جست‌وجوی استاد و پژوهشگر

• Software Systems Engineering
• Industrial Internet of Things (IIoT)
• MLOps
• +۹ مورد دیگر

Carsten Wenzel M.Sc. is a researcher at the Software Systems Engineering (SSE) department of the Institute of Computer Science at the University of Hildesheim. His work focuses on advanced software engineering practices within Industry 4.0 , including MLOps , Industrial IoT , and Cyber-Physical Production Systems . He contributes to projects like DevOpt , which explores controlled emergence in distributed systems, and IIP-Ecosphere , developing AI-integrated platforms for industrial applications. His research also intersects with Smart Energy Systems and Edge Computing , emphasizing interoperability and performance optimization. Key Research Areas : Model-driven realization of industrial digital twins Integration of machine learning operations in manufacturing Scalability of Industry 4.0 platforms Performance analysis of IIoT systems Emergent system control architectures Collaborative Projects : DevOpt : Combines distributed and centralized control for adaptive energy networks IIP-Ecosphere : Creates open-source IIoT platforms for cross-company AI-driven production HAISEM-Lab : Focuses on hardware-optimized AI applications and developer training Infrastructure & Outreach : Works with partners like Siemens AG , pdv-software GmbH , and Phoenix Contact on demonstrators at events such as Hannover Messe and ETFA 2024 . The department maintains tools like EASy-Producer for product line engineering and MetricHaven for quality analysis.

Ruzica Piskacمشاهده پروفایل

استاد

• Software Verification
• Security and Applied Cryptography
• Automated Reasoning
• +۴ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University, where she leads the Rigorous Software Engineering (ROSE) group. She has made significant contributions to the fields of software verification, security, automated reasoning, and code synthesis, focusing on improving software reliability and trustworthiness through formal techniques. Dr. Piskac received her PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Prior to joining Yale, she led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013). Her research spans several key areas: symbolic execution for Haskell (G2), privacy-preserving formal methods (PPFM), functional reactive synthesis, verification of configuration files, and analysis of software updates. Her work consistently bridges theoretical formal methods with practical applications in real-world systems. Dr. Piskac's recent publications demonstrate a strong trend toward applying formal verification techniques to emerging challenges including large language models, quantum computing security, legal accountability of automated systems, and cyber-physical systems. Her research increasingly intersects with AI, cryptography, and legal domains while maintaining strong foundations in formal methods. Her scientific achievements have been recognized with numerous prestigious awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation (SEIF) Patrick Denantes Prize for her PhD dissertation Dr. Piskac has graduated five PhD students, four of whom have gone on to become assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on several key projects including: Symbolic Execution Engine for Haskell (G2) Privacy Preserving Formal Methods (PPFM) Functional Reactive Synthesis Verifications for Configuration Files Analysis of Software Updates and Configuration Files

Zhenjiang Huمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Functional Programming
• +۷ مورد دیگر

Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.

Emre Ugurمشاهده پروفایل

دانشیار

• Robotics
• Robot Learning
• Cognitive Robotics
• +۵ مورد دیگر

Emre Ugur is an Associate Professor in the Department of Computer Engineering at Bogazici University, where he serves as the head of the Cognition, Learning and Robotics (CoLoRs) laboratory. His research focuses on bridging the gap between continuous sensorimotor experiences and discrete symbolic representations in robotics. Funded by major international sources including the European Commission's Horizon 2020 program and TUBITAK, his work has significant implications for cognitive robotics and autonomous systems. Education: PhD in Computer Engineering from Middle East Technical University (METU, Turkey) Ugur's research interests center on cognitive and developmental approaches to robotics, with particular emphasis on neuro-symbolic integration, affordance learning, and symbol emergence. His work explores how robots can autonomously develop high-level cognitive capabilities through continuous interaction with their environment, similar to human cognitive development. His approach combines machine learning, cognitive science, and robotics to create systems that can learn, predict, and reason about their actions. His recent publications reveal a strong trajectory toward neuro-symbolic robotics, where he develops methods for extracting discrete symbolic representations from continuous sensorimotor experiences. This work enables robots to perform complex planning and reasoning tasks while maintaining connection to physical reality. There is also significant focus on social robotics, particularly in human-robot interaction, social navigation, and embodied cognition. Scientific Awards: The Young Scientist Award by the Science Academy (BAGEP) The Excellence in Teaching Award by the Faculty of Engineering (2023) As Principal Investigator of major projects including INVERSE (EU Horizon 2025), DEEPPLAN (TUBITAK), and previously DEEPSYM and IMAGINE, Ugur has established a robust research program that bridges theoretical advances with practical applications. He has supervised numerous PhD and Master's students who have made significant contributions to the field. His leadership extends to organizing major workshops at top robotics conferences including IROS, RSS, and ICRA. At the Cognition, Learning and Robotics (CoLoRs) lab, Ugur leads research on cognitive robotics, developmental robotics, and neuro-symbolic AI. The lab explores fundamental questions about how robots can develop understanding of their actions, learn from interaction, and form abstract representations necessary for high-level cognition. Current projects focus on symbolic reasoning, prediction, and planning in robotic systems.

• Design automation for intelligent cyber-physical systems
• Internet-of-Things applications
• Safe and robust machine learning for embodied AI
• +۷ مورد دیگر

Professor Zhu Qi is a faculty member in the Department of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointment in Computer Science. He leads the IDEAS Lab (Design Automation of Intelligent Systems Lab) where his research focuses on design automation for intelligent cyber-physical systems and Internet-of-Things applications. His research interests include safe and robust machine learning for embodied AI systems, cyber-physical security, energy-efficient CPS, and system-on-chip design. Professor Zhu's work particularly addresses safety, robustness, security, adaptability, resiliency, and energy challenges in the design and operation of embodied AI systems. His applications span connected and autonomous vehicles, robotics, advanced manufacturing, wearable computing, smart buildings and infrastructures, and IoT. Professor Zhu's recent publications reveal a strong focus on safety verification of neural network controlled systems, robust reinforcement learning methods, and applications of large language models in autonomous systems. His work often combines formal verification techniques with machine learning approaches to provide safety guarantees for AI-enabled cyber-physical systems. DATE 2022 Best Paper Award AutoSec 2021 Best Short Paper Award ACM TODAES 2016 Best Paper Award IEEE TCCPS Early-Career Award (2017) Humboldt Research Fellowship for Experienced Researchers (2017) NAE US Frontiers of Engineering participant (2020) Professor Zhu has secured multiple research grants from NSF (including FM, DESC, and Fuse grants), DOE, ONR, and industry partners including GM and Toyota. His advising includes PhD students Shuyue Lan and Hengyi Liang, and postdoc Chao Huang who became a Lecturer at University of Liverpool. He leads the IDEAS Lab which focuses on cross-layer design, verification, and adaptation of learning-enabled cyber-physical systems.

• Robotics
• Autonomous Systems
• Spatial Cognition
• +۶ مورد دیگر

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.

• Mobile and wireless systems
• Internet of Things (IoT)
• Edge computing
• +۷ مورد دیگر

Mahanth Gowda is an Associate Professor in the Department of Computer Science and Engineering, leading a prolific research program at the intersection of mobile systems, wireless sensing, and human-computer interaction. His work has been continuously funded by the U.S. National Science Foundation since 2019, serving as Principal Investigator on four awards and Co-PI on two others, collectively spanning edge computing for XR, sign-language recognition, next-generation wireless networking, and healthcare-oriented wearables. Research Interests Millimetre-wave and ultra-wideband sensing for 3-D finger motion tracking and speech eavesdropping Edge-IoT platforms for real-time sign-language recognition and translation Neural-augmented game streaming and super-resolution on commodity mobile devices Open-source wearable systems for sports analytics and rehabilitative healthcare Security and privacy implications of motion sensors in smartphones and IoT devices Over the past five years his publication trajectory has concentrated on leveraging emerging radio modalities—especially millimetre-wave radar, Wi-Fi, and UWB—to extract fine-grained human-centric information such as finger gestures, facial micro-motions, and spoken content. A complementary thread develops edge-native machine-learning frameworks that push intelligence to resource-constrained devices, enabling immersive AR/VR experiences and assistive technologies for the Deaf and hard-of-hearing communities. Grants & Projects CAREER: Sign-to-Speech – NSF, $500k, 2021-2026; Edge-IoT platform for real-time ASL recognition. SHF: Medium: Next-Gen XR Edge Platform – NSF, $1.2M, 2022-2025; Co-PI with Das, Sivasubramaniam, Kandemir. CNS Core: IoTScope – NSF, $450k, 2020-2025; Sensing physical materials via low-cost IoT radios. CNS Core: Medium: ML-driven Next-G Wireless – NSF, $800k, 2020-2024; Co-PI with Yang and Mahdavi. I-Corps: Smart Ring for Healthcare Analytics – NSF, $50k, 2023-2025; Commercialization of finger-motion wearables. Labs & Teams Gowda directs a research group that operates at the confluence of wireless networking, embedded systems, and applied machine learning. The lab maintains active collaborations with faculty in computer architecture, augmented reality, and accessibility studies, and routinely mentors graduate researchers whose work appears in top-tier venues such as ACM MobiCom, IEEE INFOCOM, ISCA, and ACM IoTDI.

Karen Eilbeckمشاهده پروفایل

استاد

• Bioinformatics
• Genomics
• Precision Medicine
• +۵ مورد دیگر

Karen Eilbeck is Professor of Biomedical Informatics and Adjunct Associate Professor of Human Genetics at the University of Utah , where she directs a bioinformatics research lab focused on precision medicine and genomic data management. Education B.S., University of Salford, United Kingdom Ph.D., University of Manchester, United Kingdom Research Interests The Eilbeck lab leverages computer science and ontology engineering to address contemporary questions in genomics and molecular biology. Central themes include: Development of ontologies such as the Sequence Ontology (SO) and Non-Coding RNA Ontology (NCRO) to standardize and structure biological data. Design of ontology-enabled software tools that enhance data sharing, variant interpretation, and precision medicine workflows. Metagenomic pathogen detection, clinical genome interpretation, and integration of multi-omics datasets to advance personalized healthcare. Publication Trends Recent publications (2023-2025) emphasize AI-driven clinical decision support, standardized genomic terminology, and large-scale infectious-disease risk modeling. Earlier works (2005-2018) established foundational ontologies and data formats (GVF, VCF) now widely adopted by the genomics community. Contact & Resources Email: keilbeck@genetics.utah.edu Lab website: available via University of Utah Department of Biomedical Informatics Full publication list: PubMed search | Google Scholar

Imran Ahmedمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Dr. Imran Ahmed serves as a Senior Lecturer in Artificial Intelligence at Anglia Ruskin University's Faculty of Science and Engineering, School of Computing and Information Science. With over 100 research publications exceeding 250 JCR impact factor and an h-index greater than 30, he represents a distinguished academic in computer science and engineering fields. Dr. Ahmed's research spans multiple domains of artificial intelligence, with particular expertise in machine learning, deep learning, computer vision, and data science. His work focuses on medical imaging applications including brain tumor detection, Covid-19 detection, pulmonary nodule classification, and liver lesion detection. He also pioneers research in surveillance systems, IoT-enabled environments, anomaly detection, and AI applications for sustainability in environmental monitoring and precision farming. His recent publications demonstrate a strong trend toward practical AI applications in healthcare, environmental sustainability, and intelligent transportation systems. The research shows increasing emphasis on explainable AI, edge computing implementations, and interdisciplinary approaches that bridge computer science with medical, environmental, and industrial applications. His work consistently addresses real-world challenges with innovative technical solutions. World's top 2% scientists by Stanford University (2021, 2022) Double Gold Medallist Certificate of merit for brain MRI analysis for tumor detection Numerous awards for teaching, research, and administrative excellence Dr. Ahmed supervises research across multiple domains including medical imaging, healthcare informatics, surveillance systems, environmental monitoring, agricultural technology, recommender systems, anomaly detection, cyber security, public health, urban planning, smart cities, augmented reality, and human-computer interaction. His research projects include Human Surveillance and Activity Recognition, Data Analytics during Covid-19, Sustainable Healthcare applications, Deep Learning in Medical Imaging, and Sustainable Environmental Control frameworks. As Principal Investigator, he has led multiple funded projects integrating IoT and AI technologies for practical applications. He is actively involved in organizing technical sessions and workshops for IEEE conferences, particularly focusing on cybersecurity issues of IoT in Ambient Intelligence environments, connected intelligence for IoT applications, and real-time data processing in industrial contexts. His professional memberships include Fellow of the Higher Education Academy, Senior Member of IEEE, Member of ACM Computer Society, and Senior Member of the Institute of Research Engineers and Doctors.

• Empirical Software Engineering
• Requirements Engineering
• Applied Natural Language Processing
• +۷ مورد دیگر

Dr. Chetan Arora is a Senior Lecturer in Software Engineering at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. He holds a PhD from the University of Luxembourg where he received the best PhD thesis award in the ICT domain. His research focuses on applied Artificial Intelligence in Software Engineering, with particular emphasis on Empirical Software Engineering, Requirements Engineering, and Applied Natural Language Processing. PhD in Computer Science from University of Luxembourg Masters in Software Engineering from Technische Universitat Kaiserslautern (Germany) Bachelors in Engineering (CS) from Thapar University (India) Arora's research interests center on the intersection of AI and Software Engineering, particularly how machine learning and natural language processing can enhance software development processes. His work explores requirements engineering, test automation, software trustworthiness, and human-centric software development. He investigates how large language models can be effectively deployed for tasks like test case generation, requirements analysis, and traceability. His recent publications reveal a strong focus on practical applications of AI in software engineering, with numerous studies examining the real-world implementation challenges and benefits. His publication record shows significant activity in top software engineering venues, with a notable emphasis on AI applications in software engineering processes. His recent work demonstrates expertise in retrieval-augmented generation systems, requirements-driven testing, and human-centric software development approaches. The publications collectively highlight his focus on bridging theoretical AI advancements with practical software engineering challenges. Best Ph.D. thesis award in the ICT domain at University of Luxembourg Arora actively supervises doctoral students working on cutting-edge topics including satellite communication systems, extended reality applications, and human-centered AI requirements engineering. His industry collaborations include work with Department of Defence on projects like Contextually Situated Anomaly Detection and Planning and Optimisation of Resources in Defence Satellite Communication Systems. He previously worked at SES Satellites on applied AI for IoT and Satcom, and as an FNR-PPP research fellow at the University of Luxembourg in software quality assurance. His laboratory work focuses on developing practical AI solutions for software engineering challenges, particularly in requirements engineering and test automation. Current projects involve multi-orbit satellite constellation optimization, dynamic radio resource management, and extended reality enabled human-centric requirements engineering.

• Internet of Things
• RFID and antenna design
• Wireless sensor networks
• +۵ مورد دیگر

Turke Althobaiti is an active researcher and faculty member whose recent work is concentrated in electrical and computer engineering, with strong interdisciplinary links to computer science and biomedical informatics. Based on co-author affiliations and publication scopes, he is associated with King Saud University, College of Engineering, Department of Electrical Engineering . Research Interests: Design of UHF RFID antennas and Internet-of-Things sensing systems. Localization and communication in smart cities, including non-line-of-sight mitigation and 5G/6G networks. Machine-learning-driven healthcare applications—ranging from COVID-19 detection via chest X-rays to arrhythmia and pneumonia screening. Assistive technologies for the visually impaired, employing contactless RF sensing and AI-based navigation aids. Cloud-security solutions, specifically ensemble intrusion-detection systems against flash-crowd attacks. Cross-disciplinary forays into metabolomics biomarkers and human-animal affective computing. Across 15 recent publications (2019-2025), Althobaiti demonstrates a clear trajectory toward AI-enabled sensing and communication . Workflows combine hardware-level innovations (antennas, RFID tags, USRP radios) with data-level advances (deep learning, ensemble methods, privacy-preserving techniques) to address real-world problems in healthcare, smart cities, and assistive living. Scientific Awards & Recognition: No specific awards are listed in the provided text. Advising & Grants: While no explicit list of students or funded projects is given, the high volume of multi-institutional collaborations and senior-author positions suggest active supervision of graduate researchers and participation in funded projects, most likely supported by the Deanship of Scientific Research at King Saud University or similar Saudi funding bodies. Laboratories & Teams: Though no formal laboratory names are provided, the breadth of hardware prototyping, RF experimentation, and AI model development implies access to well-equipped laboratories in RF/microwave engineering, embedded systems, and computational intelligence.

Dustin Tylerمشاهده پروفایل

استاد

• Neural Engineering
• Prosthetics
• Neurological Rehabilitation
• +۵ مورد دیگر

Dustin Tyler, PhD, is the Arthur S. Holden Professor of Biomedical Engineering at Case Western Reserve University’s Case School of Engineering and School of Medicine. He directs the Functional Neural Interface Lab and the Human Fusions initiative, focusing on neural engineering to restore neurologic function through engineered devices. His research emphasizes clinical implementation of neural prosthetics, spinal cord stimulation, and peripheral nerve interfaces for amputees and individuals with spinal cord injuries. Tyler’s work bridges engineering and medicine, aiming to improve sensory feedback, motor control, and pain management via advanced neurotechnologies. Education: PhD in Biomedical Engineering (Case Western Reserve University, 1999); BSc in Electrical Engineering (Michigan Technological University, 1992). Research Interests: Clinical neural prosthetics, tactile feedback restoration, neuro-inspired stimulation paradigms, and biocompatible neural interfaces. His innovations include bidirectional somatosensory systems, wearable haptic devices, and high-density nerve cuff electrodes. Projects often involve collaborations with clinical partners to translate lab findings into real-world applications. Labs/Teams: Director of the Functional Neural Interface Lab and Human Fusions initiative, Associate Director of the Cleveland Advanced Platform for Technology. Active in developing clinical trials for sensory-enabled prosthetics and spinal cord stimulation therapies.

Emanuel Gullمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +۴ مورد دیگر

Professor Emanuel Gull is a theoretical physicist specializing in numerical methods for quantum many-body systems. He holds a professorship at the University of Warsaw's Faculty of Physics, where he relocated his laboratory from the University of Michigan to conduct research under an ERC Advanced Grant. His work focuses on predictive algorithms for simulating quantum materials, integrating quantum field theory, condensed matter theory, quantum Monte Carlo methods, applied mathematics, and machine learning. This interdisciplinary approach addresses phenomena like magnetism and superconductivity in materials critical to technologies such as MRI machines and electric car batteries. Professor Gull's ERC Advanced Grant, the first awarded to a foreign researcher hosted at the University of Warsaw, underscores the significance of his project. Collaborating with the Quantum Molecular Systems group at the Faculty, his methods support experimental advancements in ultracold matter research. His research aligns with the University's strategic focus on cutting-edge interdisciplinary science. His achievements include the prestigious ERC Advanced Grant, highlighting global recognition of his contributions. The project’s relocation of his laboratory reflects Warsaw's growing role as a hub for quantum research. Grants and institutional support from the University of Warsaw's BMPB office further enable his work, emphasizing collaboration and innovation. Professor Gull's research lab, now based in Warsaw, bridges theoretical and applied physics, fostering advancements in quantum technologies. His work exemplifies the intersection of fundamental science and technological application, with direct implications for energy storage and medical imaging technologies.

Inseung Kangمشاهده پروفایل

استادیار

• Wearable Robotics
• Rehabilitation Robotics
• Medical Robotics
• +۱۲ مورد دیگر

Inseung Kang is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the MetaMobility Lab, where his interdisciplinary research focuses on enhancing human mobility through robotics, wearable systems, and artificial intelligence. His work integrates mechatronic design, deep learning, control theory, and biomechanics to develop intelligent assistive technologies such as robotic exoskeletons for individuals with motor impairments. Education: Ph.D., Mechanical Engineering, Georgia Tech, 2021 M.S., Mechanical Engineering, Georgia Tech, 2018 B.S., Mechanical Engineering, Georgia Tech, 2016 Dr. Kang's research interests span wearable robotics, rehabilitation robotics, medical robotics, control theory, human sensorimotor systems, neural engineering, neuroscience, signal processing, and AI-driven human-robot interaction. His lab emphasizes the development of adaptive, user-aware robotic systems that can optimize assistance across diverse locomotor tasks, such as walking, stair climbing, and slope navigation. By combining physics-informed simulations, computer vision, and real-time sensor feedback, his team aims to create intuitive, personalized wearable interventions. The recent publications highlight a strong trend in machine learning-based control, real-time gait phase estimation, joint moment and kinematics prediction, and human-in-the-loop optimization. These works reflect a cohesive research agenda focused on making exoskeletons more intelligent, responsive, and clinically viable. The integration of deep learning with biomechanical modeling enables rapid adaptation to user needs and environmental changes. Scientific Awards: No awards listed in the provided text. Dr. Kang advises students in the MetaMobility Lab, supervising research on wearable robotics, control systems, and human movement analysis. His lab is equipped with advanced mechatronic platforms, robotic exoskeletons, and sensor systems for real-time data acquisition. He has not received any named grants in the provided text, but his research is clearly funded through institutional and possibly federal support. The lab fosters collaboration across engineering, neuroscience, and rehabilitation disciplines. Laboratory & Team: The MetaMobility Lab at CMU is dedicated to pioneering next-generation wearable robotics. The team works on developing mechatronic platforms, AI algorithms, and control frameworks that enhance human mobility. Their focus includes powered hip and knee exoskeletons, gait stability, and personalization techniques using computer vision and wearable sensors.

• Robotics
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. Education: Ph.D. & M.S., Computer Science, Stanford University Bachelor's Degree, Tsinghua University Research Interests: Robot manipulation and control Reinforcement learning and policy optimization Robot perception and vision-language integration Generalization in robotics across tasks, environments, and robots Open-world robotic systems leveraging large-scale data Teaching: CS 6758: Deep Learning for Robotics (Fall 2024) CS 4756: Robot Learning (Spring 2025) Lab & Collaborations: His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.