جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Dr. Xianfang Sun is a Senior Lecturer at Cardiff University's School of Computer Science and Informatics, where they have been a faculty member since 2005. Their research bridges traditional control theory with modern computer vision and machine learning applications. They actively supervise postgraduate students and maintain collaborations across multiple disciplines. Dr. Sun's research spans machine learning, computer vision, and graphics with a focus on medical imaging applications. Their work demonstrates a clear evolution from early control theory research toward applied computer vision systems, particularly in medical diagnostics and agricultural technology. Current projects emphasize lightweight architectures for real-world deployment, especially in brain tumor segmentation and fruit ripeness detection systems. Their publication record reveals strong trends in medical image analysis (brain tumor segmentation, cancer cell manipulation), agricultural computer vision (fruit ripeness detection), and media forensics. Recent work prioritizes clinically adaptable systems with computational efficiency, showing practical implementation focus rather than purely theoretical contributions. Dr. Sun has secured funding through EPSRC projects and previously led NSFC and 863 Program projects in China. Their supervision interests include AI for medical diagnosis, image processing, pattern recognition, and 3D geometry processing. Based at Cardiff University's Abacws building, Dr. Sun maintains active research in both theoretical and applied aspects of visual computing, with recent work showing increasing emphasis on real-world medical applications requiring robust and efficient implementations.

Srinath Sridharمشاهده پروفایل

استادیار

• 3D Computer Vision
• Artificial Intelligence
• Human-Object Interaction
• +۳ مورد دیگر

Srinath Sridhar serves as the John E. Savage Assistant Professor in the Department of Computer Science at Brown University, where he directs the Interactive 3D Vision & Learning Lab (IVL). His research program centers on 3D computer vision and artificial intelligence, specifically developing foundational methods for 4D spatiotemporal visual understanding of objects, human motion, and human-object interactions in dynamic environments. His academic foundation includes a PhD from the Max-Planck Institute (2016), an MSE from the University of Michigan (2012), and a BE from Anna University (2010). Prior to his faculty position, he completed a postdoctoral fellowship at Stanford University under Leonidas Guibas and gained industry experience at Microsoft Research and Honda Research Institute. Research interests span 3D computer vision, artificial intelligence, human-object interaction, robot perception, and deep learning. The IVL lab pioneers techniques for modeling complex 4D visual phenomena, with emphasis on real-world applicability in robotics and virtual environments. Current work integrates geometric computer vision with deep learning to solve challenges in dynamic scene understanding. Analysis of recent publications (2024-2025) reveals dominant trends in 3D vision, generative modeling, and human-robot interaction. Key themes include Gaussian splatting optimization, diffusion-based 3D generation, hand-object manipulation, and multimodal perception. The work consistently bridges theoretical advances with practical robotics applications, particularly in pose estimation, tactile sensing, and dynamic scene decomposition. His scientific recognition includes: NSF CAREER award Google Research Scholar award Best Student Paper award at WACV 2025 Best Paper Honorable Mention at Eurographics 2019 Sridhar teaches core computer vision courses including CSCI 1430 (Computer Vision) and specialized graduate seminars on 3D vision and robot perception. He maintains active research partnerships through his roles as Amazon Scholar and Visiting Faculty at the Indian Institute of Science (IISc), supported by grants from NSF and industry collaborators. The Interactive 3D Vision & Learning Lab (IVL) operates as a multidisciplinary hub for advancing 3D perception, with ongoing projects in generative 3D modeling, real-time pose tracking, and embodied AI. The lab emphasizes open collaboration and maintains strong ties with robotics research groups at Brown and industry partners.

Jose Santosمشاهده پروفایل

مدرس

• Networking
• Wireless Communications
• Machine Learning
• +۳ مورد دیگر

Dr Jose Santos serves as Associate Head of the School of Computing and Lecturer in the Faculty of Computing, Engineering and the Built Environment at Ulster University. Based at the Belfast campus (2-24 York Street), he maintains an active research profile with 54 publications since 2000 and a Scopus h-index of 9. His leadership extends to the Computer Science Research group where he contributes to the university's UN Sustainable Development Goal initiatives. Dr Santos' research centers on networking and communications, with foundational work in geographical routing protocols for mobile ad-hoc networks (MANETs) evidenced by his highly-cited survey (193 citations). His expertise spans quality of service optimization , location-aware routing for disaster telemedicine , and multimedia streaming in emergency scenarios . Recent expansions include Robotics: Bio-inspired control systems using reinforcement learning for coverage and navigation Emerging technologies: Blockchain-based edge computing (BECA architecture) and SLAM systems for autonomous navigation Sustainability applications: Low-cost environmental monitoring and battery health estimation His work consistently bridges theoretical networking concepts with real-world emergency response challenges. Analysis of recent publications (2021-2025) reveals strategic convergence of AI with physical systems. Key trends include sensor fusion for robotics (monocular-LiDAR SLAM), blockchain for secure IoT edge computing, and neural networks for battery diagnostics. This evolution maintains his core networking expertise while addressing sustainability through pollution monitoring and energy-efficient systems, aligning with Ulster University's focus on practical technological solutions for global challenges. Dr Santos has supervised 4 research students and secured funding for impactful projects including: KTP Programme with Fire Glass Direct Ireland Ltd (2012-2015): Transferred academic knowledge to industrial applications Ja.NET location awareness proposal (2008): Pioneered cooperative positioning techniques for indoor environments His external engagement includes serving as External Examiner at Griffith College (2015-2017), demonstrating commitment to academic standards beyond Ulster University. As part of Ulster's Computer Science Research group, Dr Santos contributes to multidisciplinary teams addressing UN Sustainable Development Goals. His current work integrates drone communications for fire emergency response, particulate matter monitoring, and blockchain architectures - reflecting a trajectory toward intelligent systems that enhance community resilience and environmental sustainability through rigorous networking research.

Alex Rutherfordمشاهده پروفایل

مدرس

• Reinforcement Learning
• Multi-Agent Systems
• Robotics
• +۴ مورد دیگر

Alex Rutherford is a PhD Student at the University of Oxford and a Teaching College Lecturer in Engineering at Pembroke College, affiliated with the Oxford Robotics Institute. Supervised by Prof. Nick Hawes, Dr. Bruno Lacerda, and Prof. Jakob Foerster, he currently maintains academic status while on leave due to Long Covid as of July 2025. His research centers on reinforcement learning with specialized focus on multi-agent systems, open-ended learning, and curriculum design. He investigates environment optimization frameworks, regret approximation techniques, and uncertainty management in robotic decision-making. His work bridges theoretical AI with practical robotics applications through frameworks like JaxMARL. Recent publications demonstrate progression from foundational motion planning (2021) to advanced multi-agent RL systems (2024-2025), with increasing emphasis on curriculum design and environment optimization. His work consistently connects reinforcement learning theory with real-world robotics challenges, particularly in multi-robot coordination and uncertain environments. Beyond research, Rutherford organizes the Tolkien Lecture series at Pembroke College and has represented Oxford in competitive fencing. He actively shares resources through his personal website and blog, including the JaxMARL open-source library for multi-agent reinforcement learning.

Heiko Mausمشاهده پروفایل

پژوهشگر

• Knowledge Management
• Semantic Technologies
• Knowledge Graphs
• +۷ مورد دیگر

Dr. Heiko Maus is a researcher and department leader at the German Research Center for Artificial Intelligence (DFKI) GmbH in Kaiserslautern, Germany. As (co-)author of over 20 publications since 2005, he specializes in knowledge management, semantic technologies, and context-aware systems. Second Deputy Head of Smart Data & Knowledge Services Department (2022–) Team Leader of Topic Field Knowledge Work (2017–) Key projects: CoMem, EPOS, ForgetIT, myRPA His research focuses on knowledge graph applications for corporate memory systems, combining context modeling with semantic desktop environments . He develops personal knowledge assistants that integrate information silos through evolving knowledge graphs , with recent work exploring LLM-based adaptive relevance prediction for entity recommendation systems. Scientific contributions include the LDK 2019 Best Research Paper Award and multiple ACM/IEEE/ESWC publications . His 2024–2025 articles examine context-aware recommendation systems for knowledge workers, cyber mapping financial systems , and real-life knowledge work datasets .

Jorge Mendez-Mendezمشاهده پروفایل

استادیار

• Lifelong machine learning
• sequential decision making
• robot learning
• +۳ مورد دیگر

Jorge Mendez-Mendez is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University. Prior to joining Stony Brook, he was a postdoctoral fellow at MIT CSAIL, and has a formal academic background from the University of Pennsylvania (PhD, 2022; MSE, 2018) and Universidad Simon Bolivar (Bachelor's, 2016). PhD and MSE from University of Pennsylvania's GRASP Lab Bachelor's in Electronics Engineering from Universidad Simon Bolivar, Venezuela His research focuses on creating versatile, intelligent, embodied agents that learn through lifelong knowledge accumulation. He explores how agents can leverage compositional and modular structures to simplify complex, continuous learning problems. His work applies to robotics, computer vision, and natural language processing, with key contributions in lifelong machine learning, sequential decision making, robot learning, and reinforcement learning. Jorge's publications highlight trends in lifelong learning, reinforcement learning, and compositional structures. His recent work includes black-box robustness improvements, bilevel planning, offline compositional reinforcement learning, and continual novelty detection. Scientific awards include: MIT-IBM Distinguished Postdoctoral Fellowship (2022-2024) Third Place in Two Sigma Diversity PhD Fellowship (2021) Best Paper at Lifelong Learning Workshop, ICML (2020) He teaches ESE 577: Deep Learning Algorithms and Software .

Riccardo Lazzerettiمشاهده پروفایل

دانشیار

• Privacy-Preserving Technologies
• Secure Multi-Party Computation
• Biometric Security
• +۵ مورد دیگر

Riccardo Lazzeretti serves as Associate Professor at Sapienza University of Rome since September 2022, following progression from Assistant Professor roles (RTD-A/B) at the same institution from 2017-2022. His academic trajectory includes post-doctoral research at the University of Siena's VIPP group, a research grant at the University of Padua, and industry experience at Italian startup Cynny s.p.a. His educational foundation comprises: MSc in Computer Science Engineering, University of Siena (2007) PhD in Information Engineering, University of Siena (2012) Lazzeretti's research centers on privacy-preserving technologies with emphasis on Secure Multi-Party Computation for encrypted signal processing. His work spans biometric security (including multi-biometric authentication), IoT security protocols, blockchain applications in healthcare, and social network misinformation detection. Recent publications demonstrate deep integration of cryptographic techniques with machine learning for resource-constrained devices and critical infrastructure protection. Analysis of his 15 most recent publications reveals dominant trends in IoT/drone security (32% of works), browser/API vulnerabilities (13%), and privacy-preserving architectures (27%). His research consistently bridges theoretical cryptography with practical system implementations, particularly for emerging technologies like NDN and programmable data planes. His scholarly recognition includes: SIGMM Test of Time Paper Honourable Mention (2021) for Multimedia Security contributions IET Biometrics Premium Best Paper Award (2021) Lazzeretti has supervised graduate students at the University of Siena and secured research funding through collaborations with NATO's CMRE, Digital Catapult (UK), and IMT Lucca. Current projects include privacy-preserving consensus algorithms for IoT and blockchain-based health applications. He maintains active partnerships with European research entities including Centre for Maritime Research and Experimentation and Digital Catapult. His laboratory affiliations encompass the VIPP research group at University of Siena and ongoing collaborations with Sapienza's cybersecurity initiatives, focusing on cross-disciplinary teams integrating signal processing, cryptography, and machine learning expertise.

• AI Safety
• Adversarial Machine Learning
• Large Language Models
• +۵ مورد دیگر

Lin Li is a Researcher in the Department of Computer Science at the University of Oxford, where he conducts postdoctoral research in the Oxford Applied and Theoretical Machine Learning (OATML) Group under Professor Yarin Gal's supervision. His work bridges theoretical advances in AI safety with real-world applications across healthcare, robotics, and economics, positioning him at the forefront of robust and trustworthy artificial intelligence development. His academic journey includes: PhD in Machine Learning from King’s College London (supervised by Prof. Michael Spratling), focusing on model robustness MSc in Computing from Imperial College London (advised by Prof. Wayne Luk) Bachelor of Music in Finance from Xiamen University (advised by Prof. Zheng Qiao) Li's research centers on critical AI safety challenges including hallucination mitigation, jailbreaking defenses, and safety alignment in large language models. He pioneers LLM-based agent systems for complex decision-making and societal simulation while developing practical applications in healthcare diagnostics, robotic dexterity, and economic modeling. His work uniquely combines adversarial machine learning techniques with domain-specific implementations to address real-world deployment challenges. Analysis of his publication record reveals a strategic evolution from foundational adversarial robustness research toward safety-critical applications. Recent work demonstrates increasing emphasis on healthcare domains—particularly women's health and ophthalmology—while maintaining strong contributions to core AI robustness through novel benchmarks like OODRobustBench and techniques such as semantic entropy regularization. His publications consistently bridge theoretical innovation with practical implementation across computer vision, natural language processing, and multimodal systems. Recognition includes: USERN Prize nomination (2025) for early-career research excellence Li actively shapes the field through program committee roles for MICCAI 2025, CVPR 2025, and AAAI 2025, while reviewing for top venues including ICML, NeurIPS, and IEEE Transactions journals. He actively seeks motivated students for collaborative research in AI safety and robustness through the OATML Group, which provides interdisciplinary infrastructure for theoretical and applied machine learning research with strong industry partnerships including Tencent's Robotics X Lab.

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Andreas Geiger is Professor and Head of the Department of Computer Science at the University of Tübingen, Germany, where he leads the Autonomous Vision Group (AVG) within CyberValley. He serves as core faculty at the Tübingen AI Center, Principal Investigator in the Excellence Cluster ML in Science and CRC Robust Vision, and ELLIS fellow coordinating the ELLIS PhD program. Prof. Geiger's research centers on machine learning for computer vision, natural language processing, and robotics, with emphasis on 2D/3D scene representations, geometry reconstruction, and robust model development. His group pioneers neural rendering techniques like Gaussian splatting while addressing real-world challenges in autonomous driving, VR/AR, and scientific document analysis through discriminative and generative approaches. Recent publications (2025) demonstrate strong focus on 3D scene understanding using Gaussian splatting, autonomous driving simulation with world models like ReSim and Vista, and generative methods for controllable 3D scenes. The group maintains Scholar Inbox for personalized academic recommendations while advancing reinforcement learning for self-driving (CaRL) and robust perception evaluation (EMPERROR). Major recognitions include: Best Paper Awards at CVPR 2024/2021, 3DV 2017/2015, GCPR 2015 Sage 10-Year Impact Award 2024 and Longuet-Higgins Prize 2022 ERC Starting Grant 2019 and IEEE PAMI Young Researcher Award 2018 nuPlan Challenge Winner 2023 and German Pattern Recognition Prize 2017 Prof. Geiger actively supervises PhD students including Reiser (3D view synthesis), Baur (Lidar scene flow), Jaeger (reinforcement learning), and Liao (urban scene transfer). His group secures substantial funding through the ERC Starting Grant, Vector Stiftung's MINT Innovation Program, ML in Science Cluster, and CRC Robust Vision, while collaborating with industry via Waymo and nuPlan challenges. The Autonomous Vision Group operates from CyberValley's new campus building with a diverse international team (12+ nationalities). They maintain Scholar Inbox for academic discovery and organize regular technical retreats and unique team-building activities like deep cave expeditions, fostering innovation in 3D vision and autonomous systems.

Vahagn Nikolianمشاهده پروفایل

استادیار

• Abdominal Wall Reconstruction
• Hernia Surgery
• Robotic Surgery
• +۵ مورد دیگر

Dr. Vahagn Nikolian serves as an Assistant Professor in the Department of Surgery at Oregon Health & Science University (OHSU) School of Medicine. A board-certified general surgeon specializing in abdominal wall reconstruction and hernia management, he leads a multidisciplinary team at OHSU's Center for Health & Healing, advancing care through laparoscopic and robotic surgical techniques while integrating digital health innovations to improve patient outcomes. His educational journey includes a B.A. from the University of Southern California (2007), M.D. from Keck School of Medicine (2012), General Surgery residency at the University of Michigan (2019), and specialized fellowships in Trauma Research (University of Michigan, 2015-2017) and Complex Abdominal Wall Reconstruction (Columbia University, 2020). Board-certified by the American Board of Surgery (2020), he maintains active memberships in the Americas Hernia Society, European Hernia Society, and American College of Surgeons. Dr. Nikolian's research program focuses on transforming surgical care through technology, with three core pillars: optimizing hernia surgery outcomes via robotic and laparoscopic approaches, implementing telemedicine and digital health strategies across the surgical continuum, and advancing surgical education through AI-driven coaching systems. His work bridges clinical innovation with practical implementation, emphasizing patient-centered solutions for complex abdominal wall conditions. Analysis of his 60+ publications reveals a strategic evolution toward technology integration in surgery, with recent work establishing telemedicine frameworks for rural patient access, developing AI-assisted procedural assessment tools, and pioneering novel techniques like TAPPRA for incisional hernias. The research consistently targets high-impact areas: reducing surgical site infections, improving long-term surveillance, and validating textbook outcomes in emergency procedures. His distinguished recognition includes: National awards: ACS Excellence in Basic Science Research Award (2016), NY Presbyterian High Value Care Shark Tank Award (2020) Teaching honors: OHSU Faculty Flame Teaching Award (2021, 2022), Faculty Flame Mentor Award (2022) Prestigious fellowships: Coller Society Traveling Fellowship (2018), Alpha Omega Alpha Honor Society (2012) Specialized recognitions: Massey Conference Outstanding Poster Award (2015), Michigan Learning Environment Excellence Award (2017) As an active researcher and educator, Dr. Nikolian mentors surgical residents through OHSU's training programs while securing NIH funding for digital health initiatives. His collaborative approach extends to surgical societies and cross-departmental teams, developing standardized protocols for hernia care and telemedicine implementation. Current projects focus on AI-driven surgical coaching systems and expanding telesurgery applications for underserved populations. He operates within OHSU's integrated surgical ecosystem, collaborating with the Digestive Health Center and Center for Health & Healing teams to provide comprehensive care. His clinical practice emphasizes personalized treatment plans developed through patient-family-physician partnerships, with ongoing research conducted through the OHSU surgical innovation laboratory focused on technology-driven care transformation.

• Artificial Intelligence
• Robotics
• Sensitivity Informatics
• +۷ مورد دیگر

Professor Shohei Kato holds a primary appointment at Nagoya Institute of Technology's Graduate School of Engineering (Omohi College) and serves as a Visiting Researcher at the National Center for Geriatrics and Gerontology. His career includes prior roles as Associate Professor at Nagoya Institute of Technology (2002–2015), Lecturer at Toyota National College of Technology (1999–2002), and Research Assistant (1998–1999). He earned his Doctor of Engineering from Nagoya Institute of Technology, where he also completed undergraduate studies in Electrical and Computer Engineering. Kato's research integrates artificial intelligence, robotics, and affective computing, with applications spanning healthcare, optimization, and human-machine interaction. Key themes include kansei (sensitivity) robotics, computational intelligence, dementia screening via speech/fNIRS analysis, and multi-agent systems. His work emphasizes adaptive algorithms, emotion modeling, and real-world AI deployment. Recent publications focus on sim-to-real transfer learning, reinforcement learning for multi-agent cooperation, and non-invasive dementia diagnosis. A consistent trend involves applying particle swarm optimization and deep learning to industrial and biomedical challenges, such as shield tunneling automation and EEG-based emotion recognition. Awards & Honors: Minister of Education Commendation for Science and Technology (2015) IEEE Outstanding Paper Awards (GCCE 2017, 2018) 日本感性工学会論文賞 (2012) Multiple Workshop on Informatics (WiNF) prizes for student collaboration (2011–2020) Kato leads externally funded projects on dementia screening, affective robotics, and optimization algorithms. He directs the Kato Lab (katolab.nitech.ac.jp), collaborating with entities like Shimizu Corporation on AI-driven construction planning. His team explores human-robot symbiosis, BCI interfaces for Duchenne muscular dystrophy patients, and therapy robot design.

Otar Akanyetiمشاهده پروفایل

مدرس ارشد

• Bio-inspired Robotics
• Health Informatics
• Underwater Robotics
• +۳ مورد دیگر

Dr. Otar Akanyeti is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. His research integrates engineering, physics, computer science, and biology to advance underwater robotics and health informatics. He holds a BSc in Electronic Engineering, an MSc in Embedded Systems and Robotics, and a PhD in robot learning from the University of Essex. He co-founded the Aberystwyth Stroke Research Group and leads projects on AI-driven healthcare interventions. His research focuses on two primary strands: 1) Bio-inspired Robotics , developing sensors and control systems for underwater autonomy using biological principles; and 2) Health Informatics , employing wearable technology and AI to improve stroke rehabilitation and chronic disease management. Current investigations include hydrodynamic imaging, collective intelligence for data mining, and predictive tools for neurorecovery. Akanyeti's recent publications emphasize interdisciplinary approaches, with articles spanning gait analysis using smartphone sensors, clinical outcome prediction in stroke, and biomimetic robotics. His work consistently bridges biomechanics, machine learning, and clinical applications. He leads multiple funded projects including 'Miscanthus AI' (plant phenotyping for Net Zero), 'Intelligent Exercise Practitioner for Stroke Survivors', and 'iNavigate' (brain-inspired navigation technologies). His lab utilizes advanced equipment like instrumented treadmills and eye-tracking systems.

Danielle Griegoمشاهده پروفایل

مدرس

• Building Integrated Solar Photovoltaics
• Building Energy Efficiency
• Participant-Driven Behavior Change via Mobile Apps
• +۳ مورد دیگر

Dr. Danielle Griego is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering and Executive Director of Design++, an interdisciplinary initiative fostering collaboration across 6 departments and over 20 professors. She holds a Ph.D. from ETH Zurich (2020), focusing on building-integrated solar photovoltaics and energy efficiency through participant-driven mobile apps. Her work bridges architecture, engineering, and sustainability, with prior roles including leading Singapore’s EcoCampus program at NTU. Her research emphasizes energy-efficient building design, community solar policy, and smart urban technologies. Education: Ph.D. in Architecture and Building Systems (ETH Zurich, 2020) M.Sc. Civil Engineering (University of Colorado Boulder, 20XX) B.Sc. Architectural Engineering (University of Colorado Boulder, 20XX) Her research interests include sustainable urban energy systems, behavioral change via technology, and the integration of renewable energy into built environments. She leads Design++ to advance interdisciplinary projects with industry partners, addressing real-world challenges in energy efficiency and smart cities. Grants & Advising: No formal advisee list provided, but actively involved in supervising Design++ initiatives and teaching courses related to building energy systems and sustainable design. Labs/Teams: Design++ at ETH Zurich serves as a central hub for cross-disciplinary research and education in design innovation and sustainability.

Kevin Buchinمشاهده پروفایل

استاد

• algorithm engineering
• computational geometry
• spatial networks
• +۲ مورد دیگر

Kevin Buchin is a Professor at the Department of Computer Science, Faculty of Computer Science at Technical University of Dortmund, where he leads Chair 11: Algorithm Engineering. His work focuses on bridging theoretical and experimental algorithm development, particularly in computational geometry and spatial data analysis. Professor Buchin's research spans several key areas in algorithm engineering, with particular expertise in computational geometry, spatial networks, algorithms for GIS (Geographic Information Systems), and motion planning algorithms. His work combines theoretical foundations with practical applications, developing algorithms that address real-world challenges in spatial data processing and analysis. He has made significant contributions to trajectory analysis, geometric spanners, Fréchet distance computations, and map construction algorithms. His research often involves interdisciplinary collaborations that connect computer science with geographic applications and movement data analysis. An analysis of Professor Buchin's recent publications reveals a strong focus on trajectory analysis and spatial network algorithms. His work consistently addresses fundamental problems in computational geometry while developing practical solutions for real-world applications. Key trends include advancements in Fréchet distance computations, trajectory clustering techniques, geometric spanner constructions, and map-matching algorithms. His research demonstrates a balanced approach between theoretical algorithm development and practical implementation, often resulting in benchmark suites and publicly available code to support reproducibility and further research. Professor Buchin actively supervises student research projects and theses. For Bachelor's theses, he requires successful completion of "Effiziente Algorithmen (EA)" and DAP2 courses, while Master's thesis students should have completed "Algorithmen und Datenstrukturen (AuD)" and ideally specialized courses and seminars in algorithms/algorithm engineering. His group offers final projects in algorithm engineering, geometric algorithms, geometric graphs, networks, GIS algorithms, and motion planning. He has supervised numerous Master's theses, with examples of completed projects available on his group's website. The Algorithm Engineering research group led by Professor Buchin includes Mart Hagedoorn (MSc), Antonia Kalb (MSc), Dr. Guangping Li, Aleksandr Popov (external, MSc), Dr. Carolin Rehs, and student assistants Jan Erik Swiadek and Torben Scheele. The group develops tools like the "Ruler of the Plane" game that illustrates geometric problems and algorithms, which is also available online. They maintain repositories containing game sources, art, and documentation for extending the games, including the "dots & polygons" game and new levels for the art gallery game. The group also produces educational videos illustrating research results, particularly in trajectory analysis, and offers a Coursera course on geometric algorithms.

Mingfei Sunمشاهده پروفایل

مدرس

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

Mingfei Sun is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Manchester, UK. His research focuses on artificial intelligence, particularly machine learning techniques including deep reinforcement learning and generative models, with applications in robotics, multi-agent systems, and video games. He holds a PhD in Computer Science & Engineering from the Hong Kong University of Science and Technology (HKUST), where he received the Top Research Postgraduate Award, and a B.S. in Electronic Engineering & Information Science from the University of Science and Technology of China (USTC). Dr. Sun's work spans theoretical advancements and practical applications, including contributions to the SMACV2 benchmark for multi-agent reinforcement learning and the development of the LoKO optimizer for large models. He has secured significant funding, such as the EPSRC Doctoral Landscape Award and the TICM Collaborative R&D program on reinforcement learning for automated system calibration. His research has been recognized through awards like the AAMAS Best Paper Award (2023) and the AAAI-20 Studentship (2020). He has held research positions at Microsoft Research Cambridge, the University of Oxford, and Tencent AI Lab. His academic service includes roles as a Senior Program Committee member for AAMAS 2024 and a reviewer for top conferences like ICML, NeurIPS, and AAAI. His GitHub repository demonstrates contributions to projects like DeepMimic_mujoco, focusing on reinforcement learning for humanoid control using Mujoco simulations.