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Mohammad Javad LatifiView profile

Researcher

• Mathematical Physics
• Geometry
• Applied Mathematics
• +3 more

Mohammad Javad Latifi is a Researcher at Dartmouth College's Department of Mathematics. He holds a PhD in Mathematics from the University of Arizona and focuses on Mathematical Physics, Geometry, Applied Mathematics, and Data Science. His research bridges theoretical work in quantum field theory and dynamical systems with applied areas like machine learning and numerical modeling of physical systems. Research Highlights: Developed kernel smoothing techniques for sea-ice dynamics modeling. Advanced the theory of star transforms and V-line tomography in imaging and inverse problems. Contributed to tensor network approximations of Koopman operators in nonlinear dynamics. Collaborated on multi-level graph spanners and optimization algorithms. Teaching: Taught undergraduate courses including Differential Equations (Math 23) and Linear Algebra (Math 22). Designed visualizations for vector calculus and ODEs, emphasizing conceptual understanding and real-world applications. Software & Projects: Developed KSPoly , a Python package for smooth field approximations in polygonal geometries. Created a sound visualization tool exploring numerical sequences as musical patterns. Contributed to Lunewave's radar and autonomous vehicle software, implementing C++ algorithms for object tracking and data analysis.

Jannick RollandView profile

Professor

• Optical Engineering
• Freeform Optics
• Optical System Design
• +5 more

Jannick Rolland is the Brian J. Thompson Professor of Optical Engineering at the University of Rochester's Institute of Optics and serves as CTO of LighTopTech, a company she co-founded. She leads the NSF I/UCRC Center for Freeform Optics (CeFO), focusing on graduate education, and directs the R.E. Hopkins Center for Optical Design and Engineering, which engages undergraduates in optical design, fabrication, and metrology. Her academic journey includes a Diploma from Institut D'Optique (1984), MS (1985) and PhD (1990) in optical science from the University of Arizona, followed by postdoctoral work at the University of North Carolina at Chapel Hill. Prior to her current role, she held positions at the University of Central Florida, where she established the Optical Diagnostics and Applications Laboratory (ODALab). Professor Rolland's research emphasizes optical instrumentation and system engineering, with a focus on freeform optics, optical coherence tomography (OCT), augmented reality (AR) displays, and biomedical imaging. Her work integrates physics-based modeling and image quality assessment across applications like medical visualization and optical system alignment. Key areas include metasurfaces, metaform optics, and the development of innovative optical systems for diverse fields such as astronomy and wearable technology. Her scientific awards include the 2014 OSA David Richardson Medal and the 2017 Edmund A. Hajim Outstanding Faculty Award. She is a Fellow of Optica, SPIE, and NYSTAR. Her editorial roles include associate editorships at Optics Letters and Optical Engineering . Her contributions span over 150 publications, reflecting her leadership in advancing optical engineering and its applications in healthcare, imaging, and emerging technologies.

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +2 more

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

Francesco PicarielloView profile

Researcher

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +7 more

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

• Machine Learning
• Data Mining
• Big Data
• +4 more

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

Adrián García GutiérrezView profile

Professor

• Aerospace Engineering
• Uncertainty Quantification
• Computational Fluid Dynamics
• +3 more

Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.

• Computer Graphics
• Augmented Reality
• 3D Modeling and Reconstruction
• +5 more

Kwan H. Lee is a prominent researcher in computer graphics and augmented reality, affiliated with the University of Alberta in Canada. With a publication record spanning over three decades (1990-2020), he has established himself as a leading figure in visual computing research with 56 documented publications. His research interests focus on Computer Graphics , Augmented Reality , 3D Modeling and Reconstruction , Computer Vision , and Cultural Heritage Computing . Dr. Lee's work demonstrates particular expertise in material appearance modeling, projection mapping techniques, and spatial augmented reality applications. His research has evolved from foundational work in manufacturing systems to cutting-edge applications in cultural heritage preservation and industrial visualization. Analysis of his recent publications (2015-2020) reveals a strong emphasis on practical applications of augmented reality in construction, museum exhibitions, and cultural heritage preservation. His work consistently bridges theoretical computer graphics concepts with real-world implementations, particularly in projection mapping systems and 3D reconstruction techniques. The publications show increasing focus on industrial applications and cultural heritage preservation in his later career. Dr. Lee has collaborated extensively with researchers from Korean institutions, suggesting strong international connections. His co-author network includes Yong Yi Lee, Jong Hun Lee, Min Ki Park, and other frequent collaborators who appear across multiple publications spanning more than a decade.

• Computer Vision
• Medical Image Analysis
• Pattern Recognition
• +5 more

Jaime S. Cardoso is a prominent researcher at the University of Porto and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) in Portugal. His extensive publication record spanning two decades demonstrates his leadership in computer vision, medical image analysis, and pattern recognition. His research primarily focuses on applying artificial intelligence to healthcare challenges, particularly in medical imaging and diagnostics. Cardoso's research interests center on explainable AI for medical applications, biometrics, and computer vision. His work bridges the gap between theoretical machine learning and practical medical solutions, with significant contributions to breast cancer diagnosis, medical image segmentation, and biometric security systems. He has developed innovative approaches to medical image analysis, including virtual staining techniques and privacy-preserving explanation methods for medical AI systems. His recent publications (2023-2025) reveal a strong emphasis on explainable AI in medical contexts, with multiple papers addressing how to make deep learning models more transparent and trustworthy for healthcare applications. He has also made significant contributions to face recognition technology, video anomaly detection, and specialized medical imaging techniques for breast cancer and neonatal EEG analysis. Among his scientific contributions are numerous collaborations with researchers across Portugal and internationally. His work has appeared in top-tier journals including IEEE Access, Medical Image Analysis, and Neurocomputing, reflecting the high impact of his research. Cardoso has supervised numerous students who have become established researchers in their own right, including Ricardo P. M. Cruz, Kelwin Fernandes, and Ana Filipa Sequeira. His research group appears to focus on the intersection of deep learning, medical imaging, and biometrics, with strong connections to clinical applications.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +4 more

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

Christos ThrampoulidisView profile

Assistant Professor

• Machine Learning
• Deep Learning Theory
• Statistical Signal Processing
• +3 more

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. Previously, he held the same position at the University of California, Santa Barbara (2018–2020) and was a Postdoctoral Researcher at MIT (2016–2018). He earned his Ph.D. and M.Sc. in Electrical Engineering from Caltech (2016 and 2012) and a Diploma in ECE from the University of Patras, Greece (2011). His research focuses on machine learning theory , optimization , high-dimensional statistics , and statistical signal processing . Specific interests include the theoretical foundations of deep learning, neural collapse phenomena, and the analysis of overparameterized models. He has contributed to understanding imbalanced data challenges, model compression, and safe optimization techniques. Key academic achievements include the Qualcomm Innovation Fellowship (2014) and the A. Mentzeolopoulos Fellowship . He teaches graduate courses on Optimization and Signals & Systems at UBC. His research group (MILD Group) actively explores topics like language model geometry, federated learning, and algorithmic reasoning in transformers. Notable publications include work on neural-collapse geometry, imbalance-aware learning, and the theoretical underpinnings of transformers. He advises multiple Ph.D., M.Sc., and undergraduate students, fostering a collaborative environment centered on equity, diversity, and inclusion.

Mai BuiView profile

Lecturer

• Computer Vision
• Medical Image Analysis
• Surgical Robotics
• +5 more

Dr. Mai Bui is a Lecturer at the Deutsches Herzzentrum München (German Heart Center Munich), affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. Her research focuses on integrating advanced computer vision and deep learning techniques into medical applications, particularly in surgical robotics, image-guided interventions, and medical augmented reality. She has contributed to critical areas like catheter tracking, pose estimation, and real-time medical imaging analysis. Dr. Bui teaches multiple courses including Computer Aided Medical Procedures , Medical Augmented Reality , and Introduction to Surgical Robotics . Her work emphasizes low-dose imaging solutions and robust navigation systems for minimally invasive procedures. She collaborates with interdisciplinary teams at labs such as DHM, IFL Lab, and NARVIS Lab to advance translational research in medical AI. Her recent publications highlight innovations in adversarial networks for pose refinement, lightweight camera localization systems, and multimodal inference in ambiguous medical scenes. These contributions address core challenges in surgical navigation and medical device tracking.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +7 more

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Avideh ZakhorView profile

Professor

• Signal Processing
• Artificial Intelligence
• 3D Computer Vision
• +6 more

Dr. Avideh Zakhor is a Professor and Qualcomm Chair at the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She is affiliated with several research centers including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive Initiative, Video and Image Processing Lab, and Berkeley Center for New Media (BCNM). Her career spans over three decades with significant contributions to signal processing, 3D computer vision, robotics, and deep learning. 1983 B.Sc. in Electrical Engineering from Caltech 1985 S.M. in Electrical Engineering and Computer Science from MIT 1987 Ph.D. in Electrical Engineering and Computer Science from MIT Dr. Zakhor's research interests focus on 3D computer vision, autonomous systems and robotics, deep learning, and signal/image processing. Her work spans diverse areas including drone navigation, medical imaging analysis, indoor positioning, and building energy audits. She has led projects on drone-based 3D building reconstruction, legged robot locomotion, and melanoma detection using AI. The 15 most recent publications highlight her work in several key areas: person search pre-training techniques, drone-based indoor navigation and 3D modeling, medical image segmentation for melanoma detection, hexapod robot locomotion, and proximity detection for public health. These publications demonstrate her expertise at the intersection of computer vision, robotics, and AI applications. Dr. Zakhor has received numerous prestigious awards throughout her career: 2022 Winner of Phases 1 and 2, Department of Energy E-Robot Competition 2018 Electronic Imaging Scientist of the Year by SPIE 2004 Okawa Research Grant 2002 IEEE Fellow 1992 Office of Naval Research Young Investigator Award 1990 Presidential Young Investigator (PYI) Award from President George H.W. Bush 1990 Junior Faculty Development Award 1984-1988 Hertz Fellowship 1983 Henry Ford Engineering Award 1982-1983 General Motors Scholarship Dr. Zakhor has advised numerous research projects and has been involved in significant research grants. She has founded successful companies including Indoor Reality, which develops technologies for rapid 3D mapping and visualization of buildings and assets. She leads research initiatives in various cutting-edge technologies: Unmanned Aerial Vehicles (UAV) with focus on autonomy and obstacle avoidance, perception, path planning and control Deep learning applications in legged locomotion, unsupervised learning for multimodal sensors, misinformation detection, and learning-based image compression Wi-Fi proximity detection methods for contact tracing of diseases Temporal graphical neural networks for disease prediction Detection of small objects in ultra-high resolution images 3D reconstruction and recognition

Michael KerberView profile

Professor

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +4 more

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

• Combustion
• Diesel Engines
• Coal Combustion
• +5 more

Dale R. Tree is a Professor in the Department of Mechanical Engineering at Brigham Young University (BYU), College of Engineering, where he has held academic appointments since 1994. He progressed from Assistant Professor (1994–2000) to Associate Professor (2000–2007) before attaining full professorship in 2007. His research focuses on experimental combustion diagnostics across diesel engines, coal, biomass, and black liquor systems, with emphasis on soot/NOx reduction and optical measurement techniques. Education: PhD, Mechanical Engineering (Minor: Chemical Engineering), University of Wisconsin-Madison, 1992 MSME, Mechanical Engineering, Purdue University, 1988 BS, Mechanical Engineering, Brigham Young University, 1986 Research Interests: Professor Tree investigates combustion dynamics using advanced diagnostics like Planar Laser-Induced Fluorescence (PLIF) and Laser-Induced Incandescence (LII). Key areas include: Soot/NOx formation mechanisms in diesel engines Black liquor droplet combustion for pulp industry applications Biomass/coal cofiring impacts on emissions Low-NOx burner design for sustainable fuels High-temperature fuel spray characterization Publication Trends: His recent articles (2004–2010) predominantly explore combustion optimization, emissions reduction, and diagnostic methods. Common themes include soot modeling in compression ignition engines, black liquor spray dynamics, biomass cofiring emissions, and innovative temperature/pollutant measurement techniques. Collaborative work with national labs (e.g., Sandia) and industry (Cummins) underscores applied research focus. Scientific Awards: No awards mentioned in the source material. Advising & Leadership: Has advised 15+ graduate students (MS/PhD) in combustion research. Additional roles: SAE Advisor & Mini-Baja/FSAE Coach Department Assessment and ABET Coordinator Sabbatical researcher at Sandia National Laboratories (1999–2000) Labs & Collaborations: Conducts experiments at BYU Combustion Laboratory. Key collaborations include Sandia National Laboratories (optical diagnostics), Cummins Engine Company (diesel emissions), and industry partnerships in biomass conversion. Leads student engineering teams for SAE competitions.