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Shirin JalaliView profile

Associate Professor

• Computational Imaging
• Machine Learning
• Information Theory
• +4 more

Shirin Jalali is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. She holds a Ph.D. and M.Sc. in Electrical Engineering and Statistics from Stanford University, with earlier degrees from Sharif University of Technology. Prior to Rutgers, she was a Research Scientist at Nokia Bell Labs and held roles at Princeton University and NYU Tandon School of Engineering. Her research focuses on computational imaging, machine learning, and information theory, addressing challenges like speckle noise mitigation and snapshot compressive imaging. She received the NSF CAREER Award in 2023 and serves as an Associate Editor for IEEE Transactions on Information Theory. Education: Ph.D. in Electrical Engineering, Stanford University, 2010 M.Sc. in Statistics, Stanford University, 2010 M.Sc. in Electrical Engineering, Sharif University of Technology, 2004 B.Sc. in Electrical Engineering, Sharif University of Technology, 2002 Research Interests: Her work spans high-dimensional inference, computational imaging, and machine learning applications in imaging systems. She develops algorithms for coherent imaging in speckle noise environments and explores structure learning through information-theoretic methods. Recent trends in her publications emphasize unrolled neural networks, Bayesian despeckling, and theoretical frameworks for compressive imaging systems. Awards and Roles: 2023 NSF CAREER Award Associate Editor, IEEE Transactions on Information Theory (since 2021) Leadership in research groups addressing imaging and machine learning challenges Grants and Labs: Her NSF CAREER grant funds theoretical work on snapshot compressive imaging systems. She collaborates across academia and industry, leading interdisciplinary projects in signal processing and AI-driven imaging solutions.

Salvatore Ivan TrapassoView profile

Assistant Professor

• Applied harmonic analysis
• Fourier analysis
• Machine learning
• +5 more

Salvatore Ivan Trapasso is a Fixed-term Assistant Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA) , Polytechnic University of Turin , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . His research focuses on Applied Harmonic Analysis , Fourier Analysis , Machine Learning , and Quantum Theory , with expertise in Mathematical Analysis (MATH-03/A) and Theoretical PDEs (PE1_11). Education : Implied PhD in Mathematics. Research Areas : Phase Space Analysis, Time-Frequency Methods, and Applications to Quantum Mechanics. His recent publications investigate phase space techniques for Feynman Path Integrals , Twisted Laplacian , Compressed Sensing , and Stability of Scattering Transforms . His work bridges Harmonic Analysis with Machine Learning and Quantum Dynamics . Notable scientific awards include the Axioms Young Investigator Award (2022) , Best Paper Award (ICGF 2020) , and the Quality Award 2019 from Polytechnic University of Turin. He serves on the Editorial Board of Advances in Operator Theory and as Associate Editor for University Texts in the Mathematical Sciences . Teaching roles include Lecturer for Mathematical Principles in the College of Architecture and Design , Collaborator for Mathematical Analysis I/II in Biomedical and Aerospace Engineering, and Contributor to advanced mathematical methods in Computer Science Engineering.

G. Wilson MillerView profile

Associate Professor

• MRI Technique Development
• Hyperpolarized Gas MR Imaging
• MR-Guided Focused Ultrasound Surgery
• +3 more

G. Wilson Miller is an Associate Professor of Radiology and Medical Imaging and Biomedical Engineering at the University of Virginia. He holds a PhD in Nuclear Physics from Princeton University (2000) and a BS in Mathematics from the University of Maryland (1993). His research focuses on MRI technique development, hyperpolarized gas imaging, and MR-guided focused ultrasound surgery. Key research areas include: Hyperpolarized noble gas MRI to map lung oxygen levels (PAO2) and microstructure Development of fast spiral pulse sequences for 3D PAO2 imaging RF coil design for hyperpolarized gas imaging MRI-guided focused ultrasound for non-invasive surgical interventions Notable projects include collaborations with the physics department to build a helium-3 hyperpolarizer and studies on focused ultrasound for blood-brain barrier opening. His work has advanced applications in lung cancer treatment planning, COPD phenotyping, and neurological disorders.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Mark IwenView profile

Professor

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +12 more

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Carlos VázquezView profile

Professor

• Computer vision
• Medical imaging
• 3D reconstruction
• +4 more

Carlos Vázquez is a Professor at the Department of Software Engineering and IT, École de technologie supérieure (ÉTS). He holds a B.Eng. and M.Sc. from ISPJAE, Cuba, and a Ph.D. from INRS, Montreal. His research focuses on computer vision, medical imaging, 3D reconstruction, and immersive video technologies. He co-leads the Summit Tech Research Chair in Immersive and Interactive Video and is affiliated with the Multimedia Research Laboratory (LABMULTIMEDIA) and the Open Innovation Laboratory in Health Technologies (LIO-ÉTS). His expertise includes stereoscopic imaging, multi-view video coding, and GPGPU programming. Notable research axes are software systems, multimedia, cybersecurity, and health technologies. He has supervised numerous doctoral and master’s theses, including works on 3D spine reconstruction, personalized femur modeling, and immersive video compression. Key contributions include advancements in medical imaging analysis, 3D reconstruction from biplanar radiographs, and efficient video coding for virtual reality. His work bridges clinical applications and engineering, with implications in surgical planning, patient rehabilitation, and multimedia systems optimization.

Javier CabreraView profile

Professor

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +5 more

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.

Navarun GuptaView profile

Professor

• Signal Processing
• Audio Signal Processing
• Bio Signal Processing
• +2 more

Navarun Gupta is a Professor and Chair of the Department of Electrical Engineering within the School of Engineering at the University of Bridgeport's College of Engineering, Business & Education. He holds a Ph.D. in Electrical Engineering from Florida International University (2003), complemented by an M.S. in Physics from Georgia State University (1992) and an M.S. in Electrical Engineering from Mercer University (1998). His educational credentials include: Ph.D., Electrical Engineering, Florida International University, 2003 M.S., Physics, Georgia State University, 1992 M.S., Electrical Engineering, Mercer University, 1998 Dr. Gupta's research centers on signal processing with specialized applications in audio and bio signals . His work spans: Digital Signal Processing for mobile and biomedical applications Pattern Recognition in biosignals (e.g., meditation impact on brain activity) Audio Spatialization and 3D sound modeling using anthropometric data Millimeter-wave propagation for 5G networks Biomedical case studies on electrical burns and respiratory flow dynamics Neuroscience applications in problem-solving and concentration His publication trends (2018-2020) emphasize biomedical signal classification, cloud security protocols, and wireless propagation modeling, demonstrating cross-disciplinary impact in healthcare and communications. Earlier works (2000-2016) established foundational contributions in audio spatialization, brain signal analysis, and biomedical engineering case reports. No scientific awards are documented in his current profile. Dr. Gupta has secured significant research funding including: National Science Foundation grant ($192,347, 2014) as Co-PI for teacher fellowship programs in urban schools United Illuminating Company grant ($11,020, 2018) as Co-PI for energy loss analysis Department of Education grant (2014) as Co-PI for aerospace/hydrospace curriculum development ENGAGE mini-grant ($2,000, 2013) as PI for engineering education initiatives As Department Chair, he leads interdisciplinary collaborations in biomedical signal processing and wireless communications, with research directly impacting healthcare monitoring systems, audio engineering, and electrical safety standards through industry partnerships.

Gonzalo NavarroView profile

Professor

• Algorithms
• Data Structures
• Compressed Data Structures
• +3 more

Gonzalo Navarro is a Full Professor at the Department of Computer Science (DCC) , within the Faculty of Physical and Mathematical Sciences at the University of Chile . His academic roles include coordinating the PhD Program , serving as Research Coordinator , and being a member of the Department Council . Co-created the Pizza&Chili site for compressed text indexes Co-authored two books: Compact Data Structures and Flexible Pattern Matching in Strings Research Interests: He focuses on algorithm design , compressed data structures , text/graph databases , and information retrieval . His work bridges theoretical and practical efficiency in problems like approximate pattern matching, regular expression searching, and dynamic data structure optimization. Recent Publications Trends: His 2025-2024 works emphasize space-time optimal data structures , graph database joins , trajectory compression , and regular expression indexing , often combining algorithmic theory with real-world implementation benchmarks. Scientific Awards: 7 Best Paper Awards in conferences 4 Google Research Awards Highest Cited Paper Award (Elsevier) Scopus Chile Award ACM Fellow (2022) Advising: He has advised 8 postdocs, 22 PhD students, 17 MSc students, and 29 undergraduate theses. His Algorithmic Wednesdays Group fosters collaborative research in algorithms. Labs & Projects: He participates in the Milennium Institute for Foundational Research on Data (IMFD) and the Basal Center for Biotechnology and Bioengineering (CeBiB) , advancing compressed data structures for biological and web-scale applications.

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +3 more

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

• Physical Medicine and Rehabilitation
• Rheumatology
• Fibromyalgia
• +5 more

Professor Ali Gur is affiliated with Gaziantep University , where he works in the Faculty of Medicine across the Department of Internal Medicine and Physical Medicine and Rehabilitation . He holds a Professor academic rank and has published extensively in both medical and social science domains. Education : Doctorate in Basic Islamic Sciences (2019-2024) at Gaziantep University Institute of Social Sciences Licence in History (2015-2019) from Anadolu University Faculty of Open Education Medical Specialization in Physical Medicine and Rehabilitation (1995-2009) from Dicle University Faculty of Medicine Research Interests span Physical Medicine and Rehabilitation , Rheumatology , and Fibromyalgia . His work focuses on musculoskeletal disorders , neuropathic pain , ankylosing spondylitis , and osteoarthritis , with recent publications addressing both clinical interventions and social science critiques of radicalism. Article Trends reflect a dual emphasis: medical research on chronic pain and rehabilitation (e.g., fibromyalgia, osteoarthritis) and social science analysis of terrorism and religious extremism. His 2024 publications include a self-critical examination of Islamophobia and a clinical study on fibromyalgia in psoriatic arthritis patients. Co-Author Networks include researchers like Mazlum Serdar Akaltun , Özlem Altindağ , and Savaş Gürsoy , with collaborative work in clinical trials , rehabilitation programs , and inflammatory disease analysis .

Marissa Lynne GrayView profile

Assistant Professor

• Biomedical Engineering
• Electrical Engineering
• Clinical Engineering
• +2 more

Marissa Lynne Gray serves as an Assistant Professor (Research) in Brown University's School of Engineering and directs the Biomedical Engineering Master's Program. She joined Brown in January 2019 after serving as Teaching Assistant Professor and Associate Chair of Graduate Programs at Stevens Institute of Technology, bringing expertise in clinical engineering and wearable sensor systems. Her educational background includes a PhD (2014) and ME (2011) in Biomedical Engineering from Stevens Institute of Technology, and a BS in Biomedical Engineering with Electrical and Computer Engineering minor from Worcester Polytechnic Institute (2009). Dr. Gray's research bridges electrical engineering principles with clinical applications through wearable sensor technology. Her work spans military-funded projects like dry EEG electrode headsets for the Army Research Laboratory and smart flotation devices for U.S. Special Operations Command, to current cancer care applications monitoring anxiety through physiological sensors. She emphasizes translational engineering solutions for real-world healthcare challenges. Recent publications (2022-2025) reveal dual research trajectories: advancing biomedical engineering education through curriculum innovation and industry partnerships, while developing wearable sensor applications for mental health monitoring in clinical populations. Her work consistently connects pedagogical development with practical clinical implementations. Her research has secured Department of Defense funding for military medical technology development. As Director of the Biomedical Engineering Master's Program, she shapes graduate curriculum and oversees student training in engineering design and innovation. Dr. Gray maintains active collaborations across Brown's ecosystem, particularly with Radiation Oncology, Computer Science, and Psychiatry departments, leveraging interdisciplinary approaches to advance wearable sensor applications in clinical settings.

• Artificial Intelligence
• Data Fusion
• Sensor Fusion
• +4 more

JESUS GARCIA HERRERO is a Full Professor at the Department of Computer Science , Carlos III University of Madrid , and serves as Director of the Postgraduate School of Engineering and Basic Sciences and the Master's Degree in Applied Artificial Intelligence. His research focuses on Artificial Intelligence , Data Fusion , and Maritime Surveillance , with applications in UAV navigation , sensor integration , and fuzzy systems . Key Research Areas: Machine Learning, Trajectory Analysis, Contextual Data Fusion, Robotics, Maritime Security, and Smart Grids. Notable Projects: ASPID (2024-2027), MARVISION (2024-2025), SIMBAT (2021-2024), and HADA (2023-2024). Contact: jesus.garcia@uc3m.es , jgherrer@inf.uc3m.es

Tobias WindischView profile

Professor

• Machine Learning
• Computer Vision
• Reinforcement Learning
• +4 more

Prof. Dr. Tobias Windisch is a Professor at the University of Applied Sciences Kempten, where he serves as head of the Institute for Machine Vision within the Faculty of Mechanical Engineering. He leads the Optical 3D Measurement and Computer Vision Laboratory (3D visionlab) and oversees research activities focused on machine learning applications for industrial automation. Dr. Windisch received his PhD in mathematics from OvGU Magdeburg under the supervision of Thomas Kahle, and holds an Honors Master's degree in mathematics from TU Munich within the elite TopMath program. Prior to his academic career, he worked on machine learning projects for Robert Bosch GmbH and Daimler TSS GmbH (now Mercedes-Benz Tech Innovation). His research spans machine learning, computer vision, and optical sensing with a strong focus on industrial applications. Windisch's work primarily explores how reinforcement learning can be combined with optical sensing to develop intelligent control strategies for manufacturing processes. His team develops mechanical processes built around machine learning models to further automate industrial applications using data from optical sensors. The research has practical applications in automotive production, quality control, and precision manufacturing. Analysis of his recent publications reveals a strong trend toward practical implementations of machine learning in industrial settings, with particular emphasis on reinforcement learning for process optimization, drift detection in high-dimensional data, and causal structure learning for manufacturing analytics. His work bridges theoretical machine learning with real-world industrial challenges. As a dedicated educator and research leader, Windisch maintains high standards for academic integrity and excellence. He believes in creating an environment where students can focus deeply, think boldly, and innovate through meaningful research. Dr. Windisch leads a dynamic research group with numerous Master's and Bachelor's students working on cutting-edge projects including reinforcement learning for active alignment, drift detection in sensory data, latent drift detection with Autoencoders, and representation learning for industrial processes. His laboratory, the 3D visionlab, serves as the physical hub for this research. The Institute for Machine Vision under his leadership develops practical tools and frameworks such as relign, lineflow, and driftbench that are openly available on GitHub, demonstrating his commitment to reproducible research and practical applications.

• Mass Spectrometry
• Imaging Mass Spectrometry
• MALDI Imaging
• +4 more

Raf Van de Plas is a researcher at Delft University of Technology specializing in mass spectrometry imaging and data analysis. His work focuses on developing advanced techniques for molecular analysis of biological tissues, particularly in kidney research and lipidomics. With a PhD in Mechanical Engineering, he bridges engineering principles with biomedical applications. His research interests include: Mass Spectrometry Imaging MALDI Imaging Techniques Lipid Analysis and Mapping Kidney Tissue Molecular Profiling Computational Data Analysis Biomolecular Imaging Algorithms Dr. Van de Plas has published extensively in high-impact journals with multiple 2025 publications in Science Advances, Nature Communications, Kidney International, and Analytica Chimica Acta. His work demonstrates a clear trajectory toward developing innovative computational workflows for mass spectrometry data while applying these techniques to solve critical biomedical problems, particularly in kidney pathology and lipid metabolism. His scientific contributions include: Development of advanced MALDI matrices for high spatial resolution imaging Creation of msiFlow automated analysis workflows Comprehensive lipid mapping of human kidney tissues Integration of mass spectrometry with immunofluorescence microscopy Dr. Van de Plas actively supervises research work and collaborates internationally across biomedical and engineering disciplines, with significant contributions to both methodological development and clinical applications of imaging mass spectrometry.