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Dana CobzasView profile

Associate Professor

• Computer Vision
• Medical Imaging
• Image Segmentation
• +7 more

Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +4 more

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

• Applications of AI in Medical Imaging
• Machine Learning
• Dark-Field X-ray Imaging
• +3 more

Tina Dorosti is a researcher at the Technical University of Munich , affiliated with the TUM Faculty of Medicine and the Department of Physics . Her work focuses on applying artificial intelligence to medical imaging, particularly in CT and X-ray technologies. Research Interests: Tina specializes in AI-driven medical imaging solutions, with emphasis on machine learning for disease detection, dark-field X-ray imaging, and spectral X-ray imaging. Her projects address challenges in low-dose imaging, artifact reduction, and lung volume quantification. Publications: Her recent work (2025) includes optimizing CNNs for COPD detection in CT scans, enhancing lung tumor imaging with sparse sampling, and developing deep learning methods for lung volume estimation from chest radiographs. Earlier studies (2024-2021) explore hemorrhage detection, artifact correction, and bone segmentation in clinical imaging. Awards: Cover image of the Radiology: Artificial Intelligence July 2025 issue Collaborations: Tina collaborates with Prof. Franz Pfeiffer and colleagues at the Chair of Biomedical Physics, contributing to interdisciplinary projects in radiology, oncology, and respiratory disease diagnostics.

Lorenzo De StefaniView profile

Lecturer

• Algorithms
• Big Data
• Statistical Learning
• +3 more

Lorenzo De Stefani serves as an Assistant Teaching Professor (formally titled Lecturer) in Brown University's Department of Computer Science, teaching core courses including Theory of Computation (CSCI1010), Design and Analysis of Algorithms (CSCI1570), and Data Science (CSCI1951A). His academic credentials feature dual doctoral degrees: a PhD in Computer Science from Brown University (2020) under Eli Upfal, and a PhD in Computer Engineering from the University of Padova (2016) under Gianfranco Bilardi. Additional qualifications include a Master of Science (2012) and Bachelor of Science (2009) in Computer Engineering from the University of Padova. PhD, Brown University, 2020 PhD, University of Padova, 2016 MSc, University of Padova, 2012 BSc, University of Padova, 2009 De Stefani's research centers on algorithmic innovation for large-scale data challenges, with emphasis on memory-efficient graph stream processing, statistical learning for multiple hypothesis testing, and Byzantine-resilient computation. His work bridges theoretical computer science and practical data analysis through rigorous probabilistic frameworks. Publication trends reveal consistent contributions to graph algorithm optimization, particularly in single-pass triangle counting for dynamic networks and I/O complexity bounds for fundamental operations like matrix multiplication. His methodologies prioritize computational efficiency while maintaining statistical validity in massive datasets. Scientific recognition includes: KDD Best Student Paper Award (2016) for Tríest streaming algorithms While advising PhD candidates during his doctoral studies at Brown and Padova, current student mentorship details remain unspecified. No major grant awards are documented in available materials. He actively participates in Brown's computer science research ecosystem through collaborative networks focused on algorithmic foundations and data science applications.

Andrés BruhnView profile

Professor

• Computer Vision
• Optical Flow
• Scene Flow
• +5 more

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

Kei HiroseView profile

Professor

• Sparse Estimation
• Multivariate Analysis
• Statistical Modeling
• +2 more

Kei Hirose serves as Professor and Division Leader of the Division of Industrial and Mathematical Statistics, with concurrent appointments in the Division of Strategic Liaison and Division of Fujitsu Mathematical Modeling for Decision Making. Division Leader: Industrial and Mathematical Statistics Concurrent Roles: Strategic Liaison, Fujitsu Mathematical Modeling for Decision Making His research pioneers sparse estimation techniques for high-dimensional data analysis, focusing on multivariate methods including factor analysis and Gaussian graphical modeling. He develops computationally efficient algorithms for parameter estimation while investigating theoretical properties of sparse models, with implementations distributed via R packages. Applications prominently include genomic data analysis such as gene expression datasets, bridging statistical theory with practical computational biology challenges. Scientific recognition: No awards or fellowships specified in source material Academic mentorship and funding details were not documented in the provided text. Similarly, laboratory infrastructure, research teams, and future research trajectories remain unspecified in the available information.

Vinkle SrivastavView profile

Researcher

• Surgical Data Science
• 3D Medical Image Analysis
• Multi-modal Pre-training
• +7 more

Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.

Giorgio ScorzelliView profile

Researcher

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Electronic Design Automation
• VLSI Design
• Computer Systems
• +7 more

Shinji Kimura is a Professor at Waseda University's Faculty of Science and Engineering, specializing in VLSI design and electronic systems. He holds a Doctor of Engineering from Kyoto University and has been with Waseda since 2002, previously serving as Associate Professor at Nara Institute of Science and Technology (1993–2002) and Assistant Professor at Kobe University (1985–1993). Kimura's research spans low-power circuit design , approximate computing , FPGA optimization , video coding (HEVC) , and hardware acceleration for AI . His work focuses on energy-efficient architectures for applications like neural networks, computer vision, and ultra-high-definition video processing. Recent publications emphasize hardware-efficient multipliers, neural network compression, and 3D-stacked memory systems. Awards include the LSI IP Design Award (2000, 1999) and the Information Processing Society of Japan Encouragement Award (1993). He leads projects on HEVC encoding/decoding, non-volatile memory optimization, and 3D integrated circuits, with VLSI implementations achieving real-time 8K video processing.

Filipe RodriguesView profile

Associate Professor

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +7 more

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Radiation Detection for Nuclear Nonproliferation
• Fission Fragment Measurement
• Muon Imaging
• +7 more

Adam Hecht is a Professor in the Department of Nuclear Engineering at the University of New Mexico since 2008. He holds a Ph.D. in Physics (Yale University, 2004) and has led multidisciplinary research in radiation detection, nuclear nonproliferation, and detector development. His educational background includes Ph.D., M.Phil., and M.S. in Physics from Yale University (2001-2004) and a B.S. in Physics from the University of California, Irvine (1997). Ph.D. in Physics (Yale, 2004) M.Phil. in Physics (Yale, 2001) M.S. in Physics (Yale, 1999) B.S. in Physics (UC Irvine, 1997) His research focuses on Radiation Detection for nuclear nonproliferation, Fission Fragment Measurement via spectrometers at LANSCE, Muon Imaging for spent fuel casks, and Novel Detector Development (perovskites, AlSb semiconductors). He directs the Radiation Research and Detector Development (R2D2) Laboratory , collaborates with LANL/INL, and advises UNM's Institute of Nuclear Materials Management (INMM) student group. Key article trends show expertise in Muon Tomography (2025), Neuromorphic Computing for radiation detection (2024), AlSb Semiconductor Detectors (2016), and Fission Data Analysis (2015-2022). His work spans Physics of Atomic Nuclei , Nuclear Instruments and Methods , and Environmental Geochemistry . Awards include the J.W. Gibbs Fellowship (Yale, 1997-1998). Students advised include Daniel Poulson (Muon Imaging), Richard Blakeley (Fission Spectrometer), Erin Vaughan (AlSb Detectors), and Joseph Morris (Refractive Index Analysis). Collaborations with electrical engineering, Portland State University, and LANL/INL are frequent.

Seyedsaeed RazavikiaView profile

Researcher

• Optimization
• Statistical Signal Processing
• Communication Systems
• +3 more

Seyedsaeed Razavikia is a Ph.D. candidate at the KTH Royal Institute of Technology , affiliated with the School of Electrical Engineering and Computer Science and the Division of Network and Systems Engineering . His work is supervised by Carlo Fischione , with co-supervision by Mairton Barros , and funded by the WASP project . B.Sc. and M.Sc. in Electrical Engineering from Iran University of Science and Technology and Sharif University of Technology , respectively. Visiting researcher at Imperial College London with Deniz Gündüz in the Information Processing and Communications Lab . Research interests span machine learning over networks , optimization , statistical signal processing , and communication theory . His work focuses on over-the-air computation , federated learning , and low-rank matrix recovery . Publications highlight advancements in digital communication , networked machine learning , and signal processing , with applications in massive connectivity and blind demixing . Awards : IEEE Sweden VT-COM-IT Best Student Journal Paper Award 2024 IEEE Sweden VT-COM-IT Top-5 Student Conference Paper Award 2024 Hans Werthén Foundation Visiting Abroad Scholarship Wallenberg AI, Autonomous Systems and Software Program Ph.D. Admission Collaborations include institutions like Imperial College London and Ericsson Research , with contributions to patents on digital channel computation .

Dimitri MavrisView profile

Professor

• Advanced Design Methods
• Aircraft Conceptual and Preliminary Design
• Air-Breathing Propulsion Design
• +4 more

Dimitri Mavris is a Professor at the School of Aerospace Engineering , Georgia Institute of Technology, holding distinguished titles including Boeing Chair in Advanced Aerospace Systems Analysis and S.P. Langley Distinguished Professor . He directs the Aerospace Systems Design Laboratory (ASDL) and serves as Executive Director of the Professional Master’s in Applied Systems Engineering (PMASE) . Education B.S., Aerospace Engineering, Georgia Institute of Technology (1984) M.S., Aerospace Engineering, Georgia Institute of Technology (1985) Ph.D., Aerospace Engineering, Georgia Institute of Technology (1988) Dr. Mavris specializes in advanced design methods , aircraft preliminary design , and non-deterministic design theory , focusing on multi-disciplinary optimization and system of systems architecting . His work spans propulsion systems, space exploration, and sustainable aviation technologies. Recent research trends include machine learning for aerospace safety , hybrid-electric propulsion analysis , and digital twin applications in energy systems. His lab produces 100+ annual publications, reflecting leadership in MBSE , UAS certification , and lunar operations modeling . Scientific Awards Class of 1934 Distinguished Professor Award (2024) NSF CAREER Award (1997) Fellow of AIAA (2009-present) NASA Blue Marble Award (2017) SGA Faculty of the Year (2012-2013) As an advisor to Georgia Tech's AIAA student chapter, Dr. Mavris mentors 1,484 alumni (300 Ph.D., 1,184 M.S.) and leads industry partnerships with FAA , Airbus , and Siemens . His labs include the Vertical Lift Research Center and Strategic Energy Institute , with global initiatives like the ICAS presidency (2023-2024).

Qianqian TongView profile

Assistant Professor

• Stochastic Optimization
• Sparse Learning
• Federated Learning
• +4 more

Qianqian Tong is an Assistant Professor in the Computer Science department at the University of North Carolina at Greensboro. Her research focuses on stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning algorithms. She has developed novel methods for efficient optimization in deep learning and federated learning frameworks, including communication-efficient distributed algorithms and decentralized systems. Education: Ph.D. Computer Science and Engineering, University of Connecticut M.S. Computational Mathematics, Zhengzhou University B.S. Mathematics, Zhengzhou University Research interests include designing algorithms for sparse learning, federated learning with privacy guarantees, and applying deep graph learning to drug discovery. Recent projects involve tensor-based models for multidimensional data analysis and improving convergence in ADAM optimization. Her publications span optimization theory, federated learning systems, and molecular modeling applications. Though no awards are listed, her work demonstrates significant contributions to efficient machine learning methodologies. Teaching includes advanced courses on data science and computer science foundations. No lab affiliations or grant details are explicitly mentioned in the provided text.