Knoxville. His research integrates mathematical modeling and evolutionary theory to explore trade-offs in biological systems, with a focus on molecular evolution, protein translation, and pathogen dynamics. Gilchrist holds a B.A. from the University of California, Berkeley (1993), an M.A. from the University of California Santa Barbara (1996), and a Ph.D. from Duke University (2001). His work spans intra-cellular processes to ecosystem-level interactions. Key research themes include codon usage bias mechanisms, viral evolution modeling, and statistical methods for genomic data analysis. Notably, his lab demonstrated that nonsense errors—not missense errors—drive codon usage bias in highly expressed genes. He also pioneered nested models linking within-host viral dynamics to population-level transmission patterns. Recent articles highlight advancements in codon usage theory, spatial phylogenetic modeling, and computational tools like AnaCoDa for codon data analysis. Gilchrist collaborates widely, including with statisticians and bioinformaticians to improve data interpretation. He is actively recruiting graduate students and mentors work on evolutionary principles in molecular systems. Labs/Teams: Gilchrist’s lab focuses on bridging mathematical models with experimental data to address questions in molecular evolution and pathogen biology. Collaborative efforts include developing statistical frameworks for high-throughput genomic datasets.