جست‌وجوی استاد و پژوهشگر

Yukyung Choiمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Autonomous Driving
• +۳ مورد دیگر

Yukyung Choi is an Assistant Professor in the Department of Artificial Intelligence and Robotics at the School of Intelligent Mechatronics Engineering, Sejong University, South Korea. She has held this position since 2018 and maintains an active research profile with 45 publications including 22 conference contributions and 22 peer-reviewed articles, demonstrating significant productivity with 9 publications in 2022, 3 in 2023, 9 in 2024, and 11 in 2025. Education: Ph.D., KAIST, 2018 M.S., Yonsei University, 2008 B.S., Soongsil University, 2006 Her research centers on Robotics and Computer Vision with specialization in Visual Perception for Autonomous Driving. She investigates multi-modal learning, 3D scene understanding, and visual perception for intelligent systems including self-driving cars and household robots, focusing on incorporating natural prior knowledge to enhance algorithm robustness against real-world variations. Her work bridges theoretical machine learning with practical robotics applications. Recent publications (2024-2025) reveal strong trends in deploying deep learning for real-world challenges: multispectral pedestrian detection for autonomous vehicles, industrial text recognition in low-contrast environments, object pose estimation under adverse conditions, and causal approaches to multimodal emotion recognition. These works demonstrate consistent focus on improving reliability of vision systems through innovative learning frameworks and robust dataset construction. Professor Choi leads the RCV Lab (Robotics and Computer Vision Lab) at Sejong University, documented through the official lab website (https://www.rcv.sejong.ac.kr) and Instagram presence (https://www.instagram.com/sejong_rcv), which serves as the primary research hub for her team's work on intelligent perception systems.

Rickard Karlssonمشاهده پروفایل

مدرس

• Swedish as a second language
• Learner language assessment
• Language development in adults
• +۴ مورد دیگر

Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.

• Machine Learning
• Computer Vision
• 3D Geometry
• +۲ مورد دیگر

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

Elin Anna Toppمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Cognitive Science
• Human-Robot Interaction
• +۳ مورد دیگر

Elin Anna Topp is a Senior Lecturer in the Department of Computer Science at Lund University's Faculty of Engineering (LTH), holding the title of Excellent Teaching Practitioner (ETP) and serving as Head of Department. She manages projects including NEXTG2COM (a Vinnova Competence Centre) and contributes to Lund's Natural and Artificial Cognition and AI and Digitalization profile areas. Her research centers on establishing common ground between humans and machines through Artificial Intelligence, Cognitive Science, and Human-Robot Interaction. She develops robotic systems that communicate effectively with users in industrial/service robotics and autonomous system monitoring (e.g., drones, self-driving cars), focusing on resolving ambiguities from mismatched human-robot expectations. This work stems from her PhD research on Human Augmented Mapping and addresses critical needs for flexible, human-comprehensible interfaces in increasingly autonomous systems. Recent publications (2023-2025) reveal strong thematic cohesion in human-robot interaction for autonomous systems, with dominant trends in situational awareness assessment (UAV operations), conflict simulation (autonomous driving), and conversational interfaces. Her work consistently bridges cognitive theory with practical robotics applications, emphasizing human-centered design for safety-critical domains. Scientific recognition includes: Excellent Teaching Practitioner title from LTH's Pedagogical Academy (2022-present) She actively shapes graduate education as WASP Graduate School Management member (since 2019) and COMPUTE Graduate School Steering group member, coordinating specialized courses like WASP's Interaction, Collaboration, and Visualisation. Major research funding comes from AISA (AI-powered situational awareness, 2023-2026) and NextG2Com (2024-2028), supporting her work on shared autonomy and digital infrastructure. Her leadership extends to supervising PhD projects including 'Get a Grip - Accurate robot–human handovers'. Topp co-manages RobotLab LTH infrastructure and collaborates across Lund's robotics ecosystem through initiatives like Robotics Week for schools and the Natural and Artificial Cognition conference series, fostering interdisciplinary teams spanning computer science, engineering, and cognitive science.

• Wireless Communication
• Optical Wireless Communication
• Satellite Communication
• +۴ مورد دیگر

Shigeru Shimamoto is a Professor at Waseda University's School of Fundamental Science and Engineering, Faculty of Science and Engineering. His research spans wireless communication systems, biomedical sensing technologies, and intelligent transportation solutions. Since 2014, he has led the Communication and Computer Engineering department at Waseda University, previously serving as Director of the Global Information and Telecommunication Institute (2020-2024). 2008: Visiting Professor at Stanford University's Electrical Engineering 2000-2002: Research Assistant at University of Electro-Communications Key research areas include: Wireless Communication: OTFS modulation, NOMA, RIS-aided systems, and microwave-based vital sensing Smart Healthcare: Non-contact blood pressure monitoring, SpO2 estimation using microwave reflection Transportation Optimization: On-street parking analysis, traffic flow modeling, and energy-efficient vehicular networks Awarded the 2024 Commendation for Science and Technology from MEXT, his work demonstrates strong interdisciplinary impact combining communication engineering with medical applications. Recent publications focus on machine learning integration in gesture recognition, vehicular detection, and resource allocation for autonomous systems.

Felix Heideمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• Optics
• +۶ مورد دیگر

Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.

• Intelligent Systems
• Deep Learning
• Graph Neural Networks
• +۵ مورد دیگر

Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.

• Computer Vision
• Autonomous Vehicles
• Deep Learning
• +۴ مورد دیگر

Franck Davoine is a CNRS Senior Researcher (equivalent to Full Professor) at the LIRIS Laboratory (UMR 5205) affiliated with INSA Lyon, where he coordinates the Imagine research team and contributes to environmental transition initiatives. His career spans 25+ years across France, Sweden, and China, including roles at Heudiasyc Lab (UTC), LIAMA Beijing, and Peking University. His research integrates computer vision , autonomous systems , and deep learning , with emphasis on: Sensor fusion for LiDAR and event cameras Real-time perception in autonomous vehicles Uncertainty modeling using Dempster-Shafer theory Self-supervised depth estimation He has supervised 14 PhD students and 21+ interns, with current projects funded by ANR (e.g., ANNAPOLIS for autonomous navigation). Awards include: Most Influential Paper over the Decade (2023) Grenoble INP PhD Thesis Prize (1995) Recent publications focus on neural networks for calibration, depth estimation, and event-based vision, reflecting his lab's work on efficient embedded systems for robotics.

Mattia Fazziniمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Software Maintenance
• +۵ مورد دیگر

Mattia Fazzini is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota's College of Science and Engineering. His primary academic appointment focuses on software engineering research and teaching, with active involvement in major conferences including ASE, ISSTA, ICSE, and MOBILESoft where he has served in leadership roles such as General Co-chair (MOBILESoft 2023) and Program Committee Co-chair. His research centers on software testing, maintenance, and security , with particular emphasis on mobile applications. Key research themes include: Developing techniques for automated Android testing and maintenance Addressing API compatibility issues across Android versions Creating tools for test oracle generation and bug reproduction Investigating security vulnerabilities in mobile ecosystems Optimizing test suites through test double analysis His recent publications (2021-2025) reveal strong focus on Android-specific challenges, with recurring themes in compatibility testing, automated test generation, and security analysis. Over 60% of his work involves tool development for practical testing scenarios, particularly targeting mobile platforms. Notable recognitions include: IEEE TCSE Distinguished Paper Award (2024) for work on test suite optimization ACM Distinguished Paper Award (2022) for COVID-19 app analysis As an educator, he advises multiple PhD and Master's students while teaching undergraduate and graduate courses including CSCI 3081W (Program Design) and CSCI 5802 (Software Engineering II). His service contributions span conference organization (MOBILESoft, ISSTA, ICSE) and extensive program committee work across top software engineering venues. He leads research projects focused on practical testing solutions with real-world applicability in mobile software development.

• Embedded Systems
• Internet of Things
• Machine Learning
• +۴ مورد دیگر

Dr. Mairo Leier is a Senior Research Fellow at Tallinn University of Technology's School of Information Technologies, Department of Computer Systems, where he leads the Internet of Things Development Centre. His career spans industry roles at Ericsson Estonia and Elisa Estonia, alongside academic positions since 2010. He holds a PhD in Computer Systems (2016) and MSc (2010) from Tallinn Tech. Research interests center on embedded systems , IoT , and edge AI , with applications in smart infrastructure, autonomous vehicles, and biomedical sensing. Key focus areas include: Machine learning optimization for resource-constrained devices Real-time sensor data processing Hardware-software co-design Industrial IoT solutions His publications emphasize embedded AI efficiency, with recent work exploring model compression, hardware-aware neural networks, and edge deployment for computer vision. Awards and recognitions include: Tallinn Entrepreneurship Award (2017, 2018) 1st Prize in TTÜ Applied Research Competition (2017) Industry collaboration awards with Bosch Sensortec He actively advises students, with 14+ bachelor supervisees in embedded systems/IoT projects. Grants include applied research funding for industrial sensor algorithms and smart wearables. He directs the IoT Development Centre, fostering industry-academia partnerships and embedded systems innovation.