جست‌وجوی استاد و پژوهشگر

Holger Meyerمشاهده پروفایل

استاد

• Experimental High Energy Particle Physics
• Neutrino Physics
• Nuclear Physics
• +۳ مورد دیگر

Dr. Holger Meyer is a Professor of Physics at Wichita State University's Fairmount College of Liberal Arts and Sciences, within the Department of Mathematics, Statistics, and Physics. He holds a Ph.D. from Virginia Tech (2002) and specializes in experimental high-energy particle physics, with a focus on neutrino physics. His research involves collaborations with major institutions like Fermi National Accelerator Laboratory (Fermilab) and Brookhaven National Laboratory, contributing to the NOvA and DUNE long-baseline neutrino experiments. Dr. Meyer also explores space-based neutrino detection in collaboration with NASA's Marshall Space Flight Center and other universities, aiming to detect neutrinos in near-solar environments. His research interests span neutrino oscillation studies, CP violation, and astrophysical applications such as supernova neutrino detection. He actively participates in detector development for DUNE, including liquid argon time projection chambers (LArTPCs) and software computing frameworks. Dr. Meyer has contributed to cross-section measurements in neutrino interactions and pioneered methods for neutrino interaction vertex reconstruction using machine learning. Publications highlight his work on DUNE's supernova pointing capabilities, Fermilab accelerator upgrades, and NOvA's oscillation parameter analyses. He is a key figure in international physics collaborations, advancing neutrino physics through both terrestrial and space-based experimental initiatives.

Scott Burdickمشاهده پروفایل

استادیار

• Seismology
• Geophysics
• Bayesian Inference
• +۵ مورد دیگر

Scott Burdick is an Assistant Professor in the Department of Environmental Science and Geology at Wayne State University, affiliated with the College of Liberal Arts and Sciences. His research focuses on seismic imaging of the Earth's crust and mantle, Bayesian inference, and geophysical inverse problems. He holds a B.S. from Purdue University (2006) and a Ph.D. from MIT (2014). Dr. Burdick’s work emphasizes quantifying uncertainties in geophysical models to refine interpretations of tectonic and mantle dynamics. Recent projects include probabilistic tomography of slab structures beneath Alaska and mantle transition zone heterogeneity. He also applies inverse theory to mass spectrometry and glacial aquifer imaging, showcasing interdisciplinary collaboration. He teaches courses such as Geology: The Science of the Earth and Environmental and Applied Geophysics. His research has been supported by grants including the NSF CAREER award for probabilistic mantle structure analysis. Notable contributions include developing the CAP22 P-wave tomographic model for North America and advancing software like Tripoli for mass spectrometry accuracy. His work bridges field observations, laboratory experiments, and computational modeling to address complex Earth science questions.

Syed Imtiazمشاهده پروفایل

استاد

• Process Control
• Process Monitoring
• Modelling and Simulation
• +۵ مورد دیگر

Syed Imtiaz is a Professor and Department Head of Process Engineering at the Faculty of Engineering, Memorial University of Newfoundland . Holding a PhD in Process Control from the University of Alberta and professional engineering credentials (P.Eng.), he specializes in Process System Safety, Risk Simulation , and Advanced Monitoring Tools for industrial applications. B.Sc., M.Sc. (BUET), M.Sc. (University of Calgary), PhD (University of Alberta) P.Eng. certification Joined Memorial University in March 2010 Research focuses on multivariate statistical tools and Bayesian belief networks for process fault diagnosis , particularly in offshore oil & gas and mineral processing industries. His work includes modeling Hydrogen Desulfurization processes and automating Managed Pressure Drilling (MPD) systems. Key article trends include: Advancing machine learning for fault detection in harsh industrial environments Developing Bayesian networks for risk assessment in offshore operations Optimizing reservoir production using entropy generation minimization Improving dynamic positioning for autonomous marine systems Scientific contributions are recognized through university awards and >30 peer-reviewed publications.

Keigo Hirakawaمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Digital Cameras
• +۳ مورد دیگر

Keigo Hirakawa is a full-time Professor and Bernhard Schmidt Chair in Engineering Leadership at the University of Dayton's School of Engineering, Department of Electrical and Computer Engineering. He holds a joint appointment with the Electro-Optics and Photonics program. His academic journey includes a Ph.D. from Cornell University (2005), an M.S. from Cornell (2003), a B.S. from Princeton University (2000), and a Master of Music in Jazz Studies/Piano Performance from the New England Conservatory (2006). His research focuses on signal and image processing, statistical modeling, color image processing, and digital camera pipeline optimization. He is actively involved in professional activities such as serving as an Associate Editor for SPIE's Journal of Electronic Imaging and IEEE Transactions on Image Processing , and participates in the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee. Recent work emphasizes event-based sensing, noise reduction in imaging systems, and advanced spectral imaging techniques. His research has led to innovations like Fourier multispectral imaging and Kalman filter-based particle velocimetry. He teaches courses such as ECE 203, 215, 340, 445, and graduate-level ECE 503 and 595. His laboratories focus on developing next-generation imaging systems and sensor technologies, with applications in aerospace, cybersecurity, and biomedical imaging. Collaborations span industry and academia to address challenges in real-time data processing and high-fidelity image reconstruction.

John Sheppardمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۷ مورد دیگر

Dr. John Sheppard is a Professor of Computer Science at Montana State University (MSU), affiliated with the College of Engineering's School of Computing. He leads the Numerical Intelligent Systems Laboratory (NISL), focusing on AI, machine learning, and probabilistic models. His research spans Bayesian networks, evolutionary algorithms, and applications in precision agriculture and health management. He holds a PhD from Johns Hopkins University (1997) and has been recognized with awards like the Provost’s Award for Graduate Research (2022) and IEEE Fellow (2006). Education: Ph.D. (1997), M.S. (1990) from Johns Hopkins University; B.S. (1983) from Southern Methodist University. Active in IEEE standards committees and serves on boards like the Bozeman Symphony and Lutheran Campus Ministries. Research emphasizes probabilistic graphical models, explainable AI, and system-level problems like equipment health management. Recent articles address neural networks for uncertainty reduction, hyperspectral imaging, and factored evolutionary algorithms. His work bridges theory and practice, with funded projects in agriculture, energy, and diagnostics. Awards highlight contributions to research mentoring and academic leadership. Advises over 30 students, including current PhD candidates like Muhammad Arju and past graduates such as Dr. Logan Perreault. Leads grants on precision agriculture and AI ethics. NISL collaborates on tools for diagnostic reasoning and PHM standards, advancing both academic and industrial applications.

Marek Reformatمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Intelligence
• Fuzzy Systems
• +۷ مورد دیگر

Dr. Marek Reformat is a Professor in the Department of Electrical and Computer Engineering at the University of Alberta, Faculty of Engineering. His research focuses on developing human-like intelligent systems through the integration of computational intelligence (fuzzy systems, neural computing, evolutionary algorithms), uncertainty theories (probability, evidence theory), and semantic web technologies (RDF, ontologies). He also explores mathematical foundations like category theory and type theory for knowledge representation. His research interests include knowledge extraction via granular computing, semantic-based decision support systems, and applications in software quality analysis. He teaches courses such as ECE 220 (Programming for Electrical Engineering) and ECE 447 (Data Analytics and Machine Learning for Engineers). Dr. Reformat actively contributes to conferences like AAAI, NeurIPS, and IEEE WCCI. His work spans topics like fuzzy ontology integration, neural networks for medical imaging, and uncertainty modeling in decision-making systems. He currently advises eight graduate students and collaborates with industry partners on projects involving AI-driven solutions for complex systems.

Nidhal C. Bouaynayaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +۵ مورد دیگر

Nidhal C. Bouaynaya is a Professor at Rowan University's Henry M. Rowan College of Engineering, holding dual roles as Associate Vice President for Artificial Intelligence and Director of MAVRC. She leads the Electrical & Computer Engineering department and focuses on AI-driven research in health informatics and engineering systems. Education: Ph.D. & M.S. in Electrical & Computer Engineering (University of Illinois at Chicago), M.S. in Pure Mathematics (UIUC), B.S. in Electrical Engineering (ENSEA, France). Research: Specializes in AI, Machine Learning, Big Data Analytics, and Medical Imaging. Her work includes brain tumor segmentation (BRATS 2016 winner), healthcare AI applications, and aviation safety systems. She co-founded MRIMATH LLC to commercialize oncology AI solutions. Grants & Funding: Over $10M+ secured from NSF, NIH, FAA, USDA, and industry partners. Active projects include aviation safety data analysis, AI-based water resource modeling, and tumor surveillance systems. Awards: Rowan Research Achievement Award (2017), Best Paper Awards (IEEE 2019, GENSIPS 2013), and recognition for entrepreneurial ventures in AI healthcare. Students: Mentors 18+ doctoral, master's, and undergraduate researchers in AI, biomedical systems, and signal processing. Labs & Teams: Leads MAVRC (Multidisciplinary Applied Vision and Reality Computing lab) and collaborates with industry partners like Lockheed Martin and NASA on applied AI solutions.

Ehsan Shareghi Nojehdehمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Deep Generative Models
• +۵ مورد دیگر

Ehsan Shareghi Nojehdeh is an Assistant Professor in the Department of Data Science and Artificial Intelligence at Monash University and an affiliated lecturer at the University of Cambridge. He leads a research team focused on predictive models for language (text and speech), with a background in postdoctoral work at Cambridge and prior roles at UCL. His research interests include probing and augmenting Large Language Models (LLMs), reasoning in legal contexts, safety of multimodal models, speech/text translation, and self-supervised learning. Education: PhD in Computer Science (Scalable Non-Markovian Sequential Modelling) from Monash University (2017). Professional roles include Deputy Course Director for the Master of AI program at Monash, service on academic committees (ECA Committee, 2022–2023), and senior reviewing roles at major conferences (ACL2025, EMNLP2023). He teaches units like FIT5217 Natural Language Processing and FIT5212 Data Analysis for Semi-structured Data. Key projects include a Paul Ramsay Foundation-funded study on media narratives around disadvantage in Australia (2022–2023). His work addresses UN SDGs through advancing education and AI ethics. Research collaborations span global institutions, with a focus on knowledge-intensive tasks like biomedical domain applications, graph-to-text generation, and generative model disentanglement. Publications emphasize safety, multimodal capabilities, and logical reasoning with LLMs, with recent work appearing in ACL, EMNLP, and NAACL venues. His team investigates LLM shortcomings, including alignment with human judgment, speech-specific vulnerabilities, and tool-based reasoning strategies.

Francesco D'Angeloمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.

Houman Owhadiمشاهده پروفایل

استاد

• Numerical Approximation
• Machine Learning
• Uncertainty Quantification
• +۳ مورد دیگر

Houman Owhadi is the IBM Professor of Applied and Computational Mathematics and Control and Dynamical Systems at the California Institute of Technology. His research explores interplays between numerical approximation, statistical inference, and learning from a game-theoretic perspective, with applications in numerical homogenization, operator-adapted wavelets, fast solvers, Gaussian process regression, and uncertainty quantification. His current research focuses on computational information games, kernel flows, and the development of universal scalable solvers. He has made fundamental contributions to uncertainty quantification through adversarial approaches and the development of optimal statistical estimators. Professor Owhadi has received numerous awards including the Vannevar Bush Faculty Fellowship (2024), Germund Dahlquist Prize (2019), and SIAM Fellowship (2022). His research has been supported by AFOSR, DARPA, DOE, NASA, NSF, and ONR.

• Robotics
• Navigation Systems
• Sensor Fusion
• +۵ مورد دیگر

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

Sander Stuijkمشاهده پروفایل

دانشیار

• Embedded Systems
• Signal Processing
• Multi-Core Architectures
• +۳ مورد دیگر

Sander Stuijk is an associate professor at the Department of Electrical Engineering of Eindhoven University of Technology, chairing the Electronic Systems (ES) group. His research focuses on design methodologies for embedded signal processing applications in high-tech systems like industrial manufacturing, automotive, and healthcare. He develops high-level compilation strategies for heterogeneous multi-core platforms, with interests in efficient code generation and resource allocation. Prof. Stuijk holds leadership roles including chairing the ES group, serving on the board of 4TU.NIRICT (a Dutch ICT research consortium), and coordinating the Embedded Systems master program. He contributes to national ICT initiatives through ICT Next Generation, a network for mid-career academics. He also chaired the Department Council (2016-2018) and participates in TPC activities. His research spans streaming applications, real-time systems, and multi-processor architectures. Notable projects include the PROMES initiative (programming embedded multi-media systems) and the MNEMEE project (automated MPSoC design). His work emphasizes predictable and energy-efficient computing, with applications in healthcare monitoring (e.g., remote PPG, thermal imaging) and reconfigurable systems. Prof. Stuijk has advised numerous PhD/Master students and led projects like FORSEE, VSM, and SenSafety. His publications include over 70 journal/conference papers, covering topics like neuromorphic computing, GPU optimization, and medical signal processing. He actively engages in academic service, including organizing SCOPES workshops and serving on editorial boards.

Vasily Demyanovمشاهده پروفایل

استاد

• Spatial Statistics
• Machine Learning
• Reservoir Modeling
• +۵ مورد دیگر

Affiliations and Roles Professor Vasily Demyanov holds the position of Professor at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, where he leads the GeoDataScience group at the Institute for GeoEnergy Engineering. His academic career includes roles as a Research Fellow and Lecturer at Heriot-Watt since 2003, preceded by a postdoc at the University of St Andrews (2000-2002) and a PhD in Physics and Mathematics from the Russian Academy of Sciences (1998). Education Bachelor of Physics, Moscow State University (1994) PhD in Physics and Mathematics, Nuclear Safety Institute, Russian Academy of Sciences (1998) Research Interests Demyanov's research focuses on spatial statistics, machine learning, and uncertainty quantification in reservoir modeling. His work integrates AI for subsurface data analysis, Bayesian inference, and geostatistical methods to enhance reservoir management and environmental mapping. Key areas include: AI-driven reservoir workflows for pattern discovery Stochastic optimization and geostatistical modeling History matching and uncertainty quantification Machine learning for petroleum and environmental systems Recent Research Trends Recent publications emphasize AI applications (e.g., GANs, graph neural networks) in reservoir modeling, methane emissions detection via satellite imagery, and subsurface fracture analysis. His work bridges geology, data science, and engineering, with contributions to both theoretical and applied geosciences. Collaborations Key collaborators include institutions like Stanford University, University of Lausanne, and Tomsk Polytechnic University. Editorial roles include Associate Editor for Computers and Geosciences (2011-2020) and guest editorships for special issues on data science in geosciences. Advising and Labs He has supervised multiple PhD students focusing on reservoir modeling and uncertainty quantification. The GeoDataScience group develops innovative data science tools for geoscience challenges.

Saeed Ghadimiمشاهده پروفایل

استادیار

• Data Analytics
• Applied Operations Research
• Energy Market
• +۳ مورد دیگر

Saeed Ghadimi is an Assistant Professor at the University of Waterloo, with affiliations in Data Analytics, Applied Operations Research, and Energy Market research groups. His work focuses on developing advanced optimization algorithms for stochastic systems, machine learning applications, and decision-making under uncertainty. He is particularly noted for contributions to bilevel programming, quantum optimal control, and robust regression techniques with missing data. Research interests include optimization theory, stochastic programming, and interdisciplinary applications in energy systems and public policy. His methodologies often address nonconvexity, nonstationarity, and high-dimensionality challenges. Recent work emphasizes projection-free algorithms, adversarial robustness in regression, and parametric cost function approximations for multistage problems. He maintains a personal webpage at https://sites.google.com/view/sghadimi .