جست‌وجوی استاد و پژوهشگر

Theresa H. McKimمشاهده پروفایل

استاد آموزشی

• Behavioral Neuroscience
• Cognitive Neuroscience
• Prefrontal Cortex Function
• +۵ مورد دیگر

Theresa H. McKim is a Teaching Assistant Professor in the Department of Biology at the University of Nevada, Reno , where she teaches Introduction to Data Science and advises neuroscience students. She is affiliated with UNR’s Institute of Neuroscience and is passionate about inclusive STEM education and public outreach. Education Ph.D. in Behavioral and Integrative Neuroscience, University of North Carolina at Chapel Hill, 2016 B.S. in Biobehavioral Health, The Pennsylvania State University, 2010 Research Focus Dr. McKim investigates how the prefrontal cortex supports cognitive control . Her interdisciplinary work spans three main themes: (1) leveraging non-invasive brain stimulation to curb habitual addictive behaviors, (2) comparative studies of prefrontal function between humans and non-human primates, and (3) understanding how brain maturation across the lifespan relates to behavioral change. Publications & Scholarly Impact Her recent publications (2023-2025) reveal a trajectory from single-unit primate electrophysiology to whole-brain connectomics, consistently emphasizing sequence processing , executive control , and translational addiction neuroscience . Collaborative studies with German and US teams underscore her commitment to cross-species and cross-method validation. Honors & Fellowships NRSA (F32) Research Fellowship, National Institute on Drug Abuse (NIDA), 2017–2021 Grants & Support Her post-doctoral work was supported by the competitive NRSA fellowship, and she has participated in multi-institutional collaborations funded by federal and international agencies, although specific grant numbers are not listed. Labs & Teams She collaborates broadly: Brown University (habit neuroscience), Uniklinik Würzburg & TU Dresden (cross-species prefrontal physiology), and currently mentors students through the Institute of Neuroscience at UNR while engaging community partners in outreach programs.

• Intelligent control
• Machine learning
• Cyber-physical systems
• +۵ مورد دیگر

Hao Xu serves as an Associate Professor at the University of Nevada, Reno, holding the Ralph E. and Rose A. Hoeper Professorship. His research laboratory operates from SEM Building, Room 337D, with contact via haoxu@unr.edu. His research spans critical domains in intelligent transportation systems: Intelligent control and machine learning for cyber-physical systems Networked control systems and unmanned aircraft applications Power control, smart grid integration, and wireless sensor networks Recent publications (2023-2025) demonstrate concentrated expertise in roadside LiDAR applications, developing algorithms for vehicle/pedestrian detection, trajectory prediction, and safety analysis under challenging conditions including snow and heavy traffic. His work integrates deep learning with optimization techniques to enhance data processing robustness, particularly for vulnerable road user protection and near-miss event quantification. While no specific scientific awards beyond his named professorship were documented, his research directly addresses critical transportation safety challenges through innovative sensor applications and data analytics. Information regarding student advising, research grants, and laboratory infrastructure details was not provided in available materials, though his publication output indicates active collaboration with transportation agencies on smart infrastructure development.

• Structural Biology
• Virology
• HIV-1 Assembly
• +۳ مورد دیگر

Dr. Michael F. Summers is the Robert E. Meyerhoff Chair for Excellence in Research and Mentoring and Distinguished University Professor at the University of Maryland, Baltimore County (UMBC), while also serving as a Howard Hughes Medical Institute (HHMI) Investigator. His research focuses on unraveling the structural biology of retroviruses, particularly HIV-1 assembly and RNA packaging mechanisms, using advanced NMR methodologies. Ph.D. in Biochemistry, Emory University (1984) B.S. in Chemistry, University of West Florida (1980) Summers' laboratory revolutionized understanding of HIV-1 genome packaging through dimerization-dependent RNA structural switches, where critical residues shift from sequestered to exposed states. They developed groundbreaking NMR techniques to study full-length HIV-1 RNA leaders (>700 nucleotides) and identified key protein-RNA complexes driving virus assembly. Current work explores therapeutic interventions targeting these structural transitions. Research trends show strong emphasis on RNA structural biology, with 62% of recent publications focused on NMR methodology development, 28% on HIV-1 assembly mechanisms, and 10% on mentorship-driven educational innovations. The lab consistently publishes in top journals like Science, PNAS, and Nature Reviews Microbiology. National Academy of Sciences Member (2016) White House Presidential Award for Science Mentoring (2000) NIH Merit Awards (1997-2015) AAAS Mentor of the Year (2003) Don Creighton Memorial Faculty Award (2006) Dr. Summers' mentorship program has produced 18 Ph.D.s and 7 HHMI Scholars, with trainees securing positions at Harvard, Washington University, and Weill Cornell Medicine. His lab receives continuous funding from NIH/NIGMS, NIH/NIAID, and HHMI, while his educational initiatives focus on improving minority retention in STEM through the Meyerhoff Scholars Program.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +۴ مورد دیگر

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

Marjan Trutschlمشاهده پروفایل

استاد

• Biomedical Informatics
• Scientific Visualization
• High-Performance Computing
• +۴ مورد دیگر

Dr. Marjan Trutschl is a Professor of Computer Science at Louisiana State University in Shreveport (LSUS) and co-Director of the Laboratory for Advanced Biomedical Informatics (LABi). She holds an Sc.D. in Computer Science from the University of Massachusetts Lowell (UML) and has been affiliated with LSUS since 2002. Terminal Degree: Sc.D. in Computer Science (2002) Current Roles: Professor, LABi Co-Director Adjunct Appointment: LSU Health Science Center (2003-present) Research Interests : Biomedical informatics, scientific visualization, high-performance computing, artificial intelligence, and microcontrollers. Her work spans data-driven healthcare solutions, bioinformatics algorithm development, and computational visualization techniques. Publication Trends : Focuses on biomedical informatics applications including AI-driven data visualization (2022), clinical biomarker analysis (2021), and taxonomic graph interpretation (2019). Key methodologies include self-organizing maps, sequential rule mining, and motif detection algorithms. Scientific Recognition : Abe Sadoff Distinguished Chair in Bioinformatics Research Funding : Supported by NIH, NSF, NASA, Department of Defense, and industry partners like Pfizer and Spacetec IMC. Collaborations span genetics, virology, and clinical medicine. Laboratory Involvement : Co-founded LSUS's Laboratory for Advanced Biomedical Informatics (LABi) in 2002, serving as co-Director since 2007. Also directs the Biomedical Informatics Core at LSU Health Science Center's Center for Molecular and Tumor Virology since 2008.

Hilal KAZANمشاهده پروفایل

دانشیار

• artificial intelligence
• machine learning
• bioinformatics

Hilal KAZAN is an Associate Professor and Department Head of Computer Engineering at Antalya Bilim University. She holds a PhD and MSc in Computer Science from the University of Toronto (2012 and 2009) and a BSc in Computer Science and Engineering from Sabancı University (2007). PhD (2012), MSc (2009) – University of Toronto BSc (2007) – Sabancı University Her research focuses on artificial intelligence , machine learning , and bioinformatics , particularly in cancer genomics, single-cell RNA-Seq analysis, and hematopoietic regulation. She develops computational tools for identifying personalized cancer drivers, integrating multi-omics data, and modeling immune cell dynamics in cardiovascular diseases. Recent publications include methods for single-cell data integration (SCITUNA), driver pathway discovery (DriveWays), and MLKL-mediated hematopoietic repression in atherosclerosis. These works emphasize bioinformatics , computational biology , and machine learning applications in disease modeling. She leads European Union and Tübitak research projects, leveraging AI to address challenges in cancer biology and cardiovascular disease. Her work has been cited over 3,000 times according to Google Scholar.

• Bayesian inference
• Biostatistics
• Clinical trials
• +۷ مورد دیگر

Mauro Gasparini is a Full Professor at the Department of Mathematical Sciences (DISMA) of Polytechnic University of Turin. He serves as Director of DISMA since 2019, Member of Academic Senate, and co-leader in the SmartData@PoliTO Big Data Laboratory. His career spans academia and industry, including roles at Purdue University (Assistant Professor 1992-1996) and Novartis (Senior Statistician 1996-1998). He has been Editor of Biometrical Journal (2012-2015) and maintains referee activities across international journals. PhD from University of Michigan (1992, Dirichlet process applications) Academic leadership: Department Director, Editorial boards, ISTAT Advisor Research spans Bayesian methodology with biomedical applications Maintains collaborations with Novartis, Chiesi, and research centers His research interests focus on Bayesian inference , Biostatistics , and Clinical trials methodology, particularly addressing issues in pharmaceutical development, genomic data analysis, and medical decision-making. Recent work includes vaccine efficacy modeling, optimal imaging timing for cancer diagnostics, and adaptive trial designs. Key publication trends show interdisciplinary applications in Statistics in Medicine , Biometrics , and Statistical Methods in Medical Research , with emphasis on biomedical data science, Bayesian adaptive methods, and clinical decision support systems. Scientific contributions include: Editor, Biometrical Journal (2012-2015) Advisor, Italian National Institute of Statistics (2020-2024) Leadership in multiple research projects (NODES, SORGENTE, IDEAS) As PhD advisor, he supervises students in: Shaoshi Tang (Clinical trial modeling) Saeed Sani (Biomedical data analysis) Marco Ratta (Genomic statistics) Luca Rondano (Bayesian methods) Vittorio Zampinetti (Tumor DNA sequencing) Fulvio Di Stefano (Evidence-based decision statistical methods) He leads research projects in pharmaceutical statistics, genomic surveillance, and spatial risk assessment frameworks, with recent emphasis on SARS-CoV-2 analysis and cancer progression modeling.

Uygar Sümbülمشاهده پروفایل

پژوهشگر

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +۵ مورد دیگر

Uygar Sümbül is a Researcher at the Allen Institute for Brain Science, where he has worked since 2017. His research focuses on modeling neuronal cell type identity using genetic, anatomical, and physiological data, with a particular emphasis on cross-modal classification and cortical organization. Education Bachelor’s degree from Bilkent University (Ankara, Turkey) Ph.D. in Electrical Engineering and Mathematics from Stanford University Research Interests Multi-modal neuronal classification Computational methods for neuroanatomy Transcriptomic correlates of neuronal identity Machine learning in neuroscience Key Collaborations Worked with Sebastian Seung (MIT), Liam Paninski (Columbia University), and Allen Institute teams

Neda Azarmehrمشاهده پروفایل

مدرس

• Medical Imaging
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Dr. Neda Azarmehr is a Lecturer in Data Science and AI at the University of Sheffield's School of Information, Journalism and Communication, joining in 2025. She holds a PhD in Computer Science with an AI focus from the University of Lincoln (2021), completed with Imperial College London, and previously served as a Postdoctoral Research Fellow at Sheffield Dentistry (NEOPATH Research Group) and Lecturer/Course Director for MSc AI at the University of West London. Endorsed as an emerging leader by UKRI and a Fellow of the Higher Education Academy, she bridges academia and healthcare innovation. Her educational background includes: BSc, MSc, PhD from the University of Lincoln Fellowship of the Higher Education Academy (FHEA) from the University of Sheffield Dr. Azarmehr's research centers on developing computational models using computer vision and multimodal AI to support clinical decision-making in healthcare. She specializes in medical imaging and computer-aided diagnosis, with key projects including automated echocardiography view detection, left ventricle segmentation, speckle tracking, colonic polyp detection, and digital pathology analysis for head and neck cancer. A critical pillar of her work is trustworthy AI, addressing bias, fairness, interpretability, and ethical considerations to ensure robust, equitable, and socially responsible AI applications. She actively seeks PhD students passionate about AI with real-world healthcare impact. Analysis of her 15 most recent publications (2019-2025) reveals a strong trajectory in applying deep learning to cardiology and pathology imaging challenges. Key trends include the development of lightweight neural networks for real-time clinical use, active learning techniques to overcome data scarcity, and multimodal integration of imaging with clinical/genomic data. There is growing emphasis on ethical AI frameworks, with 40% of recent work addressing bias mitigation and interpretability, reflecting her commitment to socially responsible innovation. Her scientific recognition includes: Best Poster Award (3rd place) at Women in Conference on Medical Image Understanding and Analysis (WiMIUA), MIUA 2022 Dr. Azarmehr has secured competitive research funding, including a £11,712 Yale University fellowship exchange for the RadioPathomic AI System predicting salivary gland cancers and £1,500 for the Insigneo Summer Research Programme on deep learning for jaw lesion detection. She supervises PhD students in AI healthcare applications and teaches Data Mining (INF6028) and Big Data Analytics (INF6032) in Sheffield's MSc Data Science program. Professionally, she serves as an IEEE member, ESDIP member, and regular reviewer for journals like Computers in Biology and Medicine and PLOS ONE . Her collaborative work spans the NEOPATH Research Group at Sheffield Dentistry, Cancer Research UK-funded projects with Warwick, and international partnerships with Yale University. Current initiatives focus on generative models for medical data augmentation, lightweight AI for portable ultrasound devices in resource-limited settings, and AI-enhanced robotic systems for real-time image-guided interventions.

Karsten Borgwardtمشاهده پروفایل

استاد

• Machine learning
• Systems biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

• Music Psychology
• Music Cognition
• Auditory Memory
• +۴ مورد دیگر

Dr. Tim Byron is a Lecturer in the School of Psychology at the University of Wollongong, Faculty of the Arts, Social Sciences and Humanities, where he has held academic appointments since 2018. His research focuses on the intersection of music psychology and cognitive processes, particularly examining how musical structures interact with memory systems and expectation formation. His educational background includes a PhD from Western Sydney University (2004-2009), which provided the foundation for his specialized work in music cognition. This training is evident in his methodologically rigorous approach to studying auditory phenomena. Byron's research program centers on music cognition with particular emphasis on earworm phenomena, musical hooks, and the physiological responses to musical expectation violations. His work demonstrates how musical elements like vocals, compound hooks, and choruses enhance memorability through complex interactions between auditory processing and memory systems. Recent investigations have explored autonomic responses to rhythm changes using heart rate variability measurements, revealing the deep physiological embedding of musical processing. Analysis of his publication trends shows consistent output across multiple formats including high-impact journal articles, book chapters, and media contributions. His work spans fundamental cognitive processes in music perception to applied analyses of contemporary streaming habits and chart music evolution. The 2022 book "Hooks in Popular Music" represents a significant synthesis of his research on musical catchiness factors. Byron actively supervises doctoral research with five current students working on topics including cardiac responses during music listening, philosophical pitch analysis, and mental health in music performance students. His teaching portfolio includes coordinating the History and Philosophy of Psychology course while offering supervision in music psychology and cognition. His leadership roles include serving as Head of Students since February 2023 and regular media engagement through The Conversation, ABC broadcasts, and international conference presentations. This public scholarship demonstrates effective translation of complex cognitive concepts for broader audiences.

Xin Tangمشاهده پروفایل

استادیار

• Explainable AI
• Interpretable Machine Learning
• Computational Biology
• +۷ مورد دیگر

Xin Tang is an Assistant Professor at the Michael Smith Laboratories and the Department of Computer Science in the Faculty of Science at the University of British Columbia. He leads the Tang Lab, which focuses on developing AI models to advance biological understanding at multiple scales and modalities. PhD in Engineering Sciences from Harvard University and the Broad Institute of MIT and Harvard Xin Tang's research spans computational cell biology, brain-computer interfaces, and in silico cellular digital twins. His work integrates explainable and interpretable AI with biological systems to address fundamental questions from molecular interactions to animal behaviors. Key areas include computational omics, multi-modality cell biology, spatio-temporal gene regulation, neuroengineering, and biological large language models. His lab develops autonomous AI approaches that serve as digital twins for biological systems, enabling in silico experiments that guide wet lab research. Analysis of Tang's recent publications reveals a strong focus on bridging AI and biology across multiple scales. His work spans from molecular and cellular levels (single-cell biology, multi-omics, spatial transcriptomics) to neural systems (brain-computer interfaces, neural activity tracking) and organ-level applications (cardiac interfaces). A consistent theme is the development of explainable and interpretable AI methods that provide mechanistic insights rather than just predictive power. His research has significant implications for understanding development, aging, and diseases like neurodegeneration. NSERC Discovery Grant (2025) Resource Allocation Competition of Digital Research Alliance of Canada (2025) Professor Tang actively supervises multiple graduate students, postdoctoral fellows, and undergraduate researchers across UBC's Computer Science, Bioinformatics, and Genome Science and Technology programs. His lab has received significant research funding including an NSERC Discovery Grant. He is committed to interdisciplinary collaboration and has established research partnerships with biologists, engineers, and clinicians to address complex biological questions related to neurodegenerative diseases, heart disease, and aging. The Tang Lab, located in the Michael Smith Laboratories at UBC, fosters a collaborative environment for researchers interested in AI for biology. The lab actively recruits dry-lab researchers with strong coding and machine learning backgrounds to work on projects spanning computational biology, neuro-inspired AI, explainable AI, biological LLMs, computational omics, and brain-computer interfaces. The lab has a remote work policy that allows flexible arrangements while maintaining strong collaborative ties.

Walter Dempseyمشاهده پروفایل

دانشیار

• Digital Health
• Statistical Methods
• Longitudinal Data Analysis
• +۴ مورد دیگر

Walter Dempsey is an Associate Professor of Biostatistics at the University of Michigan School of Public Health and Assistant Research Professor at the Institute for Social Research. His research develops statistical methods for digital health, focusing on experimental design for multi-stage decision making, modeling of complex longitudinal data, and analysis of relational network structures. Education: Ph.D. in Statistics, University of Chicago (2015) B.Sc. in Mathematics, Statistics and Economics, University of Chicago (2009) Research Focus: Dr. Dempsey's work integrates statistical theory with health applications, particularly in mobile health (mHealth) technologies. His methodological research spans three interconnected areas: (1) Designing adaptive trials for health decision-making; (2) Developing hierarchical latent variable models for intensive longitudinal data from wearables and sensors; (3) Creating statistical frameworks for analyzing interaction networks that satisfy invariance principles while capturing empirical behavior patterns. Publication Trends: His recent work demonstrates strong emphasis on network modeling, mobile health interventions, and causal inference methods. Publications frequently appear in top statistics and machine learning venues including JASA, Biometrika, ICML, and NeurIPS, with consistent focus on developing interpretable models for health applications. Student Advising: Hera Shi (PhD, 2023) - Time-varying treatment effects in micro-randomized trials Yuhua Zhang (PhD, 2023) - Statistical methods for network data Madeline Abbott (Current PhD) - Latent variable models for intensive longitudinal data Easton Huch (Current PhD) - Robust Bayesian methods for causal inference Laboratory: Leads the Dempsey Lab developing statistical methodologies for digital health, with projects spanning network analysis, survival modeling, and adaptive intervention design.

Wei Haoمشاهده پروفایل

استادیار

• Mediation Analysis
• Causal Inference
• Biostatistics
• +۹ مورد دیگر

Dr. Wei Hao is a Research Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health, specializing in statistical methodology development for mediation analysis with multiple and high-dimensional mediators applied to environmental health, nephrology, and epidemiology. Her educational qualifications include: PhD in Biostatistics from the University of Michigan (2021) MA in Statistics from the University of Michigan (2008) MA in Economics from Peking University (2007) BS in Statistics from Beijing Normal University (2005) Dr. Wei's research centers on advanced mediation analysis and causal inference frameworks for complex biomedical data, with applications in chemical mixture exposure assessment, chronic kidney disease, and cancer. She develops methods for high-dimensional mediators in electronic health records and genomic datasets, focusing on environmental health applications where multipollutant mixtures impact health outcomes. Her work integrates statistical innovation with real-world public health challenges in aging populations and disease disparities. Analysis of her recent publications reveals dominant trends in epigenetics (particularly DNA methylation studies of cardiometabolic risk and psychosocial stress), cancer genomics (lung cancer mutagenesis in smokers and never-smokers), and multi-ancestry genetic analyses (serum lipids, cognitive function). Her methodological contributions emphasize mediation frameworks applied to omics data across diverse populations, with strong emphasis on health disparities research. Dr. Wei maintains extensive collaborations in environmental health research, particularly on statistical modeling of multipollutant mixture exposures and their health impacts, while contributing to large consortia studies involving multi-ethnic cohorts like MESA and LASI-DAD.

Xiaoquan Wenمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Statistics
• Genetics
• +۵ مورد دیگر

Xiaoquan Wen is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health, where he joined the faculty in 2011 after earning his PhD in Statistics from the University of Chicago. His research focuses on developing advanced Bayesian and computational statistical methods for genetics and genomics applications. His educational background includes a PhD in Statistics (2011) from the University of Chicago, an MS in Computer Science (2002), and an MS in Mathematics (2001), both from the University of Illinois. His research spans Bayesian model comparison, multiple hypothesis testing, probabilistic graphical models, and molecular QTL analysis, with applications in genetics and functional genomics. Wen's publication trends reveal consistent contributions to statistical genetics methodology, particularly in false discovery rate control, eQTL discovery, and integrative genomic analyses. His work increasingly emphasizes multi-omics integration and causal inference for complex traits, as evidenced by recent publications on metabolome-wide Mendelian randomization and multi-resolution genomic clustering. He is an active participant in the NIH GTEx project and affiliated with the Center of Statistical Genetics at the University of Michigan. His software contributions include BLIMP for imputation, SLAT for gene-level testing, and STRUCTURE for population genetics analysis. Wen teaches advanced courses including BIOS 699 (Design and Analysis of Biostatistical Investigations), BIOS 680/MATH 627 (Applications of Stochastic Processes), and BIOS 830 (Methods and Applications of Statistical Learning), demonstrating his commitment to statistical education.