جست‌وجوی استاد و پژوهشگر

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

Yang Huمشاهده پروفایل

استادیار

• Autonomous Systems
• Cybersecurity
• Cloud Computing
• +۳ مورد دیگر

Dr. Yang Hu is an Assistant Professor of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on autonomous systems, cybersecurity, and cloud computing, with a particular emphasis on rethinking autonomous driving infrastructure through multi-system networking. He has received an NSF CAREER Award for a project aiming to redesign autonomous driving systems using advanced computing and communication technologies, benefiting AR/VR and industrial IoT applications. Dr. Hu’s work bridges semantic gaps in virtual machine introspection and explores dynamic binary instrumentation frameworks like PEMU. His publications span cybersecurity, embedded systems, and graph processing optimizations. The NSF-funded project ($508,339) highlights his leadership in transforming autonomous systems through networked multi-object architectures. His research achievements include contributions to guest OS fingerprinting in cloud environments and efficient graph processing algorithms. Collaborations involve hypervisor-layer automation tools like HyperShell and hybrid techniques for semantic gap reduction.

Henry Hanمشاهده پروفایل

استاد

• Data Science
• Fintech
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Henry Han is a Professor and holds the McCollum Family Chair in Data Science at Baylor University’s Hankamer School of Business, Department of Data Science. His interdisciplinary research spans data science, fintech, artificial intelligence, bioinformatics, health informatics, cybersecurity, and quantum computing. PhD, Applied Mathematical & Computational Sciences, University of Iowa (2004) MS, Computer Science, University of Iowa (2001) MS, Applied Mathematics, University of Iowa (2001) Dr. Han’s research integrates advanced machine learning, optimization, and data analytics techniques to solve complex problems in finance, healthcare, and cybersecurity. His work emphasizes manifold learning , deep neural networks , evolutionary computation , and graph-based learning for real-world applications such as high-frequency trading, automobile damage classification, and single-cell genomics. He applies these methods across domains including financial modeling, biomedical data analysis, and sports forecasting. His recent publications demonstrate a strong trend toward interdisciplinary machine learning , with applications in computational finance, bioinformatics, and intelligent systems. Many of his works utilize optimization-based deep learning and unsupervised representation learning to extract meaningful patterns from high-dimensional data. Dr. Han has received recognition through prestigious appointments: McCollum Family Chair in Data Science He actively collaborates on research projects and has secured funding for advanced data science initiatives, though specific grant details are not listed. His work supports both academic advancement and practical innovation in data-driven decision-making. He advises graduate students and contributes to the development of next-generation data science methodologies. Dr. Han is a key member of the data science research team at the Hankamer School of Business, contributing to interdisciplinary labs and research groups focused on fintech, AI, and health informatics.

• Computer Vision
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

Beatriz Flamia Azevedoمشاهده پروفایل

پژوهشگر

• Optimization
• Mathematics Modeling
• Machine Learning
• +۴ مورد دیگر

Beatriz Flamia Azevedo is a multidisciplinary researcher at the Research Center for Digitization and Intelligent Robotics (CeDRI-IPB), affiliated with the Department of Industrial and Systems Engineering. She obtained her Ph.D. in Industrial and Systems Engineering from the University of Minho (Portugal), a master’s in Industrial Engineering from the Polytechnic Institute of Bragança, and a bachelor's in Control and Automation Engineering from the Federal Technological University – Paraná (Brazil). Ph.D. , Industrial and Systems Engineering, University of Minho (Portugal) M.Sc. , Industrial Engineering, Polytechnic Institute of Bragança (Portugal) B.Sc. , Control and Automation Engineering, Federal Technological University – Paraná (Brazil) Her research spans Optimization , Mathematics Modeling , Machine Learning , Recommendation Systems , and STEM , with additional focus on Simulation and Advanced Data Analytics . She actively contributes to clustering algorithm development and data-driven decision systems. Beatriz's publications highlight trends in multi-objective clustering algorithms , fuzzy clustering , and distance metric optimization , with applications in educational platforms and medical diagnostics. Her work emphasizes clustering validity indices and hybrid modeling approaches. Scientific Awards : No awards listed in provided data.

Jasmin Jelovicaمشاهده پروفایل

دانشیار

• Structural Analysis
• Structural Optimization
• Computational Methods
• +۶ مورد دیگر

Dr. Jasmin Jelovica is an Associate Professor in the Department of Civil Engineering and Mechanical Engineering at the University of British Columbia (UBC), with a joint appointment since 2017. He holds a D.Sc. (Tech.) from Aalto University and an M.Sc. in Naval Architecture from the University of Rijeka. His research focuses on computational methods for structural analysis and optimization, emphasizing lightweight structures and mechanical response analysis. He leads the Structural Efficiency Laboratory, exploring advanced materials, welding techniques, and machine learning applications in structural design. Research Interests: Structural optimization, computational methods, advanced materials, marine vessels design, machine learning, and welding technologies. His work spans structural mechanics, vibration analysis, and multi-objective optimization frameworks. Awards: NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels (2020). Lab & Collaborations: The Structural Efficiency Laboratory at UBC focuses on innovative structural components, topology optimization, and advanced welding processes. Research is supported by collaborations with Civil and Mechanical Engineering departments. Advising & Grants: Advises graduate students in structural analysis and optimization projects. Active in conferences such as MARSTRUCT and OMAE, publishing on topics like neural network-assisted constraint handling and underwater acoustics.

Laurent Najmanمشاهده پروفایل

استاد

• Mathematical Morphology
• Image Processing
• Computer Vision
• +۴ مورد دیگر

Laurent Najman is a Professor of Computer Science at ESIEE Paris, part of Université Gustave Eiffel, within the Laboratoire d'Informatique Gaspard-Monge (LIGM) and the A3SI research team. His work focuses on Mathematical Morphology, Image Processing, and applications in Computer Vision and Medical Imaging. He has authored influential books such as Mathematical Morphology: from Theory to Applications and contributed to advancements in hierarchical segmentation, graph-based methods, and discrete geometry. Key research interests include morphological filtering, watershed algorithms, and topological data analysis. Notable achievements include a top-cited paper on ultrametric watersheds and collaborations in medical imaging simulations, such as myocardial perfusion modeling. He advises students like Camille Couprie, recipient of prestigious awards. Najman's interdisciplinary work bridges theoretical foundations with practical applications in fields like bioinformatics and remote sensing. His affiliations include ESIEE Paris and LIGM, where he leads research initiatives. Activities include organizing conferences on discrete curvature and co-developing tools like the Higra library for hierarchical graph analysis. Najman’s contributions emphasize both algorithmic innovation and their interpretability in AI systems.

• Nonlinear Optimization
• Discrete Optimization
• Operational Research
• +۲ مورد دیگر

Professor Joerg Fliege holds the position of Professor of Operational Research at the University of Southampton, leading its Operational Research Group, the largest in the UK. He is a Fellow of the Operational Research Society and a member of the Council for Mathematical Sciences, advising the UK government. His research focuses on Nonlinear Optimization, Discrete Optimization, and interdisciplinary applications in AI and healthcare. He co-founded the startup Nucleolus and collaborates with industries like Airbus, Boeing, and the European Space Agency. Current research includes UAV navigation, stochastic optimization frameworks, and healthcare decision-making models. Prof. Fliege supervises four PhD students and has been awarded grants from the Royal Society, EU FP7, and others. His work spans academic and industrial domains, emphasizing practical problem-solving and innovation. Education: PhD in Mathematics from the University of Dortmund (1997), with visiting roles at UC Berkeley, Université Libre de Bruxelles, and University of Erlangen-Nuremberg. He became a professor at the University of Birmingham (2004) before joining Southampton in 2007. Research Interests: His core areas include optimization theory, AI-driven solutions, and operational research applications in healthcare, aerospace, and maritime systems. Recent projects address UAV trajectory optimization, predictive wear analysis in materials, and computational approaches to single-cell sequencing. Grants & Awards: Notable funding includes Royal Society grants for network visualization models and EU FP7 support for robust optimization. His 2025 publications highlight healthcare decision-making and autonomous systems, reflecting a blend of theoretical and applied research.

Wei Wen Wongمشاهده پروفایل

پژوهشگر

• Epitaxy
• Semiconductor Materials
• Photonics
• +۵ مورد دیگر

Dr. Wei Wen Wong is a Postdoctoral Research Fellow at the Research School of Physics, Australian National University (ANU), affiliated with the ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS). He holds a PhD from ANU (2023) and a Bachelor's degree from Multimedia University, Malaysia (2016). Previously, he worked as a Product and Failure Analysis Engineer at Cypress Semiconductor (2016–2018). His research focuses on epitaxy of low-dimensional semiconductor structures and design/fabrication of photonic, optoelectronic, and metasurface devices. Key areas include quantum dot lasers, microring resonators, parity-time symmetric cavities, and InP-based nanowire structures. He has co-authored over 20 peer-reviewed papers in journals like Nano Letters , Laser & Photonics Reviews , and ACS Nano . Education: Bachelor of Engineering, Multimedia University (2016) PhD in Physics, ANU (2023) Dr. Wong’s work emphasizes Bayesian optimization for laser design, single-photon sources , and metasurface integration . His research bridges semiconductor epitaxy with advanced optical systems, targeting applications in quantum photonics and low-threshold lasers. Notable Awards: Jim Williams Award (ANU, 2023) Global Young Scientists Summit (2023) His lab collaboration focuses on InP nanowire heterostructures and quasi-bound states in micro-cavities . Current projects explore scalability of single-photon sources using droplet epitaxy and AI-driven laser optimization.

Heng Huangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Big Data Computing
• +۷ مورد دیگر

Heng Huang is the John A. Jurenko Endowed Professor at the University of Pittsburgh with dual appointments in the Department of Electrical and Computer Engineering and Department of Biomedical Informatics. His research spans machine learning, bioinformatics, and medical image analysis with significant contributions to Alzheimer's disease research, neuroinformatics, and precision medicine. His educational background includes a Ph.D. in Computer Science from Dartmouth College and M.S./B.S. degrees from Shanghai Jiao Tong University. Research focuses on Machine Learning , Big Data Computing , and Biomedical Informatics with emphasis on: Neurodegenerative disease prediction through transferable deep networks Medical image analysis for macular degeneration and Alzheimer's Watermarking techniques for LLM security and protein design Optimization methods for non-convex problems Recent publications show strong trends in multimodal learning (TV-LSTM, MIRROR), LLM security (CoTGuard, Web IP protection), and biomedical applications (Alzheimer's classification, single-cell analysis). He actively mentors students including Yanfu Zhang (recent tenure-track appointment at William & Mary) and seeks new PhD candidates/postdocs for research in big data, machine learning, and biomedical image analysis. His lab maintains active participation in top conferences including ICML, CVPR, NeurIPS, and MICCAI as evidenced by consistent paper acceptances (3 at ICML 2023, 5 at AAAI 2023).

Gulesin Sena Dasمشاهده پروفایل

مدرس

• Circular Economy and Sustainable Supply Chains
• Mathematical Modeling for Sustainable Production
• Multi-objective Modelling
• +۷ مورد دیگر

Dr. Gulesin Sena Das is a Lecturer in Supply Chain Management at De Montfort University, affiliated with the Faculty of Business and Law and based in the School of Leadership, Management and Marketing. Her work bridges operations research, artificial intelligence, and sustainable supply chain systems, focusing on real-world industrial applications. Her research interests include: Circular Economy and Sustainable Supply Chains Mathematical Modeling for Sustainable Production Multi-objective Modelling Green Supply Chain Management Logistics and Transportation Management Sustainable Aviation Combinatorial Optimization Dr. Das's recent publications reflect a strong trend in applying advanced optimization techniques—particularly stochastic, bi-objective, and column generation methods—to complex logistical and industrial problems such as airport gate assignment, satellite design, industrial symbiosis networks, and team formation. Her work integrates sustainability goals with operational efficiency, often leveraging AI and mathematical programming frameworks. She actively supervises MSc and PhD students and welcomes research proposals in sustainability-driven supply chain modeling. She also serves as a reviewer for top-tier journals including Omega , Applied Soft Computing , and IEEE Transactions on Systems, Man, and Cybernetics: Systems . Teaching areas include Supply Chain Management, Project Management, Management Information Systems, and Sustainable Aviation.

Reza Sedaghatمشاهده پروفایل

استاد

• Optimization Problems
• VLSI Design
• Algorithms
• +۳ مورد دیگر

Dr. Reza Sedaghat is a Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. He holds a PhD in Electrical Engineering from the University of Hannover (1999), an MASc (1994), and BSc (1992) from the University of Kassel. His research focuses on optimization problems, VLSI design, and cross-layer modeling of IoT systems. Professional Engineer (P.Eng) in Ontario IEEE Senior Member Reviewer for IEEE Transactions and Elsevier journals Research interests include: Single/Multi-objective optimization Quadratic assignment problems Security architecture for ciphers Design automation and FPGA systems Scientific contributions highlighted in articles from 2018-2019 demonstrate expertise in swarm computation, hardware security, and IoT optimization frameworks.

Manuel Mucientes Molinaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Manuel Mucientes Molina is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) within the University of Santiago de Compostela . His research focuses on Artificial Intelligence , particularly in Computer Vision and Machine Learning , with applications in object detection, process mining, and healthcare diagnostics. Research Areas : Machine learning, Computer vision, Process mining, Deep learning, AI for healthcare. Projects : Protonterap-IA (2025), AZOR (2024), RAI4P (2021), eXplica-IA (2018), DronePlan (2014), SoftLearn (2012). Publications highlight advancements in few-shot object detection, small object tracking, and AI-driven conformance checking. Notable collaborations include work on X-ray vision systems and medical mask detection in operating rooms. Awards : Best Student Paper Nomination (2015), Runner-up best industry-oriented paper award (2008). Teaching includes courses on Statistical Learning, Deep Learning, and Automata Theory at the M.Sc. and B.Sc. levels.

Henrik Ebelمشاهده پروفایل

استادیار

• Robotics
• Cooperative Robotics
• Model Predictive Control
• +۶ مورد دیگر

Henrik Ebel is an Assistant Professor (Tenure Track) in Mechanical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the LUT School of Energy Systems. He joined LUT in 2024 after previously working at the University of Stuttgart's Institute of Engineering and Computational Mechanics from 2016 to 2024. Dr. Ebel's research focuses on the intersection of artificial intelligence, machine learning, and mechanical engineering, with particular emphasis on: Distributed cooperative robotics Model predictive control systems Non-holonomic robot control Data-driven approaches to mechanical engineering problems Automation systems development His research aims to bring cooperative robotics from laboratory settings to real-world applications in Finland, focusing on using multiple simple and cost-efficient robots that collaborate to solve problems rather than relying on single complex machines. He believes automation promotes sustainability through better energy conservation, generation, and storage compared to manual processes. His recent publications demonstrate consistent advancement in cooperative robotics, control systems, and machine learning applications in mechanical engineering. As an educator, Dr. Ebel supervises doctoral students and finds fulfillment in seeing them grow and succeed in their research. He values the human aspect of academia and enjoys teaching as a complement to his research work, noting that honing teaching skills helps him explain research better and pass on important future-oriented knowledge.

Mauro Salazarمشاهده پروفایل

استادیار

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +۷ مورد دیگر

Mauro Salazar is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with co-affiliations at the Eindhoven AI Systems Institute (EAISI) in both Health and Mobility domains. He leads his own research group focused on optimization models and methods for systems and control. His educational background includes: B.Sc. and M.Sc. in Mechanical Engineering from ETH Zürich (2012, 2015) Master thesis conducted at EPFL's Automatic Control Lab Ph.D. in Mechanical Engineering from ETH Zürich (2019) in collaboration with Ferrari Formula 1 Postdoctoral Scholar at Stanford University's Autonomous Systems Lab (2019-2020) Salazar's research centers on optimization models for multi-scale cyber-socio-technical systems with applications spanning mobility systems, pandemic response, and material design. His work bridges theoretical optimization with practical implementation, particularly in sustainable mobility solutions where he develops methods for optimal design and operation of transportation systems from single vehicles to entire networks. He investigates mesoscopic user behavior modeling and designs incentive schemes to align individual and system-level objectives in transportation networks. His recent publications (2025) demonstrate a strong focus on sustainable mobility systems, with recurring themes in electric vehicle fleet management, battery degradation modeling, ride-pooling algorithms, and energy management for transportation electrification. His work spans multiple disciplines including automotive engineering, control systems, energy management, and public health, characterized by rigorous mathematical optimization approaches applied to real-world challenges. His scientific recognitions include: Two ETH Medals (for Master's and PhD theses) Best Student Paper awards at ITS 2018 and ECC 2022 Best Teacher Award (2021) Nomination for TU/e Young Researcher Award (2022) Salazar actively advises research projects and secured funding through the Dutch Research Council's NEON project (Crossover research program, Grant 17628). His teaching portfolio includes Optimal Control and Reinforcement Learning, Engineering Optimization, and Advanced Full-Electric and Hybrid Powertrain Design. He leads the 'Group Salazar' within the Control Systems Technology section and contributes to EAISI Health and Mobility initiatives.