جستجوی استاد راهنمای دانشگاهی

John T. Evans IVمشاهده پروفایل

استادیار

• Machine Systems Engineering
• Precision Agriculture
• Agricultural Automation
• +۳ مورد دیگر

John T. Evans IV is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University. He specializes in Machine Systems and Automation, focusing on agricultural machine design, precision agriculture technologies, and automated systems modeling. His research bridges robotics, digital agriculture, and sustainable farming practices, contributing to advancements in autonomous navigation and machine learning applications for crop management. Bachelor's in Biosystems Engineering, University of Kentucky Master's in Biosystems Engineering, University of Kentucky PhD in Biological Systems Engineering, University of Nebraska–Lincoln Evans' work centers on optimizing agricultural machinery through synthetic vision systems, digital twin environments, and deep learning algorithms. Key areas include autonomous roadside mowing , corn row identification , and transfer performance optimization for robotic systems. His publications reveal interdisciplinary trends combining robotics, precision agriculture, and computational modeling. He actively mentors Purdue's Quarter Scale Tractor Team and contributes to the American Society of Agricultural and Biological Engineers. His research involves collaboration with Purdue's ADM Agricultural Innovation Center and Discovery Park facilities, focusing on automation, data science, and machine learning in agricultural contexts.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۴ مورد دیگر

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

Laurent Charlinمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۶ مورد دیگر

Laurent Charlin is an Associate Professor at HEC Montréal and holds an adjunct appointment in Computer Science at Université de Montréal. His research focuses on machine learning for decision-making with applications in recommender systems, reinforcement learning, and optimization.

Mohammadhossein Malmirمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Robotic Manipulation
• Sim2Real Transfer
• +۳ مورد دیگر

Mohammadhossein Malmir is a Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technische Universität München (TUM). He joined the chair in November 2019 and currently contributes to the A-IQ Ready project (successor of AI4DI ), focusing on learning algorithms for industrial manipulators and mobile robots in manufacturing and intralogistics tasks. Education B.Sc. in Electrical Engineering (Control Systems) from Amirkabir University of Technology (Tehran Polytechnic) (2014) M.Sc. in Automation and Control Engineering from Politecnico di Milano (2018) His research addresses Sim2Real Transfer , Reinforcement Learning , Robust Control , and Model Predictive Control for autonomous robotic systems. He has published extensively on sim2real policy transfer, domain randomization, and control algorithms for manipulation and navigation tasks. Recent publications highlight his work in dexterous grasping , continual domain adaptation , and deep reinforcement learning for trajectory optimization . His advising roles include supervising 13 master's students and interdisciplinary projects, with co-advisorship on topics like vision-based robotic grasping and socially compliant path planning . He teaches courses on Cognitive Systems and Cloud-Based/Simulation-Based Machine Learning in Robotics , emphasizing reinforcement learning and sim2real methodologies.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +۱۰ مورد دیگر

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

• Robotics
• Agricultural Robotics
• 3D Imaging
• +۶ مورد دیگر

Dr. Alen Alempijevic is the Deputy Head of School (Teaching and Learning) at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). He holds the academic rank of Senior Lecturer and coordinates Mechatronic Engineering programs. His research focuses on robotic perception, multi-modal sensor fusion, and AI applications in physical systems, with industry-driven projects in livestock monitoring, critical infrastructure, and autonomous systems. He completed his PhD in Robotics at UTS and contributed to the DARPA Urban Grand Challenge with the Sydney-Berkeley team. Education: PhD in Robotics (2004-2008), B.Eng (Computer Systems) with First Class Honors (2000-2003), B.E and B.Sc in Computer Science and Engineering from University of Belgrade (1995-1999). Research Interests: Livestock monitoring via 3D imaging, autonomous vehicles, sensor fusion, and robotics perception. His work includes developing technologies for cattle trait estimation, crowd dynamics prediction, and industrial automation. Funded Research: Notable grants include UTS Wool Bioharvesting (2024-2027), Objective real-time live cattle assessments (2020-2023), and Advanced livestock measurement technologies (2017-2018).

• Computer Vision
• 3D Graphics
• Robotics
• +۴ مورد دیگر

Li Yi is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology, where they lead cutting-edge research at the intersection of computer vision, 3D graphics, and robotics. Their work focuses on advancing neural rendering, point cloud processing, and embodied AI with applications in human-object interaction and robotic manipulation. Research interests span Computer Vision , 3D Graphics , Robotics , Point Cloud Processing , Neural Rendering , and Human-Object Interaction . Recent work explores language-grounded spatial reasoning, dexterous manipulation, and 4D dynamic content generation, with publications appearing in top venues like CVPR, ICCV, and NeurIPS. Their research bridges theoretical advances with practical applications in embodied AI systems. The publication trends reveal a strong focus on neural rendering techniques (particularly NeRF variants), embodied AI for robotic manipulation , and multimodal understanding integrating vision, language, and action. Recent work increasingly incorporates large language models and focuses on generalizable approaches that transfer from simulation to real-world settings. As an advisor, Professor Li has mentored numerous students including Yunze Liu, Xueyi Liu, Zekun Qi, and Runpei Dong, who frequently appear as first authors on collaborative publications. Their research has been supported by significant grants enabling work on human-robot interaction, 3D scene understanding, and embodied AI systems. Professor Li leads a research group focused on developing comprehensive frameworks for spatial reasoning, object manipulation, and dynamic scene understanding. The team works on creating benchmarks like TACO for tool-action-object understanding and developing systems like MobileH2R for human-robot handover tasks. Current work emphasizes real-world applicability with a focus on generalizable solutions that work across diverse settings.

John Evansمشاهده پروفایل

استادیار

• Machine Systems and Automation
• Precision Agriculture Technologies
• Automated Systems Design and Modeling
• +۳ مورد دیگر

John Evans is an Assistant Professor in Agricultural & Biological Engineering at Purdue University, specializing in Machine Systems and Automation. His research focuses on precision agriculture technologies, autonomous systems design, and digital twin applications. He holds degrees from the University of Kentucky and University of Nebraska-Lincoln. Evans advises Purdue's Quarter Scale Tractor Team and collaborates on projects like autonomous roadside mowing simulators and robotic crop sampling systems. His work integrates robotics, computer vision, and machine learning to optimize agricultural machinery and field operations. Key themes include autonomous vehicle development, sensor fusion for real-time perception, and economic analysis of autonomous farming technologies. Education: PhD Biological Systems Engineering (UNL), MS/BS Biosystems & Agricultural Engineering (UK) Research Labs: Autonomous Systems Lab, Agricultural Robotics Group Recent projects include digital twin environments for testing autonomous mowers and developing physics-based driveline control strategies. His work appears in journals like Transactions of the ASABE and IEEE Robotics. Evans contributes to open-source initiatives like OSCAR rover hardware and LATTICE data frameworks for scalable agriculture.

Bo Tangمشاهده پروفایل

دانشیار

• Bio-Inspired Artificial Intelligence
• AI Security
• Edge AI
• +۵ مورد دیگر

Dr. Bo Tang is an Associate Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI), with collaborative appointments in Computer Science and Data Science. His research focuses on bio-inspired AI, AI security, edge AI, and applications in Cyber-Physical Systems such as wireless networks, autonomous vehicles, and power systems. He holds a Ph.D. from the University of Rhode Island (2016) and previously served as an Assistant Professor at Mississippi State University (MSU). Education: Ph.D., University of Rhode Island, 2012–2016 M.S., Chinese Academy of Sciences, 2007–2010 B.E., Central South University, 2003–2007 Research interests include bio-inspired neural networks, AI security frameworks, edge computing for IoT, and autonomous systems. He leads the Computational Intelligence and Learning Systems (CILS) Lab, developing AI solutions for real-world challenges like secure federated learning, underwater robotics, and smart grid resilience. Notable awards include the NSF CAREER Award (2021), NIJ Early Career Award (2019), and MSU's Emerging Research Scholar Award (2022). He is a Senior IEEE Member and Associate Editor for IEEE Transactions on Neural Networks and Learning Systems . Grants and Projects: NSF CISE CCRI Grant ($1.8M, Co-PI, 2021) NTIA Grant ($1.9M, 2023) ONR Grant ($249K, Co-PI, 2019) Lab & Teams: The CILS Lab collaborates on projects like Open AI Cellular (OAIC), secure O-RAN networks, and autonomous vehicle navigation. Recent student projects include federated learning algorithms and underwater robot localization.

Stephan Pareigisمشاهده پروفایل

استاد

• Autonomous Systems
• Robotics
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Dr. Stephan Pareigis is the Head of the Department of Computer Science at Hamburg University of Applied Sciences. He holds a professorship in Applied Mathematics and Technical Informatics. His career includes leadership roles such as Chair of the Examination Board for Computer Science (2005–2018) and founding director of the autosys research lab . His expertise spans autonomous systems, reinforcement learning, robotics, and embedded machine learning. He has extensive industry experience, including roles at Heidelberger Druckmaschinen AG and Siemens AG before joining academia in 2004. His research focuses on bridging simulation and real-world applications in autonomous systems, particularly in miniature platforms for reliable AI development. Education and Academic Journey: 1992: Diplom Mathematiker (LMU Munich, minor in Computer Science) 1998: Dr.rer.nat in Numerical Methods for Reinforcement Learning (CAU Kiel) Research Interests: Autonomous and Intelligent Systems Reinforcement Learning for robotics Embedded Machine Learning Miniature autonomy testing frameworks His work emphasizes real-world applicability through projects like Miniature Autonomy for scalable AI validation. Labs and Teams: Leads the autosys research lab , focusing on autonomous systems research and industry collaboration through the Forschungs- und Transferzentrum Smart Systems .

Daniel Büscherمشاهده پروفایل

پژوهشگر

• Autonomous Robot Navigation
• Deep Learning
• Computer Vision
• +۱ مورد دیگر

Daniel Büscher is a post-doctoral researcher at the Robot Learning Lab of the University of Freiburg. His work focuses on autonomous robot navigation , deep learning , and probabilistic state estimation . He has contributed to robotics research through projects involving mobile manipulation and audio-visual navigation . Research Interests : Autonomous navigation, deep learning, computer vision, probabilistic estimation Teaching : Lecturer and tutor for Introduction to Mobile Robotics and Robot Mapping (2017–2023) Publication Trends : Recent work emphasizes robot design optimization , language-grounded scene graphs , and uncertainty-aware perception . His research connects reinforcement learning with mobile manipulation and cross-domain navigation challenges.

• Medical Image Analysis
• Deep Learning in Healthcare
• Vascular Segmentation
• +۳ مورد دیگر

Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.

• Computer Vision
• Human Activity Forecasting
• First-Person Vision
• +۹ مورد دیگر

Kris M. Kitani is an Associate Research Professor at the Robotics Institute of Carnegie Mellon University and a Research Scientist at Meta's Artificial Intelligence division. He co-directs the Extended Reality Technology Center (XRTC) at CMU and holds courtesy appointments in the Electrical and Computer Engineering Department at CMU and as a Guest Professor at the University of Tokyo's Institute of Industrial Science. Education : BS in Electrical Engineering (University of Southern California), MS and PhD in Computer Science (University of Tokyo) Labs : Cognitive Assistance Laboratory His research focuses on computer vision , human activity forecasting , and inverse reinforcement learning with applications in human-centered robotics and assistive technologies . Recent work explores 3D scene understanding , multi-modal sensing , and generative models for physical simulation . Publications analyze pedestrian interaction modeling , human pose estimation , and zero-shot learning for robotic applications. Scientific Awards : Marr Prize Honorable Mention, ICCV 2017 Best Paper Honorable Mentions at CHI 2017/2020 Best Papers at W4A 2017/2019 Best Application Paper, ACCV 2014 Kitani has advised 8 current PhD students and 18 past PhD/MSc students. He has taught graduate-level courses in Computer Vision and Statistical Techniques in Robotics since 2013. The lab develops autonomous systems for real-world perception and interactive decision-making.

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

Fotios Lygerakis is a doctoral student and university assistant at the Chair of Cyber-Physical Systems, University of Leoben, Austria. He focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and robot learning through imitation and reinforcement learning. His work aims to enable robots to mimic human learning processes via abstraction and creativity, targeting tactile-rich manipulation tasks and human-robot interaction. Education: Pursuing a doctoral degree in Cyber-Physical Systems at the University of Leoben. Research Interests: Representation Learning and Visuotactile Fusion using self-supervised methods Robot Learning: Reinforcement Learning, Imitation Learning, Sim2Real Transfer Human-Robot Interaction and tactile-aware manipulation Recent Achievements: Received the Best Student Paper Award at the 2024 Ubiquitous Robotics Conference for his work on M2CURL. His research has been presented at venues like ICRA, IROS, and NYU. He is also a contributor to projects like MViTaC and CR-VAE. Engagement: Served on organizing committees for ECESCON 9 and PETRA 2021, and volunteered at TED AI Vienna 2024. Active in teaching as a Machine Learning lab assistant and through outreach initiatives like Neural Coffee Reading Group.

• Autonomous Driving
• Multi-Object Tracking
• Sensor Calibration
• +۳ مورد دیگر

Leah Strand is a Ph.D. candidate and research assistant at the Technical University of Munich (TUM) in the Department of Robotics, Artificial Intelligence and Real-time Systems under Prof. Knoll. She joined TUM in March 2020 and contributes to projects like Providentia++ , which focuses on creating real-time digital twins of traffic on the Autobahn A9. Education: M.Sc. in Mechatronics and Information Technology, B.Sc. in Engineering Science (both from TUM) Research interests include: Autonomous Driving Multi-Object Tracking Sensor Calibration and Fusion Her recent publications emphasize image enhancement , multi-camera calibration , and traffic surveillance datasets . She actively participates in teaching and project advising, with expertise in real-time systems and infrastructure-based perception for autonomous vehicles. Contact: leah.strand@tum.de