جستجوی استاد راهنمای دانشگاهی

Rainer Wallnyمشاهده پروفایل

استاد

• High-Energy Physics
• Particle Physics
• Higgs Physics
• +۳ مورد دیگر

Prof. Rainer Wallny is a Full Professor of Physics at ETH Zurich and Head of the Institute for Particle Physics and Astrophysics. His research focuses on high-energy particle physics, particularly through the CMS experiment at the Large Hadron Collider (LHC), emphasizing Higgs boson studies and detector upgrades. He leads projects on Higgs boson characterization in photon and b-quark final states, as well as CMS pixel detector upgrades for Phase-2. His group also explores future collider technologies and contributes to teaching at all academic levels. Education: Studied Physics at Universities of Tübingen, Washington (M.Sc., 1994), and Heidelberg (Diplom, 1996) PhD in Physics from University of Zurich CERN Research Fellow (2001–2003) Faculty at UCLA (2003–2010), promoted to Full Professor in 2010 Joined ETH Zurich as Full Professor in 2010 Research Interests: Higgs boson properties and decay channels Supersymmetry searches in CMS data Detector development for CMS (pixel trackers, diamond sensors) Phase-2 LHC upgrade technologies Experimental particle physics at high-luminosity colliders Grants and Advising: Supervised over 20 PhD students since 2010 Leadership roles in CMS collaboration and detector R&D initiatives Active in curriculum design for physics education at ETH Labs & Teams: Wallny Group at ETH Zurich Institute for Particle Physics and Astrophysics (D-PHYS) Collaborations with CERN and global CMS teams

Kevin Vinsenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• High-performance Computing
• Peta-scale systems
• +۷ مورد دیگر

Kevin Vinsen is a Senior Research Fellow at the University of Western Australia, working in the Data Intensive Astronomy (DIA) Program of the International Centre for Radio Astronomy Research (ICRAR) since 2009. He is also affiliated with the UWA Defence and Security Institute and holds an ORCID ID of 0000-0001-5332-3784. His work focuses on translating ICRAR software capabilities into practical industry applications across diverse domains. His research interests include: Peta-scale systems High-performance Computing Machine Learning applications in multiple fields Wave and weather forecasting Digital Assistive Technologies Agricultural applications of ML Large language models Vinsen heads the Translation and Impact work of the DIA team and leads the development of Machine Learning systems. His current projects include ML for wave forecasting on the NW shelf, wind and temperature forecasting, honey traceability and provenance, and digital assistive technology for people with disabilities. His work contributes to UN Sustainable Development Goals related to industry, oceans, agriculture, food, disability, and defense. His research output demonstrates a strong trend toward applying machine learning techniques to solve real-world problems across astronomy, environmental science, agriculture, and disability support. This interdisciplinary approach showcases the versatility of his computational expertise across scientific and social domains. Vinsen has an h-index of 11 with 621 citations across 33 research outputs. As the ICRAR/UWA Summer Studentship Co-ordinator, he mentors emerging researchers and contributes to building research capacity. His collaborative network spans multiple institutions and research areas, reflecting his ability to bridge academic research with practical applications.

Ajay Pillarisettiمشاهده پروفایل

استادیار

• Household Energy Use
• Air Pollution Exposure
• Human Health Impacts
• +۵ مورد دیگر

Ajay Pillarisetti is an Assistant Professor at the University of California, Berkeley's School of Public Health, Department of Environmental Health Sciences. His research focuses on the interplay between household energy use, air pollution exposure, health outcomes, and climate change in low- and middle-income countries. A graduate of UC Berkeley (PhD) and Emory University (MPH, BS), he has led global projects in India, Mongolia, Nepal, Guatemala, Peru, and Rwanda. PhD – Environmental Health Sciences, UC Berkeley MPH – Global Environmental Health, Emory University BS – Biology, Emory College His work employs low-cost air quality sensors, longitudinal surveys, and randomized controlled trials like the HAPIN study to assess health impacts of clean fuel interventions. Key subfields include exposure assessment, implementation science, pollution's metabolic effects, and policy-driven energy transitions. He collaborates with teams across four continents and has published extensively on household air pollution's multi-scale health burdens. Recent articles (2023–2025) highlight trends in quantifying PM2.5's health effects, optimizing sensor networks, and evaluating LPG interventions for maternal/child health. His studies span Guatemala's RESPIRE cohort, Rwanda's HAPIN trial, and India's community monitoring systems, addressing gaps in exposure-response modeling, biomarker analysis, and policy advocacy.

• Particle Physics
• High Energy Physics
• ATLAS Collaboration
• +۳ مورد دیگر

Jonas Strandberg is an Associate Professor at KTH Royal Institute of Technology's Department of Physics, part of the School of Engineering Sciences. His research focuses on particle physics, particularly within the ATLAS Collaboration at the Large Hadron Collider (LHC). He contributed to the Higgs boson discovery and currently studies its properties. Strandberg has been involved in detector development, including the HGTD timing detector for the LHC upgrade. He holds a PhD from Stockholm University (2006) and worked as a postdoc at the University of Michigan (2006-2011) before joining KTH. His teaching responsibilities include courses on experimental particle physics, statistical methods, and engineering skills. Research interests span high-energy physics, collider technology, and detector systems. Research Highlights: Member of the ATLAS Collaboration since 2011 Key contributor to Higgs boson measurements Developed timing detector systems for LHC upgrades Published extensively on particle physics and accelerator technology Teaching & Supervision: Course responsible for Experimental Particle Physics (SH2203) Teaching roles in Applied Modern Physics (SH1015), Embedded Systems Design (IL2232), and more Professional Activities: ATLAS Data Preparation Coordinator (2015-2017) Member of the Particle and Astroparticle Physics Group at AlbaNova University Centre

Rodrigo Gonzálezمشاهده پروفایل

استادیار

• Data-driven modeling
• System identification
• Control systems engineering
• +۴ مورد دیگر

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

• Geochemistry
• Isotope Geochemistry
• Environmental Geology
• +۴ مورد دیگر

Dr. Diego Fernandez is a Research Professor in the Department of Geology & Geophysics at the University of Utah, specializing in isotope geochemistry, mineral resource characterization, and environmental dust dynamics. His work spans four decades of academic appointments (2006–present) with expertise in strontium isotope applications for migration tracking, critical mineral assessments, and dust pollution health impacts. PhD in Thermodynamics, University of Buenos Aires (1992) Research Professor (2021–present) Research Associate Professor (2014–2021) Research Assistant Professor (2006–2014) Research focuses include: strontium isotope geochemistry for reconstructing animal migration and geological processes; critical mineral characterization of coal-bearing strata for rare earth element extraction; environmental health impacts of dust from the Great Salt Lake playa; dust source apportionment in urban settings; and magmatic system petrochronology of the Alta Stock region. Recent publications (2024–2025) demonstrate methodological innovations in LA-ICP-MS analysis, Bayesian isotope turnover modeling, and dust-metal contamination frameworks. His studies document elevated health risks from thallium, arsenic, and lead in urban dust, while proposing solutions through dust suppression and international emission reductions. 2024 Distinguished Educator Award, University of Utah College of Science Current grants include USGS-funded terminal lake geochemistry, NSF projects on critical zone dust dynamics, and Bering Sea fisheries baseline studies Teaching activities include directing SRI Research courses (2024) and mentoring interdisciplinary projects on environmental isotopes. Collaborators include institutions across North America and Europe, with fieldwork in Utah, Alaska, Wyoming, and Kenya.

Sanat K. Sarkarمشاهده پروفایل

استاد

• Multiple Testing
• Statistical Methodologies
• High-Dimensional Statistical Inference
• +۱ مورد دیگر

Sanat K. Sarkar serves as a Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. An internationally renowned expert, he has pioneered foundational work in multiple testing theory with applications spanning genomics, neuroimaging, and high-dimensional data analysis. His methodological innovations address critical challenges in false discovery rate control under complex dependency structures. Research Interests: Dr. Sarkar specializes in Multiple Testing, Statistical Methodologies, High-Dimensional Statistical Inference, and Multivariate Statistics. His work develops rigorous frameworks for hypothesis testing in modern scientific contexts where thousands of simultaneous tests are performed, ensuring reliable discoveries in fields like genetic association studies and brain connectivity mapping. Key contributions include adaptive FDR procedures and methods for structured hypothesis groups. Publication Trends: Over 2020-2025, his 11 publications demonstrate sustained leadership in refining false discovery rate methodologies. Recent work tackles correlated data (2025), knockoff variable selection (2022), and hierarchical hypothesis structures (2021-2024), reflecting his focus on real-world applicability in biomedical big data. His research bridges theoretical statistics with practical computational solutions. Honors and Awards: Fellow, Institute of Mathematical Statistics Fellow, American Statistical Association Elected Member, International Statistical Institute Musser Award for Research Excellence (Fox School) Multiple Dean's Research Honor Roll Inductions Research Support and Service: Funded continuously by NSF and NSA grants, Dr. Sarkar co-organized the NSF-CBMS conference on Multiple Comparisons and serves on editorial boards of Annals of Statistics , American Statistician , and Sankhya . He regularly delivers invited talks at international venues and mentors junior researchers in statistical methodology development.

Yi Liمشاهده پروفایل

استاد

• Survival Analysis
• Data Science
• High-dimensional Inference
• +۱۱ مورد دیگر

Yi Li is the M. Anthony Schork Collegiate Professor of Biostatistics at the University of Michigan School of Public Health. With a PhD in Biostatistics from the University of Michigan (1999) and postdoctoral training at Harvard (1999-2000), Dr. Li has established himself as a leading researcher in statistical methodology with applications across multiple biomedical domains. Dr. Li's research spans survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease modeling. His methodological work finds application in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research, and opioid overuse studies. With over 230 publications in major statistical journals including JASA, Biometrika, JRSSB, and Biometrics, as well as premier subject matter journals like PNAS, JAMA, and JCO, Dr. Li's work has significantly impacted both statistical theory and biomedical applications. His research portfolio demonstrates consistent evolution from foundational methodological work in survival analysis and spatial statistics to cutting-edge applications in high-dimensional data, machine learning, and deep learning approaches for complex biomedical problems. The recent publications reveal increasing focus on integrating multiple data sources, causal inference in observational studies, and developing interpretable machine learning models for clinical applications. Dr. Li's work has been continuously supported by NIH funding since 2003, including multiple National Cancer Institute grants (R01 CA95747, 1P01CA134294-010002, R21CA157219, R01CA249096, R01CA269398) and a National Institute on Aging grant (R21AG058198). He actively collaborates with researchers from the University of Michigan and Harvard University on clinical and observational studies. As an educator, Dr. Li has taught advanced courses in survival analysis and statistical methods, mentoring the next generation of biostatisticians. His methodological contributions have been widely recognized through invitations to serve on NIH study sections (BMRD 2008-2012, EPIC 2015-2019) and as Associate Editor for leading statistical journals including Journal of the American Statistical Association, Biometrics, and Scandinavian Journal of Statistics.

Song Jun-myeongمشاهده پروفایل

استاد

• Analytical Pharmacology
• Nanomedicine
• 3D Bioprinting
• +۵ مورد دیگر

Song Jun-myeong is a Professor at Seoul National University (SNU) College of Pharmacy, specializing in Analytical Pharmacology and Nanomedicine. He has held leadership roles including Director of the Library and Information Center at SNU College of Pharmacy and Director of the Korean Biochip Society. Bachelor of Science, Sogang University (1991) Master of Science, Sogang University (1993) Ph.D., Kyushu University (1997) His research focuses on tumor clearing imaging, 3D bioprinting, drug analysis, and photodynamic therapy. He utilizes quantum dot-based imaging and single-cell cytometry for cancer diagnostics and drug mechanism studies. Analysis of his publications reveals a consistent emphasis on nanotechnology applications for pharmacological research, particularly in cancer diagnostics, drug resistance mechanisms, and antimicrobial nanomaterials. His work bridges analytical chemistry with biomedical applications. He serves on multiple scientific advisory boards and committees, including the Korean Society for Biotechnology and Bioengineering and the Pharmacist National Examination Question Development Committee. Song leads the Laboratory for Nanomedicine at SNU College of Pharmacy, advancing technologies in quantum dot imaging and nanomaterial synthesis for pharmaceutical applications.

Scott E. Crouterمشاهده پروفایل

استاد

• Physical Activity Measurement
• Wearable Sensors
• Machine Learning Applications
• +۵ مورد دیگر

Scott E. Crouter is a Professor in the College of Education, Health, and Human Sciences at the University of Tennessee, Knoxville. He serves as director of the Applied Physiology Laboratory and is a Fellow of the American College of Sports Medicine. His research focuses on improving physical activity and energy expenditure measurement using wearable monitors and machine learning algorithms, with applications in youth, adults, and special populations like pregnant individuals and cancer survivors. Education: Postdoctoral in Nutritional Sciences (Cornell, 2005-07), PhD in Exercise Physiology (University of Tennessee, 2005), MS in Cardiac Rehabilitation/Adult Fitness (University of Wisconsin, 2000), BS in Exercise Science (Linfield College, 1998) Dr. Crouter's work spans physical activity assessment , sedentary behavior analysis , and technology validation . He has developed machine learning models for energy expenditure estimation using ActiGraph GT9X and Cosmed K5 devices. His recent studies examine pregnancy hyperglycemia interventions, urban environment impacts on activity patterns, and robotic exercise aids. He has secured significant NIH funding for projects including digital health weight management , pediatric obesity treatment translation , and fall risk factors in the elderly . As associate editor for Medicine and Science in Sports and Exercise and Journal for the Measurement of Physical Behaviors , he contributes to methodological advancement in activity monitoring. Scientific Awards: Fellow, American College of Sports Medicine Editorial Leadership National Physical Activity Plan Alliance Participation CDC/National Collaborative on Childhood Obesity Research Contributions National Academy of Sciences Committee Member Dr. Crouter's student collaborations include works with Natalie Butte on youth compendiums, Allan Opotowsky in cardiac rehabilitation trials, Patrick Hibbing in metabolic equivalent research, and Sara LaMunion in consumer monitor validation. His lab has produced over 100 publications and 15 recent articles focusing on sensor technology and population health.

Sanford R. Studentمشاهده پروفایل

استادیار

• Educational Measurement
• Psychometrics
• Quantitative Methods
• +۳ مورد دیگر

Dr. Sanford R. Student is an Assistant Professor in the School of Education at the University of Delaware and a Resident Faculty member of the Data Science Institute. His research focuses on connecting psychometric methodologies with practical educational implications, particularly in academic growth measurement, large-scale science assessments, and instrument design. He holds a Ph.D. in Research and Evaluation Methodology from the University of Colorado Boulder (2023) and dual B.A.s in Philosophy and Computer Science from Brown University (2013). Dr. Student’s professional experience includes roles as a Research Associate at Lyons Assessment Consulting (2021–2023), Doctoral Researcher at the Center for Assessment Design, Research and Evaluation (2018–2023), and Lead Researcher for the American Bar Association’s Bar Exam study (2019–2020). Prior to academia, he worked as a Software Engineer at edX (2016–2018). His awards include selection for the 2020 AIR/NCES NAEP Data Training Workshop. Current research trends in his publications emphasize Bayesian methods, vertical scaling, and crosscutting concepts in science education. He actively contributes to educational policy discussions through work with state agencies and assessment developers. Dr. Student advises graduate students and collaborates on grants related to growth measurement and educational data systems. He is affiliated with the National Council on Measurement in Education and maintains a lab focusing on applied psychometric challenges in K-12 systems.

Charles Gillanمشاهده پروفایل

مدرس ارشد

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +۴ مورد دیگر

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Robert Jacobمشاهده پروفایل

استاد

• Human-Computer Interaction
• Brain-Computer Interfaces
• Implicit Interaction Techniques
• +۳ مورد دیگر

Robert Jacob is a Professor of Computer Science at Tufts University's School of Engineering, where he leads research in human-computer interaction with particular focus on implicit brain-computer interfaces. His work bridges computer science, cognitive science, and interface design to create adaptive systems that respond to users' cognitive states without explicit input. His educational background includes a Ph.D. from Johns Hopkins University. Professional milestones include: ACM CHI Academy membership (2007) ACM Fellow designation (2016) Leadership roles as ACM SIGCHI Vice President and conference chair for CHI, UIST, and TEI Professor Jacob's research centers on implicit interaction techniques, particularly using fNIRS brain sensing to create adaptive interfaces. His work has evolved from foundational studies in reality-based interaction and tangible programming to current neuroadaptive systems that measure cognitive workload in real-time. This research spans domains including music learning, museum education, and general user interface adaptation. His publications reveal consistent focus on brain-computer interfaces since 2012, with increasing sophistication in physiological measurement and machine learning techniques. Recent work integrates multiple physiological signals beyond brain data to create comprehensive user state models. Major recognitions include: CHI 2016 Best Paper Award for music learning research CHI 2014 and 2012 Best Paper Honorable Mentions Keynote addresses at major conferences including Neuroadaptive Technology Conference (2017) Extensive media coverage in New Scientist, IEEE Computer, and Boston Globe Professor Jacob has mentored 17 PhD students who now hold faculty positions at institutions including Worcester Polytechnic Institute, Northwestern University, and Carleton University. His HCI Lab, located in the Joyce Cummings Center, receives funding from NSF and other sources supporting neuroadaptive interface research. Current projects focus on broadening implicit interaction to include multiple physiological measurements while maintaining user privacy and system transparency.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.