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Ira AssentView profile

Professor

• Machine Learning
• Data Mining
• Clustering Algorithms
• +5 more

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

• Machine Learning
• Optimization
• Computer Vision
• +5 more

Tom Goldstein is the Volpi-Cupal Endowed Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. His research focuses on AI system development, optimization methods, and their applications in computer vision and signal processing. He emphasizes the intersection of theoretical foundations and practical hardware implementations. Education: PhD in Applied Mathematics from UCLA (2010), BA from Washington University (2006). Prior to UMD, he held postdoctoral positions at Stanford University and Rice University. Research interests include AI security/privacy, algorithmic bias, diffusion models, and large language model robustness. His work addresses challenges in model watermarking, adversarial attacks, and ethical AI deployment. Notable awards include the Sloan Research Fellowship (2017), DARPA Young Faculty Award, and JPMorgan Faculty Research Award. He directs the Maryland Center for Machine Learning and has advised over 20 graduate students in AI-related disciplines. Recent research trends emphasize multimodal systems, generative model analysis, and scalable training techniques. His work on diffusion models explores style similarity and content authenticity, while watermarking studies address anti-plagiarism and data provenance. Labs/Teams: Leads the Maryland Center for Machine Learning, collaborating with UMIACS and ECE departments. Active in open-source AI initiatives and supercomputing applications for large model training.

• Computer Architecture
• Embedded Systems
• RISC-V
• +5 more

Prof. Daniel Müller-Gritschneder is a Full Professor of Computer Architecture at TU Wien's Institute of Computer Engineering (Faculty of Informatics) since 2024. Previously, he held roles as research group leader at TU Munich's Chair of Electronic Design Automation and acting professor for Real-Time Systems at TU Munich. He earned his Dipl.-Ing. (2003), Dr.-Ing. (2009), and Habilitation (2019) from TU Munich. His research focuses on Electronic System Level Design, RISC-V domain-specific architectures, embedded ML compiler toolchains, functional safety, and hardware security. He collaborates extensively with industry partners like Infineon, Bosch, BMW, and Mercedes. Active in the RISC-V community, he co-initiated the RISC-V Summit Europe and serves on steering committees for conferences such as DAC, ICCAD, and DATE. Key Research Themes: RISC-V ISA extensions and toolchain development Edge AI/ML on embedded systems Secure and safety-critical processor design High-level synthesis and performance simulation Awards: Best Student Paper Award (2024), IEEE Winter Conference on Computer Vision Habilitationspreis (2019), TU Munich Alumni Association Senior IEEE Member (2019) Teaching includes courses like 'High-Level Synthesis', 'Computer Organization', and 'Embedded Systems Design'. Current projects involve lightweight software for IoT devices and pre-silicon validation of safety-critical RISC-V systems. His work bridges academic research with industrial application through collaborative projects and open-source tool development.

• Fluid Mechanics
• Coastal Sciences and Engineering
• Numerical Modeling
• +15 more

Cihan Bayındır is an Associate Professor at Istanbul Technical University's Faculty of Civil Engineering, Department of Civil Engineering, specializing in fluid mechanics, coastal engineering, and computational methods. His research integrates advanced mathematical techniques with practical engineering applications across multiple disciplines. His primary research areas include Fluid Mechanics, Coastal Sciences and Engineering, Numerical Modeling, Fluid Physics, Acoustics and Vibrations, Nonlinear Dynamics, and Quantum Hydrodynamics. Bayındır's work demonstrates a strong interdisciplinary approach, bridging traditional civil engineering with cutting-edge computational methods and quantum phenomena. His research has significant applications in coastal protection, renewable energy systems, disaster management, and infrastructure resilience. Analysis of his recent publications reveals a clear trend toward applying artificial intelligence and machine learning techniques to traditional civil engineering challenges. His work increasingly focuses on using compressive sensing, deep learning (particularly LSTM networks), and fuzzy logic systems to solve complex problems in wave dynamics, vibration analysis, and tsunami prediction. The integration of quantum computing concepts with classical fluid mechanics represents another distinctive aspect of his research portfolio. ITU Doctoral Special Award-2024-Advisor (2025) ITU 2023 Academic Performance Award (2024) Turkish Academy of Sciences (TUBA) Outstanding Young Scientist Award (GEBİP) (2022) Elsevier Frontiers Article Award (2020) Kaleidoscope Award (American Physical Society, 2016) Bayındır leads multiple research projects funded by ITU's Scientific Research Projects Unit (BAP), including studies on dam break wave propagation using fractional equations, optimization of wave energy concentrators with AI methods, and three-dimensional vibration control in marine structures. His work involves collaborations with international institutions, including previous engagement with CERN on accelerator design. He maintains an active research laboratory focused on hydraulics and coastal engineering, with specialized equipment for wave and vibration analysis in the Hydraulics Laboratory HL 216 at ITU.

• Body Imaging (CT)
• Chronic Liver Disease
• Gastrointestinal Conditions
• +3 more

Caroline Taylor is an Associate Clinical Professor in Radiology & Biomedical Imaging at Yale School of Medicine, affiliated with VA Connecticut Healthcare System since 1984. She previously served as Chief of VA Connecticut Diagnostic Imaging Service until 2022. Her clinical focus includes body CT imaging, chronic liver disease evaluation, gastrointestinal conditions, and CT colonography. She has driven initiatives to enhance cancer screening and management, including lung cancer screening programs and cancer tracking systems. Dr. Taylor completed her MD at Yale University (1978), residency at Yale-New Haven Hospital (1982), and fellowship at Yale School of Medicine (1984). She is board-certified in Diagnostic Radiology (ABR, 1982). Her research spans imaging in cirrhosis, portal hypertension, and telemedicine applications. She has contributed to educational innovations like automated test-item generation in radiology training. Over 50 peer-reviewed articles reflect her work in diagnostic imaging, technology, and clinical protocols. She holds administrative roles in radiology education and clinical service coordination at Yale and VA facilities. Her work emphasizes improving diagnostic accuracy and healthcare quality through systems integration and evidence-based practices.

Stefan GumholdView profile

Professor

• Computer Graphics
• Visualization
• Medical Imaging
• +3 more

Professor Stefan Gumhold is a leading academic in computer graphics and visualization at TU Dresden. He has held roles including Head of the Chair for Computer Graphics and Visualization since 2005 and served as Dean of the Faculty of Computer Science from 2010 to 2012. His expertise spans visualization techniques, medical imaging, and immersive technologies. Gumhold earned his Ph.D. in 2000, focusing on mesh compression, and later received a Dissertation Award in the same year. He has contributed to projects like the Heisenberg Scholar Research Group and led the Kommission Umwelt at TU Dresden. His teaching includes courses such as Introduction to Computer Graphics, Data Visualization, and Scientific Visualization. Gumhold’s research emphasizes visualization platforms (e.g., ISAAC), image fusion, and neural network applications in medical diagnostics. His work often integrates virtual reality for data analysis, as seen in tools like VRCellLabeler and ExtremeWeatherVis. Education: Diploma in Computer Science (University of Tübingen, 1998), M.Sc. in Applied Physics (University of Massachusetts Boston, 1996). Research Highlights: Development of visualization frameworks, 3D reconstruction, and invertible neural networks for outlier detection. Awards: Dissertation Award 2000. Grants & Labs: Leadership in TU Dresden’s Computer Graphics lab and contributions to collaborative research projects like Fast-Haptic.

Israel Leyva MayorgaView profile

Associate Professor

• Satellite Communications
• Non-Terrestrial Networks (NTNs)
• Internet of Things (IoT)
• +7 more

Israel Leyva Mayorga is an Associate Professor at Aalborg University within the Department of Electronic Systems , part of the Technical Faculty of IT and Design. His research focuses on satellite communications, non-terrestrial networks (NTNs), and IoT connectivity in 5G/6G systems. Key research interests include LEO satellite constellations , random access protocols , energy-efficient wireless systems , and edge computing . He leads or collaborates on projects like SATNEX V WI Y4.6 (ESA) and WITS: Wireless access for heterogeneous IoT Services , addressing challenges in mesh satellite communications and IoT service integration. Awarded the Novo Nordisk Foundation's Prize for Excellence in Technical Science Teaching (2025) and Outstanding PhD thesis (2020) , his work bridges academia and industry, with media coverage on topics like Arctic connectivity and satellite mega-constellations. He serves as an editor for IEEE Wireless Communications Letters and is a member of the one6G consortium. His research outputs span 58 publications , emphasizing satellite routing optimization, IoT traffic management, and energy-autonomous nodes in vehicular networks. Current projects explore semantic image encoding for Earth observation and reconfigurable intelligent surfaces in wireless access.

Yingke ChenView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Multiagent Systems
• +7 more

Dr. Yingke Chen is an Associate Professor at the Department of Computer and Information Sciences, Northumbria University. He holds a PhD in Computing Science from Aalborg University (Denmark) and has conducted postdoctoral research at Queen’s University Belfast (UK) and Georgia University (USA). His research focuses on Artificial Intelligence, particularly machine learning, multiagent systems, and formal methods such as model checking. He has secured over £1.3M in Innovate UK grants, collaborating with industries in transportation, logistics, autonomous systems, and e-commerce to apply AI and data science for business growth. Education: PhD in Computing Science (Aalborg University, 2013). Key research areas include machine learning applications, formal verification, and data-driven decision-making. He has published in top venues like Journal of AI Research, AAMAS, AAAI, and IJCAI. Collaborations involve projects with businesses to address real-world challenges, emphasizing practical solutions through theoretical advancements. His work spans anomaly detection, autonomous systems, and cross-domain data analysis. He is open to supervising PhD students and engaging with media inquiries. Research Grants: Over £1.3M in Innovate UK funding (PI/Co-I). Industry Partnerships: Transportation, logistics, autonomous underwater vehicles, education, and e-commerce sectors. Labs/Teams: Actively involved in interdisciplinary teams applying AI to industrial challenges, though specific lab names are not mentioned.

Timothy HoriuchiView profile

Associate Professor

• Computational Neuroscience
• Neuromorphic Engineering
• VLSI System Design
• +5 more

Dr. Timothy Horiuchi is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Maryland, affiliated with The Institute for Systems Research, Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. He leads the Computational Sensorimotor Laboratory (CSSL) and contributes to the Microelectronics at Maryland group. His research bridges computational neuroscience and neuromorphic VLSI design, focusing on sensorimotor systems inspired by biological mechanisms like bat echolocation. Education: B.S. and Ph.D. in Electrical Engineering from Caltech (1989, 1997), followed by postdoctoral research at Johns Hopkins University. Industrial experience includes roles at Hughes Aircraft, Boeing, IBM, HP, and Tanner Research. Research interests include bio-inspired neuromorphic systems, acoustic signal processing, mobile robotics, and neural circuit modeling. Recent work explores bio-hybrid robots, acoustic metamaterials, and spike-based navigation algorithms. His NSF-funded projects include the 'ExLENT' experiential learning initiative and the DREEM microelectronics workforce program. Notable achievements: NSF CAREER Award (2004) for bat echolocation modeling, ISR Outstanding Faculty Award (2004), and 2019 Elevate Fellowship to redesign ENEE 303. His lab has pioneered neuromorphic chips for auditory localization, motor control, and spike-sorting tools for neurophysiology. Advising: Supervises graduate students like Nathan Renegar. Grants include contributions to NSF neuroengineering initiatives and collaborative industry partnerships. Current projects address odor-guided drones, Luneburg lens-based acoustic waveguides, and neuromorphic social robots. Labs/Teams: CSSL focuses on sensorimotor systems; Microelectronics group develops advanced VLSI technologies. Collaborations span neuroscience, robotics, and biomedical engineering domains.

• Machine Learning
• Computer Vision
• Signal Processing
• +3 more

Louise Rixon Fuchs is an Industry doctoral student at KTH Royal Institute of Technology's Division of Robotics, Perception and Learning. She specializes in advanced sonar imaging techniques, machine learning applications, and signal processing for robotics. Her research focuses on enhancing sonar data analysis through deep learning methodologies, optimization algorithms, and compressive sensing models. Her work bridges robotics and computer vision, addressing challenges in low-resolution sonar image processing, object detection, and beamforming optimization. Key contributions include GAN-based sonar image simulations and transfer learning for object recognition in sonar environments. Louise has published extensively on sonar technology advancements, with recent articles emphasizing dense point correspondence algorithms and wide-beam sonar system improvements. She currently holds no recorded scientific awards but is actively contributing to robotics and marine sensing research.

Robert BregovicView profile

Assistant Professor

• Light Field Engineering
• Signal Processing
• 3D Displays
• +3 more

Robert Bregovic holds the position of Assistant Professor (tenure track) in Computing Sciences. He earned his Engineer degree from the University of Zagreb, Croatia (1998) and a Doctor of Science (Technology) in Information Technology (2003). His research focuses on Light Field Engineering, Signal Processing, 3D Displays, and Filter Design. Key contributions include advancements in light field reconstruction techniques (e.g., Shearlet Transform applications), datasets like the CIVIT dataset for integral microscopy, and editorial roles in journals such as IEEE Transactions on Multimedia. He actively reviews for prestigious conferences and journals, including ISPA and ICASSP. His work spans over 90 publications since 2000, with recent emphasis on projection-based light field displays and anti-aliasing solutions. Education: PhD in Information Technology (2003) Engineer in Technology (University of Zagreb, 1998) Research Contributions: Light Field Displays 3D Scene Rendering Signal Processing for Imaging Systems Activities: Guest Editor for Signal Processing: Image Communication (2024) Reviewer for multiple top-tier journals and conferences

• Computational Fluid Dynamics (CFD)
• Turbulent Combustion
• High Performance Computing (HPC)
• +5 more

Professor Karl Jenkins is a Professor of Computational Engineering at Cranfield University , where he leads the Centre for Computational Engineering Sciences . His expertise spans Computational Fluid Dynamics (CFD) , Turbulent Combustion , High Performance Computing (HPC) , and Multiphase Flow Modeling . Jenkins has published over 100 papers and received the Gaydon Prize for his contributions to combustion research. His research focuses on reacting flows , turbulence modeling , and compressible multiphase flows , with recent work addressing green hydrogen production , aircraft component segmentation , and virtual reality applications in aviation safety. He has developed high-order numerical methods for shock wave analysis and interface-capturing in unstructured mesh environments . A former Sir Arthur Marshall Research Fellow at Cambridge University, Jenkins combines academic rigor with industrial collaboration , having worked with companies like Rolls-Royce plc , Airbus SE , and Siemens AG . He mentors research students including Yiren Tong and actively contributes to LES/DNS computational frameworks for aerospace and environmental applications .

Greg HamerlyView profile

Professor

• Machine Learning
• Unsupervised Learning
• Data Clustering
• +10 more

Greg Hamerly is a Professor and former Interim Chair (2022–2025) in the Department of Computer Science at Baylor University’s School of Engineering and Computer Science. He holds a PhD from the University of California, San Diego (2003) and specializes in machine learning, particularly unsupervised learning and data clustering, with real-world applications in medical imaging and ecological monitoring. PhD, University of California San Diego (2003) MS, University of California San Diego (2001) BS, California Polytechnic State University (1999) His research centers on the development and application of efficient machine learning algorithms. Key areas include improving k-means clustering techniques, federated learning, and applying AI to early detection of retinoblastoma through leukocoria in photographs and monitoring invasive aquatic species using video data. He has also contributed to computational thinking curriculum development. His recent publications reflect strong trends in unsupervised and self-supervised learning, with a focus on algorithmic efficiency, robustness, and real-world deployment in healthcare and environmental science. His work often combines clustering, deep learning, and computer vision for practical applications. Dubious awards (as humorously noted on personal website) Chief Judge for ICPC South Central USA regional competition (2018, 2019) Site Director for ICPC South Central USA regional competition (2010–2013, 2015–2019) Member of ICPC Live Analytics team at ICPC World Finals (2011–2015) Participant in ICPC World Finals as contestant (2000, 2001) Greg Hamerly has advised numerous graduate students, including PhD candidates now at Texas A&M Kingsville and Meta. He has secured research funding from the NSF, NIH, Department of the Interior, and Texas Parks and Wildlife Department. He is also deeply involved in competitive programming, having coached students and developed the Competitive Learning course at Baylor with over 300 original problems. He leads research projects on eye disease detection and invasive species monitoring, often in collaboration with interdisciplinary teams. His lab integrates algorithmic innovation with practical deployment, emphasizing accessibility and real-world impact.

• Robotics
• Machine Learning
• Electrical Engineering
• +3 more

Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.

Guillaume TochonView profile

Assistant Professor

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
• +4 more

Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.