جست‌وجوی استاد و پژوهشگر

• Electronic Design Automation (EDA)
• VLSI Circuit Design
• Algorithmic Aspects of Chip Design
• +۵ مورد دیگر

Martin D. F. Wong is the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean of the College of Engineering at the University of Illinois. A pioneer in Electronic Design Automation (EDA) and VLSI circuit design, his work has significantly advanced chip design methodologies through algorithmic innovations. He holds over 450 publications and has been recognized with prestigious awards, including the ASP-DAC Most Frequent Author Award and the inaugural EDA Research Award from Synopsys. Wong’s research focuses on EDA, computational lithography, and 3D integrated circuits. He has mentored 48 PhD students, many of whom have excelled in academia and industry. His contributions include foundational frameworks like OpenILT (Inverse Lithography Technique) and Xplace (global placement). He is an IEEE Fellow and has served as a Distinguished Lecturer for the IEEE Circuits and Systems Society. Key Achievements: Recipient of six best-paper awards in chip design and routing optimization Developed GPU-accelerated tools for static timing analysis and global routing Advances in machine learning applications for EDA, including congestion prediction and hotspot detection Wong’s legacy combines technical innovation with mentorship, shaping the future of semiconductor design and manufacturing.

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.

Nigel Boschمشاهده پروفایل

استادیار

• Machine Learning
• Human-Computer Interaction
• Educational Technology
• +۳ مورد دیگر

Nigel Bosch is an Assistant Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign, with a joint appointment in the Department of Educational Psychology. He is also a faculty affiliate at the National Center for Supercomputing Applications (NCSA) and Illinois Informatics. His primary research focuses on machine learning and human-computer interaction applications in education, with particular emphasis on affective computing, metacognition, and online learning environments. Bosch holds a PhD in Computer Science from the University of Notre Dame, followed by a postdoctoral research position at the National Center for Supercomputing Applications. His research explores machine learning applications in education, including automatic emotion measurement in programming education, metacognition analysis through natural language processing, and ethical implications of AI in learning. He also investigates wearable technologies for health monitoring and algorithmic bias mitigation in educational data. Bosch’s work is supported by grants from the National Science Foundation (NSF), the Institute of Education Sciences (IES), and the University of Illinois. He leads the (Human + Machine) Learning lab, which develops innovative technologies for educational analytics, AI ethics, and human-centered computing.

• Biofuels
• Grain Processing
• Food Safety
• +۴ مورد دیگر

Dr. Kent D. Rausch serves as Professor across three departments at the University of Illinois Urbana-Champaign: Agricultural and Biological Engineering, Animal Sciences, and Food Science and Human Nutrition. His research bridges agricultural engineering and food science through advanced process optimization and analytical techniques. His core research domains include: Biofuels and Ethanol Production (focusing on dry-grind corn processes) Grain Processing and Storage Chemistry (corn, soybeans) Food Safety and Fraud Detection (using NIR/hyperspectral imaging) Co-products Valorization (distillers grains, corn germ) Lipid Oxidation Mechanisms Recent work demonstrates increasing integration of machine learning with spectroscopic methods for non-targeted fraud detection in high-value foods like organic spices and quinoa, while maintaining strong contributions to biofuel process engineering. His 2022-2025 publications reveal dual trajectories: advancing ethanol yield through fiber conversion and developing portable detection systems for global food supply chains. Research Impact: 144 scholarly outputs including two widely disseminated datasets on corn dry-grind ethanol processes, with significant Mendeley readership and news coverage. International collaborations span six key research profiles in dry grinding and fermentation domains. Scientific Awards: No awards explicitly listed in source materials. Advising & Infrastructure: No student lists or laboratory details provided, though fingerprint analysis confirms active research groups in dry-grind processing and spectroscopic analysis.

David Frazierمشاهده پروفایل

استاد

• Simulation-based inference
• Financial Econometrics
• Nonparametric and semiparametric econometric modeling
• +۳ مورد دیگر

David Frazier is a Professor in the Department of Econometrics & Business Statistics at Monash University, specializing in simulation-based inference, financial econometrics, and nonparametric/semiparametric modeling. He teaches ETC 1010: Data Modeling and Computing. His research focuses on robust statistical methods, Bayesian computation, and model misspecification. Key projects include 'Consequences of Model Misspecification in Approximate Bayesian Computation' (2020-2025) and 'Loss-based Bayesian Prediction' (2020-2025). Recent work addresses forecasting in misspecified models, weak identification in econometric frameworks, and robust variational Bayes techniques. His contributions align with UN Sustainable Development Goals related to economic and environmental sustainability. Projects: 4 active/funded projects with ARC, Brown University, and international collaborators. Publications: Over 37 peer-reviewed articles in journals like the Journal of the American Statistical Association and Journal of Econometrics. Research interests include advancing Bayesian methodologies for complex models, with applications in asset pricing and economic forecasting. His work emphasizes reliability in statistical inference under model uncertainty and computational efficiency.

• Solar Physics
• Space Physics
• Heliospheric Physics
• +۵ مورد دیگر

Zigong Xu is a Postdoctoral Scholar Research Associate in Physics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics, and Astronomy. His research focuses on solar energetic particles (SEPs), heliospheric physics, and cosmic ray dynamics. His work leverages data from missions like Solar Orbiter, Parker Solar Probe, and Chang’E-4 to study particle acceleration mechanisms, interplanetary shock dynamics, and the propagation of energetic particles in the solar environment. His research interests span solar flares, coronal mass ejection interactions, and the interplay between solar eruptions and the Earth-Moon radiation environment. He has contributed to understanding phenomena such as inverse velocity dispersion in SEPs, cosmic ray cavities in near-Earth space, and the composition variations of 3He-rich SEP events. Collaborations with multi-spacecraft missions highlight his expertise in analyzing particle data across diverse heliospheric distances. Zigong has explored topics including galactic cosmic ray shielding on the lunar surface, thermodynamic properties of solar protons, and the role of coronal shocks in particle acceleration. His studies often involve advanced statistical methods and comparative analyses of observations from instruments like EPT and HET aboard Solar Orbiter, and ISOIS on Parker Solar Probe. No formal awards or grants are explicitly mentioned, though his extensive publication record reflects active engagement in the field. He collaborates with international teams on missions such as Chang’E-4’s Lunar Lander Neutron and Dosimetry (LND) experiment, advancing lunar surface radiation studies.

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.

Yun Zhangمشاهده پروفایل

استاد

• Remote Sensing
• Image Processing
• Computer Vision
• +۴ مورد دیگر

Dr. Yun Zhang is a Professor and Canada Research Chair in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick. He holds a PhD from the Free University of Berlin and has pioneered research in remote sensing, image processing, and computer vision since 2000. His patented technologies are licensed to global companies including PCI Geomatics and DigitalGlobe. Research Focus: Optical/radar image processing, digital photogrammetry, AI applications in geomatics, and sensor fusion for UAV systems. His work enables advanced geospatial analysis across environmental, urban, and defense sectors. Distinctions: First Giuseppe Inghilleri Award (ISPRS 2012) NSERC Synergy Innovation Award from Governor General of Canada (2011) ASPRS Talbert Abrams Grand Award (2005) Featured in CFI 20th Anniversary Book for breakthrough innovations Technology Impact: Solutions deployed by NASA, USGS, Google Earth, and DND Canada across five continents. Recognized among top 9 Canadian research achievements in AUTM's global case studies alongside MIT and Stanford innovations.

• Dark Matter Research
• Particle Physics
• Plasma Physics
• +۳ مورد دیگر

Prof. Dieter H.H. Hoffmann is a distinguished academic in the Department of Physics , specializing in high-energy physics, dark matter detection, and plasma-based fusion research. His work focuses on particle astrophysics, including axion searches via helioscopes like CAST, nuclear fusion mechanisms (particularly proton-boron reactions), and plasma dynamics in extreme conditions. He collaborates on major projects such as the Cherenkov Telescope Array (CTA) for gamma-ray astronomy and heavy-ion beam experiments at facilities like FAIR. Research interests include: Dark matter axion detection and theoretical modeling Proton-boron fusion as an alternative energy pathway Plasma interactions in high-intensity laser and beam experiments Stopping power and beam transport in dense matter High-energy-density physics for inertial confinement fusion Recent work highlights advancements in: CAST experiment sensitivity improvements for solar axions Experimental validation of proton-boron fusion yields in dense plasmas Development of NectarCAM cameras for CTA's gamma-ray detection Simulation of proton beam dynamics in solid-state materials His contributions bridge fundamental physics with applied research in energy and detector technology, with active involvement in international collaborations like CTA and FAIR experiments.

• Electrochemical Sensors
• Materials Science
• Corrosion Protection
• +۳ مورد دیگر

Dr. Sina Jamali is a Senior Lecturer at Griffith University's School of Environment and Science within the Chemistry and Forensic Science department. He is affiliated with the Queensland Quantum and Advanced Technologies Research Institute (QUATRI) and the Queensland Micro and Nanotechnology Centre. His research focuses on electrochemical sensors, nanomaterials, corrosion protection, and sustainable energy materials. He holds a PhD from the University of Wollongong and previously served as a DECRA Fellow at UNSW. Dr. Jamali's work addresses challenges in biosensing technologies, nanotechnology applications, and material degradation mechanisms. His funded research includes the Australian Research Council's DECRA grant (DE210101137) exploring stochastic electrochemical biosensors. He collaborates on projects related to 3D printable energy materials, antimicrobial coatings, and wearable health sensors. Research Themes: Electrochemical biosensors, material degradation, nanotechnology, sustainable materials, and biomedical applications. Key Projects: Developing green 3D-printable materials for energy devices, porous metal-organic frameworks biosensors, and graphene-based wearable health monitoring systems. Dr. Jamali has supervised multiple doctoral and master’s students in areas like bioengineered wearable sensors, antimicrobial materials, and energy device manufacturing. His publications span journals like Small Science , Angewandte Chemie , and Advanced Materials , with a focus on both fundamental and applied electrochemistry. He actively contributes to Griffith's teaching and supervision, emphasizing practical applications of chemistry and forensics. His work aligns with the Sustainable Development Goals for clean energy and sustainable cities.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +۴ مورد دیگر

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

• Bayesian statistics
• Computational statistics
• Environmental statistics
• +۴ مورد دیگر

Roles & Affiliations: Distinguished Research Professor in Statistical Science at Queensland University of Technology (QUT), Director of QUT Centre for Data Science, and Associate Member of University of Oxford's Department of Statistics. Served as Deputy Director of ARC Centre of Excellence in Mathematical and Statistical Frontiers (2015–2021) and ARC Laureate Fellow (2015–2021). Education: BA (Hons) and PhD in Mathematical Statistics from University of New England, Australia. Completed post-doctoral roles at multiple Australian universities. Research Interests: Specializes in Bayesian statistical modelling, computational methods, and their applications in environmental science, genetics, healthcare, and industry. Leads projects on coral reef recovery, cancer epidemiology (Australian Cancer Atlas), and virtual citizen science platforms like Virtual Reef Diver. Her work emphasizes interdisciplinary collaboration, integrating complex data sources with advanced statistical techniques to address real-world challenges. Publications & Grants: Over 350 refereed journal publications and attracted >30 major grants. Recent focus areas include influenza epidemiology, spatial health disparities, and AI-driven early warning systems for climate-sensitive diseases. Active in developing methodologies for spatial statistics, small-area estimation, and federated learning. Awards & Recognition: 2024 Ruby Payne-Scott Medal (Australian Academy of Science), Pitman Medal (2016), first female recipient of this award in 35 years. Elected Fellow of Australian Academy of Science (2018), Academy of Social Sciences (2018), and Queensland Academy of Arts and Sciences (2018). Holds international roles including Vice-President of International Statistical Institute (2021–2025) and Scientific Council Member at Centre International de Rencontres Mathématiques (France). Supervision & Leadership: Supervised over 36 PhD students and leads teams in >50 collaborative projects. Current supervision includes 5 PhD and 4 Masters students at QUT. Founded the QUT Centre for Data Science and previously led the Collaborative Centre for Data Analysis, Modelling and Computation. Labs & Initiatives: Core contributor to the Australian Cancer Atlas 2.0, Virtual Reef Diver project, and Queensland's Learning Potential Fund. Active in global initiatives like the World of Statistics campaign and UN Big Data Task Teams.

Eleanor Jenningsمشاهده پروفایل

مدرس ارشد

• Geochemistry
• Planetary Science
• Petrology
• +۳ مورد دیگر

Eleanor Jennings is a Senior Lecturer at the School of Natural Sciences, Birkbeck, University of London. Her research focuses on Earth's origin and evolution, employing geochemical methods to study planetary differentiation processes. She investigates trace element partitioning between liquid metals and silicates under high pressure, ferropicrite magma origins, and igneous fractionation processes using mineral inclusions. Jennings teaches courses such as Planetary Interiors, Planetary Materials, and Volcanism in the Solar System. Her work integrates experimental petrology, thermodynamic modeling, and analytical techniques to explore core-mantle interactions, mantle melting dynamics, and extraterrestrial materials (e.g., lunar samples). She supervises three doctoral researchers at Birkbeck, focusing on topics like mantle heterogeneity and planetary geochemistry. Jennings' publications span experimental studies on metal-silicate partitioning, mantle melting models, and lunar petrogenesis. Her research outputs emphasize quantitative geochemical analyses and their implications for understanding Earth's formation and internal processes.

• Robotics and Autonomous Systems
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Dr. Sen Wang is an Associate Professor in Robotics and Autonomous Systems at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with I-X (Imperial's AI initiative), the Grantham Institute, and the Robotics Forum. He directs the Sense Robotics Lab and founded the MSc in Artificial Intelligence Applications and Innovation. His research focuses on advancing robotic autonomy through probabilistic and machine learning methods, addressing challenges in unstructured environments such as underwater infrastructure inspection and climate change solutions. Key projects include leading the £18M UKRI ORCA Hub, developing underwater robotics for offshore energy infrastructure inspection, and achieving the first autonomous wind farm foundation inspection at EDF's Blyth site. He holds editorial roles at IEEE Transactions on Robotics and other journals. Research interests span robotics, computer vision, SLAM, and AI applications, with recent work emphasizing underwater systems, sensor fusion, and safety-critical autonomy. His publications bridge theoretical advancements with real-world deployments in marine robotics and environmental monitoring. Awards: 2024 AI Most Influential Scholar Award Honourable Mention Grants: £18M ORCA Hub funding (UKRI) Labs: Sense Robotics Lab, I-X AI Initiative

• Robotics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Nicolas RAGOT is an Associate Professor at CESI, affiliated with the Engineering and Numerical Tools department. He teaches Digital and embedded electronics, Microcontroller programming, System control, and Sensors at the Bachelor and Master levels. His research focuses on Environment perception for robotics and Computer vision, particularly in unconventional applications. He leads or collaborates on major research programs including ROJUNACO (2023–2025), FUSION (2023–2027), OASIS (2022–2024), and COLIBRY (2021–2024), all addressing robotics, digital twins, and industrial automation. He co-supervises PhD students Y. Feddoul, S. Ouarab, and S. Choudhary. His work spans smart mobility, assistive technologies, and energy-efficient systems. He is a member of the Secure Electronic Transactions (TES) competitiveness cluster's expert committee. Education: PhD in Computer Vision (University of Rouen, 2009), Master in Electrical Engineering (University of Paris XI, 2003), Engineering diploma (ESIGELEC, 1999). Research interests emphasize robot perception, human-robot collaboration, and extended reality integration. His publications span object detection, SLAM algorithms, and smart wheelchair systems. Current projects emphasize industrial robotics, digital twins, and real-time 3D reconstruction. Advising and grants include leadership roles in multiple research programs and co-supervision of three PhDs. His lab work focuses on Engineering and Numerical Tools, with contributions to CESI LINEACT's collaborative robotics initiatives.