جست‌وجوی استاد و پژوهشگر

• Stochastic Optimization
• Risk-Averse Decision-Making
• Mathematical Programming
• +۷ مورد دیگر

Darinka Dentcheva is a Professor in the Department of Mathematical Sciences at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She holds a PhD in Mathematics from Humboldt University (2006) and an earlier PhD in Mathematics (1989) and MS in Mathematics and Computer Science (1981), also from Humboldt University. Her research focuses on stochastic optimization, risk-averse decision-making, and their applications in machine learning, finance, healthcare, and energy systems. Dr. Dentcheva’s work emphasizes developing mathematical frameworks for managing risk under uncertainty, including stochastic dominance constraints and risk-averse control of Markov systems. She has authored influential books such as *Risk-Averse Optimization and Control: Theory and Methods* (2024) and co-edited volumes on stochastic optimization theory. Her research has been supported by grants from the National Science Foundation (NSF), Office of Naval Research, and Air Force Office of Scientific Research, totaling over $3 million in funding. Her honors include the Davis Memorial Research Award (2007), recognition from the Stevens Board of Trustees for research excellence, and awards from the Bulgarian Ministry of Education. She has held leadership roles, including Chair of the Department of Mathematical Sciences (2018–2021) and co-director of the CRAFT Center (Center for Research toward Advancing Financial Technologies). Her teaching spans courses in nonlinear optimization, stochastic control, and data science. Dr. Dentcheva’s contributions to stochastic optimization theory and applications have been published in over 100 peer-reviewed articles and book chapters. Her recent work explores risk-averse machine learning models, dynamic resource allocation, and bias reduction in sample-based optimization. She actively collaborates with institutions like Rutgers University and serves on editorial boards for journals such as *SIAM Review* and *ESAIM: Control, Optimisation and Calculus of Variations*.

• Quantum Computing
• High-Performance Computing (HPC)
• Optimization Algorithms
• +۳ مورد دیگر

Katarzyna Rycerz is an academic researcher specializing in quantum computing, high-performance computing (HPC), and optimization algorithms. Her work spans hybrid quantum-classical systems, complex network analysis, and multiscale simulations. Collaborating with institutions such as AGH University of Science and Technology (inferred via co-authors like Marian Bubak), her research focuses on advancing quantum algorithms for optimization problems, quantum annealing applications, and software frameworks for distributed computing environments. Her notable contributions include developing libraries like QHyper for hybrid quantum-classical optimization, analyzing quantum walk-based image segmentation, and exploring the application of quantum computing to classical problems like the Traveling Salesman Problem. She has also contributed to foundational studies in quantum game theory and functional programming paradigms in HPC. Rycerz's publications reflect a sustained focus on interdisciplinary research, bridging quantum mechanics, computer science, and applied mathematics. Her work often addresses practical challenges in computational efficiency, algorithm design, and scalable simulation frameworks for complex systems.

Christoforos Kachrisمشاهده پروفایل

استادیار

• Hardware Accelerators
• FPGAs
• Edge AI
• +۵ مورد دیگر

Christoforos Kachris is an Assistant Professor at the Department of Electrical and Electronics Engineering, University of West Attica since 2023. Previously, he co-founded and led InAccel, a startup pioneering FPGA deployment in the cloud, securing €500k in funding and winning awards like the Venture Impact Award (2021) and DeepBlue Cup (2019). He led EU projects such as VINEYARD (€6.3M, 2016–2018), the first to deploy FPGAs in cloud data centers. His research spans reconfigurable computing, hardware accelerators, cloud computing, and optical interconnects, with over 90 publications and 2,700+ citations. Education: Ph.D. (2007, Delft University of Technology), M.Sc. (2003, Technical University of Crete), and Diploma (2001, Technical University of Crete). His Ph.D. focused on FPGA-based network processing, with internship at Xilinx Research Labs, resulting in a US patent on configurable transactional memories. Research interests include machine learning on embedded systems, FPGAs, edge AI, reconfigurable computing, and optical interconnects. He has advised numerous students, many now in tech roles at Cisco, IBM, Ericsson, and startups. Notable contributions include a highly cited survey on optical interconnects in data centers and leadership in projects like VINEYARD and CloudAccel (Principal Investigator). Awards include Venture Impact Award and recognition in international hardware competitions.

Thanh Trung Nguyenمشاهده پروفایل

دانشیار

• Inverse Problems
• Numerical Analysis
• Optimization
• +۶ مورد دیگر

Thanh Trung Nguyen is an Associate Professor and Graduate Program Coordinator in the Department of Mathematics at Rowan University's College of Science & Mathematics. He holds a PhD in Engineering Mathematics from Vrije Universiteit Brussel and completed postdoctoral research at the Austrian Academy of Sciences, UNC Charlotte, and Iowa State University. Dr. Nguyen's research focuses on inverse problems for partial differential equations with applications in subsurface detection, through-wall imaging, water quality modeling, and smart farming. His work combines mathematical theory with computational methods to solve practical engineering challenges, including infrared thermography for buried object detection, electromagnetic induction for subsurface imaging, and AI-driven agricultural monitoring. Dr. Nguyen has secured research funding from NSF, US Army Corps of Engineers, and NJ Department of Agriculture, and serves as a member of the Society for Industrial and Applied Mathematics. Nguyen's publication record demonstrates consistent contributions to inverse problem methodologies applied to environmental monitoring, nondestructive testing, and advanced material analysis. His current projects include developing globally convergent methods for coefficient identification and applying artificial intelligence to agricultural challenges.

• Biomedical Signal Processing
• Digital Biomarkers
• Medical Sensor Systems
• +۳ مورد دیگر

Prof. Dr. Reto Wildhaber is a Professor for Digital Biomarkers and Signal Processing at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), School of Life Sciences. He holds a dual doctorate (Dr. sc. ETH and Dr. med.) and serves as a Senior Researcher at the Signal and Information Processing Laboratory (ISI) at ETH Zürich. His academic roles include lecturing at the Institute for Medical Engineering and Medical Informatics, focusing on biomedical signal processing and medical device development. Education: PhD in Signal and Information Processing (Dr. sc. ETH), ETH Zürich (2013), awarded ETH Medal 2019 Medical Doctorate (Dr. med.), University of Zürich (2011) Electrical Engineering Degree from Fachhochschule Rapperswil (2002) Research Interests: Model-based signal processing, digital biomarkers, medical sensor systems, and clinical trials for medical devices. His work emphasizes ECG analysis, cochlear implantation, coronary stenosis assessment, and wearable medical technologies. Publications Trends: Focus on real-time signal analysis in cardiology and electrophysiology, with contributions to ECG-based diagnostics, cochlear implant optimization, and medical device validation. His work bridges algorithm development and clinical applications, emphasizing translational research. Awards: ETH Medal 2019 (best thesis award, ETH Zürich) and Zellweger Preis (awarded for thesis at Fachhochschule Rapperswil). Projects: Co-developed open-source tools like lmlib.ch (model-based signal processing library) and CLabUZH (cardiopulmonary simulation software for education). Labs/Teams: Active in the Institute for Medical Engineering and Medical Informatics (FHNW) and the Signal and Information Processing Laboratory (ETH Zürich).

• Inverse Problems
• Computational Algorithms
• Medical Imaging
• +۳ مورد دیگر

Rosemary Renaut is a Professor and Associate Director for Faculty Development at the School of Mathematical and Statistical Sciences, Arizona State University. Her research focuses on computational algorithms for solving inverse problems in medical and geophysical applications, emphasizing regularization techniques and numerical linear algebra. She holds a Ph.D. in Applied Mathematics from the University of Cambridge (1985). Education: Ph.D. in Applied Mathematics, University of Cambridge, UK (1985) Research Interests: Dr. Renaut specializes in developing algorithms for solving ill-posed inverse problems, particularly in medical imaging (e.g., PET, MRI) and geophysics (e.g., gravity/magnetic data inversion). Her work addresses challenges like data noise, model ill-posedness, and parameter regularization. She has contributed to methods like total variation regularization, χ²-based parameter estimation, and iterative solvers (e.g., LSQR). Articles Trends: Recent publications (2020–2025) emphasize large-scale inversion techniques, sparsity-driven reconstruction, and efficient regularization parameter estimation. Key applications include geophysical exploration (e.g., iron oxide deposits, kimberlites) and medical imaging (e.g., EEG source localization). Her work often combines numerical methods with domain-specific challenges, such as joint inversion of multimodal geophysical data. Awards: SIAM Fellow (2022) Technische Universitaet Muenchen Ambassador (2013) J.T. Knight Prize in Mathematics (1982) Grants & Service: Dr. Renaut has led numerous grants (e.g., NSF, DOD) on inverse problems and computational methods. She serves on editorial boards (BIT, SIAM News) and has held roles like Program Director at the NSF. Her service also includes mentoring junior faculty and advancing computational biosciences education. Labs/Teams: Collaborates with interdisciplinary teams in geophysics (e.g., gravity/magnetic data inversion) and biomedical imaging (e.g., PET parametric imaging). Active in conferences like International Conference on Spectral and High Order Methods.

Sam Baughمشاهده پروفایل

استادیار

• Spatial Statistics
• Climate Science
• Environmental Statistics
• +۳ مورد دیگر

Sam Baugh is an Assistant Professor of Statistics at Pennsylvania State University's Department of Statistics since Fall 2023. He holds a PhD from UCLA (2022) and MS/BS degrees from the University of Chicago. His research focuses on developing statistical methods for climate change impacts, spatio-temporal modeling, Bayesian hierarchical approaches, and psychometrics, with collaborations in medicine/public health. Education: Ph.D. in Statistics, UCLA (2022) MS in Statistics, University of Chicago (Advisor: Michael Stein) BS in Mathematics, University of Chicago Research interests emphasize climate science applications, including ocean/atmosphere changes and extreme event probabilities. Methodologically, he specializes in: Bayesian hierarchical models Spatio-temporal statistics Computational approximations Psychometric modeling Recent work spans climate attribution studies, medical AI applications, and latent variable modeling. His publications demonstrate cross-disciplinary impact across climate science, surgery outcomes, and educational measurement. Awards include the NSF Postdoctoral Fellowship (2022-2023) and UCLA's Outstanding Ph.D. Student Award (2022). Collaborates with institutions like Lawrence Berkeley National Lab and medical teams in plastic surgery research. Active in both environmental and health-related statistical method development.

• Artificial Intelligence
• Sensor Technologies
• Drug Delivery Systems
• +۴ مورد دیگر

Professor Amit Kumar-Mishra is affiliated with Halmstad University's Department of Artificial Intelligence and Sensor Technologies. His research spans interdisciplinary areas including AI-driven solutions for precision agriculture, drug delivery systems, cancer biology, and molecular imaging. He has authored over 30 publications in 2024-2025 alone, focusing on topics like pH-responsive nanocarriers for cancer therapy, circular RNA roles in cytokine signaling, and weed management strategies in mustard crops. Notable research contributions include development of deep neural network architectures for sensor technologies, investigation of salt stress adaptations in eggplant, and exploration of proteases for cancer diagnostics. His work integrates machine learning with agricultural challenges, such as real-time weed detection systems and hydroponics automation. Recent studies also explore novel biomarkers for ovarian cancer detection using circulating DNA analysis. Publications demonstrate a strong focus on translational research with applications in both clinical oncology and agricultural productivity. His research bridges computational methods with biological systems, emphasizing cross-disciplinary innovation in healthcare and food security domains.

Boxiang Dongمشاهده پروفایل

دانشیار

• Cybersecurity
• Big Data Analytics
• Deep Learning
• +۴ مورد دیگر

Boxiang Dong is an Associate Professor in the Department of Computer Science at Montclair State University. He holds a Ph.D. in Computer Science from Stevens Institute of Technology (2016) and a Bachelor's degree from Dalian University of Technology (2011). His research focuses on the intersection of cybersecurity and big data analytics, including deep learning applications in intrusion detection, causal inference in environmental change, and privacy-preserving data computations. Education: Ph.D., Stevens Institute of Technology (2016) Bachelor of Science, Dalian University of Technology (2011) Research interests emphasize cybersecurity (e.g., intrusion detection, privacy-preserving techniques) and big data analytics (e.g., anomaly detection, causal inference). Notable contributions include secure data outsourcing frameworks, verified deep learning systems, and patent applications on intrusion detection. Awards include Best Paper Awards at IRI 2017 and KDD Workshop 2019, and the NEC Excellent Invention Award 2017. Recent projects include NSF-funded REU Site grants for cybersecurity education. His work spans 47+ publications across journals like IEEE Transactions and conferences like Big Data, ECML-PKDD, and ICDM.

Dong Ryeol Leeمشاهده پروفایل

استادیار

• Machine Learning
• Large-Scale Data Analysis
• Computational Geometry
• +۳ مورد دیگر

Dr. Dong Ryeol Lee is an Assistant Professor in the Data Science Department at Saint Peter's University. His research focuses on large-scale machine learning, geometric problems in natural science, web, and IoT applications. Previously, he worked as a research scientist on web search engines, fleet prognostics, and revenue forecasting models. He holds a Ph.D. in Computer Science from Georgia Institute of Technology (2012), M.S. in Mathematics (2011) from the same institution, and dual B.S. degrees in Mathematical Sciences and Computer Science from Carnegie Mellon University (2005). Education : B.S. Mathematical Sciences & Computer Science, Carnegie Mellon University (2005) M.S. Mathematics, Georgia Institute of Technology (2011) Ph.D. Computer Science, Georgia Institute of Technology (2012) Dr. Lee’s research bridges computational methods and real-world applications, emphasizing efficient algorithms for high-dimensional data and metric space problems. His work spans celestial body simulations, web search optimization, and predictive maintenance in IoT systems. Recent publications explore dual-tree algorithms, kernel summation frameworks, and multitree methods in astrostatistics. His articles reflect a focus on algorithm efficiency, statistical analysis, and cross-disciplinary applications. Notable trends include scalable machine learning infrastructure and geometric problem-solving in astronomy and computer vision. No scientific awards have been listed. He currently teaches DS-600 (Data Mining) and DS-630 (Machine Learning). No grants or advising records are mentioned. His academic contributions emphasize algorithmic innovation and industrial applicability without explicit lab affiliations detailed in the source text.

Erwan LE PENNECمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Statistics
• +۵ مورد دیگر

Erwan LE PENNEC is a Professor in the Department of Applied Mathematics at École Polytechnique, France, where he has been since 2013. He is affiliated with the Center for Applied Mathematics (CMAP) and the SIMPAS team, focusing on data science, artificial intelligence, statistics, and signal processing. His research spans health applications (notably women’s health) and reinforcement learning, often collaborating with interdisciplinary teams. He leads programs like the MScT Data Science and AI for Business, and contributes to executive education initiatives at Polytechnique. Education: Habilitation à Diriger des Recherches (HdR), Université Paris Sud, 2013 PhD in Applied Mathematics, École Polytechnique, 2002 Master in Mathematics and Artificial Intelligence, ENS Cachan, 1997 Research interests emphasize health and AI-driven solutions, with contributions to medical diagnostics (e.g., neonatal acidemia detection via DeepCTG®), image processing, and robust reinforcement learning. His work bridges theoretical foundations and practical applications, including collaborations with industry partners like Air Liquide and Sonio. Teaching includes courses on data science, statistics, and Python/R tools. He has advised over 20 PhD/PostDoc students, focusing on topics like reinforcement learning, medical imaging, and statistical modeling.

Frédéric PICHONمشاهده پروفایل

استاد

• Uncertain Reasoning
• Information Fusion
• Machine Learning
• +۳ مورد دیگر

Frédéric Pichon is a Full Professor at Artois University's Laboratory of Computer Engineering and Automation of Artois (LGI2A), part of the Faculty of Applied Sciences in Béthune, France. He holds a Ph.D. in Information and Communication Systems (2009) and an H.D.R. (2018) from Artois University. His research focuses on uncertain reasoning, information fusion (particularly belief functions theory), and machine learning applications in decision-making under uncertainty. Education: B.Eng. (Hons) in Computer Networks and Distributed Systems, Edinburgh Napier University, 2003 M.Sc. in Information Technology, Aalborg University Esbjerg, 2005 Ph.D., University of Technology of Compiègne, 2009 H.D.R., Artois University, 2018 Research Interests: Belief functions theory and applications Optimization under severe uncertainty Machine learning with uncertain data Information fusion for decision support Articles Trends: Recent work emphasizes belief function-based solutions for optimization problems (e.g., shortest path, vehicle routing) and deep learning integration with uncertainty quantification. Early-career contributions include foundational work on Dempster-Shafer theory and conflict analysis. Awards: Notable recognitions include the IJAR Early Career Researcher Award (2022), multiple best paper awards at LFA and BELIEF conferences, and distinctions for student research supervision. Advising & Grants: Supervised 9 PhD students (3 in progress). Active in organizing international conferences (BELIEF, LFA) and editorial roles (Area Editor for International Journal of Approximate Reasoning). Research spans theoretical advancements and applied projects in logistics, image processing, and simulation. Labs/Teams: Core member of LGI2A, collaborating with institutions like Heudiasyc and IBISC labs on uncertainty modeling and fusion systems.

Longxiu Huangمشاهده پروفایل

استادیار

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +۴ مورد دیگر

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Elaine Crooksمشاهده پروفایل

استاد

• Nonlinear partial differential equations
• Singular limits of elliptic and parabolic systems
• Reaction-diffusion-convection systems
• +۳ مورد دیگر

Professor Elaine Crooks is a faculty member at Swansea University's School of Mathematics and Computer Science within the Faculty of Science and Engineering. Based at the Computational Foundry on Bay Campus, her research focuses on nonlinear partial differential equations with applications spanning materials science, ecology, and image processing. She specializes in singular limits of PDEs, reaction-diffusion systems, travelling waves, and geometric methods for feature detection in images. Her research investigates fundamental mathematical phenomena including: Asymptotic behavior of reaction-diffusion-convection systems Front propagation in anisotropic media Spatial segregation in population dynamics Compensated convex transforms for geometric singularity detection Phase transitions in liquid crystals Ecological invasion modeling Analysis of her recent publications reveals strong emphasis on: nonlinear diffusion phenomena, theoretical foundations of convex transforms, travelling wave solutions, and applications of PDEs to biological systems. Her work consistently bridges theoretical mathematics with practical applications in materials science and computational imaging. Professor Crooks actively supervises PhD students in areas including reaction-diffusion systems, mathematical neuroscience, and wave propagation analysis. She teaches foundational mathematics courses including Introduction to Analysis 1 & 2, offered in both English and Welsh.

• Hydrology
• Civil Engineering
• Sustainability
• +۳ مورد دیگر

Auroop Ratan Ganguly is a Professor of Civil and Environmental Engineering at Northeastern University in Boston, MA, serving as Director of the Sustainability and Data Sciences Laboratory (SDS Lab). He holds courtesy appointments across multiple university departments and colleges, emphasizing his interdisciplinary focus. As a hydrologist and civil engineer, his work bridges sustainability challenges with data-driven approaches. His research integrates: Hydrology and water resource systems Climate extremes and resilience modeling Sustainability science and equitable development Machine learning applications in environmental domains Geospatial analytics and remote sensing Infrastructure-climate interactions This interdisciplinary nexus addresses pressing global challenges through computational innovation. Analysis of 159 publications shows recurring themes: climate extremes modeling (floods, heatwaves, precipitation), AI-enhanced environmental monitoring (satellite imagery, crop mapping), sustainability transitions, and complex system resilience. Recent work demonstrates increased focus on deep learning architectures, transboundary water systems, and urban-scale climate impacts. He leads the Sustainability and Data Sciences Laboratory, which pioneers interdisciplinary research at the nexus of climate science, civil engineering, and artificial intelligence. The lab's work supports decision-making for environmental sustainability and infrastructure resilience.