Find Academic Supervisors

Massimo FranceschettiView profile

Professor

• Mathematical Engineering
• Networked Control Systems
• Information Theory
• +7 more

Massimo Franceschetti is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), with faculty affiliation at Calit2. His research spans mathematical engineering, focusing on control, communication, computation, and sensing, particularly in complex networks and systems. He integrates tools from statistical physics, wave propagation, and information theory to analyze and design networked systems. Born in Naples, Italy, he studied at the University of Naples Federico II and the University of Edinburgh (European exchange program), graduating in 1997. He earned his M.Sc. (1999) and PhD (2003) from Caltech, where he received the Walker von Brimer Award and the C.H. Wiltz Prize for outstanding research and thesis. After postdoctoral work at UC Berkeley (2003-2004), he joined UCSD as faculty and held visiting positions at Vrije Universiteit Amsterdam, EPFL (Switzerland), and the University of Trento (Italy). He became an IEEE Fellow in 2018 and was nominated a Guggenheim Fellow in 2019. His research includes networked control systems , stochastic geometry , electromagnetic information theory , and social dynamical systems . Recent work explores non-invasive emotional contagion in social networks, quantum limits on information entropy, and the physics of wave propagation. His publications bridge information theory , machine learning , and network science , often applying percolation theory and random walks to explain scaling laws and wireless signal behavior. Scientific accolades include the S.A. Schelkunoff Transactions Prize , IEEE Communications Society Best Tutorial Paper Award , and the IEEE Ruberti Young Researcher Prize . He co-authored two books: Random Networks for Communication (2007) and Wave Theory of Information (2018). His students have pursued careers in academia (e.g., IIT-Bombay, Notre Dame) and industry (e.g., Google, IBM, Tesla). He teaches courses on network science , information theory , and control systems , emphasizing data-driven analysis and the physical foundations of communication. His group’s work impacts cyber-physical systems , quantum network coding , and epidemic modeling on networks .

• Signal Processing
• Statistical Signal Analysis
• High-Dimensional Data Analysis
• +3 more

Pascal Vallet is an Associate Professor at University of Bordeaux, affiliated with IMS Bordeaux (Integration Laboratory from Material to System) where he conducts research in the Signal and Image Processing group and Spectral team. His work focuses on theoretical and applied aspects of statistical signal processing with emphasis on high-dimensional data analysis. Vallet's research interests center on statistical signal processing methodologies, particularly in high-dimensional settings. His work addresses fundamental problems in covariance matrix analysis, spectral coherence, and change detection within Gaussian models. He develops theoretical frameworks for analyzing complex signals with applications in areas such as SAR imaging and multivariate time series analysis. His approach combines rigorous mathematical statistics with practical signal processing applications, focusing on scenarios where dimensionality challenges traditional statistical methods. Analysis of Vallet's recent publications reveals a consistent research trajectory focused on high-dimensional statistical signal processing. His work primarily addresses covariance structure analysis in Gaussian low-rank models, with applications to SAR imaging and multivariate time series. The publications demonstrate increasing sophistication in handling high-dimensional asymptotic regimes where both sample size and dimensionality grow large. His research shows strong theoretical foundations combined with practical validation through synthetic and real-world data experiments, particularly in radar imaging applications. Vallet maintains active research collaborations with several colleagues including Rémi Beisson, Audrey Giremus, Guillaume Ginolhac, and Philippe Loubaton, as evidenced by his co-authored publications across multiple years. His research contributes to advancing theoretical understanding of statistical signal processing in high-dimensional settings while maintaining relevance to practical applications in remote sensing and signal analysis.

Matthew GrilliView profile

Associate Professor

• Neuropsychology
• Cognitive Neuroscience
• Memory
• +6 more

Matthew Grilli is an Associate Professor in the Department of Psychology at the University of Arizona , where he directs the Clinical Program, Neuropsychology Minor, and Human Memory Lab. His research spans the neuropsychology and cognitive neuroscience of autobiographical memory, imagination, and cognitive aging, with a focus on disorders like amnesia and Alzheimer’s disease. His work investigates individual differences in autobiographical thought , the impact of aging on memory specificity, and innovative neuropsychological assessment methods. Recent publications highlight aging-related changes in memory dynamics, brain structure-function relationships, and real-world applications such as phishing vulnerability and bilingualism’s role in cognitive aging. Grilli’s research utilizes naturalistic observation and computational approaches (e.g., latent brain state analysis, large language models) to study memory coherence, emotional simulation, and intergenerational conversations. He is also involved in developing tools like the Phishing Email Suspicion Test (PEST) to assess cognitive mechanisms in aging populations. The Human Memory Lab under his direction explores memory’s intersection with well-being, identity, and brain health, employing multimodal methodologies to address both theoretical and applied questions in cognitive neuroscience.

• Computational Chemistry
• Artificial Intelligence in Chemistry
• Molecular Modeling
• +6 more

Professor Neil Berry is a distinguished academic and researcher in the Department of Chemistry at the University of Liverpool's School of Physical Sciences. He currently serves as Head of Department (2018-2024) and has held various significant administrative roles including Director of Post Graduate Research for the School of Physical Sciences (2015-2018) and Departmental PGR Lead. His academic journey began at Exeter College, University of Oxford where he earned his MChem in Chemistry (1999) followed by a DPhil (2002) under the supervision of Professor Paul Beer. Professor Berry's research expertise spans computational chemistry with a focus on applying AI, machine learning, and molecular modeling to solve complex problems in chemistry. His work primarily centers on four key areas: medicinal chemistry (particularly antimalarial and antiparasitic drug discovery), materials chemistry (including metal-organic frameworks and supramolecular gels), reaction mechanisms, and chemoinformatics. His group adopts a rational approach to research through molecular and material design, integrating computational modeling with experimental validation through close collaborations with the Liverpool School of Tropical Medicine, University of Washington, and industry partners including GSK, Bayer, and Unilever. His publication record demonstrates a strong focus on applying computational methods to drug discovery for neglected tropical diseases and advanced materials science. Recent work shows increasing integration of machine learning techniques across all research areas, with significant contributions to antimalarial drug development, snakebite treatment, and materials science applications. His research consistently bridges computational prediction with experimental validation, demonstrating the practical impact of his work. Treasurer - Royal Society of Chemistry Chemical Information and Computer Applications Group (2021 - present) Invited committee member - Royal Society of Chemistry Chemical Information and Computer Applications Group (2015 - present) Fellow of Higher Education Academy (2012 - present) Member of Royal Society of Chemistry (2005 - present) Referee for journals including Nature Communications, Journal of Medicinal Chemistry, and Journal of Computer Aided Molecular Design As an educator, Professor Berry has supervised numerous PhD students and postdoctoral researchers, with a particular emphasis on integrating computational approaches with experimental chemistry. His teaching innovations include Chemtube3D (web-based interactive 3D simulations of organic reactions), lecture recording systems, and ChemPreLab (a pre-lab interactive tutoring system). His research group operates at the intersection of chemistry, biology, and computer science, securing substantial funding from EPSRC, MRC, Wellcome Trust, and industry partners.

Saleh JavadiView profile

Senior Lecturer

• Systems engineering
• Signal processing
• Machine learning
• +1 more

Saleh Javadi is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He is actively engaged in research and teaching within the field of systems engineering. His educational background includes: B.Sc. in Electrical-Control Engineering from Amirkabir University of Technology (2009) M.Sc. in Electrical, Electronic and Systems Engineering from The National University of Malaysia (2013) Ph.D. in Systems Engineering from Blekinge Institute of Technology (BTH) (2021) Saleh Javadi's research focuses on signal processing, machine learning, and computer vision , with applications spanning remote sensing, intelligent transportation systems, and AI-driven industrial optimization. His work bridges theoretical advancements with practical implementations, particularly in SAR imagery analysis, drone-based agricultural monitoring, and traffic surveillance systems. His recent publications demonstrate a strong focus on remote sensing technologies, particularly Synthetic Aperture Radar (SAR) image processing and analysis. There's a clear trend toward applying machine learning techniques to solve complex problems in aerial and satellite imagery, traffic monitoring, and agricultural applications. His research shows interdisciplinary connections between computer vision, signal processing, and practical engineering applications. Saleh Javadi has received significant recognition for his innovative work: Innovator of the Year award (SKAPA – Innovation Prize in Memory of Alfred Nobel) in Blekinge for innovative efforts in optimizing and reducing energy consumption in industries by using artificial intelligence ÅForsk Entrepreneur's prize at the Swedish Innovation Council Day – Swedish Incubators & Science Park's annual conference in May 2019 Dr. Javadi is involved in practical applications of his research through projects such as "Artificiell intelligens AI kan reducera ogräsfrön i utsäde" (ongoing) and "Bekämpa Renkavle med hjälp av drönare och Artificiell Intelligens (AI)" (completed). His work demonstrates a strong commitment to translating academic research into real-world solutions that address industrial and environmental challenges. His research appears to be conducted within a collaborative framework, working with colleagues on drone technology, SAR image analysis, and AI applications across multiple domains including agriculture, maritime monitoring, and transportation systems.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +5 more

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

Florian ZaussingerView profile

Professor

• Thermal Convection
• Fluid Dynamics
• Astrophysics
• +6 more

Prof. Florian Zaussinger is a faculty member at the Faculty of Applied Computer and Life Sciences at Mittweida University of Applied Sciences. His research focuses on thermal convection, fluid dynamics, and numerical simulations in both geophysical and astrophysical contexts. He has contributed extensively to studies on microgravity experiments, including the GeoFlow and AtmoFlow projects conducted on the International Space Station (ISS). University: Mittweida University of Applied Sciences Faculty: Applied Computer and Life Sciences Department: Mathematics Contact: +49 3727 58-1381 | florian.zaussinger@hs-mittweida.de | Building 6, Room 6-131 His research involves advanced numerical modeling of complex fluid systems, including spherical convection, dielectric heating, and double-diffusive processes. He has developed and applied computational tools like the ANTARES code to simulate convection in DA white dwarfs, planetary atmospheres, and Earth's mantle. His work bridges theoretical fluid mechanics with experimental validation in space-based microgravity environments. Recent publications highlight his expertise in thermo-electrohydrodynamic convection, planetary fluid flow analysis, and microgravity-induced instabilities. While the scraped data does not list scientific awards or students directly, his academic profile emphasizes interdisciplinary collaboration with engineering and life sciences, particularly in applied mathematics for fluid dynamics and experimental data processing.

Steven NeubergView profile

Professor

• Evolutionary Psychology
• Social Cognition
• Prejudice and Stereotyping
• +4 more

Steven Neuberg is a Foundation Professor in the Department of Psychology at Arizona State University (ASU), where he directs the Evolution, Ecology, and Social Behavior Lab and co-directs the Kenrick-Neuberg Social Cognition Laboratory. He founded the ASU Global Group Relations Project, a multidisciplinary initiative examining intergroup conflict drivers like religion. Affiliated with ASU’s Center for Social Dynamics and Complexity and the Global Futures Laboratory, his work integrates evolutionary theory and social cognition to explore prejudices, stereotypes, and ecological influences on behavior. Education: A.B. Cornell University (1983) M.A. Carnegie Mellon University (1984) Ph.D. Carnegie Mellon University (1987) NATO Postdoctoral Fellowship, University of Waterloo (1987–1988) Research Interests: Neuberg investigates how fundamental motivations—such as self-protection, disease avoidance, and mating—shape social cognition and behavior. His research addresses stigma, prejudice, and intergroup conflict through an evolutionary lens, examining how ecological factors (e.g., resource scarcity, disease threats) modulate social perceptions. Recent work explores COVID-19’s impact on prejudices and environmental aesthetics in migration decisions. Publication Trends: Neuberg’s recent articles emphasize evolutionary psychology frameworks applied to social phenomena like stereotyping, life satisfaction, and cultural variation. Key themes include affordance management (how people perceive and act on social opportunities/threats), ecological influences on cognition, and functional approaches to prejudice. His work consistently bridges theoretical innovation with empirical rigor across cross-cultural and experimental studies. Awards and Honors: Gordon Allport Intergroup Relations Prize Daniel M. Wegner Theoretical Innovation Prize Fellow, American Association for the Advancement of Science (AAAS) ASU Outstanding Doctoral Mentor Award ASU College of Liberal Arts and Sciences (CLAS) Outstanding Teaching Award Advising and Grants: Neuberg has mentored over 50 doctoral and master’s students, many now faculty at institutions like UCLA, UC Irvine, and Hamilton College. His research is funded by the National Science Foundation (NSF), National Institutes of Health (NIH), Templeton World Charity Foundation, and Army Research Institute. Notable grants include a study on religious fasting’s paradoxical effects on prosociality and a RAPID project analyzing COVID-19’s impact on prejudices. Labs and Teams: He leads the Evolution, Ecology, and Social Behavior Lab, investigating stereotypes, prejudices, and ecological affordances. The Kenrick-Neuberg Lab focuses on social cognition and decision-making. Neuberg also oversees Psych for Life®, an ASU initiative promoting applied psychology for public benefit.

Jinzhao DuView profile

Associate Professor

• Platform-based marketing
• Multi-sided media markets
• Matching platforms
• +5 more

Jinzhao Du serves as Associate Professor of Marketing at The University of Hong Kong's Faculty of Business and Economics, where he also holds the position of BBA Deputy Programme Director and Admissions Tutor. His academic journey includes a Ph.D. in Marketing from Duke University (2018) and a B.A. in Economics from Tsinghua University (2012). His educational background includes: Ph.D. in Marketing, Duke University, 2018 B.A. in Economics, Tsinghua University, 2012 Professor Du's research centers on platform-based marketing, with particular emphasis on media platforms and matching platforms. His work examines strategic interactions among multiple players in media ecosystems (publishers, consumers, advertisers, content suppliers, and news aggregators) and investigates how information design, strategic pricing, and AI development can improve matching outcomes. He employs game-theoretic models to analyze these complex market dynamics, with growing attention to how consumer data rights and AI tools are reshaping platform marketing landscapes. His publication portfolio reveals consistent contributions to top marketing and operations journals, with recent work focusing on personalized product offerings, ride-hailing platform operations, customer review systems, media platform competition, and publisher-aggregator relationships. The research demonstrates a strong theoretical foundation while addressing practical challenges faced by platform companies. Notable recognition includes: 2022 Research Output Prize in the Faculty of Business and Economics at HKU Professor Du actively mentors doctoral students, including executive professionals pursuing DBAs, and teaches courses including Pricing Strategies and Tactics (MSMK 7031), Pricing Strategies (MKTG3527), and Introduction to Marketing (MKTG2501). His industry engagement informs his research on organizational communication and platform strategy, bridging academic theory with practical business applications. He has also shared insights on platform competition strategies, ethical pricing approaches, and the importance of quantitative skills in modern marketing.

Haiyang AiView profile

Associate Professor

• Corpus Linguistics
• Natural Language Processing
• Second Language Acquisition
• +3 more

Haiyang Ai is an Associate Professor in the Literacy and Second Language Studies program at the University of Cincinnati's School of Education. His research applies corpus linguistics and natural language processing to investigate second language acquisition, writing complexity, and bilingual language processing. His educational background includes a Ph.D. in Applied Linguistics from The Pennsylvania State University (2015), an M.A. in Linguistics & Applied Linguistics from the University of Chinese Academy of Sciences (2006), and a B.A. in English from Shaanxi Normal University (2003). Dr. Ai's research spans corpus linguistics, natural language processing, second language acquisition, and computer-assisted language learning. He specializes in compiling and analyzing native and learner corpora to develop intelligent language learning systems and investigate lexical/syntactic complexity in L2 writing. His methodological approach integrates computational tools with theoretical linguistics to address practical language teaching challenges. His recent publications (2019-2023) demonstrate consistent focus on lexical bundles in professional communication, automating complexity measurement in Chinese, speech perception mechanisms in bilinguals, and grammatical puzzles in English learning. These works bridge corpus-based analysis with psycholinguistic experimentation across diverse subfields including morphosyntax, pragmatic competence, and cognitive processing in L2 acquisition. Dr. Ai has secured multiple University of Cincinnati grants including three CECH Faculty Development Grants ($2500 in 2017-2018 for verb-noun collocation research, $2000 in 2016-2017 for corrective feedback tools) and the NCFDD Faculty Success Program ($3250 in 2017), all supporting his development of computational language learning resources.

ZHUANG YizhouView profile

Assistant Professor

• Weather and climate extremes
• Climate change and event attribution
• Atmospheric circulation and thermodynamics
• +3 more

Prof. ZHUANG Yizhou is an Assistant Professor in the Department of Geography at Hong Kong Baptist University. His research focuses on weather and climate extremes, climate change attribution, and land-atmosphere coupling. With a Ph.D. in Meteorology from Peking University and extensive postdoctoral experience at UCLA, he brings significant expertise in atmospheric sciences to his academic role. Dr. Zhuang's educational background includes: 2019-2024: Postdoctoral Scholar, University of California, Los Angeles (UCLA), USA 2017: Visiting Graduate Researcher, University of California, Los Angeles (UCLA), USA 2015-2017: Visiting Research Scholar, University of Texas at Austin, USA 2013-2019: Ph.D., Meteorology, Peking University, China 2009-2013: B.S., Atmospheric Sciences (Remote Sensing Focus), Nanjing University of Information Science and Technology, China Dr. Zhuang's research spans multiple critical areas in climate science. His work on weather and climate extremes examines phenomena like wildfires, droughts, and floods. In climate change attribution , he investigates the human influence on extreme weather events, with several publications in PNAS demonstrating how anthropogenic warming has altered drought mechanisms and fire risks. His research on land-atmosphere coupling explores the complex feedback mechanisms between Earth's surface and the atmosphere. Additionally, he applies machine learning techniques and remote sensing technologies to analyze cloud formations and precipitation patterns. Analysis of Dr. Zhuang's recent publications reveals a consistent focus on drought mechanisms and fire weather risk in western North America. His work frequently employs advanced statistical methods like self-organizing maps and canonical correlation analysis to understand complex climate phenomena. A notable trend is his investigation of how anthropogenic climate change is fundamentally altering the nature of droughts, shifting from precipitation-deficit dominated to temperature-driven events, with significant implications for water resource management. Dr. Zhuang has received several prestigious awards for his research contributions: JIFRESSE Outstanding Leadership/Service Award, UCLA, 2023 Richard P. and Linda S. Turco Exceptional Research Publication Award, UCLA, 2023 China Scholarship Council (CSC) Joint Ph.D. Scholarship, 2015-2017 As an academic mentor, Dr. Zhuang supervises graduate students, with evidence of at least one student (G. Wang) whose work has been published under his supervision. He serves as a reviewer for numerous high-impact journals including Proceedings of the National Academy of Sciences (PNAS), Earth's Future, and Geophysical Research Letters. Additionally, he has mentored students in the UCLA Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) Summer Internship Program, with his mentee Annie Rosen winning the 2024 Best JSIP Presentation Award. Dr. Zhuang maintains an active research group, as indicated by his personal website www.zhuangyz.org. His team focuses on climate extremes, attribution studies, and land-atmosphere interactions, with ongoing projects examining drought mechanisms, fire weather risks, and precipitation variability across different regions of the United States, particularly the western states and Great Plains.

Eman AlmehdaweView profile

Professor

• Modeling Stochastic Systems
• Healthcare Operations Management
• Supply Chain Management
• +1 more

Eman Almehdawe serves as Professor of Operations Management and Associate Dean of Graduate Programs at the University of Regina's Levene School of Business, where she holds the CPA Scholar in Data Analytics designation and maintains professional engineering licensure through APEGS. Her academic foundation includes: PhD in Management Science from University of Waterloo (2012) MASc in Management Science from University of Waterloo (2007) BASc in Industrial Engineering from University of Jordan (2000) Her research integrates stochastic modeling with practical applications in healthcare systems optimization and supply chain resilience, emphasizing operational research methodologies to address uncertainty in complex environments. This focus manifests in her teaching of core courses including BUS 275, BUS 435AW, and MBA/EMBA 816 in Operations Management. Recognition includes: CPA Scholar in Data Analytics award Professional affiliations encompass membership in the Canadian Operational Research Society (CORS) and licensure as a Professional Engineer with APEGS. No details regarding student supervision or research funding were documented in the source material. Her operational research expertise is applied through industry collaborations though specific research teams or laboratory facilities were not referenced in the available documentation.

Peter ZijlstraView profile

Professor

• Single-Molecule Sensing
• Plasmonics
• Nanophotonics
• +4 more

Peter Zijlstra is a Full Professor in the Department of Applied Physics at Eindhoven University of Technology (TU/e), leading the Molecular Plasmonics group. His research focuses on single-molecule sensing using plasmonic and nanophotonic approaches to study biomolecular interactions in complex environments. He is a core member of the Institute for Complex Molecular Systems at TU/e, collaborating across disciplines like chemistry, biomedical engineering, and mathematics. Education: MSc in Applied Physics, University of Twente (2005) PhD from Swinburne University of Technology (2009), studying plasmonic nanoparticles in optical data storage Postdoctoral fellowship at Leiden University under Prof. Michel Orrit Research Interests: Developing novel sensing concepts via nanophotonics and super-resolution microscopy. Key areas include plasmon-enhanced fluorescence, real-time biomolecular dynamics, and applications in cancer management. His work contributes to UN Sustainable Development Goals through advancements in biosensing technologies. Awards: 2013 NWO Vidi Award for research on plasmonic imaging of enzymes in living cells Teaching & Activities: Teaches courses like Advanced Optical Microscopy and Electromagnetism Supervised 32 academic works Contributed to conferences and editorial roles for journals like npj Biosensing Labs & Collaborations: Molecular Plasmonics group website: www.molecular-plasmonics.nl Marie Curie ITN SuperCol project: www.supercol.eu

Nick MoseyView profile

Associate Professor

• Computational Chemistry
• Theoretical Chemistry
• Materials Science
• +7 more

Nick Mosey is an Associate Professor in the Department of Chemistry and Associate Dean (Research) in the Faculty of Arts & Science at Queen’s University. He holds a PhD from the University of Western Ontario and completed postdoctoral research at Princeton University. His research focuses on computational and theoretical chemistry, particularly in developing simulation methods to study chemical reactions under mechanical stress and tribological conditions. Key areas include molecular dynamics, exact exchange calculations in periodic systems, and the design of electrocatalysts for fuel cells. His work integrates method development (e.g., contracted planewave basis functions, temporal QM/MM) with applied studies in tribology (e.g., friction modifiers, lubricants) and electrocatalysis (nickel-based materials for alkaline fuel cells). He has led interdisciplinary projects involving collaborations across chemistry, materials science, and engineering. Awards include the NSERC Doctoral Prize (2007), Early Researcher Award (2009-2014), and leadership in securing funding from NSERC and Ontario government programs. Mosey teaches courses in quantum mechanics and computational chemistry, with a focus on bridging theory and practical applications. His group has produced over 70 peer-reviewed publications and trained numerous graduate students and postdoctoral researchers. Current research emphasizes extending simulation time-scales for reactive systems and exploring novel materials for energy applications.

Jie PengView profile

Professor

• Graphical Models
• High Dimensional Inference
• Functional Data Analysis
• +4 more

Jie Peng, Ph.D., is a Professor and Vice-Chair for Graduate Affairs in the Department of Statistics at the University of California, Davis. Her research focuses on statistical methodologies with applications in genomics, neuroimaging, and functional data analysis. She holds a Ph.D. from Stanford University and is actively involved in advancing statistical theory and its practical implementations in biomedical and computational sciences. Education Ph.D. in Statistics, Stanford University Research Interests Dr. Peng’s work spans several key areas including graphical models, high-dimensional inference, and neuroimaging analysis. She develops innovative statistical tools for analyzing complex biological data, such as tumor genomics and brain connectivity patterns derived from diffusion MRI. Her research emphasizes the interplay between theoretical statistics and real-world applications in healthcare and precision medicine. Professional Contributions She serves as an Associate Editor for the Journal of Computational and Graphical Statistics and has contributed to numerous high-impact publications. Her methods have been applied to identify biomarkers in ovarian cancer and to study brain lateralization using Human Connectome Project (HCP) data. Dr. Peng also leads efforts in developing computational frameworks for spatial transcriptomics and dynamic network modeling.