جست‌وجوی استاد و پژوهشگر

• Computer Science
• Programming Languages
• Software Engineering
• +۳ مورد دیگر

Xinyu Wang is a Research Fellow at the University of Michigan's Department of Physics, affiliated with the School of Literature, Science, and the Arts (LSA). Their primary office is located in the Randall Laboratory, and they are also associated with the Homer A. Neal Lab. Wang's work focuses on advancing program synthesis techniques, web automation systems, and data-driven methodologies to enhance software development and user interaction. Their research addresses challenges in SQL query verification, cloud computing simplification, and integrating human feedback into automated processes. While specific educational background details are not provided in the text, Wang's research demonstrates expertise in computer science, programming languages, and software engineering. Their projects include developing tools like VeriEQL for SQL equivalence verification, Miwa for user-controlled web automation, and SlabCity for query optimization. These efforts highlight a trend toward creating systems that blend formal methods with practical usability. Key Research Themes: Program synthesis, compiler design, database systems, human-computer interaction, neurosymbolic AI, and automated workflows Wang’s contributions include innovations in data structure refinement, cloudless computing architectures, and tensor transformation tools. Their work often emphasizes reliability, user confidence, and scalable solutions. No formal advisees or grants are explicitly listed in the provided texts. However, their involvement in advising resources like 'Advising Career Resources' suggests a role in supporting academic and professional development within the department. Wang collaborates with labs and teams focused on physics and computational sciences, contributing to interdisciplinary projects. Their contact information includes the email wongxy@umich.edu and office phone numbers 734-764-4437 and 734-763-9694.

Heng Fanمشاهده پروفایل

استادیار

• Computer Vision
• Video Analysis
• Robotics
• +۲ مورد دیگر

Heng Fan is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, with affiliated faculty status in the Department of Data Science. He leads the LLaVi Lab and holds editorial positions at Pattern Recognition and serves as an Area Chair for major conferences including ICCV and NeurIPS. He earned his B.S. from Huazhong Agricultural University (2013) and Ph.D. from Stony Brook University (2021), advised by Professor Haibin Ling. His research focuses on: Computer vision, especially video analysis using vision-language integration Robotics with an emphasis on perception systems Medical image analysis for diagnostic applications Recent publications demonstrate strong emphasis on transformer architectures for spatio-temporal video understanding, 3D object detection in autonomous systems, and multimodal AI applications. Dominant themes include UAV tracking, video grounding, and privacy-preserving NLP, reflecting cross-disciplinary innovation. He actively recruits PhD/master's students for computer/robotic vision projects and leads the LLaVi Lab, focusing on cutting-edge perception systems.

• Robotics
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Abdeslam Boularias is an Associate Professor in the Department of Computer Science at Rutgers, The State University of New Jersey. His research focuses on robotics, artificial intelligence, and reinforcement learning, with a strong emphasis on robotic manipulation, model-based control, and perception in cluttered environments. He leads research groups in Artificial Intelligence, Intelligent Systems, and Robotics. Key contributions include advancing techniques for dynamic object tracking, sensorimotor learning in unstructured settings, and integrating physics-based reasoning with deep learning. His work has been recognized with prestigious grants such as the NSF CAREER Award and collaborative grants with institutions like Yale University. Grants: NSF NRI Grant (2022), NSF SA&S Grant (2021), NSF CAREER Award (2024) Research Themes: Robotics in clutter, reinforcement learning for manipulation, physics-aware perception, and end-to-end systems for autonomous robots His recent publications emphasize scalable manipulation learning, one-shot imitation, and diffusion models for affordance prediction. He actively contributes to advancing robotic systems that operate reliably in complex real-world scenarios.

Kevin Leoمشاهده پروفایل

پژوهشگر ارشد

• Constraint Programming
• Modelling
• Presolving
• +۳ مورد دیگر

Kevin Leo is a Research Fellow in the Department of Data Science & AI at Monash University. His work contributes to the United Nations Sustainable Development Goals (SDGs), though specific goals are not detailed in the text. **Research Interests**: Constraint Programming Modelling and Presolving Techniques Operations Research Programming Languages Algorithm Design for Optimization Applications in Medical Science and Autonomous Systems His recent research emphasizes core-guided reformulation, human-centred feasibility restoration, and globalization of constraint models. These advancements address both theoretical challenges and real-world applications such as UAV autonomy and medical treatment planning. Collaborations with international researchers are noted, though specifics are not elaborated. Kevin has no recorded part-time status or scientific awards. His advising and grants sections remain empty, with no listed students or active grants. His contributions focus on peer-reviewed publications in artificial intelligence and constraint satisfaction domains.

• Informatics Education
• Algorithms
• Computer Science Education
• +۳ مورد دیگر

Regula Lacher is a Researcher at ETH Zürich’s Department of Computer Science, affiliated with the Professorship for Algorithms and Didactics. Her work focuses on advancing computer science education through innovative pedagogical methods, emphasizing algorithm design, problem-solving techniques, and the integration of computational thinking into K-12 curricula. She specializes in developing tangible, non-digital teaching tools and game-based approaches to enhance abstract concept comprehension. Her research explores how informatics education can improve general educational outcomes, particularly through constructive induction and genetic Socratic methodologies. She has contributed to curriculum design frameworks for both Swiss Maturität and international K-12 systems, advocating for cross-disciplinary learning and teacher training programs. Publications span topics like robotics-based programming, cybersecurity education, and multilingual educational materials. Key themes include bridging concrete and abstract learning, promoting equity through accessible resources, and leveraging historical pedagogical tools like LOGO in modern classrooms. Her work addresses challenges in standardizing informatics education while maintaining adaptability for diverse cultural and linguistic contexts, as seen in her French and Italian language publications.

• Industrial Automation Systems
• Distributed Control Systems
• IEC 61499 Standard
• +۶ مورد دیگر

Valeriy Vyatkin is a Professor at Aalto University's Department of Electrical Engineering and Automation within the School of Engineering. He also maintains an affiliation with Luleå University of Technology in Sweden. His research spans industrial automation, distributed control systems, and digital transformation of manufacturing processes. Vyatkin leads a significant research group focusing on next-generation industrial control architectures and has established himself as a leading authority in the IEC 61499 standard for distributed industrial automation systems. Professor Vyatkin's research interests center around industrial automation systems with particular emphasis on distributed control architectures, formal methods for verification of industrial control systems, and digital twin technologies. His work bridges theoretical computer science with practical industrial applications, developing methods for model checking, virtual commissioning, and formal verification of industrial automation systems. He has pioneered approaches for applying reinforcement learning to industrial process control, particularly in steel manufacturing and energy systems. His recent work explores the integration of generative AI with industrial control systems, focusing on rapid prototyping and code generation for IEC 61499 applications. The analysis of Professor Vyatkin's recent publications (2023-2025) reveals a strong research trajectory focused on advancing industrial automation through multiple complementary approaches. His work demonstrates a consistent emphasis on the IEC 61499 standard as a foundation for distributed control systems, with growing integration of AI techniques, particularly generative models and reinforcement learning. A notable trend is the progression from theoretical foundations toward practical implementation frameworks, with increasing attention to security, privacy, and human factors in industrial settings. His research group has expanded into new application domains including energy systems, horticulture, and nuclear instrumentation while maintaining core expertise in manufacturing automation. Professor Vyatkin has made significant contributions to the field through his editorial work, including serving as guest editor for special issues in IEEE Transactions on Industrial Informatics. His research has been supported by multiple grants from national and international funding agencies focusing on industrial digitalization and smart manufacturing initiatives. Professor Vyatkin actively supervises numerous PhD and Master's students, with a consistent research group of 8-10 students and postdoctoral researchers. His advising approach emphasizes both theoretical rigor and industrial relevance, with many students collaborating directly with industry partners. His research group has established collaborations with major industrial players in manufacturing, energy, and automation sectors across Europe. The research activities are centered around the Industrial Automation research group at Aalto University, which maintains strong connections with the international IEC 61499 community. The team operates a dedicated laboratory for industrial automation research with capabilities for virtual commissioning, digital twin development, and formal verification of control systems. The group participates in multiple European research projects focused on Industry 4.0 and 5.0 technologies, with particular emphasis on human-centric automation and secure industrial systems.

Pratap Tokekarمشاهده پروفایل

دانشیار

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

Pratap Tokekar is an Associate Professor in the Department of Computer Science at the University of Maryland, with appointments in UMIACS and Electrical and Computer Engineering. He leads the RAAS Lab and focuses on robotics, particularly algorithms for mobile robots in active sensing and environmental monitoring. His research spans AI, machine learning, and multi-robot systems. Education: Ph.D., Computer Science, University of Minnesota (2014); B.Tech., Electronics and Telecommunication, College of Engineering Pune (2008). Awards include the NSF CAREER Award (2020) and CISE Research Initiation Initiative Award (2016). Teaching highlights include courses on Decision-Making for Robotics, Artificial Intelligence, and Robot Motion Planning at both UMD and Virginia Tech. He advises six Ph.D. students and collaborates with the Maryland Robotics Center and Center for Machine Learning. Awards: NSF CAREER (2020), NSF CISE CRII (2016) Labs/Teams: RAAS Lab, Maryland Robotics Center Key Focus: Multi-robot coordination, active perception, and decision-making under uncertainty.

Chris Lehnertمشاهده پروفایل

مدرس ارشد

• Agricultural Robotics
• Robotic Perception
• Grasping Systems
• +۴ مورد دیگر

Dr Chris Lehnert is a Senior Lecturer at the Queensland University of Technology (QUT) within the Faculty of Engineering, School of Electrical Engineering & Robotics. With over a decade of experience, he specializes in robotic perception and grasping systems, particularly in agricultural robotics. His research focuses include Robotic manipulation in cluttered environments Vertical farming automation Agricultural robotics Dynamic grasping systems He leads projects like 'Moving to See' and 'Harvey – The Robotic Capsicum Harvester', aiming to revolutionize agricultural automation through advanced robotic solutions. Dr Lehnert's publications and supervised research emphasize cooperative learning algorithms and 3D shape completion for harvesting tasks. He collaborates with prominent researchers including Peter Corke and Niko Sünderhauf, advancing practical applications in indoor cropping systems.

Gavin Suddreyمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۳ مورد دیگر

Dr Gavin Suddrey is a Researcher at the Queensland University of Technology (QUT) Centre for Robotics, specializing in Artificial Intelligence and Electrical Engineering. His research focuses on human-robot interaction, natural language instruction for robots, and the development of autonomous systems. He holds a Bachelor of Information Technology (Honours) from QUT. Key research interests include social robotics, wellbeing applications via robotic interventions, natural language processing for task execution, and vision-based industrial robotics solutions. His work emphasizes practical applications such as automated laboratory tours, healthcare robotics, and industrial automation. Dr Suddrey has contributed to over a dozen publications since 2014, exploring topics ranging from path algebra for mobile robots to robot-delivered wellbeing training. His projects include the Mini Autonomous Vehicles initiative and competitive robotics entries like Team ACRV's OCRTOC participation. He develops software systems for human-robot interaction, including HTML/JavaScript-based interfaces on Pepper robots, and investigates user perceptions of robots through scales like the Robot Self-Efficacy measure. While no specific awards are listed, his work aligns with QUT's commitment to interdisciplinary robotics innovation.

• Information Retrieval
• Machine Learning
• Big Data Stream Processing
• +۶ مورد دیگر

Dr. Richard McCreadie is a Senior Lecturer in Computing Science at the University of Glasgow, specializing in Information Retrieval, Machine Learning, and Digital Finance. His research focuses on real-time data processing, financial asset recommendation systems, and crisis informatics. He leads the Information Retrieval Group and co-chairs the TREC Incident Streams initiative, advancing methods for actionable information extraction from social media during emergencies. Education: PhD in News Vertical Search using User-Generated Content (2012). Active collaborations include the FAR-AI platform for investment recommendations and the Event Tracker system for disaster response. Research Interests: His work bridges academia and industry, emphasizing practical applications in fintech, social media analysis, and event detection. Recent trends include leveraging large language models (LLMs) for personalized financial advising and robust vision transformers for crisis image classification. Grants & Projects: Key contributions include the TREC-IS track evaluations, Agile IR experimentation frameworks with Terrier, and the SUPER project for emergency management using social sensors. He has led over 75 publications in top venues like SIGIR and ECIR. Labs/Teams: Part of the Information, Data and Analytics section and collaborates internationally on AI-driven financial systems and disaster informatics platforms.

Dennis Hoodمشاهده پروفایل

استاد آموزشی

• Project Management
• Software Engineering
• Computer Science Education
• +۳ مورد دیگر

Dennis Hood is an Associate Teaching Professor of Computer Science and the Graduate Admissions and Advising Coordinator at Illinois Institute of Technology's College of Computing. He holds an M.S. in Computer Science from Stevens Institute of Technology and a B.S. in Computer and Systems Engineering from Rensselaer Polytechnic Institute. Education focuses on Computer Science Education , Software Engineering , and Technology Management . His work emphasizes innovative teaching methods, such as using simulations for project management training and LEGO-based tools for programming concept instruction. Notable publications include studies on educational simulations and hands-on learning strategies in computer science. His research bridges practical software development skills with pedagogical innovation. As an Advising Coordinator, he guides students through academic and career development. No grants or lab affiliations are explicitly listed in the provided information.

Jie Caoمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۴ مورد دیگر

Dr. Jie Cao is an Assistant Professor at the School of Computer Science, University of Oklahoma, affiliated with the Gallogly College of Engineering and the Data Science and Analytics Institute. He holds a PhD from the University of Utah, and MS/BS degrees from Huazhong University of Science and Technology. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), dialogue systems, structured prediction, and trustworthy AI applications in education and healthcare. Prior to OU, he was a postdoctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, and worked as a research scientist at WeChat AI, Amazon, and in software engineering at Alibaba and Baidu. Education: PhD in Computer Science, University of Utah MS in Computer Science, Huazhong University of Science and Technology BS in Information Security, Huazhong University of Science and Technology Research Interests: Dr. Cao’s work spans multi-party/multimodal dialogue analysis, LLM alignment, efficient structured prediction, and robust AI deployment. Notable projects include AI partners for collaborative classrooms, discourse analysis in psychotherapy, and database workload characterization. His research bridges foundational NLP/ML advancements with interdisciplinary applications in education, healthcare, and material science. Key Achievements: Recipient of the iSAT Trainee Grant (2023) and Alternative Textbook Grant (2025) Lead developer of MatterChat, a multi-modal LLM for material science Published over 20 peer-reviewed papers in top venues like ACL, COLING, and IEEE TVCG Co-instructor for NLP courses integrating LLM and agentic AI materials Labs & Teams: Leads the OUNLP lab at OU, focusing on advancing dialogue systems and trustworthy AI solutions.

J.W.M. Gerritsمشاهده پروفایل

استاد

• Innovation Management
• Information Technology in Business
• Internet Business Models
• +۳ مورد دیگر

Prof. J.W.M. Gerrits is a Full Professor at Vrije Universiteit Amsterdam’s School of Business and Economics, Department of Knowledge, Information and Innovation, and affiliated with the Amsterdam Business Research Institute and KIN Center for Digital Innovation. His work bridges organizational innovation and technology, with a focus on IT-driven business process optimization, internet applications, and technology’s role in enhancing organizational capabilities. He holds ancillary roles as an advisor at Woxsen University and director at Newtech Solutions. Research interests include innovation management, e-business models, entrepreneurship, and agent-based simulations of social and behavioral phenomena. Notable areas of exploration involve emotion recognition in virtual environments, legal/ethical AI implications, and crowd behavior analysis. His teaching includes the Digital Entrepreneurship Project course. Recent work emphasizes AI ethics, social robotics in healthcare, and computational models of emotion contagion. His publications span interdisciplinary topics like panic amplification in crowds, gamified mental health interventions, and domain-adaptive sentiment analysis tools like SentiMap. Labs/teams: Active contributor to the KIN Center for Digital Innovation and Amsterdam Business Research Institute.

Rudolf Hanelمشاهده پروفایل

دانشیار

• Complex Systems
• Statistical Physics
• Non-Equilibrium Thermodynamics
• +۱۲ مورد دیگر

Rudolf Hanel is an Associate Professor at the Medical University of Vienna and a faculty member at the Complexity Science Hub. His interdisciplinary research bridges theoretical physics, complex systems, medical imaging, and socio-economic modeling. He is deeply involved in advancing the foundations of statistical mechanics and non-equilibrium thermodynamics. His research interests include complex systems, non-equilibrium thermodynamics, information theory, medical robotics, and social physics. He investigates how systems evolve far from equilibrium, focusing on phase transitions, tipping points, and the emergence of structure. His work applies these principles to diverse domains such as firm dynamics, social cohesion, and pandemic response. The recent trend in his publications reveals a strong focus on generalized entropy, sample space reduction, network-based social modeling, and practical applications in public health. His articles span foundational physics, computational medicine, and socio-economic systems, reflecting a unifying framework of complexity science across disciplines. Thermodynamics of driven systems Generalized entropy and information theory Social fragmentation and homophily Pooled testing for pandemics Firm performance via information consumption Structure-forming systems Rudolf Hanel has received no explicitly mentioned scientific awards in the provided text. He has not been stated to advise any formal students, though he collaborates widely with researchers such as Stefan Thurner, Jan Korbel, and Peter Klimek. He has contributed to major interdisciplinary grants and projects, particularly through the Complexity Science Hub, including work on pandemic testing strategies and economic modeling. He is a key member of the Complexity Science Hub, where he collaborates on foundational and applied research in complex systems. The Hub serves as a central platform for his work in integrating physics-inspired models into social, biological, and economic systems.

• Cognitive Neuroscience
• Developmental Psychology
• Medical Robotics
• +۳ مورد دیگر

Anneke Vermeulen is a researcher affiliated with the University of Groningen, associated with the Robotics and image-guided minimally-invasive surgery (ROBOTICS) and Perceptual and Cognitive Neuroscience (PCN) departments. Her work focuses on cognitive development in hearing-impaired children and interdisciplinary applications of robotics in medical contexts. Scientific Award: ZonMw Programma Expertisefunctie Zintuiglijk Gehandicapten (2025) for research on Theory of Mind and language training in children.