جست‌وجوی استاد و پژوهشگر

Xiangmin Xuمشاهده پروفایل

استاد

• Neural Circuits
• Alzheimer's Disease
• Neuroscience
• +۳ مورد دیگر

Xiangmin Xu is a Chancellor's Professor in the Department of Anatomy & Neurobiology at the University of California, Irvine (UCI), and Director of the Center for Neural Circuit Mapping. His research focuses on the structure, function, and development of neural circuits, with particular emphasis on Alzheimer’s disease mechanisms, viral vector technologies, and neurobiological processes underlying cognition and disease. He leads interdisciplinary projects involving advanced imaging, genetic tools, and computational methods to map brain circuits and understand their role in neurological disorders. Dr. Xu’s work integrates molecular, cellular, and systems neuroscience approaches. Key areas of investigation include the application of viral tracers (e.g., rabies and HSV vectors) to map neuronal connectivity, the impact of microglia and endothelial cells in neurodegeneration, and translational studies using animal models like the Octodon degus to study aging and Alzheimer’s pathology. He also explores therapeutic interventions, such as BDNF augmentation, to mitigate cognitive decline. His publications span topics like neural circuit plasticity, spatial coding deficits in Alzheimer’s models, and the molecular basis of psychiatric disorders. Collaborations with clinicians and engineers highlight his commitment to bridging basic science and clinical applications. The Center for Neural Circuit Mapping under his direction fosters innovation in neurotechnology and data-driven neuroscience.

Zhijun Yinمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Health Informatics
• +۴ مورد دیگر

Zhijun Yin is an Assistant Professor in the Department of Biomedical Informatics and Computer Science at Vanderbilt University's School of Engineering. His research focuses on developing machine learning and data mining techniques to analyze health behaviors through online data sources like social media and electronic health records (EHRs). He holds dual appointments in Biomedical Informatics and Computer Science, reflecting his interdisciplinary work in health informatics and computational methods. Yin earned a Ph.D. in Computer Science and an M.S. in Biostatistics from Vanderbilt University. His research spans ethical AI applications in healthcare, predictive modeling for disease recurrence, and understanding caregiver dynamics in online communities. He has published extensively on topics including AI ethics, health data privacy, and leveraging social media for medical insights. Notable areas of focus include optimizing word embeddings for small medical datasets, improving risk prediction models using EHR longitudinal data, and addressing algorithmic bias in healthcare AI systems. His work often bridges biomedical data analysis with computational methodologies, aiming to enhance patient care through technology-driven solutions.

Douglas Phanstielمشاهده پروفایل

دانشیار

• 3D Chromatin Structure
• Genome Editing
• Cellular Differentiation
• +۷ مورد دیگر

Douglas Phanstiel, PhD is an Associate Professor in the Department of Cell Biology and Physiology at the University of North Carolina at Chapel Hill School of Medicine. His research focuses on understanding the molecular mechanisms of chromatin architecture and its role in cellular differentiation, particularly in diseases like rheumatoid arthritis, osteoarthritis, and Alzheimer’s Disease. He leads a lab utilizing genomic, proteomic, and computational tools to study DNA looping, epigenetic regulation, and CRISPR-based genome editing. Education: B.S., University of California, San Diego Ph.D., University of Wisconsin Postdoc, Stanford University Research Interests: 3D chromatin structure and its role in gene regulation Phase separation-driven chromatin looping in cancer Genome editing techniques for disease modeling Software development for genomic data analysis (e.g., Coral, Plotgardener) Recent Article Trends: Recent work highlights the lab’s focus on osteoarthritis mechanisms via chromatin accessibility and eQTL mapping, Alzheimer’s risk gene identification through 3D chromatin analysis, and CRISPR screening for cancer drivers. Tools like Coral enable kinome visualization, while Mariner aids Hi-C data exploration. Awards: 2024 HHMI Gilliam Fellow 2017 Junior Faculty Development Award 2015 K99/R00 Pathway to Independence Award 2012 Damon Runyon Fellowship Grants & Advising: Leads NIH-funded projects on osteoarthritis and Alzheimer’s, including a $2.5M NIAMS grant. Advises students like JP Flores (Gilliam Fellow) and Marielle Bond (F31 NIH grant recipient). Collaborates with labs at UNC and beyond to integrate genomics, proteomics, and computational biology. Labs & Teams: The Phanstiel Lab is part of UNC Lineberger Comprehensive Cancer Center and Thurston Arthritis Research Center. Projects include developing high-throughput CRISPR screens and analyzing chromatin dynamics in disease models.

Sarah Jai Hazell Pickeringمشاهده پروفایل

پژوهشگر ارشد

• Chromatin Structure
• Transcript Expression
• Bioinformatics
• +۳ مورد دیگر

Sarah Jai Hazell Pickering is a Research Fellow in Stem Cell Epigenetics at the University of Oslo's Faculty of Medicine, affiliated with the Department of Basic Medical Sciences. She holds a Bachelor of Genetics (Honours) from the Australian National University. Her research focuses on chromatin structure's impact on transcript expression, leveraging bioinformatics and genome-level data analysis. Key projects include studying nuclear lamins in lipodystrophic laminopathies and investigating nuclear architecture in adipose and cancer stem cells. Her work spans epigenetic mechanisms in adipocyte differentiation, with recent publications in Frontiers in Endocrinology , Communications Biology , and Genes . Funded by the Institute of Basic Medical Sciences, her contributions highlight interdisciplinary approaches to understanding genomic regulation and cellular processes.

Mark Borodovskyمشاهده پروفایل

استاد

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

Mark Borodovsky is a Regents' Professor at Georgia Institute of Technology, jointly affiliated with the Wallace H. Coulter Department of Biomedical Engineering and the School of Computational Science and Engineering within the College of Computing. His research focuses on developing machine learning algorithms for computational analysis of biological sequences, particularly DNA, RNA, and proteins. He specializes in predicting protein-coding genes and regulatory sites in genomic DNA using probabilistic models and extrinsic data integration. As the Bioinformatics PhD Coordinator, he oversees interdisciplinary training in computational biology. His work emphasizes automated genome annotation tools like GeneMark-ETP and BRAKER3, which utilize RNA-seq and protein evidence for accurate gene prediction in eukaryotic and prokaryotic genomes. These tools address challenges in plant, animal, and microbial genomics, including phage and metagenomic studies. His research also explores frameshift detection in prokaryotic genomes, transcriptome-based annotation, and computational methods for analyzing large-scale genomic datasets. Collaborations span institutions like Emory University and the Georgia Tech-Emory biomedical engineering program. Borodovsky’s lab develops open-source bioinformatics pipelines, contributing to advancements in genome assembly, comparative genomics, and functional genomics. His work bridges computational methods with practical applications in agriculture, medicine, and environmental microbiology.

Roberto Barrero Gumielمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Plant Virology
• +۳ مورد دیگر

Associate Professor Roberto Barrero Gumiel is an academic at Queensland University of Technology (QUT), leading the eResearch-Bioinformatics team since 2019. He holds a PhD from the University of Tokyo (2002) and has held prior positions including Assistant Professor at the National Institute of Genetics (Japan, 2004–2006) and Bioinformatics roles at Murdoch University (2006–2018). His research focuses on leveraging genomics and scalable bioinformatics workflows to enhance Australia's biosecurity, particularly in plant virus/viroid diagnostics for imported horticultural commodities. Key research interests include phytopathogen detection, agricultural biosecurity, and the application of next-generation sequencing technologies. Collaborations with the Department of Agriculture, Fisheries and Forestry aim to accelerate phytosanitary testing, supporting Australia's Agriculture 2030 vision. His team developed tools like GA-VirReport for rapid plant pathogen screening. Publications span bioinformatics methodologies, plant virology, and diagnostic tool development. Notable recent work includes miRNA biomarker studies for oral and oropharyngeal cancers, and proteomics-based high-throughput workflows. His contributions align with improving food security and advancing genomic technologies in agricultural and medical contexts.

Dragan Milenkovicمشاهده پروفایل

پژوهشگر

• Molecular Genetics
• Cardiovascular Health
• Neurodegenerative Diseases
• +۵ مورد دیگر

Dragan Milenkovic, Ph.D., is an Associate Researcher at the University of California, Davis (UC Davis), Department of Nutrition. His work focuses on the impact of bioactive compounds (e.g., polyphenols) on cardiovascular and neurodegenerative diseases, using multi-omic approaches including genomics, epigenetics, and systems biology. Education: B.S. in Cell Biology and Physiology (University of Tours/Paris 7, France), M.D. in Genome Analyses and Molecular Modeling (Paris 7), and Ph.D. in Molecular Genetics (University of Versailles). Research Interests: Investigates how nutrients and plant compounds influence disease mechanisms. Key areas include nutrigenomic modifications in animal models and humans, polyphenol metabolism, and translational research combining in vitro, animal, and clinical trial methodologies. He employs multi-omics to integrate genomic, proteomic, and epigenetic data. Key Findings: His work demonstrates protective effects of polyphenols against inflammation, cognitive dysfunction, and atherosclerosis. Recent studies highlight the role of epicatechin metabolites in vascular health and microbiome-derived metabolites in endothelial function. Awards: No awards explicitly listed in provided text. Advising/Grants: No student advisees listed. Research has been supported by collaborative projects (e.g., with UC Davis, European COST Action POSITIVe). Labs/Teams: Affiliated with UC Davis Department of Nutrition labs, focusing on nutrigenomics and systems biology.

Yuan Heمشاهده پروفایل

استاد

• Structural Biology
• Biophysics
• Chromatin Biology
• +۳ مورد دیگر

Yuan He is the Bloomberg Distinguished Professor of Structural Biophysics and Chromatin Biology at Johns Hopkins University, holding appointments in the Department of Biophysics and the Department of Biology. He leads research into the molecular mechanisms governing genome processes, including DNA reading, writing, and repair. His work employs advanced techniques like cryo-electron microscopy and structural biology to study large protein-DNA complexes. He obtained his PhD in Biochemistry and Biophysics from Northwestern University, followed by postdoctoral training at UC Berkeley under Eva Nogales. Before joining Johns Hopkins in 2024, he established his independent lab at Northwestern in 2015. His research spans structural biology, biophysics, and epigenetics, with a focus on transcriptional regulation and chromatin dynamics. Key research interests include understanding how cells interact with the genome at structural and mechanistic levels. He has made seminal contributions to mapping the human transcription pre-initiation complex and elucidating DNA repair mechanisms. His work integrates structural biology with genomic approaches to unravel disease-relevant processes, such as cancer immunology and viral entry mechanisms. Awarded the Burton Medal by the Microscopy Society of America, Dr. He’s research has been featured in high-impact journals. His lab collaborates broadly, leveraging interdisciplinary tools to address fundamental biological questions. Current projects explore tissue-specific gene regulation, CRISPR-based screening, and the role of epigenetic factors in disease pathogenesis.

Matteo Reمشاهده پروفایل

دانشیار

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

Matteo Re is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on Machine Learning, Computational Biology, and Bioinformatics, with applications in genomics, drug network analysis, and semi-supervised learning. He has contributed to developing tools like RANKS for node label ranking in biological networks and HyperSMURF for predicting genetic variants. His work integrates graph-based methods, ensemble learning, and network modeling to address challenges in disease association, drug repositioning, and biomolecular data analysis. Re’s research interests include semi-supervised node classification, imbalanced data handling, and functional gene prioritization. He has collaborated on projects involving clinical phenotype prediction using patient biomolecular networks and drug therapeutic categorization. His methods emphasize scalable learning, secondary memory usage, and multi-species data fusion. Awards and grants are not explicitly mentioned, but his extensive publication record reflects strong contributions to computational biology and machine learning.

Jan Byškaمشاهده پروفایل

دانشیار مدعو

• Molecular Visualization
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

Jan Byška is an Adjunct Associate Professor at the University of Bergen's Department of Informatics and an Assistant Professor at Masaryk University in Brno, Czech Republic. He is affiliated with Visitlab and VisGroup research laboratories, focusing on molecular and time-dependent data visualization. His research addresses challenges in molecular dynamics simulations, protein structure analysis, and interactive visualization techniques for biological systems. Education: While specific educational details are not provided, his professional roles indicate advanced academic training in computer science and bioinformatics. Research Interests: Byška's work spans molecular visualization, bioinformatics tool development, and computational methods for analyzing protein structures and dynamics. Key areas include protein tunnel analysis, ligand transportation visualization, and immersive virtual reality applications in molecular modeling. Publications Trends: Recent work emphasizes VR-based molecular design platforms, automated snow height extraction from time-lapse data, and tools for chromatin architecture analysis. His contributions often bridge visualization techniques with domain-specific challenges in biology and medicine. Labs/Teams: Active member of Visitlab and VisGroup, collaborating on projects like LoopGrafter (protein loop engineering) and InVADo (molecular docking analysis). These groups focus on developing open-access visualization tools for scientific research.

• Plant Stress Biology
• Crop Genetics
• Metabolomics
• +۳ مورد دیگر

Giovanni Melandri is an Assistant Professor at the University of Arizona, leading the Melandri Lab which focuses on identifying physiological, biochemical, and genetic mechanisms to enhance drought and heat stress tolerance in crops like sorghum, durum wheat, and cotton. His research integrates physiological phenotyping, omics analyses, predictive modeling, and genetic mapping in field trials across Arizona's desert environment. Education includes a PhD in Plant Sciences from Wageningen University (NL), postdoctoral research at INRAE (France) and the University of Arizona, and earlier degrees from the University of Dundee (UK) and University of Bologna (Italy). Teaching responsibilities include ACBS PLS 312 Animal and Plant Genetics, alongside expertise in Biochemistry, Environmental Stress Biology, and Genomics. Research interests emphasize leveraging genetic diversity through systems biology approaches, particularly in crops critical to arid regions. His lab's methods blend field-based experimentation with computational tools like machine learning and bioinformatics pipelines (e.g., HAMRLNC) to uncover novel stress tolerance mechanisms. Recent work highlights mRNA modifications in sorghum, microbial interactions in anaerobic digesters, and metabolic biomarkers for crop resilience. Labs/Teams: Melandri Lab at Keating Bioresearch, University of Arizona.

• Infectious Diseases
• Sepsis Pathophysiology
• Immunology
• +۷ مورد دیگر

Professional Overview Jan Kristian Damås is a Professor at the Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU). His research focuses on sepsis pathophysiology, infectious diseases, and translational epidemiology, with particular emphasis on leveraging genomics and machine learning for clinical prediction. Research Interests His work spans multiple domains including: Infectious Disease Mechanisms : Investigating immune responses to pathogens like scrub typhus and pneumocystis pneumonia Sepsis Outcomes : Developing predictive models for mortality and long-term morbidity using electronic health records Genetic Epidemiology : Identifying risk loci for infections through Mendelian randomization and GWAS Clinical Informatics : Optimizing diagnostic coding practices and healthcare data systems Key Contributions Recent work includes: Systematic review of machine learning approaches for bloodstream infection prediction Nationwide registry studies on sepsis trends and outcomes Discovery of canonical notch pathway dysregulation in scrub typhus patients Awards & Collaborations Collaborates extensively with clinical teams at St. Olavs Hospital and international groups. Active in translational research bridging basic immunology and clinical practice.

• Data Compression
• Indexing Data Structures
• Algorithms
• +۷ مورد دیگر

Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.

Byung-Jun Yoonمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning for Science
• Optimal Experimental Design
• +۵ مورد دیگر

Dr. Byung-Jun Yoon is a Professor in the Department of Electrical & Computer Engineering at Texas A&M University and a Scientist at Brookhaven National Laboratory's Computing and Data Sciences division. His research focuses on AI-driven scientific discovery, optimal experimental design under uncertainty, and bioinformatics. Yoon holds a joint appointment at BNL since 2019 and has served as a founding faculty member at Hamad bin Khalifa University (2014–2016). He earned his Ph.D. from Caltech (2007) and has received awards including the NSF CAREER Award and multiple best paper recognitions. Education: B.S.E. (summa cum laude) from Seoul National University (1998), M.S. and Ph.D. in Electrical Engineering from Caltech (2002, 2007). His theoretical interests include objective-based uncertainty quantification, machine learning, and signal processing. Application areas span bioinformatics, computational network biology, and AI-driven drug/materials discovery. Key contributions include advancements in optimal experimental design, Bayesian optimization, and neural network applications in scientific contexts. His work is published in top venues like IEEE Transactions and ACM conferences. He serves as an associate editor for multiple journals and chairs major bioinformatics conferences. Current projects include ARPA-H-funded vaccine design platforms and quantum chemistry benchmarking.

Victoria Crawfordمشاهده پروفایل

استادیار

• Approximation algorithms
• Optimization
• Machine learning
• +۳ مورد دیگر

Victoria Crawford is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, where she has held this position since 2022. She holds a Ph.D. in Computer Engineering (2022), an M.S. in Mathematics (2016), and a B.S. in Mathematics (2012), all from the University of Florida. Her research focuses on approximation algorithms, optimization, machine learning, and submodular optimization, with an emphasis on scalable solutions for large datasets and theoretical guarantees. Her educational background includes advanced studies in mathematics and computer engineering, complemented by teaching experience in algorithms and theory courses at Texas A&M. Crawford has received prestigious awards such as the Gartner Group Graduate Fellowship (2019) and the Harris Fellowship (2017), and her work includes high-impact publications in venues like IJCAI, ICML, and NeurIPS. Crawford actively contributes to academic service, serving on committees including the Texas A&M Graduate Admissions Committee and the Data Science Hiring Committee. She also chairs and advises doctoral students such as Wenjing Chen and oversees research projects funded through initiatives like the Texas A&M Targeted Proposal Teams and the TAMIDS Seed Program for AI/Computing/Data Science. Her research spans theoretical and applied domains, including algorithm design for submodular functions, bandit optimization, and network resilience. Recent work emphasizes fairness in algorithmic design and scalable solutions for billion-scale networks.