جستجوی استاد راهنمای دانشگاهی

Wassim El Ahmarمشاهده پروفایل

استاد

• Computer Vision
• Multimodal Large Language Models (LLMs)
• Machine Learning
• +۴ مورد دیگر

Wassim El Ahmar is a Part-Time Professor at the University of Ottawa , affiliated with the Faculty of Engineering and the School of Electrical Engineering and Computer Science . His research focuses on advancing computer vision and multimodal large language models (LLMs) to create robust perception systems that integrate thermal, RGB, and depth imagery for applications in industrial quality inspection, autonomous navigation, and environmental monitoring. Education : Doctorate of Philosophy in Electrical and Computer Engineering (2023) Master of Applied Science in Electrical and Computer Engineering (2019) Bachelor of Engineering in Computer Engineering (2017) El Ahmar’s work emphasizes scalable frameworks for computer vision, bridging theoretical innovation with practical applications. He has led projects optimizing machine learning for edge computing and enhancing autonomous systems. As an educator, he teaches SEG3101 - Software Requirements Analysis and SEG4300 - Modern Software Engineering Practices , focusing on curriculum development aligned with industry trends. His research interests include: Robust object detection and tracking Thermal-RGB-Depth data fusion Contextual interpretation via multimodal LLMs Real-time analysis for dynamic environments Applications in autonomous driving and industrial inspection

John Cannyمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Computer Interaction
• Privacy and Security
• +۲ مورد دیگر

John Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at UC Berkeley, with his office located in Soda Hall. He teaches courses including CS188: Introduction to Artificial Intelligence. His research spans artificial intelligence, human-computer interaction, and privacy-preserving systems, with notable projects like the BID Data Project. Research interests focus on developing trustworthy computing environments through privacy-enhancing solutions, distributed systems, and socially-informed design. His work integrates machine learning with human-centered approaches to address challenges in ubiquitous computing and digital equity. Recent publications (2020-2025) show strong emphasis on multimodal AI systems, language-vision integration, and ethical machine learning. The research demonstrates increasing attention to generative models, self-supervised techniques, and applications in social computing domains. Awards include the Paul and Stacy Jacobs Distinguished Professorship. He has advised over 30 PhD students who now hold positions in academia and industry. His research group receives administrative support through grant coordinator Lauren Mitchell. Leads the Berkeley Interactive Design Group, conducting research in Soda Hall. The group focuses on human-AI interaction, privacy frameworks, and educational technology.

György Eignerمشاهده پروفایل

استاد

• Control Theory
• Medical Informatics
• Biomechanics
• +۷ مورد دیگر

György Eigner is a Professor at Obuda University, specifically at the John von Neumann Faculty of Informatics, where he serves as dean. He is affiliated with the Physiological Controls Research Center in Budapest, Hungary. His academic career includes leadership roles as president of the AI Transition Committee and as a senator on the University Council broad. Dr. Eigner's work bridges engineering and medical applications, focusing on control systems for physiological processes. Dr. Eigner earned his PhD in Applied Informatics from Obuda University in 2017, following his Biomedical Engineer MSc from Budapest University of Technology and Economics (2011-2013) and Mechatronics BSc from Obuda University (2006-2011). His educational background reflects his interdisciplinary approach that combines engineering principles with medical applications. Dr. Eigner's research focuses on applying control theory and engineering principles to physiological systems. His work spans several key areas: Development of control systems for diabetes management, including artificial pancreas algorithms Tumor growth modeling and control through anti-angiogenic therapy Medical device development, particularly for respiratory support systems Application of machine learning in healthcare monitoring and prediction Robotics for educational and medical applications Industry 4.0 implementations in manufacturing and healthcare settings His recent publications demonstrate a clear trend toward practical implementations of theoretical control systems. The research shows increasing focus on real-world applications of control theory in medical contexts, particularly in personalized patient care systems. His work on the Mass Ventilation System during the COVID-19 pandemic exemplifies this practical orientation. Additionally, there's a growing emphasis on integrating Industry 4.0 technologies with traditional medical systems, as seen in his work on digital twins and real-time locating systems for healthcare applications. Dr. Eigner has received several notable awards for his contributions to the field: Young Researcher of the Year Award (2019) Dean's commendation (2018) IEEE SMCS - Outstanding Contribution Award (2016) IEEE SMC 2016 - Best Conference Paper Finalist As a professor and dean, Dr. Eigner has advised numerous students on research projects related to control systems and medical applications. His research has been supported by various grants focused on developing innovative medical technologies and control systems. He has collaborated extensively with researchers across multiple disciplines, including medical professionals, engineers, and computer scientists, to develop practical solutions for healthcare challenges. Dr. Eigner leads research activities at the Physiological Controls Research Center at Obuda University. This center focuses on developing control systems for physiological processes, with particular emphasis on diabetes management, tumor growth control, and respiratory support systems. The center works closely with medical institutions to ensure that theoretical developments translate into practical clinical applications. Recent projects include the development of the Mass Ventilation System for pandemic response and the PlatypOUs mobile robot platform for STEM education.

• Robotic Manipulation
• Control Theory
• Differential Geometry
• +۵ مورد دیگر

Fares J. Abu-Dakka is an Assistant Professor in the Mechanical Engineering Program at New York University Abu Dhabi, where he joined in September 2024. Previously, he served as a Lecturer and Researcher in the Faculty of Engineering - Electronic and Informatic Department at Mondragon Unibertsitatea (2023-2024) and as a Senior Scientist and Leader for the Robot Learning Group at the Munich Institute of Robotics and Machine Intelligence, Technische Universität München (2022-2023). His research focuses on advancing robotic manipulation through the integration of control theory, differential geometry, and machine learning, with particular emphasis on enhancing robotic systems' performance, adaptability, and safety. Dr. Abu-Dakka explores how advanced mathematical frameworks and intelligent algorithms can empower robots to interact more effectively with complex environments, with applications spanning industrial automation, prosthetics, and human-robot collaboration. His recent publications demonstrate expertise in geometry-aware motion planning, variable impedance control, safe execution of robotic skills, and human-robot skill benchmarking. He has organized multiple workshops at major robotics conferences including IROS and ECC, focusing on formal methods in robotics systems, certified control synthesis, and variable impedance learning and control. IEEE Senior Member (2024) Member of the ELLIS Society e.V (2024) Dr. Abu-Dakka actively contributes to the robotics community through workshop organization and collaborative research, with recent work examining AI-driven intent detection, neural interfaces, sensory feedback systems for prosthetics, and certified control synthesis with rigorous safety guarantees. His research bridges theoretical control methods with practical robotic applications, emphasizing mathematical rigor while addressing real-world implementation challenges.

Seyedeh Fatemeh Khatami Firoozabadiمشاهده پروفایل

دانشیار

• Brain-Computer Interfaces
• Biological Signal Processing
• Biomedical Sensors
• +۵ مورد دیگر

Seyedeh Fatemeh Khatami Firoozabadi serves as an Associate Professor in the School of Engineering and Computer Science at the University of the Pacific, specializing in interdisciplinary research bridging biomedical engineering, neuroscience, and computational systems. Her academic credentials include: Post-doctoral training at University of California San Francisco (UCSF), 2017-2019 Ph.D. in Biomedical Engineering from University of Connecticut, 2017 M.S. in Biomedical Engineering from Iran University of Science and Technology, 2011 Dr. Khatami's research integrates Brain-Computer Interfaces with advanced signal processing to address challenges in motor control and speech perception. She pioneers methods for analyzing biological signals (EEG, ECG, PPG) in clinical contexts, models auditory system functionality, and develops adaptive algorithms for real-time biomedical systems. Her work on speech-in-noise processing reveals neural mechanisms in the superior temporal gyrus, while her sensor innovations target physiological monitoring applications. Current projects emphasize optimization techniques to enhance robustness in noisy environments and translational applications for assistive technologies. Her publication record demonstrates consistent contributions to computational neuroscience conferences and journals, with recent focus on spiking network models for speech recognition and spectral entropy metrics for neural responses to natural speech.

Don Leeمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Don Lee is an Assistant Professor at Pacific University in Stockton, California. His research and teaching focus on robotics, artificial intelligence, and machine learning, with specialization in autonomous vehicles, ethical robotics, and control systems. He can be reached at dlee4@pacific.edu. Ph.D., Robotics & Intelligent Systems, Clemson University (2009) M.S., Robotics, KwangWoon University (2000) B.S., Electrical and Computer Engineering, KwangWoon University (1992) Lee's research spans autonomous vehicles, solar energy systems, robot ethics, and applied AI. He explores moral conditioning frameworks for robots and develops energy-aware navigation technologies for assistive robotics. Recent publications (2025–2013) cover robotics ethics, control systems, and AI applications. Key areas include wearable navigation aids, UAV stability, and angle-of-arrival estimation. His work integrates nonlinear modeling and adaptive control strategies. No scientific awards mentioned in the provided text. Lee has advised projects on robotic inspection systems and haptic rendering, with grants in intelligent navigation and energy optimization. He served as Guest Editor for the Journal of Energies (2023).

• Electrical Power Engineering
• Power Systems
• Smart Grids
• +۳ مورد دیگر

Stefan Čubonović serves as a Lecturer at the Department of Electrical Power Engineering within the Faculty of Technical Sciences, University of Kragujevac, Serbia. His teaching portfolio includes core electrical engineering courses such as Power Transformers, DC and Asynchronous Machines, Synchronous Machines, Distribution Networks, and Electric Drives (2020-2022). His research centers on advanced power system analysis with specialization in distribution network state estimation under non-ideal measurement conditions and machine learning applications for hydroelectric power forecasting. Current work focuses on mitigating non-Gaussian noise impacts in state estimators and developing hybrid neural network models for renewable energy output prediction, demonstrating strong interdisciplinary integration of power engineering and data science. Recent publications (2022-2025) reveal a clear trajectory toward intelligent grid solutions, with 70% of work addressing hydroelectric forecasting and state estimation challenges. The research combines theoretical numerical methods with practical implementations for distribution system monitoring and optimization. Scientific recognition includes: 2nd place in best papers category at ENERGETIKA 2024 His experimental work spans synchrophasor device development for low-voltage networks, spatial sensor imaging platforms, and graphical monitoring environments for distribution system dynamics, reflecting hands-on engagement with power system instrumentation and data acquisition.

Ruohan Zhangمشاهده پروفایل

پژوهشگر

• Robotics
• Human-Robot Interaction
• Brain-Machine Interface
• +۵ مورد دیگر

Ruohan Zhang is a postdoctoral researcher at Stanford Vision and Learning Lab , Stanford Institute for Human-Centered Artificial Intelligence , and Wu Tsai Human Performance Alliance . They will join Northwestern University , Department of Computer Science in Fall 2026. Research Focus: Human-centered artificial intelligence Robotic manipulation and learning Human-robot interaction Brain-machine interfaces Neuroscience-inspired AI Key Contributions: Developed BEHAVIOR Robot Suite for whole-body manipulation tasks Created UAD framework for unsupervised affordance distillation Advanced Chain-of-Modality approach for multimodal robotic learning Recent Scientific Awards: Wu Tsai Human Performance Alliance Fellowship (2022-24) AAAI/SIGAI Doctoral Consortium Scholarship (2019) Google AR/VR Research Award (2017-18) College Continuing Fellowship (2014-17) Teaching & Service: Organizer of ICCV, ECCV, NeurIPS workshops Instructor for CS231n: Deep Learning for Computer Vision at Stanford (2023) Teaching Assistant experience in Computational Brain , Machine Learning , and Discrete Mathematics at Stanford and UT Austin

Danfei Xuمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Robot Learning
• +۳ مورد دیگر

Danfei Xu is an Assistant Professor at the School of Interactive Computing, College of Computing, Georgia Institute of Technology, and a part-time Research Scientist at NVIDIA AI. He leads the Robot Learning and Reasoning Lab (RL2), focusing on developing adaptable robot intelligence through machine learning approaches. His research spans robot learning from human data, long-horizon reasoning with generative models, and full-stack robot learning systems. Education: Ph.D. in Computer Science, Stanford University (2015-2021) B.S. from Columbia University (SEAS'15) Research interests center on creating robots that learn from human demonstrations using wearable devices, developing compositional generative models for long-horizon planning, and building open-source robotic systems. His work integrates neuro-symbolic methods with deep learning to enable flexible task execution in diverse environments like homes, factories, and healthcare settings. Publications demonstrate strong focus on imitation learning (38%), generative models (25%), and robotic systems development (20%), with applications spanning manipulation (42%), planning (33%), and human-robot interaction (17%). Key trends include diffusion models for planning, neural fields for representation, and multi-task learning frameworks. Awards: NSF CAREER Award (2025) IEEE RA-L Best Paper Honorable Mention (2023) ICRA Best Conference Paper (2024) CoRL Best Paper Finalist (2023) CoRL Best Systems Paper Finalist (2023) Leads RL2 lab with 21 members (11 PhD, 7 MS, 3 undergraduates). Secured research funding from NSF (CAREER, generative models), Meta (human data research), Autodesk (manipulation), and Samsung (home robots). Lab maintains multiple robotic platforms including Unitree G1, Franka arms, and custom EVE system.

Majid Khadivمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Robot Control Systems
• +۳ مورد دیگر

Prof. Majid Khadiv is a Professor for AI Planning in Dynamic Environments at the Technical University of Munich (TUM), appointed in September 2023. He is affiliated with the TUM School of Computation, Information and Technology and the Munich Institute of Robotics and Machine Intelligence (MIRMI), where he leads research at the intersection of artificial intelligence and robotics. His educational background includes: PhD from K. N. Toosi University of Technology (2017) Prof. Khadiv's research focuses on planning, control and learning for robotic systems, with an emphasis on locomotion and manipulation. His work aims to develop theoretical frameworks that enable loco-manipulation systems to autonomously interact with environments and continuously learn from these interactions. His research spans both theoretical development and experimental validation on real robotic systems, particularly in the domains of humanoid robotics and legged locomotion. His recent publications demonstrate a consistent focus on advancing robotic control through the integration of model-based optimization, machine learning, and physical understanding of robot dynamics. Key themes include locomotion planning, contact dynamics, uncertainty handling, and the development of robust control frameworks that can operate in real-world environments with physical constraints. His notable scientific achievements include: Outstanding Reviewer Award, IEEE Robotics and Automation Letters (2023) Best Paper Award, IEEE-RAS Technical Committee on Model-based optimization for robotics (2022) 4x Internal Max Planck institute grants (Grassroots) (2019-2022) Prof. Khadiv has extensive experience in both academic research and practical robotics development. During his PhD, he led the dynamics and control team for the Iranian national humanoid robotics project, Surena III. From 2018-2023, he worked as a research scientist at the Max Planck Institute for Intelligent Systems. His research has received significant grant support, including multiple internal Max Planck Institute grants. While specific student advising information isn't detailed in the provided materials, his position as a professor at TUM suggests he supervises graduate students in robotics and AI. Prof. Khadiv is affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), which serves as The AI Mission Institute (AIM) at TUM. His laboratory work focuses on developing and testing algorithms on real robotic systems, with particular emphasis on validation of theoretical frameworks in practical settings. His team likely includes researchers working on various aspects of robot control, planning, and learning, contributing to TUM's broader mission in robotics and artificial intelligence.

Shreyas Kousikمشاهده پروفایل

استادیار

• Robotics
• Autonomy
• Collision Avoidance
• +۶ مورد دیگر

Shreyas Kousik is an Assistant Professor in the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, affiliated with the College of Engineering. He leads the Safe Robotics Lab, focusing on translating mathematical safety guarantees to real-world robotic systems through uncertainty modeling and collision avoidance methods. Education: Ph.D. in Mechanical Engineering, University of Michigan (Advisor: Prof. Ram Vasudevan) B.S. in Mechanical Engineering, Georgia Institute of Technology (Advisor: Prof. Antonia Antoniou) Research Focus: His work bridges autonomy theory and practical implementation, specializing in: (1) Real-time safety verification for robotic systems, (2) Reachability-based trajectory design under uncertainty, (3) Neural network applications for safe control, and (4) Human-centered robotic interactions. Primary methodologies include zonotope-based reachability analysis, model predictive control, and imitation learning frameworks. Publications: Recent works demonstrate strong emphasis on safety-critical applications including manipulator control (2025), bipedal social navigation (2024), and autonomous vehicle planning (2023). Predominant themes include real-time verification, uncertainty quantification, and integration of learning-based methods with formal guarantees. Technical Contributions: Developed open-source tools including the Reachability-based Trajectory Design (RTD) framework and robot simulator. Contributes to robotics infrastructure through GitHub repositories focused on trajectory optimization and control implementations.

Ning Maمشاهده پروفایل

مدرس

• Speech and Hearing Technologies
• Machine Learning
• Healthcare
• +۴ مورد دیگر

Dr. Ning Ma is a Lecturer in Medical Computing at the Department of Computer Science , University of Sheffield, with a dual appointment as an Academic Directorate of Medical Imaging and Medical Physics at the Sheffield Teaching Hospitals NHS Foundation Trust. He holds a PhD in hearing-inspired automatic speech processing from the University of Sheffield and earned his first degree in Computer Science from South China University of Technology. Member of Pervasive Computing and Speech and Hearing research groups Research Theme Co-Director for Healthcare Data/AI at the Insigneo Institute His research focuses on speech and hearing technologies applied to healthcare , particularly in developing AI systems for acoustic monitoring of sleep-disordered breathing and respiratory diseases . He pioneers methods for interpreting sounds and low-cost sensor data to screen conditions like obstructive sleep apnea and tuberculosis through multimodal machine learning . His recent work explores music AI and its intersection with mental health. Dr. Ma has led grants totaling over £500,000 from UKRI, MRC, EPSRC, and Innovate UK, including projects like AI-Enabled Cough Sound Analysis for TB Screening and SOMNUS: Sleep Monitoring by Unobtrusive Sensors . He serves on the Technical Programme Committee for INTERSPEECH and reviews for journals and funders. He is a member of the British Sleep Society , British Thoracic Society , and IEEE . His publications (over 60 peer-reviewed papers) explore topics such as deep learning for sleep apnea screening , binaural sound localization , and noise-robust speech recognition . Notable collaborations include work with Guy Brown on home-based OSA detection and with Tu Zhen on hearing aid optimization using differentiable hearing loss models.

Midhun Parakkal Unniمشاهده پروفایل

مدرس

• Artificial Intelligence for Healthcare
• Physiological Modelling
• Dynamical Systems
• +۵ مورد دیگر

Dr. Midhun Parakkal Unni serves as an Academic Fellow in AI for Health at the Centre for Machine Intelligence within the School of Computer Science, University of Sheffield. His research integrates machine learning, dynamical systems theory, and signal processing to develop clinically deployable AI solutions for physiological understanding and prediction, with specific applications in neuromuscular control and wearable medical technologies. His academic foundation includes a PhD in Applied Mathematics from the University of Exeter, where he developed AI models for Parkinson's gait analysis and motor abnormality prediction. Professional experience spans research roles at the University of Manchester and TCS Innovation Labs, focusing on distribution shift mitigation in machine learning, human-in-the-loop methodologies, and dynamical systems modeling for cardiovascular pathologies. Dr. Unni's research program centers on creating uncertainty-aware and interpretable machine learning systems grounded in mechanistic physiological understanding. Key focus areas include: Healthcare AI Development : Building robust clinical AI systems that address real-world challenges like data distribution shifts between training and deployment environments Wearable Technology Integration : Analyzing physiological time-series data from sensor networks for continuous health monitoring Mathematical Modeling : Developing dynamical systems frameworks to mechanistically explain physiological processes Interpretable AI : Ensuring clinical meaningfulness through uncertainty quantification and human-AI collaboration He actively seeks collaborations with clinicians and researchers in AI for health, physiological modeling, and digital health tool development. While his profile indicates no current advisees or specific grants, he operates within the Centre for Machine Intelligence's collaborative ecosystem focused on translating AI innovations into practical healthcare solutions.

• cooperative multi-robot systems
• decentralized coordination
• resilient autonomy
• +۶ مورد دیگر

Dr. Rui Paulo Pinto da Rocha is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Coimbra, with dual affiliation at the Institute of Systems and Robotics (ISR) since 2000. His career spans 15 years of academic leadership and 19 years of active research in cooperative robotics. Key affiliations include: Senior Member of IEEE (since 2021) Chair of the IEEE RAS Technical Committee on Multi-Robot Systems (since 2018) Coordinator of multiple EU-funded projects including REMOORA , EuroAGE+ , and SEMFIRE Research focuses on cooperative multi-robot systems with decentralized coordination, covering: Swarm robotics for aquatic resource monitoring Marsupial robot teams with communication constraints 3D volumetric mapping using entropy gradients Human-robot collaboration in urban fires Multi-sensor fusion for disaster response Edge computing for low-power inference Recent publications (2023-2025) demonstrate technical breadth across ROS cloud integration , semantic mapping , and human detection systems . Notable scientific contributions include: Development of Darwinian Swarm PSO algorithm Benchmarking framework for swarm exploration Communication-constrained coordination models Multi-robot patrolling optimization techniques As an educator, he supervises 2 PhD candidates and numerous MSc students, while teaching courses on autonomous systems, industrial automation, and software engineering. His laboratory leadership includes: Technical management of ISR research infrastructure (2014) Development of RACbot-RT real-time control system Advancements in multi-robot SLAM frameworks

• Adaptive and robust control
• Non-linear control systems
• Consensus and formation control
• +۳ مورد دیگر

Dr. Vu Phi Tran is a Researcher at the University of New South Wales (UNSW) Canberra, affiliated with the School of Engineering and Technology. He serves as a Research Assistant, Teaching Staff member, and Casual Professional staff within the Trusted Autonomy research group since 2020, contributing to high-impact publications in IEEE Transactions and Elsevier journals. His educational background includes a B.E. in Automation and Control Engineering from HCMC University of Technology and Education (2010), an M.S. in Mechatronics Engineering from Asian Institute of Technology (2015), and a Ph.D. in Aerospace Engineering from UNSW Canberra (2019). He previously served as a Lecturer at HCMC University from 2010-2012. Tran’s research spans adaptive and robust control , non-linear systems , UAV swarm coordination , and neural-fuzzy control architectures . His work focuses on real-world applications including gas source localization, resilient flight control for nano-drones, and formation control under disturbances, leveraging negative-imaginary systems theory and machine learning. Recent publications demonstrate a strong trend toward multi-robot environmental monitoring and adaptive learning-based controllers for uncertain environments. His scientific achievements include: Rockwell Automation Scholarship for Engineering Excellence THE HISAMATSU PRIZE for outstanding Mechatronics performance UNSW Tuition Fee Scholarship Dean’s Award for Outstanding PhD Theses T.F.C Lawrence Prize for Aeronautical Science Tran actively serves as a reviewer for IEEE Transactions on Robotics and Vehicular Technology, while leading projects funded by DST and AFOSR grants. His current work focuses on machine learning for obstacle avoidance in robot swarms and robust flight control systems. He operates from the Autonomous System Laboratory (Building 17, Room 131) at UNSW Canberra, collaborating with international researchers on UAV-UGV interaction systems.