جست‌وجوی استاد و پژوهشگر

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Garth V. Crosbyمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Industrial Internet of Things (IIoT)
• Cyber-physical Systems Security
• +۵ مورد دیگر

Garth V. Crosby is an Associate Professor at Texas A&M University's Department of Engineering Technology and Industrial Distribution within the College of Engineering. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on IoT and IIoT security, cyber-physical systems, and STEM education innovation. Crosby holds a Ph.D. in Electrical Engineering from Florida International University (2007), an M.S. in Computer Engineering, and a B.S. in Electronics (Applied Physics) from the University of the West Indies. His work spans cybersecurity frameworks for emerging technologies, including blockchain-based federated learning, post-quantum cryptography, and IoT threat mitigation. He has contributed to educational advancements through online lab design and faculty efficacy studies in hybrid learning environments. Crosby's recent publications emphasize securing robotic IoT systems, supply chain blockchain applications, and ransomware evasion techniques using generative AI. His research portfolio includes over 40 peer-reviewed articles in journals like IEEE Transactions and conferences such as ASEE and FiCloud. Key themes include volunteer cloud reliability models (ProTrust), edge computing security, and educational technology evaluation. Crosby's interdisciplinary approach bridges engineering systems with pedagogical innovation, addressing both technical and human factors in modern technological challenges.

Gaurav Sharmaمشاهده پروفایل

استاد

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +۳ مورد دیگر

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

David Brownمشاهده پروفایل

استاد

• General Relativity
• Theoretical Physics
• Gravitational Dynamics
• +۳ مورد دیگر

David Brown is a Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. He holds an office in Riddick Hall 319F and can be reached at jbrown@ncsu.edu. Brown earned his PhD from The University of Texas at Austin in 1985 and held postdoctoral positions at the University of Vienna, Austria, The University of Texas, and the University of North Carolina. He joined NC State in 1992 as a visiting professor, transitioning to a regular faculty position in 1995. His research focuses on general relativity, with particular emphasis on gravitational dynamics, spacetime geometry, and relativistic continuum mechanics. His work explores topics such as hyperelastic models in curved spacetime, singular Lagrangians, and numerical relativity techniques for simulating black hole systems. Key areas of interest include geodesic deviation in cosmological models, Mathisson-Papapetrou-Dixon equations for extended bodies, and the Dirac-Bergmann algorithm for constrained Hamiltonian systems. Brown’s recent publications (2021–2023) highlight advancements in elasticity theory within general relativity and the application of hyperelastic models to Schwarzschild spacetime. His earlier work (2009–2012) includes foundational contributions to numerical relativity, such as BSSN formulations and moving-puncture methods for black hole simulations. While no specific honors are listed, his extensive publication record underscores his expertise in theoretical physics and cosmology. He has advised no formally documented students in the provided materials. His research has been supported by methodologies involving both analytical and computational approaches, though specific grants are not detailed here. No affiliated laboratories or research teams are explicitly mentioned in the text.

• Security
• Embedded Systems Security
• Cyber-Physical Systems Security
• +۱۳ مورد دیگر

Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.

Christos Thrampoulidisمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Theory
• Mathematical Optimization
• +۴ مورد دیگر

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Vancouver. Previously, he held positions at the University of California, Santa Barbara (UCSB). His research focuses on machine learning theory, deep learning principles, mathematical optimization, and statistical signal processing. He leads the Machine Intelligence and Learning Design (MILD) group at UBC, emphasizing foundational studies of large language models, neural collapse, and high-dimensional data analysis. Education: PhD and MS in Electrical Engineering from the California Institute of Technology (Caltech), with a minor in Applied and Computational Mathematics. Undergraduate degree (Diploma) in Electrical and Computer Engineering from the University of Patras, Greece. Research interests include the implicit geometry of next-token prediction, optimization bias in neural networks, and robust generalization in overparameterized models. Key contributions include work on adversarial training, neural collapse phenomena, and safe reinforcement learning. His group actively explores theoretical frameworks for language models and develops methodologies to analyze their mechanics and limitations. Awards: NSERC Discovery Grant (2021). Teaching includes courses on optimization and signals & systems at UBC. He has advised numerous graduate students (PhD and MSc) and undergraduates, focusing on rigorous mathematical approaches to machine learning problems. Active collaborations span academia and industry, with publications in top venues such as NeurIPS, ICASSP, and AAAI. Current projects include understanding language model geometry, decentralized learning, and mitigating imbalances in training data. The group prioritizes equity and diversity, welcoming students with strong mathematical backgrounds and curiosity about foundational ML questions.

Anya Helene Baggeمشاهده پروفایل

دانشیار

• Programming Language Design and Implementation
• Software Language Engineering
• Semantics of Programming Languages
• +۸ مورد دیگر

Anya Helene Bagge is an Associate Professor in the Department of Informatics at the University of Bergen, where she is a researcher at the Bergen Language Design Laboratory. Her work focuses on programming languages and software language engineering, with strong ties to education and tool development. Research Interests: Programming language design and implementation Software language engineering Semantics of programming languages Domain-specific languages and language extensions Program transformation using Rascal, MPL, and Stratego/XT Aspect-oriented programming, especially Domain-Specific Aspect Languages Her recent publications reflect a consistent focus on software language engineering, refactoring, microservice security, and programming education. Themes across her work include language design, program analysis, and the development of educational tools and methodologies. Scientific Awards: The Realist Committee's Teaching Award 2016/17 Lecturer of the Year in Computer Science (Spring 2015) Anya Bagge actively supervises students, including PhD and Master’s candidates such as Tero Hasu, Kristoffer Haugsbakk, and Nina Andersen. She has been involved in numerous academic activities, including organizing the OOPSLE workshop and serving on program committees for conferences like SLE, HILT, and WCRE. She has also taught core courses including INF101, INF225, and INF328, demonstrating a strong commitment to both research and teaching. Labs and Teams: She is affiliated with the Bergen Language Design Laboratory , collaborating with researchers such as Magne Haveraaen, Eva Burrows, and Tero Hasu.

• Machine Vision
• Supply Chain Optimization
• Optical Imaging
• +۵ مورد دیگر

Professor Sang-Heon Lee is a faculty member at the University of South Australia, affiliated with the UniSA STEM division. His research spans interdisciplinary domains including machine vision, supply chain optimization, and material science. His research interests focus on developing advanced imaging systems for food safety (e.g., aflatoxin detection in almonds), optimizing reverse supply chains for electronic waste and solar panels, and engineering nanocomposites for construction materials. He also investigates biomedical imaging techniques for mucociliary transit assessment and clinical coding methodologies. The 15 most recent publications highlight his work in multispectral/hyperspectral imaging, fuzzy logic optimization, and nanomaterials. Key trends include non-destructive food testing, sustainable e-waste management, and biomedical imaging analysis. As a research degree supervisor, he contributes to academic training in these areas. No scientific awards or lab affiliations were explicitly mentioned in the provided text.

Sourav Medyaمشاهده پروفایل

استادیار

• Machine Learning
• Graph Mining
• Data Science
• +۳ مورد دیگر

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

Haim Dubossarskyمشاهده پروفایل

مدرس

• Natural Language Processing
• Artificial Intelligence
• Historical Linguistics
• +۳ مورد دیگر

Haim Dubossarsky is a Lecturer in Computer Science at the School of Electronic Engineering and Computer Science , Queen Mary University of London , and an Affiliated Lecturer at the Language Technology Lab , University of Cambridge. His work bridges Natural Language Processing (NLP) , Artificial Intelligence (AI) , and interdisciplinary studies in linguistics , cognitive neuroscience , and mathematical modeling . Dr. Dubossarsky's research explores semantic change and language variation through computational methods, focusing on: Detecting historical and contemporary linguistic shifts Integrating neurocognitive insights into NLP systems Developing interpretable AI models for language tasks Recent publications highlight his contributions to multilingual NLP , word-in-context (WiC) tasks , and robust semantic analysis . His work includes collaborations on hate speech detection , topological representations , and temporal modeling in language evolution. Dr. Dubossarsky leads projects funded by: UKRI AI Hub (£1,026,205, 2024–2029) RJ Riksbankens Jubileumsfond (£381,511, 2022–2027) Innovate UK (£320,887, 2024–2025) He supervises PhD students at Queen Mary's Centre for Human-Centred Computing , including Alaa Bazaid , Roksana Goworek , and Dina Pisarevskaya , working on topics like multilingual polysemy disambiguation and language variation analysis .

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Separation Methods
• Liquid Chromatography
• Chemometrics
• +۳ مورد دیگر

Jelena Dj. Trifkovic serves as Full Professor in the Department of Analytical Chemistry at the Faculty of Chemistry, University of Belgrade, where she was promoted from Assistant Trainee (2003) through Associate Professor (2019) to her current position (2024). She concurrently holds leadership roles as Vice-Dean for Teaching and Secretary of the Serbian Chemical Society since 2020. Her academic foundation includes undergraduate (1995-2001), Master's (2003-2006), and Doctoral studies (2011-2013) in Chemical Sciences, all completed at the University of Belgrade's Faculty of Chemistry. Trifkovic's research centers on chromatographic separation techniques and chemometrics for food authenticity verification, particularly focusing on bee products like honey and propolis. Her methodology integrates advanced analytical instrumentation with multivariate data analysis to establish quality control protocols and detect adulteration, significantly contributing to food safety standards in the Balkan region. Analysis of her publication record reveals consistent innovation in HPTLC image analysis and multi-technique approaches (GC, LC, electrochemical methods) for natural product authentication. Her work demonstrates increasing international collaboration, with recent publications emphasizing standardized protocols for regulatory compliance and health benefit validation of food components. She actively secures competitive research funding, including Horizon Europe's PFAStwin project (2022-2025) and UNDP-funded valorization initiatives (2023-2024), alongside leadership in bilateral projects on bee product authentication with Croatian and Slovenian institutions. Trifkovic maintains strong laboratory networks through her affiliation with Slovenia's National Chemical Institute Food Chemistry Laboratory and coordinates multiple university committees focused on curriculum development and quality assurance in chemical education.

Andreas Dafnosمشاهده پروفایل

پژوهشگر

• Social Movement Theory
• Political Extremism and Radicalisation
• Social Media Analytics
• +۵ مورد دیگر

Dr. Andreas Dafnos is a Researcher at the Institute of Political Science, Bundeswehr University Munich, focusing on comparative digital dynamics of riots and multi-method integration within the SPARTA research project. He previously completed his Ph.D. at the University of Sheffield (2021) with a thesis on the mobilization processes of the British far-right (2009–2019) and holds a double Master’s Degree in Public Policy and Human Development from Maastricht University and the United Nations University, as well as a Bachelor’s in Business Administration from Athens University of Economics and Business. Current affiliations: Institute of Political Science, Bundeswehr University Munich; Centre for Analysis of the Radical Right (CARR) Fellow Education: Ph.D. (Sheffield), MSc (Maastricht/UNU), BSc (Athens UoE) His research interests span Social Movement Theory , Political Extremism and Radicalization , Social Media Analytics , and Far-Right Politics . His work includes protest event analysis, automated event extraction tools for far-right studies, and analysis of violent conspiratorial narratives in extreme right manifestos. Recent projects include transnational radical-right mobilization in the UK, the Right-Wing Terrorism and Violence (RTV) Dataset, and the European Violent Right-Wing Extremism Monitoring Project, where he serves as a country expert for Greece, Cyprus, and the UK. His publications address topics such as the 2009–2019 far-right mobilizations in Britain, the Battle of Cable Street's legacy, and methodologies for countering radicalization. He is fluent in Greek and English.

Ren, Leiمشاهده پروفایل

استاد

• Image-guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Medical Imaging Physics
• +۶ مورد دیگر

Lei Ren, PhD is a Professor and Associate Chief of Physics Research in the Department of Radiation Oncology at the University of Maryland School of Medicine. Previously, he held faculty positions at Duke University Medical Center from 2011-2021, progressing from Assistant Professor to Professor and Director of Physics Research. A clinical medical physicist certified by the American Board of Radiology (ABR), Dr. Ren leads a research program focused on advancing precision radiation therapy through innovative imaging and AI technologies. Dr. Ren's research centers on image-guided radiation therapy (IGRT) and AI applications in radiotherapy. His seminal contributions include developing digital tomosynthesis (DTS) for fast target localization, creating the limited-angle intrafraction verification (LIVE) system, and pioneering cone-beam CT (CBCT) scatter correction methods. Current work focuses on deep learning for deformable image registration, virtual MRI/CBCT generation, XMAN phantom development, and proton dose verification using prompt gamma imaging. His research integrates biomechanical modeling with AI to improve soft tissue contrast and motion management. His 15 most recent publications (2023-2025) reveal a strong trend toward clinical translation of AI in radiation oncology, with emphasis on interpretable AI for clinical decision making, radiomics for cancer prognostics, and novel imaging techniques for proton therapy verification. Approximately 70% of recent work involves deep learning applications, while 30% focuses on advanced imaging physics for motion management and dose verification. Award highlights include: Fellow of the American Association of Physicists in Medicine (AAPM), 2020 Thomas Gorrie Clinical Leadership Impact Award, Duke University, 2019 Top 5 team in AAPM Grand Challenge for 4D-CBCT reconstruction, 2018 Multiple Excellence in Research/Mentorship awards from Duke Medical Physics Program Distinguished Graduate Award from Tsinghua University, 2003 Dr. Ren actively mentors postdoctoral fellows and has secured substantial NIH funding as Principal Investigator for projects including 3D prompt gamma imaging (R01CA279013), XMAN phantoms (R01EB032680), and hybrid virtual-MRI/CBCT systems (R01EB028324). His lab collaborates with industry partners including Varian Medical Systems and ScandiDos AB on clinical implementation of imaging technologies. He serves on NIH study sections and as Deputy Editor for Medical Physics journal. His research group operates within the Radiation Oncology Department's imaging physics division, developing clinical tools for liver SBRT, proton therapy verification, and AI-driven treatment planning. Current projects include FLASH proton therapy dosimetry and virtual clinical trials using XMAN phantoms.

• Geophysics
• Machine Learning
• Joint Inversion
• +۷ مورد دیگر

Jiajia Sun is an Associate Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston. Her research focuses on advancing subsurface imaging, uncertainty quantification, and mineral exploration through interdisciplinary approaches combining geophysics, machine learning, and computer vision. Education : PhD in Geophysics (2015, Colorado School of Mines); BS in Geophysics (2008, China University of Geosciences, Wuhan). Research Interests : Jiajia specializes in deep learning for geophysical inversion, multi-physics data integration, and probabilistic geological modeling. Her work leverages computational resources like GPUs and clusters to solve inverse problems and tackle magnetic remanence challenges. Recent Publications : Her research includes applying Bayesian frameworks, deep generative models, and joint inversion algorithms to airborne geophysics for critical mineral mapping and hydrogen reservoir detection. She emphasizes open-source tools like SimPEG for reproducibility. Awards : J. Clarence Karcher Award (SEG) Advising & Collaborations : She mentors PhD students in geophysics and collaborates with institutions like Amazon’s Generative AI Innovation Center, Stanford University, and University College Dublin. Her team also tests drones and magnetometers at the UH Coastal Center.