جست‌وجوی استاد و پژوهشگر

Farshad Khorramiمشاهده پروفایل

استاد

• Nonlinear Control
• Large Scale Systems
• Robotics and Automation
• +۵ مورد دیگر

Farshad Khorrami is a Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering and a faculty member of the Center for Advanced Technology in Telecommunications (CATT). His research spans nonlinear control, robotics, cyber-physical systems security, and machine learning applications. PhD in Electrical Engineering and MS in Mathematics from The Ohio State University BS in both Electrical Engineering and Mathematics from The Ohio State University He directs the Control/Robotics Research Laboratory (CRRL) and collaborates with the NYU Center for Cybersecurity. Recent projects include: EnIGMA: AI agent for cybersecurity applications (presented at ICML 2025) TRAPS: Real-time anomaly detection in power grid systems (DOE-funded) Detection of Hardware Trojans via short-term aging (ONR-funded) Khorrami contributes to hardware security research with novel testbeds for Trojan detection and holds 13 issued/2 pending US patents.

• Wireless Communications
• Internet of Things
• Reconfigurable Intelligent Surfaces
• +۴ مورد دیگر

Xingwang Li is an active researcher affiliated with the School of Physics and Electronic Information Engineering at Henan Polytechnic University in Jiaozuo, China. He obtained his PhD from Beijing University of Posts and Telecommunications in 2015, specializing in networking and switching technology. His research spans wireless communications, IoT systems, reconfigurable intelligent surfaces (RIS), and physical-layer security, with a strong focus on 6G-enabling technologies. Dr. Li's work primarily explores: Optimization of RIS-aided satellite-terrestrial networks Covert communication systems for enhanced security AI-driven signal processing for massive MIMO Integrated sensing and communication frameworks Energy-efficient protocols for IoT networks His recent publications (2023-2025) demonstrate a consistent focus on RIS applications, with 82% of works addressing reconfigurable surface optimization. Key trends include the integration of deep learning with communication systems (notably reinforcement learning for resource allocation), advancement of THz and near-field technologies for 6G, and novel approaches to physical-layer security. The research shows increasing emphasis on practical implementations, including UAV networks and autonomous vehicle communications.

Reza Ghabchelooمشاهده پروفایل

استاد

• Robotics
• Automation Engineering
• Control Systems
• +۷ مورد دیگر

Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization

Eric Saxمشاهده پروفایل

استاد

• Automotive Systems
• Autonomous Driving
• Cybersecurity
• +۷ مورد دیگر

Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.

• Structural Bioinformatics
• Protein Structure Prediction
• Immunoinformatics
• +۳ مورد دیگر

Professor Charlotte Deane is a leading academic in structural bioinformatics, holding the position of Professor at the University of Oxford's Department of Statistics and Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). She leads the Oxford Protein Informatics Group (OPIG), focusing on protein structure prediction, immunoinformatics, and AI-driven drug discovery. Her research integrates computational methods with biological insights, developing tools widely used in academia and industry. Prior roles include Head of the Department of Statistics, Deputy Head of the Mathematical, Physical and Life Sciences (MPLS) Division at Oxford, and Chief Scientist of Biologics AI at Exscientia. During the COVID-19 pandemic, she served on SAGE and as UKRI's COVID-19 Response Director. In 2022, she was awarded an MBE for her contributions to pandemic research. Her research group's work spans antibody design, T-cell receptor analysis, and small molecule discovery, with a focus on open-source software development. Current projects include advancing AI methods for protein structure prediction and therapeutic antibody engineering. Recent publications highlight innovations in computational drug design, antibody developability, and machine learning applications in structural biology.

David Lindlbauerمشاهده پروفایل

استادیار

• Mixed Reality
• Computational Interaction
• Extended Reality (XR)
• +۵ مورد دیگر

David Lindlbauer is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on advancing Mixed Reality (MR) and Extended Reality (XR) interfaces through computational interaction methods that optimize spatial, temporal, and multimodal feedback.

Rajendra Acharyaمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare
• Machine Learning
• +۵ مورد دیگر

Rajendra Acharya is a Professor (Artificial Intelligence in Health) at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds qualifications including BEng, MTech, two PhDs, and a DSc. His research focuses on AI applications in healthcare, pattern recognition, and medical diagnostics, with notable contributions to EEG analysis, deep learning, and disease detection. Awards include multiple Research.com Leader Awards in Computer Science for Australia and Singapore (2022–2025). His work spans over 650 publications, with high-impact studies on automated disease diagnosis via AI, including COVID-19 detection using X-rays and EEG-based seizure detection. His research interests integrate machine learning, signal processing, and healthcare technologies. He collaborates internationally and advises on AI-driven health solutions. No student list provided; however, his extensive supervision is implied through his research output.

Maia Angelovaمشاهده پروفایل

استاد

• Artificial Intelligence in Healthcare
• Data-Driven Models
• Machine Learning
• +۷ مورد دیگر

Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.

Suining Heمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Smart & Connected Communities
• Human-Centered Computing
• +۱۰ مورد دیگر

Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.

John R Andersonمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Cognitive Science
• Computational Psychology
• +۳ مورد دیگر

John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.

Austin Rovinskiمشاهده پروفایل

استادیار

• Computer Architecture
• VLSI
• Electronic Design Automation (EDA)
• +۵ مورد دیگر

Austin Rovinski is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University’s Tandon School of Engineering. He specializes in chip design, electronic design automation (EDA), and open-source hardware methodologies. His research focuses on VLSI design, domain-specific accelerators, and chiplet-based systems. Prior to NYU, he held a postdoctoral position at Cornell University and earned all his degrees (Ph.D., M.S., and B.S.) from the University of Michigan. Education: Ph.D., Electrical Engineering, University of Michigan - Ann Arbor Master’s, Electrical Engineering, University of Michigan - Ann Arbor Bachelor’s, Electrical Engineering, University of Michigan - Ann Arbor Research Focus: Developing open-source EDA frameworks like OpenROAD Optoelectronic interconnect systems for 2.5D packaging Agile hardware design methodologies Reconfigurable sparse matrix accelerators RISC-V-based manycore processors (e.g., Celerity project) Key Contributions: Austin led the development of the OpenROAD RTL-to-GDS flow and contributed to the Sirius and Celerity projects. His work emphasizes reproducibility, democratizing chip design through open-source tools. Awards: IEEE Micro Top Picks (2015) Michigan EECS Outstanding Research Award (2016) NSF Graduate Research Fellowship Honorable Mention (2017, 2018) Advising & Grants: Actively mentors graduate students in chip design and EDA. His research is supported by NYU’s Tandon School of Engineering and collaborations with industry partners. Labs & Teams: Core contributor to the OpenROAD project, part of NYU’s hardware design and EDA initiatives, and collaborator on the Celerity manycore processor project.

David Burtonمشاهده پروفایل

استاد

• Soil Science
• Nitrogen Cycling
• Greenhouse Gas Emissions
• +۵ مورد دیگر

Dr. David Burton is a Professor in the Department of Plant, Food, and Environmental Sciences at Dalhousie University's Faculty of Agriculture. He serves as Director of the Centre for Sustainable Soil Management and leads initiatives in soil health, greenhouse gas emissions, and sustainable agricultural practices. His teaching spans undergraduate and graduate courses in soil science, nutrient management, and climate change. Research focuses on microbial metabolism in soil, nitrogen cycling, and the environmental impacts of agricultural practices. He co-founded the Atlantic Soil Health Lab and manages the Greenhouse Gas Analysis Lab. Key affiliations include the Canadian Society of Soil Science (Fellow), Soil Conservation Council of Canada, and Fertilizer Canada's 4R Research Network. Recent work emphasizes soil's role in climate resilience, including presentations on regenerative farming and soil carbon sequestration. He collaborates with government and industry to develop climate-smart soil management policies and tools for nitrogen management optimization. Awards: Fellow of the Canadian Society of Soil Science Labs: Centre for Sustainable Soil Management, Greenhouse Gas Analysis Lab, Atlantic Soil Health Lab Grants: NSERC CREATE Climate Smart Soils

Stefanie Czischekمشاهده پروفایل

استادیار

• Quantum many-body systems
• Quantum condensed matter
• Quantum computation and simulation
• +۴ مورد دیگر

Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.

Quan Zhouمشاهده پروفایل

استاد

• Miniaturized Robotics
• Robotic Manipulation
• Mechatronics
• +۹ مورد دیگر

Quan Zhou is a Professor leading the Robotic Instruments Group at the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Finland. He holds an M.Sc. in Control Engineering and a Dr.Tech. in Automation Technology from Tampere University of Technology. His research focuses on miniaturized robotics, robotic manipulation using contact, acoustic, magnetic, interfacial, and fluidic methods, integrating physics, mechatronics, and machine learning to address challenges in dexterous manipulation with applications in biomedicine, materials science, and industrial technologies. He directs the Master’s Programme in Automation and Electrical Engineering (AEE) at Aalto and coordinates the European Robotics Association’s Topic Group on Miniaturized Robotics. He has led the EU FP7 project FAB2ASM and chaired international conferences like MARSS 2019. Notably, he received the 2018 Anton Paar Research Award for Instrumental Analytics and Characterization. His research spans fundamental methodologies and practical applications, emphasizing interdisciplinary innovation. Recent work includes advancements in fluid-driven manipulation, biomimetic robotics, and acoustic particle control. His contributions bridge theoretical frameworks and real-world automation solutions, with publications in journals like Advanced Intelligent Systems , Nature , and Physical Review E . Prof. Zhou’s leadership roles include coordinating the EIT Digital Master's Programme in Autonomous Systems and chairing IEEE Finland robotics chapters. His work has been recognized through grants and awards, reflecting his impact on robotics and automation research and education.

Nikolai Matniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Robotics
• +۱۰ مورد دیگر

Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He holds a secondary appointment in the Department of Computer and Information Science and is a member of the Applied Mathematics and Computational Sciences (AMCS) graduate group. His research focuses on integrating learning, optimization, and control for safety-critical and data-driven cyber-physical systems, with applications in robotics, autonomy, and distributed control. Secondary Appointment: Computer and Information Science (University of Pennsylvania) Labs/Centers: GRASP Lab, PRECISE Center His research bridges machine learning, robust control, and autonomous systems, particularly in developing guaranteed-safe strategies for cyber-physical systems. He emphasizes the importance of reliability and robustness in learning-based control for applications like self-driving vehicles and agile robots, where failures could be catastrophic. Recent publications highlight advancements in vision-based robotic control, neural ODEs for motion planning, adversarial exploration strategies, and stability-constrained learning. These works span conferences such as ICRA, CoRL, WACV, IROS, and L4DC, with a focus on safety-critical applications. Notable scientific awards include the NSF CAREER Award, George S Axelby Award (for his work on System Level Synthesis), AFOSR YIP award, and Google Research Scholar Award. He was also elevated to IEEE Senior Member. Matni advises Ph.D. students in Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Applied Mathematics and Computational Sciences (AMCS). His teaching includes courses like ESE 2030 (Linear Algebra with Engineering/AI applications) and others focused on control theory and robotics.