جست‌وجوی استاد و پژوهشگر

Shuyang Lingمشاهده پروفایل

استادیار

• Mathematics of Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at the Tandon School of Engineering, New York University. He joined NYU Shanghai in September 2019 as an Assistant Professor Faculty Fellow of Data Science, after serving as a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU from 2017-2019. Dr. Ling received his PhD in Applied Mathematics from the University of California, Davis in 2017 under the supervision of Thomas Strohmer. He also earned an MS in Statistics from UC Davis in 2016 and completed his undergraduate studies in Mathematics and Applied Mathematics at Fudan University in Shanghai, China in 2012. Dr. Ling's research focuses broadly on the mathematics of data science, with particular interest in tackling inverse problems from engineering applications and extracting meaningful information from large-scale and heterogeneous datasets. His work spans a broad spectrum of subjects including: Optimization (convex and non-convex) Probability and statistics Computational harmonic analysis Numerical linear algebra Signal processing and machine learning theory His recent publications demonstrate a strong focus on synchronization problems, neural collapse phenomena, and optimization landscapes. He has made significant contributions to understanding the theoretical foundations of spectral methods, non-convex optimization, and convex relaxations in data science applications. His work often bridges theoretical guarantees with practical algorithms for problems in signal processing and machine learning. Dr. Ling has received several prestigious awards and grants including the SIAM Student Paper Prize in 2017, Shanghai Eastern Scholar for Young Professionals (2019), Shanghai Rising Star Program (A-type) (2024), and grants from the Natural Science Foundation of Shanghai and National Key R&D Program of China. He is actively involved in mentoring the next generation of researchers, currently advising three Ph.D. students at NYU Shanghai: Jiayang Yin (co-advised with Prof. Mathieu Lauriere), Wanli Hong, and Ziliang Samuel Zhong. He has also supervised numerous undergraduate thesis projects at NYU Shanghai since 2020. Dr. Ling is part of the Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai, where he organizes seminars and reading groups on data science and machine learning.

• Internet of Things (IoT)
• Embedded Systems
• Precision Agriculture
• +۳ مورد دیگر

Deniz Taşkın is an Associate Professor at the Department of Computer Engineering , Trakya University , Turkey. He has served in various academic and administrative roles since 2002, including Head of Department of Information Technology (since 2024) and Deputy Head of Department (since 2016). Education: PhD in Computer Engineering (2007) Master's in Computer Engineering (2004) BSc in Computer Engineering (2002) Edirne Science High School (1998) Research Interests: His research focuses on Internet of Things (IoT) , Embedded Systems , and Precision Agriculture , with applications in sensor networks, wireless communication, and agricultural monitoring. He explores 3D Modeling techniques for optical systems and contributes to Cybersecurity in multimedia and IoT environments. Publication Trends: Deniz has published 15+ articles on IoT architectures, Bluetooth Low Energy sensor networks, 3D calibration methods, and video encryption. His work bridges Digital Circuit Design with Agricultural Technology , emphasizing energy efficiency and real-time data processing. Scientific Awards: 2017 Trakya Project Market: Electric Motor Driver for Autonomous Vehicles 2016 TÜBİTAK: Electromobile Category Runner-Up and Promotion Award 2015 TÜBİTAK: Solar-Powered Vehicle Design Award 2015 Edirne Governorship: Provincial Achievement Certificate Advising and Grants: He has supervised 5 PhD theses and 7 Master's theses , including projects on IoT in Precision Agriculture and FPGA Circuit Design . As a principal investigator, he led 8 scientific projects funded by Trakya University and KOSGEB, focusing on embedded systems, data security, and smart automation. Labs and Teams: He contributes to IoT Research and Embedded Systems Development at Trakya University, collaborating with multidisciplinary teams in agricultural technology and digital design. His work includes designing self-powered sensor nodes and FPGA-based hardware.

Xiaobai Sunمشاهده پروفایل

استاد

• Numerical Analysis
• Matrix Theory
• High-Performance Scientific Computing
• +۸ مورد دیگر

Xiaobai Sun is a full-time Professor of Computer Science at Duke University's Trinity College of Arts & Sciences, with a career spanning from 1994 to present. His research focuses on Numerical Analysis, Matrix Theory, High-Performance Scientific Computing, Parallel Computing, and Graph Clustering . He received his PhD from the University of Maryland, College Park in 1991. Education : Ph.D., University of Maryland, College Park (1991) Research Interests encompass: Numerical Linear Algebra Graph Algorithms Parallel Computing Scientific Simulation Medical Imaging Optical Engineering Publication Trends show a consistent focus on graph algorithms, numerical methods, and high-performance computing applications across biomedical imaging, optical systems, and physical simulations. Grants include: NIH funding (2019-2021) for Hybrid virtual-MRI/CBCT in liver SBRT NSF funding (2009-2015) for Integral Equation-Based Fast Algorithms NIH funding (2014-2020) for LIVE System in SBRT Treatments ARO funding (2013-2014) for computing platform development Professional Activities include Bass Connections Faculty Team Memberships (2013-2016) focusing on energy systems analysis and scientific document analysis.

• Machine Learning
• Neural Networks
• Data Science
• +۳ مورد دیگر

Giulia Fracastoro is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , where she contributes to teaching and research in Systems and Control Engineering (scientific sector IINF-04/A). Her research focuses on Machine Learning , Neural Networks , and Multimedia Signal Processing , with a strong emphasis on Deep Learning and Automatic Control applications. Teaching Roles: Course Lecturer for Discrete-Event Models and Systems in Computer Engineering (2021–2025), Automatic Controls in Electronic Engineering (2024–2025), and collaborations on courses like Optimization for Machine Learning . Research: Member of the Automatica (DET) research group , supervised PhD student Luca Savant Aira , and contributed to projects involving neural implicit world models , image/video processing , and deep neural network sparsification . Her publications span topics such as financial network stability, 3D super-resolution, and neural network quantization, reflecting interdisciplinary applications of machine learning. She holds multiple patents for image/video encoding/decoding technologies and has co-authored works with researchers like Enrico Magli and Sophie Fosson.

• Artificial Intelligence
• Quantum Machine Learning
• Deep Learning
• +۶ مورد دیگر

Prayag Tiwari is a Senior Lecturer at Halmstad University's School of Information Technology, where he conducts cutting-edge research at the intersection of artificial intelligence, quantum computing, and healthcare applications. With an SMIEEE designation and an active research profile, Dr. Tiwari contributes significantly to both academic and practical advancements in intelligent information systems. His research interests span a diverse spectrum including Artificial Intelligence, Quantum Machine Learning, Deep Learning, Healthcare, Bioinformatics, NLP/LLM, Computer Vision, Multimodal Learning, and Intelligent Systems. This interdisciplinary focus enables him to tackle complex problems across multiple domains, particularly in developing quantum-inspired algorithms for healthcare applications and advancing multimodal learning techniques for real-world challenges. Dr. Tiwari's publication record demonstrates consistent high-impact contributions across multiple domains, with recent work showing a clear trend toward quantum machine learning applications, particularly in healthcare and bioinformatics. His research frequently combines traditional deep learning approaches with quantum computing principles to develop novel solutions for drug interaction prediction, medical data processing, and multimodal analysis. The interdisciplinary nature of his work is evident in publications spanning computer vision, natural language processing, and blockchain applications for healthcare security. 2022 Best Paper Award for a publication in Neural Networks Multiple Highly Cited Papers in various journals Editor for Neural Networks and IEEE Transactions on Fuzzy Systems As an academic leader, Dr. Tiwari serves as an editor for prestigious journals including Neural Networks and IEEE Transactions on Fuzzy Systems, contributing to the advancement of knowledge in his fields of expertise. His extensive publication record in top-tier venues such as TMLR, IJCV, TNNLS, AAAI, ACL, CIKM, ICASSP, ECIR, and ECML demonstrates significant scholarly impact. While specific grant information isn't detailed in the provided materials, his research output suggests active funding for projects in quantum machine learning and healthcare applications. Dr. Tiwari's work appears to be conducted within collaborative research environments at Halmstad University, particularly within the School of Information Technology's research groups focused on artificial intelligence and quantum computing applications. His publications frequently involve multi-institutional collaborations, suggesting active participation in both national and international research networks focused on advancing machine learning techniques for practical applications.

Peter Vangorpمشاهده پروفایل

استادیار

• Computer Graphics
• Material Perception
• Realistic Rendering
• +۴ مورد دیگر

Peter Vangorp is an Assistant Professor in the Visualization and Graphics group of the Department of Information and Computing Sciences at Utrecht University in the Netherlands. He leads research in computer graphics, visual perception, and virtual reality, with a particular focus on material perception and realistic rendering techniques. Dr. Vangorp obtained his Ph.D. in Computer Science at the University of Leuven (Belgium) in 2009. His doctoral research focused on "Human Visual Perception of Materials in Realistic Computer Graphics." Prior to his current position, he held postdoctoral positions at REVES/Inria Sophia-Antipolis (France), Giessen University (Germany), Max Planck Institute for Informatics (Germany), and Bangor University (UK). He also served as a Senior Lecturer at Edge Hill University (UK) from 2016 to 2022. Dr. Vangorp's research interests span several interconnected domains within computer graphics and visual perception. His primary focus is on understanding how humans perceive materials and gloss in computer-generated imagery, which has direct applications in realistic rendering. He has made significant contributions to the study of hazy gloss perception, BRDF modeling, and material editing techniques. His work bridges the gap between computer graphics and human visual perception, using rigorous experimental methods to inform rendering techniques. More recently, his research has expanded into virtual reality applications, particularly in medical visualization and rehabilitation. Analysis of Dr. Vangorp's recent publications reveals a consistent focus on material perception and realistic rendering, with increasing attention to virtual reality applications. His work often combines computer graphics techniques with psychophysical experiments to understand human visual perception. Recent publications show expansion into medical applications of VR, 3D point cloud processing, and gamification in educational contexts. The interdisciplinary nature of his research is evident in collaborations with researchers from computer science, psychology, medicine, and education fields. Dr. Vangorp has received research funding through an NWO grant for the VR4eVR project (Virtual Reality for enhanced Visual Rehabilitation), which runs from 2024 to 2030. This project involves collaboration with multiple institutions including UMCG, Royal Visio, RUG, and UT. Dr. Vangorp has supervised numerous graduate students, including PhD candidate Vanderfeesten (2025) and multiple Master's students in Game & Media Technology and Artificial Intelligence programs. His students have worked on diverse topics including real-time rendering techniques, neural denoising, 3D Gaussian splatting, and volumetric sampling methods. He serves as a PhD supervisor and researcher in the VR4eVR project, mentoring students working at the intersection of computer graphics and medical applications. Dr. Vangorp is actively involved in the Visualization and Graphics research group at Utrecht University, where he contributes to research on advanced rendering techniques, material perception, and virtual reality applications. His work on the VR4eVR project demonstrates his commitment to applying computer graphics research to real-world medical challenges, particularly in visual rehabilitation.

Julien Milleمشاهده پروفایل

دانشیار

• Computer Science
• Pattern Recognition
• Image Analysis
• +۵ مورد دیگر

Julien Mille is an Associate Professor at INSA Centre Val de Loire and Ecole Polytechnique de l'Université de Tours , France. He conducts research with the RFAI team (Reconnaissance des Formes et Analyse d'Images) at the Laboratoire d'Informatique Fondamentale et Appliquée in Tours. Previously, he was an associate professor at Université Claude Bernard Lyon 1 (2009-2015), affiliated with the LIRIS laboratory and Imagine team . Key research areas: Pattern recognition, image analysis, active contours/surfaces, optimal transport, human activity recognition Technical contributions: Developed open-source tools for image labeling ( PixelLabeling ), DeepFlowCUDA optimization Scientific contributions span computer vision, biomedical imaging, and plant science applications. Notable work includes: Optimal transport methods for image restoration Hierarchical skeletonization for shape matching Pose-driven attention mechanisms for video analysis 3D segmentation of medical images His research has been published in leading journals like SIAM Journal on Imaging Sciences , International Journal of Computer Vision , and Computer Vision and Image Understanding , along with major conferences including CVPR , ECCV , and ICIP .

• Chemical Engineering Thermodynamics
• Phase Equilibrium
• Thermodynamic Modeling
• +۳ مورد دیگر

Prof. Dr. Ivona Radović is a Full Professor in the Department of Chemical Engineering at the Faculty of Technology and Metallurgy, University of Belgrade. Her academic career is deeply rooted in chemical engineering thermodynamics, with a focus on high-pressure systems, phase equilibria, and industrial drying processes. She has made significant contributions to the fields of biodiesel characterization, thermodynamic modeling, and energy integration in chemical processes. Department of Chemical Engineering, Faculty of Technology and Metallurgy University of Belgrade Research focus on thermodynamics and process engineering Prof. Radović's research interests center on chemical engineering thermodynamics, particularly phase equilibrium studies involving vapor-liquid, liquid-liquid, and solid-liquid systems. Her work spans high-pressure density measurements, thermodynamic modeling of complex mixtures, and industrial drying processes. She has extensively investigated biodiesel and petro-diesel blends, hydrocarbon-alcohol systems, and polymer-solvent interactions. Her research has practical applications in energy integration, particularly using Pinch methodology for nitric acid production optimization, and in the development of efficient drying processes for chemical production. Her publication record demonstrates a strong focus on experimental thermodynamics, with numerous papers on density measurements, volumetric properties, and phase behavior of various chemical systems. The research trends show consistent work on high-pressure systems, biodiesel characterization, and molecular interactions in complex mixtures. Her work bridges fundamental thermodynamic principles with industrial applications, particularly in energy efficiency and process optimization. Prof. Radović has supervised numerous doctoral, master's, and undergraduate students across a wide range of thermodynamics and process engineering topics. Her mentoring portfolio includes research on hydrogen storage, biomass valorization, separation processes, and energy efficiency in industrial applications. She has participated in multiple research projects including the European Commission's NESSHY project on hydrogen storage and various national projects focused on chemical thermodynamics applications. Her research group has developed specialized equipment for high-pressure density measurements and has worked on innovative catalyst development for biodiesel production from waste materials. The team maintains strong connections with both academic institutions, including post-doctoral work at Delft University of Technology, and industrial partners in the chemical and energy sectors.

• Image Processing
• Deep Learning
• Signal Processing
• +۶ مورد دیگر

Adrian Munteanu is a Professor in the Department of Electronics and Computer Science at Free University of Brussels (Vrije Universiteit Brussel). He has been actively contributing to research in image processing, deep learning, and signal processing since 1995, with over 414 research outputs and an h-index of 29. His work spans diverse fields such as video compression, autonomous vehicle pose estimation, greenhouse gas emissions prediction, and real-time distributed video coding.

Soheil Kolouriمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Deep Learning

Soheil Kolouri is an Assistant Professor in the Department of Computer Science at Vanderbilt University. He leads the Machine Intelligence and Neural Technologies (MINT) Lab, focusing on cutting-edge research in artificial intelligence and computational systems. Education includes a PhD in Computer Science from Carnegie Mellon University (2015), an MSc from Colorado State University (2012), and a BSc from Sharif University of Technology (2010). Research interests center on Machine Learning , Computer Vision , and Deep Learning , with specialized applications in optimal transport theory, medical imaging, continual learning systems, and neural network efficiency. Recent work demonstrates strong cross-disciplinary collaboration with medical and engineering domains. Publications (2024-2025) reveal three dominant themes: Advancements in optimal transport methodologies (Wasserstein distances, partial transport) Innovations in continual/lifelong learning systems Medical AI applications including surgical gesture recognition and cancer detection The MINT Lab explores neural network architectures, reinforcement learning, and efficient model compression techniques for real-world deployment.

Bo Yangمشاهده پروفایل

استاد پژوهشی

• Thoracic Aortic Aneurysms
• Cardiac Surgery
• Stem Cell Biology
• +۵ مورد دیگر

Dr. Bo Yang is a Frankel Research Professor of Aortic Surgery at the University of Michigan Medical School, affiliated with the Department of Cardiac Surgery at the Frankel Cardiovascular Center. He completed his cardiothoracic surgery fellowship at Stanford University and joined the University of Michigan in 2011. His clinical expertise focuses on complex aortic and cardiac surgeries, including valve-sparing aortic root replacement (David procedure) and treatment of aortic dissections. Education & Training: Xiangya Medical School, Central South University (1995) Xiangya Hospital, Cardio-thoracic Surgery Residency (1998) University of Arizona Cancer Center, General Surgery Residency (2008) Stanford University Medical Center, Cardiothoracic Surgery Fellowship (2011) Research Interests: Dr. Yang’s lab investigates mechanisms of thoracic aortic aneurysms using patient-derived induced pluripotent stem cells (iPSCs) and CRISPR-Cas9 gene editing. He explores novel therapies targeting genetic mutations like those in Loeys-Dietz syndrome, Marfan syndrome, and bicuspid aortic valve (BAV). Collaborations with bioengineers focus on tissue-engineered vascular grafts. Clinically, his team studies outcomes of aortic surgery and adult cardiac procedures, including long-term results of the David procedure and surgical management of endocarditis. Awards & Grants: AHA Vivien Thomas Young Investigator Award (Finalist) Young Investigator Award (Loeys-Dietz Syndrome Foundation) NIH Grant Recipient Clinical & Research Impact: Dr. Yang combines surgical innovation with basic science to address aortic pathology. His lab’s iPSC models enable personalized approaches to aneurysm prevention, while clinical research evaluates surgical outcomes and device efficacy. He leads a multidisciplinary team advancing both surgical techniques and regenerative medicine.

Yu Panمشاهده پروفایل

استاد پژوهشی

• Graph Learning
• Manifold Learning
• Representation Learning
• +۴ مورد دیگر

Yu Pan is a Research Assistant Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln (UNL). He holds a Ph.D. in Computer Science from UNL, an M.S. in Information Management from Illinois Institute of Technology, and a B.S. in Computer Science and Technology from the University of Electronic Science and Technology of China. His research focuses on Graph Learning, Manifold Learning, Representation Learning, and Data Visualization, with applications in interdisciplinary fields such as biological systems and meteorology. He designs and maintains the Scientific Data Management Infrastructure for the Institute of Agriculture and Natural Resources (IANR), implementing FAIR principles to standardize data pipelines and enhance accessibility for researchers. His work also involves High Performance Computing (HPC) frameworks like MPI and CUDA to optimize data processing and analysis. Education: Ph.D., Computer Science, University of Nebraska-Lincoln M.S., Information Management and Technology, Illinois Institute of Technology B.S., Computer Science and Technology, University of Electronic Science and Technology of China Research Interests: Dr. Pan’s work bridges computational methods with scientific data challenges. He explores techniques to extract meaningful insights from complex datasets using graph-based approaches and manifold learning, while advancing visualization tools to aid domain scientists. His infrastructure projects emphasize scalable, FAIR-compliant systems to manage heterogeneous data across disciplines. Current collaborations with biological and environmental researchers aim to enhance data-driven decision-making through advanced analytics. Advising and Infrastructure: While no formal advisees are listed, his role in IANR’s data infrastructure project underscores his leadership in collaborative scientific computing. The infrastructure facilitates interdisciplinary research by integrating spatially and temporally co-located datasets, enabling researchers to explore patterns in agriculture, climate, and natural resources. Labs/Teams: He is affiliated with the Department of Biological Systems Engineering and contributes to the IANR’s data management initiatives, fostering cross-disciplinary innovation through computational solutions.

Sanja Fidlerمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

Karianne Wiger Gammelsrudمشاهده پروفایل

دانشیار

• Antimicrobial Resistance
• Microbial Genomics
• Infectious Diseases
• +۳ مورد دیگر

Karianne Wiger Gammelsrud is an Associate Professor in the Department of Microbiology at the University of Oslo. Her research focuses on antimicrobial resistance, microbial genomics, and clinical microbiology with a particular emphasis on infectious diseases and molecular epidemiology. She leads the VIP Laboratory, mentoring PhD students in areas such as deep learning-based image compression and coding for machines. Her work spans antibiotic susceptibility testing, pharmacokinetic analysis in ICU patients, and genomic studies of pathogens like Enterococcus faecium and Klebsiella pneumoniae . Notable contributions include investigating the molecular epidemiology of carbapenemase-producing Enterobacterales in Norway and developing novel diagnostic methods like the Direct MIC-gradient Strip Method. Dr. Gammelsrud collaborates extensively with national and international institutions, contributing to clinical guidelines for conditions such as septic arthritis. Her research integrates genomic, pharmacological, and epidemiological approaches to address pressing public health challenges in antimicrobial resistance and infection control. Students under her supervision include Tianma Shen (image coding), Zhongpeng Zhang (machine learning), and Calvin Dong (point cloud coding). Alumni include Pengli Du (now at Apple) and Junhe Cui (continuing studies at SCU).

• Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Werner Martin is a Professor of Big Geospatial Data Management at the Technical University of Munich (TUM), affiliated with the School of Engineering and Design and the Department of Aerospace and Geodesy. His research focuses on georeferenced data processing, distributed computing, quantum algorithms, and machine learning applications in geospatial contexts. Prof. Martin holds a doctorate from LMU Munich and has held academic and research positions at institutions including LMU Munich, Leibniz-University Hannover, the German Aerospace Center (DLR), and UniBW Munich. His work bridges theoretical advancements with practical applications in spatial data analysis, visualization, and high-performance computing. Awards ACM SIGSPATIAL GIS Certificate of Appreciation (2019) 1st place ACM SIGSPATIAL GIS Cup (2015) IPIN Best Paper Award (2014) His recent publications emphasize geospatial AI, quantum computing for data processing, and environmental monitoring systems. Collaborations with industry partners like DLR highlight his focus on real-world geospatial challenges.