جست‌وجوی استاد و پژوهشگر

Dimitri Van De Villeمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Functional Neuroimaging
• +۹ مورد دیگر

Dimitri Van De Ville is a Full Professor of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva (UniGE), affiliated with the School of Engineering at EPFL and the Faculty of Medicine at UniGE. He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech in Geneva and is a key figure at the CIBM Center for Biomedical Imaging. His work bridges signal processing, computational neuroscience, and clinical neuroimaging. Education: M.S. and Ph.D. in Computer Science, Ghent University, Belgium (1998, 2002) Post-doctoral Fellow, Biomedical Imaging Group, EPFL (2002–2005) His research focuses on advancing non-invasive brain imaging through methodological innovations in signal and image processing. He investigates the dynamic and network aspects of brain function using fMRI and EEG, with a special emphasis on dynamic functional connectivity, graph signal processing, and real-time neurofeedback. His work has demonstrated that EEG microstate sequences exhibit scale-free dynamics, linking fast electrophysiological events to slow hemodynamic changes. He pioneered connectivity decoding and contributed to the development of sparsity-based deconvolution methods for fMRI. The recent articles reflect a strong trend toward modeling brain function as a dynamic network process. Key themes include graph signal processing on brain connectomes, decomposition of transient brain activity, and the use of machine learning to decode brain states. His work increasingly integrates structural and functional data to understand brain organization at multiple scales. Scientific Awards: Technical Achievement Award, IEEE EMBS (2024) Fellow, EURASIP (2023) Distinguished Lecturer, IEEE Signal Processing Society (2021–2022) Fellow, IEEE (2020) Leenaards Award (2016) NARSAD Independent Investigator Award (2014) NeuroImage Editors' Choice Award (2013) Pfizer Research Award (2012) Van De Ville has secured substantial research funding through grants such as the SNSF Professorship and has advised numerous researchers. He plays a major role in the scientific community as founding chair of the EURASIP BISA SAT and former chair of the IEEE BISP TC. He has held editorial roles in top journals including IEEE Transactions on Signal Processing , SIAM Journal on Imaging Sciences , and Imaging Neuroscience . He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech, which specializes in developing advanced signal processing tools for neuroimaging. The lab is part of a broader collaborative ecosystem involving EPFL, UniGE, and the CIBM, fostering interdisciplinary research in biomedical imaging and brain science.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۱۲ مورد دیگر

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

Nicolas Verzelenمشاهده پروفایل

پژوهشگر

• Statistics
• Machine Learning
• Agroecological Data Analysis
• +۴ مورد دیگر

Nicolas Verzelen is a Senior Research Scientist at the MISTEA Laboratory within INRAE , affiliated with Université de Montpellier . His work bridges Statistics and Machine Learning , focusing on agroecological applications. Roles: Associate Editor for Annals of Statistics, Bernoulli, and Electronic Journal of Statistics; Editor-in-Chief for ESAIM: P&S Supervision: Mentoring 5 PhD/post-doc researchers; alumni include current professors at ENSAI, Centrale Supélec, and Telecom-Paris Research Interests: High-dimensional minimax theory, active learning, unsupervised learning (clustering, ranking), and applications to seed exchange networks and agroecology. His work addresses computational-statistical trade-offs in modern data science. Publication Trends: Recent outputs emphasize clustering algorithms, covariance adaptation, latent space modeling, community detection, and active learning frameworks. Collaborations span institutions like Université Paris-Saclay and Telecom-Paris. Teaching: Instructs Statistical Machine Learning (M2) at Université de Montpellier.

Mostafa Kavehمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Image Processing
• +۳ مورد دیگر

Mostafa Kaveh is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, College of Science and Engineering. His research spans statistical signal processing, wireless communications, image processing, and biomedical ultrasound imaging. He has held leadership roles including Department Head (1990-2005), Associate Dean for Research and Planning (2005-2018), and Dean of the College of Science and Engineering (2018-2022). His academic credentials include a B.S. and Ph.D. from Purdue University (1969, 1974) and an M.S. from UC Berkeley (1970). Kaveh’s research focuses on: Statistical signal processing for antenna arrays and mobile localization Wireless communication systems with multiple antennas and collaborative transmission Image restoration algorithms based on partial differential equations Medical image processing for tomography and reconstruction Signal processing applications in genomics and biotechnology Google Scholar publications highlight trends in array signal processing, direction-of-arrival estimation, wireless channel modeling, and medical imaging techniques. His work has influenced GRAPPA algorithms for MRI, space-time interference-canceling receivers, and genomic data analysis.

Tianlong Chenمشاهده پروفایل

استادیار

• Machine Learning
• Sparsity
• Robustness
• +۵ مورد دیگر

Tianlong Chen is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he established the UNITES Lab (University of North Carolina, AI Trustworthiness, Efficiency, and for Science Group). He joined UNC in Fall 2024 after completing his Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin (2023) and serving as a postdoctoral researcher at MIT (CSAIL), Harvard University (BMI), and the Broad Institute of MIT & Harvard. His research focuses on building accurate, trustworthy, and efficient machine learning systems , with emphasis on: Sparsity and model compression techniques Robustness against adversarial challenges Learning-to-optimize methodologies Graph representation learning Diffusion models and generative AI Quantum computing applications Bioengineering and healthcare AI solutions His publications demonstrate strong trends in large language model optimization , efficient multimodal systems , and trustworthy AI frameworks , with frequent appearances at top venues (NeurIPS, ICML, CVPR, ICLR). Significant scientific honors include: IBM and Adobe Ph.D. Fellowships Best Paper Award at Learning on Graphs Conference 2022 AdvML Rising Star (2023) Amazon Research Award (2024) Two UNC Accelerating AI Awards (2025) NAIRR Pilot Award (2025) He leads 19+ advisees in the UNITES Lab and has secured research funding from Cisco, Amazon, and institutional grants. Current projects explore sparsity-aware architectures, LLM alignment, and efficient multimodal learning systems.

Joseph Cavanaughمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Public Health
• +۳ مورد دیگر

Joseph Cavanaugh is a Professor in the Department of Biostatistics at the University of Iowa, specializing in biostatistical methods and their applications to public health challenges. His work integrates advanced statistical modeling with real-world epidemiological and safety research, particularly focusing on injury prevention and diagnostic delay analysis across diverse populations. His core research areas include: Biostatistics : Developing novel methodologies for model selection and time series analysis. Epidemiology : Investigating disease patterns and risk factors in population-based studies. Injury Prevention : Analyzing road safety for adolescents and older drivers through naturalistic studies. Diagnostic Delays Research : Quantifying missed opportunities in diagnosing infectious diseases like dengue and sepsis. Statistical Modeling : Advancing information criteria and regularization techniques for heterogeneous data. His recent publications (2024-2025) demonstrate a cohesive research trajectory centered on adolescent road safety interventions, diagnostic delay quantification in infectious diseases, and methodological innovations in model selection. He frequently leverages population-based cohort studies and insurance claims data to identify actionable risk factors, with strong emphasis on translating statistical findings into public health policy recommendations for injury prevention and healthcare quality improvement. Scientific Awards: No awards were documented in the provided sources. Advising and Grants: The available information does not specify any graduate students supervised or research funding secured by Professor Cavanaugh. Laboratories and Teams: No details regarding dedicated research laboratories or collaborative teams were mentioned in the source material.

Giuseppe Carlo Calafioreمشاهده پروفایل

استاد

• Convex Optimization
• Control Systems
• Randomized Algorithms
• +۶ مورد دیگر

Giuseppe Carlo Calafiore is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin , where he has been since 1998. He has held visiting positions at institutions like Stanford University , University of California at Berkeley , and Vietnam National University . His research focuses on convex optimization , control systems , and randomized algorithms , with applications in finance , robotics , and machine learning . Education : Laurea in Electrical Engineering (1993) and Ph.D in Information and System Theory (1997) from Politecnico di Torino. Research Interests : Span large-scale optimization , robust portfolio optimization , and distributed algorithms , with emphasis on sparsity-based methods and data-driven control for autonomous systems. Recent Publications include works on nonlinear model predictive control , financial network robustness , and adaptive gain control in population games. His scientific awards include the IEEE CSS George S. Axelby Outstanding Paper Award (2008) and Fellow of the IEEE (2018) . He has advised students in artificial intelligence and communications engineering programs. His projects include research on smart energy allocation and financial risk modeling .

Mikael Brixمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Computed Tomography
• X-ray Detectors
• +۳ مورد دیگر

Mikael Brix serves as a post-doctoral researcher and project manager at the Research Unit of Health Sciences and Technology within the Faculty of Medicine, University of Oulu, Finland, while concurrently working as a physicist at Oulu University Hospital's Department of Diagnostics. He leads the Medical Imaging in Diagnostics, Algorithms and Software (MIDAS) research group as vice-leader. His research centers on medical imaging physics, with primary focus on X-ray detectors, computed tomography physics, and reconstruction algorithm development. He actively bridges fundamental physical phenomena research with clinical translation through industrial partnerships and diagnostic expert collaboration, increasingly leveraging artificial intelligence to enhance imaging techniques for practical healthcare applications. Analysis of his 2024-2025 publications reveals consistent innovation in CT technology, particularly photon counting detectors, metal artifact reduction, and low-dose reconstruction algorithms. His work spans cardiology, orthopedics, and forensic science applications, with notable emphasis on cost-effectiveness analysis and real-world healthcare system integration, especially within Finnish medical infrastructure. Scientific Awards: Young Investigator Award of the Radiological Society of Finland (2023) No information regarding student advising or grant funding is provided in available sources. Dr. Brix directs the MIDAS research group at University of Oulu, which develops cutting-edge medical imaging solutions, specialized algorithms, and diagnostic software tools with strong industry-academia collaboration for clinical deployment.

Vasilis Syrgkanisمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Econometrics
• +۵ مورد دیگر

Vasilis Syrgkanis is an Assistant Professor at the Department of Management Science and Engineering , Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. He leads the Stanford Causal AI Lab and is an Associated Director of the Stanford Causal Science Center . His research spans machine learning, causal inference, econometrics, online learning, reinforcement learning, game theory/mechanism design, and algorithm design . Education PhD in Computer Science, Cornell University (advised by Eva Tardos) Diploma in EECS, National Technical University of Athens Research Focus Develops methods for causal machine learning , including courses on Applied Causal Inference and Foundations of Causal ML. Works on treatment effect estimation , instrumental variable regression , and robust policy learning under unobserved heterogeneity. Explores intersections of game theory and machine learning , particularly in auction design and strategic exploration. Scientific Contributions Proposes neural causal partial identification and adaptive instrument design frameworks. Develops doubly robust learning and minimax IV regression algorithms with theoretical guarantees. Introduces incentive-aware synthetic control and structure-agnostic causal effect estimation methods. Awards & Recognition 2023 Bodossaki Distinguished Young Scientist Award in Applied Sciences 2022 Amazon Research Award in Machine Learning Algorithms and Theory Best Paper Awards at COLT 2019, EC 2015, NeurIPS 2015, and others PhD Advisees Ravi Sojitra (MS&E, co-advised with Guido Imbens) Hui Lan (ICME) Jikai Jin (ICME) Jiyuan Tan (MS&E, co-advised with Jose Blanchet) Keertana Veeramony Chidambaram (MS&E) Contact Email: vsyrgk@stanford.edu Office: Huang Engineering Center, Room 252, Stanford, CA 94305

• Heterogeneous computing systems
• High-level synthesis (HLS)
• Hardware specialization for machine learning
• +۳ مورد دیگر

Zhiru Zhang is a Professor at Cornell University in the School of Electrical and Computer Engineering , leading research at the Computer Systems Laboratory . His work focuses on algorithms, methodologies, and design automation tools for heterogeneous computing systems. Recent publications emphasize high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Education: Ph.D. in Computer Science, UCLA B.S. in Computer Science, Peking University M.S. in Computer Science, UCLA Research Interests: Heterogeneous computing systems High-level synthesis (HLS) optimization FPGA-based hardware acceleration Sparse data format compilers Machine learning for electronic design automation Scientific Awards: IEEE Fellow Intel Outstanding Researcher Award NSF CAREER Award DARPA Young Faculty Award Multiple Best Paper Awards at ASPLOS, ISPD, FPGA, AutoML, FCCM Research Group: Mentors 12 current students including Jordan Dotzel, Jie Liu, and Grace Dinh, with 14 alumni now at institutions like AWS AI, NVIDIA, Google DeepMind, and Microsoft. His lab develops tools like UniSparse for sparse format customization, presented at OOPSLA'24 and IEEE CAL.

Olalekan Babaniyiمشاهده پروفایل

استادیار

• Inverse Problems
• Biomechanical Imaging
• Computational Mechanics
• +۳ مورد دیگر

Olalekan Babaniyi is an Assistant Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His research focuses on inverse problems with applications in biomechanical imaging, computational mechanics, and PDE-constrained optimization. He develops mathematical techniques to improve medical imaging and environmental modeling, including real-time imaging of soft tissue mechanical properties and ice flow prediction. He teaches courses such as Probability and Statistics, Boundary Value Problems, and Partial Differential Equations. His work has been highlighted in media for innovative applications in noninvasive disease diagnosis and climate science. Babaniyi runs the Inverse Problems Seminar at RIT (InvPrS) and has published extensively in journals like The Cryosphere, Journal of Nonlinear and Variational Analysis, and Physics in Medicine & Biology. His research emphasizes uncertainty quantification, image denoising in biomedical contexts, and solving inverse problems in viscoelasticity and geophysics. While no personal awards are listed, his contributions have supported student achievements, such as Quinn Kolt’s Goldwater Scholarship in 2021.

• Machine Learning
• Operations Research
• Optimization
• +۳ مورد دیگر

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

Maryam Mehri Dehnaviمشاهده پروفایل

دانشیار

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Maryam Mehri Dehnavi is an Associate Professor of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing. Her research group, ParaMathics, focuses on scalable numerical methods, high-performance libraries, and compilers for cloud and parallel computing platforms. Her research interests span cloud computing, machine learning, numerical analysis, and compiler optimization. She has been recognized with awards including the Ontario Early Researcher Award (2021) and an NSF CRII grant. She has advised numerous students, many of whom have achieved notable academic and industry roles. Teaching: Applied Parallel Computing, Parallel Computing, Cloud Computing Leadership Roles: General Chair of PPOPP 2023, Keynote Speaker at SIAM PP 2024 Labs/Teams: ParaMathics Research Group Her work bridges theoretical advances in numerical methods with practical applications in high-performance computing, emphasizing efficiency and scalability across diverse architectures.

• Computer Vision
• Deep Learning
• Remote Sensing
• +۳ مورد دیگر

Trishna Barman is a Research Associate at Ulster University's School of Computing, Engineering and Intelligent Systems, affiliated with the Computer Science Research department. Her work focuses on interdisciplinary applications of artificial intelligence and computer vision in environmental technology, industrial automation, and remote sensing. Her research spans deep learning for hyperspectral imaging, image super-resolution techniques, and biomimetic engineering. Education: No specific qualifications listed in the provided text. Her research interests include developing algorithms for industrial visual inspection (e.g., solder quality assessment), optimizing e-waste recycling through AI-driven PCB segmentation, and enhancing remote sensing data through advanced super-resolution frameworks. She also explores motion-based gesture recognition and biomimetic prosthetics. Her publications reflect a progression from early work in prosthetic design (2013) to modern applications of deep learning in imaging (2025). Key themes include sparse representation techniques, GPU-accelerated processing, and attention-driven neural networks. Awards: None explicitly mentioned in the provided text. Advising & Grants: No student advisement or grant details found. Focus appears centered on technical research and development within her current role. She is part of the Computer Science Research team at Ulster University's Derry~Londonderry campus, contributing to innovations at the intersection of computing and engineering.

• Statistics
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Dr. Nicos Pavlidis is a Senior Lecturer in the Department of Management Science at Lancaster University's Management School. His research focuses on developing novel methods for clustering and classification in high-dimensional and time-varying data environments, with applications in machine learning and statistics. He teaches courses such as MSCI 562 (Intelligent Data Analysis and Visualisation) and MSCI 331 (Data Mining for Direct Marketing and Finance), supported by DataCamp platforms. His research interests include statistics, data mining, and machine learning, with a particular emphasis on adaptive methods for dynamic data streams. Pavlidis has contributed to projects like STOR-i: DSI (2017–2021), which aimed to improve clustering efficiency in high-dimensional datasets. He is affiliated with the STOR-i Centre for Doctoral Training and the Centre for Marketing Analytics & Forecasting. His publications span topics such as spectral clustering, metric learning, and ensemble methods. He has advised PhD student Danielle Notice and has reviewed for journals like IEEE Transactions on Evolutionary Computation and Applied Soft Computing. Pavlidis actively participates in conferences and workshops, including the International Symposium on Intelligent Data Analysis and the IEEE Big Data Conference.