Find and connect with academic supervisors, professors, and scholars for your research journey.
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Researcher
Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.
Senior Lecturer
Björn Åstrand serves as a Senior Lecturer at Halmstad University's School of Information Technology, specializing in mechatronics and autonomous systems with the additional qualification of Associate Professor (Docent). His research develops perception and cognition systems for mobile robots, focusing on agricultural robotics, automatic guided vehicles, and self-driving vehicles. He applies signal processing and machine learning to semantic mapping, human tracking, agent behaviour modelling, intention estimation, object detection/classification, and autonomous vehicle safety systems. Dr. Åstrand teaches Electrical Circuits and Electronics, Design of Mechatronical Systems, Intelligent Vehicles, Robotics, Sensors, Signals and Systems, and Signals and Sensors courses. He acts as course responsible and examiner for Bachelor of Science engineering theses while supervising undergraduate, postgraduate, and PhD students. No scientific awards were documented in the source materials. His academic supervision spans undergraduate theses, postgraduate research, and PhD candidate mentorship, with primary responsibility for engineering program thesis examination.
Assistant Professor
Mizan Rahman is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama's College of Engineering. He serves as the Director of the Connected and Automated Mobility Laboratory (CAM Lab) at UA, leading research in cutting-edge transportation technologies. His work bridges civil engineering with computer science, focusing on the intersection of physical infrastructure and digital systems. Dr. Rahman's educational background includes: B.S. in Civil Engineering from Bangladesh University of Engineering and Technology (2008) M.S. in Civil Engineering from Clemson University (2013) Ph.D. in Civil Engineering from Clemson University (2018) His research interests span artificial intelligence, machine learning, cybersecurity, and digital twins applied to transportation systems. Dr. Rahman takes an interdisciplinary approach to solving evolving mobility challenges, with particular focus on connected and automated transportation systems for smart cities. His work addresses critical issues in AI-based predictive analytics, cybersecurity and privacy for connected mobility, driver behavior modeling, heterogeneous wireless communication, and transportation cyber-physical systems. Analysis of Dr. Rahman's recent publications reveals a strong emphasis on digital twin technology for traffic management, cybersecurity for autonomous vehicles (particularly GNSS spoofing detection), and connected vehicle applications. His work demonstrates a clear trajectory toward creating safer, more efficient transportation systems through the integration of advanced computing technologies with traditional civil infrastructure. The research spans theoretical development, numerical validation, and real-world experimental implementation. Dr. Rahman has received significant recognition for his work: NSF CAREER Award (2024) IEEE George N. Saridis Best Transactions Paper Award (2020) U.S. Department of Transportation Dwight D. Eisenhower Doctoral Fellowship (2013-2014) U.S. Department of Transportation Dwight D. Eisenhower Master's Fellowship (2012-2013) He has secured multiple federally funded projects from the National Science Foundation (NSF), the U.S. Department of Transportation (USDOT), and the Federal Motor Carrier Safety Administration (FMCSA). His research has practical applications in developing low-cost solutions for self-driving cars to detect GPS hacking and improve overall transportation safety. Dr. Rahman actively collaborates across disciplines, taking advantage of UA's cybersecurity program in the computer science department to advance research in connected and automated transportation systems. As director of the CAM Lab, Dr. Rahman leads a team working at the forefront of transportation innovation, with a vision to build smart transportation systems that enhance quality of life and contribute to economic prosperity. His work is particularly relevant as cities become increasingly connected and automated, moving toward the reality of smart cities.
Professor
Jonas Sjoberg is a Professor of Mechatronics at Chalmers University of Technology and leader of the Mechatronics research group. His work spans over 30 years with 143 publications and leadership in 29 research projects. His current research focuses on autonomous vehicle systems, vehicle dynamics, and traffic safety applications. Dr. Sjoberg's research interests center on mechatronic systems for transportation applications. His work bridges theoretical control systems with practical automotive implementations, particularly in autonomous vehicle technology. His research group investigates vehicle dynamics, path planning, intersection management, and safety systems, with growing emphasis on micromobility applications including autonomous bicycles and pedestrian interaction. Recent work demonstrates strong focus on real-world applications of control theory to improve vehicle safety and performance. His publication record shows consistent output with 15+ papers annually, demonstrating sustained research activity. The work spans fundamental control theory (system identification, nonlinear control) to applied transportation problems (intersection management, road surface estimation, autonomous docking). Recent publications show increasing focus on micromobility applications, particularly autonomous bicycles, and integration of machine learning techniques with traditional control approaches. Dr. Sjoberg leads multiple significant research projects including MicroSIM (2026-2027), Mintox (2025-2026), and MicroITS, with funding from VINNOVA, EU, and industry partners. His projects address critical challenges in autonomous vehicle safety, micromobility integration, and traffic management. As leader of the Mechatronics research group, Dr. Sjoberg oversees research on vehicle control systems, autonomous driving technologies, and safety applications. His laboratory work focuses on practical implementation of theoretical control concepts, with recent emphasis on bicycle dynamics and micromobility safety systems.
Associate Professor
Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.
Professor
Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.
Assistant Professor
Dr. Siobahn Day Grady is an Assistant Professor of Information Science/Systems at North Carolina Central University (NCCU) and serves as the Founding Director of the Institute for Artificial Intelligence and Emerging Research (IAIER), which she established in 2025. She also holds leadership roles as Co-Director of The Center fOr Data Equity (CODE), Program Director of the Information Science Program, and Faculty Fellow in the Office of Faculty and Professional Development. Dr. Grady reports to the Provost with oversight of a $1M+ annual budget and $3M+ grant portfolio, managing a staff of 5 plus advisory boards. Ph.D. in Computer Science, North Carolina Agricultural & Technical State University (2018) M.S. in Computer Science, North Carolina Agricultural & Technical State University (2018) M.S. in Information Science, North Carolina Central University (2009) B.S. in Computer Science, Winston-Salem State University (2005) Dr. Grady's research focuses on the ethical implementation of artificial intelligence, with particular emphasis on fairness, bias mitigation, and equity in AI systems. Her work bridges technical AI development with social justice considerations, especially in healthcare applications and educational contexts. She has pioneered initiatives to increase AI literacy at HBCUs and developed frameworks for operationalizing fairness in AI governance. Her research interests include natural language processing, machine learning applications for social good, digital literacy programs for marginalized communities, and strategies to increase diversity in STEM fields through her STEM-It-Yourself program. Analysis of Dr. Grady's recent publications reveals a strong trajectory toward practical applications of AI ethics in real-world settings, particularly in healthcare and education. Her work demonstrates a consistent focus on creating frameworks that translate theoretical AI ethics principles into actionable guidelines for practitioners. There's a clear progression from technical AI research toward more interdisciplinary work that bridges computer science with social sciences, nursing, and education. Her publications increasingly address the needs of underrepresented communities and focus on practical implementation strategies rather than purely theoretical contributions. Winston-Salem State University 2023 Distinguished Alumni Award Durham Section of the National Council of Negro Women 2024 Distinguished Educator Sigma Iota Omega Chapter of Alpha Kappa Alpha Sorority, Incorporated® 2022 Soaring to Greater Heights in Science Technology Engineering Arts Mathematics Honoree The Links, Inc., Raleigh (NC) Chapter 2022 Emerald Award Honoree Association for Educational Communications and Technology (AECT) Culture, Learning, and Technology (CLT) Division 2023 Outstanding Publication Award Dr. Grady has secured significant grant funding totaling over $3 million, including a $1 million Google.org investment, $100K+ from Cisco, $15K from FICO, and funding from NTIA and NIH. She serves as Principal Investigator for the Digital Equity Leadership Program (DELP) and the Genomic Research and Data Science Center for Computation and Cloud Computing (GRADS-4C). Her mentoring extends to numerous students through programs like STEM-It-Yourself, which focuses on cultivating STEM identity among adolescent girls. Dr. Grady has established three endowed scholarships supporting economically disadvantaged students at multiple HBCUs, demonstrating her commitment to educational access. As Founding Director of the Institute for Artificial Intelligence and Emerging Research (IAIER), Dr. Grady leads North Carolina Central University's strategic vision for AI education, research, and policy. The institute includes the AI Emerging Scholars and Leaders Programs, which engage students, faculty, and staff in cross-disciplinary AI innovation. She has developed NCCU's first AI minor (pending approval) and serves as co-facilitator for the UNC AI Faculty Learning Community. Dr. Grady also holds leadership positions on the Governor's AI Council and multiple advisory boards, positioning NCCU as a national leader in responsible AI development and implementation.
Professor
Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.
Researcher
Ksander de Winkel serves as Project Leader of the Motion Perception and Simulation research group within the Department of Human Perception, Cognition and Action at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, a position he has held since March 2017. His role involves leading both fundamental and applied research on self-motion perception, with direct applications to motion simulation and autonomous vehicle technologies. His academic credentials include a Ph.D. in Multisensory Perception of Spatial Orientation and Self-Motion from Utrecht University (2013), an M.Sc. in Applied Cognitive Psychology earned cum laude (2008), and a B.Sc. in Psychology, all completed at Utrecht University. Additional training includes specialized coursework in Applied Bayesian Statistics and Mathematical Statistics. De Winkel's research program centers on how the brain integrates sensory information during self-motion, with particular focus on causal inference mechanisms when sensory cues conflict. His experimental work measures perceptual thresholds, investigates motion sickness triggers, and develops computational models of multisensory integration. This fundamental research directly informs applied projects including motion cueing algorithm development, simulator fidelity assessment, and solutions for autonomous vehicle passenger comfort. His recent publications (2017-2018) demonstrate consistent focus on causal inference in spatial orientation and heading perception, with studies appearing in high-impact journals including Scientific Reports, Journal of Vision, and PLoS ONE. These works reveal how individual differences and sensory discrepancies affect motion perception, providing critical insights for engineering applications. Scientific recognition includes: Young Researcher Grant from ESA Scientific Committee (2012) He actively supervises graduate researchers from multiple institutions including University of Twente, University of Tübingen, and Delft University of Technology, with several students graduating cum laude . His research group maintains active collaborations with automotive and simulation industries to translate theoretical findings into practical motion solutions. Current projects address autonomous driving side-effects and next-generation motion cueing systems. The Motion Perception and Simulation group operates within the Department of Human Perception, Cognition and Action, conducting psychophysical experiments using motion platforms, virtual reality systems, and neuroimaging techniques to advance understanding of self-motion perception.
Associate Professor
Saurabh Gupta is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign (UIUC), where he is based at the Coordinated Science Lab (CSL 319). He previously served as a Research Scientist at Facebook AI Research in Pittsburgh working with Prof. Abhinav Gupta. His educational background includes a PhD in Computer Science from UC Berkeley advised by Prof. Jitendra Malik, and an undergraduate degree in Computer Science and Engineering from IIT Delhi, India. Gupta's research focuses on building intelligent agents that can interact with the physical world, with particular emphasis on computer vision, robotics, and machine learning. His work explores representations that enable physical interaction and learning from active engagement with environments. Key research directions include spatio-semantic and topological representations for visual navigation, skill discovery, learning from videos, and active visual learning. His recent publications reveal a strong trend toward practical robotics applications with an emphasis on real-world deployment. The research spans multiple domains including humanoid robotics, egocentric vision, physical reasoning, and wireless sensing. Significant attention is given to bridging the sim-to-real gap and developing systems that work in practical environments rather than controlled laboratory settings. As an educator, Gupta teaches advanced courses including Deep Learning for Computer Vision (CS 444/ECE 494), Computer Vision (ECE 549/CS 543), and Learning-Based Robotics (ECE 598 SG). Gupta advises multiple PhD students including Arjun Gupta, Shaowei Liu, Aditya Prakash, Xiaoyu Zhang, Runpei Dong, and Xialin He, with former student Matthew Chang completing his PhD in 2024 on Robot Learning from Videos. His research group operates within the Coordinated Science Laboratory at UIUC, collaborating with researchers across computer vision, robotics, and machine learning domains. The group maintains strong connections with industry research labs including Meta AI Research, where former students have pursued research scientist positions.
Associate Professor
Dr. Martin v. Mohrenschildt is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. He holds academic positions as Undergraduate Advisor for Mechatronics and Mechatronics Coordinator. His expertise spans control systems, signal processing, hybrid systems, and immersive simulation. He earned a Dr. sc. math. ETH and Dipl. math. ETH (Swiss Federal Institute of Technology), and is a Professional Engineer (P.Eng.). Research focuses include Model Predictive Control, vibration analysis for industrial machinery, and multisensory integration in motion simulation. His Motion Simulator Laboratory features a 6-DOF simulator with advanced visualization and physiological measurement capabilities. The Embedded Systems Laboratory explores mechatronics and robotics, including alternative fuel injection systems. Teaching includes courses like CAS-748 (Time Series Analysis) and SFWR ENG 2SO3. Recent work emphasizes sensor fusion (LiDAR/camera/thermal), biofeedback systems for muscle interventions, and cognitive effects of motion cues in virtual environments. Collaborations with Dr. Shedden investigate perceptual neuroscience in immersive simulators. His lab facilities support interdisciplinary projects combining engineering, computer science, and cognitive science. Publications span 30+ years, covering hybrid systems theory, control algorithms, and applied sensor technologies. Current research directions include autonomous vehicle perception systems and aging-related changes in multisensory integration.
Associate Professor
Melike Erol-Kantarci is an Associate Professor and Tier 2 Canada Research Chair in AI-enabled Next-Generation Wireless Networks at the School of Electrical Engineering and Computer Science, University of Ottawa. She is the founding director of the Networked Systems and Communications Research (NETCORE) laboratory. She holds a PhD and has over 140 peer-reviewed publications, with an h-index of 37. Her research focuses on AI-driven wireless networks, 5G/6G communication systems, smart grids, and cybersecurity. Education: PhD in Electrical Engineering (not explicitly stated, inferred from profile). Research interests include AI-enabled wireless networks, smart grids, IoT, and underwater sensor networks. She has received awards such as the 2019 N2Women recognition and IEEE Best Tutorial Paper Award (2017). She serves on editorial boards of IEEE journals and has organized international conferences. Her recent articles emphasize AI applications in 6G networks, secure communications, federated learning, and resource allocation. Key trends include leveraging LLMs for network orchestration, combating adversarial attacks, and optimizing next-gen wireless architectures. Her work on beam selection, ISAC, and RIS-assisted systems addresses critical challenges in 5G/6G. Awards include the Canada Research Chair and best paper recognitions. She actively contributes to open RAN standards and has co-edited books on smart grids and intelligent transportation. Her lab, NETCORE, drives innovation in networked systems.
Researcher
Pau Dietz Romero is a Doctoral Researcher at Forschungszentrum Jülich GmbH, affiliated with the Peter Grünberg Institute (PGI) and specifically working within the Integrated Computing Architectures (PGI-4) department. His research is conducted at the Wilhelm-Johnen-Straße campus in Jülich, Germany, with a focus on computational and data science challenges in autonomous systems. Research Focus: His work centers on developing robust semantic segmentation techniques for self-driving tasks, emphasizing the creation of large-scale, cost-effective, and diverse datasets. This aligns with broader interests in artificial intelligence, computer vision, and data engineering applications in autonomous vehicle technology. Labs & Teams: Pau is part of the Quantum Computing Hardware Systems research group (PGI-4) at Jülich, contributing to advancements in computational architectures for emerging technologies.
Assistant Professor
Holger Caesar is a tenured Assistant Professor at Delft University of Technology (TU Delft) in the Intelligent Vehicles Lab . He leads research on scalable approaches for autonomous vehicle perception, prediction, and data annotation, with a focus on sensor fusion, domain adaptation, and minimal supervision. His work has been cited over 15,000 times. Education PhD in Computer Vision, University of Edinburgh Prior studies at KIT Karlsruhe, EPF Lausanne, and ETH Zurich Research Interests span autonomous driving perception, weakly supervised learning, novel view synthesis (NeRFs), diffusion models, and collaborative perception. He emphasizes reducing reliance on manual annotations through active learning and partial labeling techniques. Recent Publications include work on 4D Gaussian Splatting, camera-radar fusion, open-set scene graph generation, and safety benchmarking. These reflect trends toward multi-modal perception, robust sensor fusion, and foundation models for scalable autonomous systems. Scientific Awards ELLIS Europe Scholar (2024) TU Delft Cohesion Grant (100k EUR, 2024) Climate Action Grant (30k EUR, 2024) TKI High Tech Systems Grant (502k EUR, 2022) AiNed XS Grant (80k EUR, 2023) Argoverse Scene Flow Challenge Winner (unsupervised track, 2024) Advising & Grants include EU Horizon funding for the MOSAIC project (1 PhD and 1 Postdoc, 2024), EU KDT Cynergie4MIE grant (450k EUR, 2024), and industrial collaborations with Bosch, Motional, and partners across Europe. Labs & Teams include the Intelligent Vehicles Lab at TU Delft and founding roles in Motional's Data Annotation, Autolabeling, and Data Mining teams. He co-organizes workshops at ICCV, CVPR, and NCCV, and leads the ELLIS Delft Unit seminar series.
Research Fellow
Dr. Jia Liu serves as a Research Fellow at the Australian National University's Research School of Biology (Division of Ecology and Evolution) and concurrently holds a Technical Officer position in the Research School of Earth Sciences. Holding a PhD in Statistics, Dr. Liu conducts interdisciplinary research spanning statistical methodology, earth sciences, and computer vision applications. Education: PhD in Statistics Research focuses on Bayesian statistics, spatial data analysis, image analysis, geostatistics, and experimental design. Dr. Liu applies these methods to paleomagnetism, atmospheric science, and computer vision, with recent emphasis on uncertainty quantification and deep learning architectures for complex data analysis. Publication trends reveal dual trajectories: earth sciences applications (2020-2022) featuring directional statistics for paleomagnetic data and cloud physics modeling, alongside computer vision advancements (2021-2025) in 3D reconstruction, object detection, and segmentation. Core methodological contributions include novel approaches for spatiotemporal survey design and aleatoric uncertainty modeling. Awards: No major scientific awards documented Student advising activities and research grant funding details are not publicly available. The researcher maintains affiliations across multiple ANU schools but specific laboratory or team memberships were not specified in source materials.
