جست‌وجوی استاد و پژوهشگر

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Program Synthesis
• +۴ مورد دیگر

Yingfei Xiong is an Associate Professor at Peking University specializing in Software Engineering and Programming Languages. His research focuses on program synthesis, automated program repair, and software analysis techniques with extensive contributions to top-tier conferences including ASE, ICSE, PLDI, and SPLASH/OOPSLA. Dr. Xiong's primary research interests include: Program Synthesis and Inductive Programming Automated Program Repair and Bug Fixing Static and Dynamic Program Analysis Machine Learning Applications in Software Engineering Compiler and Language Implementation Techniques Software Testing and Verification His publication trends show a strong shift toward neural approaches in program repair, with significant integration of large language models in synthesis tasks since 2023. His work bridges theoretical foundations with practical applications, evidenced by industrial collaborations such as the Alibaba case study on static taint analysis. Recent contributions include GrammarT5 for code generation, Tare for type-aware neural repair, and Equality Saturation techniques guided by LLMs. At Peking University, Dr. Xiong is affiliated with the School of Electronics Engineering and Computer Science (SEI) as indicated by his institutional website domain. He actively contributes to the academic community through program committee service for major software engineering conferences and mentoring student researchers.

Shun Shiramatsuمشاهده پروفایل

استاد

• Natural Language Processing
• Knowledge Graph
• Civic Tech
• +۳ مورد دیگر

Shun Shiramatsu is a Professor at the Graduate School of Engineering, Nagoya Institute of Technology, where he also serves as Director of the Center for Community Creation Education and Research. Since August 2024, he has held a concurrent position as Specially Appointed Professor in the Civic Tech and Design Science Creation Endowed Research Division at the Center for Spatial Information Science, The University of Tokyo. In April 2024, he established Socianotter Inc. to implement his laboratory's research results in society. Dr. Shiramatsu has been actively involved in civic tech activities with Code for Nagoya since 2014. Dr. Shiramatsu earned his Master's degree in Information Science from Tokyo University of Science in 2003 and his PhD in Informatics from Kyoto University in 2008. His academic journey began as a JST CREST Research Assistant at AIST in 2003, followed by positions as JSPS Research Fellow (DC2) in 2007, JSPS Research Fellow (PD) in 2008, Assistant Professor at Nagoya Institute of Technology in 2009, Associate Professor and head of the Shiramatsu Laboratory in 2015, and Professor in 2021. His research focuses on civic tech support and consensus building using natural language processing and knowledge graphs. Dr. Shiramatsu's work bridges the gap between advanced AI technologies and practical community applications, particularly in supporting democratic processes and citizen engagement. His recent research has increasingly incorporated large language models to enhance discussion structuring, information recommendation, and consensus building in both physical and digital spaces. He has developed systems for structuring oral discussions, estimating optimal timing for information recommendations, and creating coaching agents for job-hunting students. Dr. Shiramatsu's publication record shows a clear evolution from foundational work in web technologies and knowledge representation toward increasingly sophisticated applications of AI for civic engagement. His recent work heavily features large language models applied to community building, discussion support, and consensus formation, demonstrating how cutting-edge AI can address real-world social challenges through practical implementations. Excellence Award in the Dataset category at the LOD Challenge Japan 2013 Excellence Award in the Application category at the LOD Challenge Japan 2018 Grand Prize at the Kyoto University Summer Design School 2022 Best Student Paper Award at the 2nd International Workshop on Democracy and AI (2023) Outstanding Performance Award at the Linked Open Data Challenge Japan 2018 Dr. Shiramatsu has served as principal investigator on multiple research projects funded by JST, NEDO, and the Japan Society for the Promotion of Science. His research has been applied in various civic tech initiatives, including the development of systems for public collaboration, consensus building, and regional revitalization. Through Socianotter Inc., he is translating academic research into practical solutions that directly benefit communities. His leadership extends to chairing the Japanese Society for Artificial Intelligence's Citizen Co-Creation Intelligence Research Group (SIG-CCI) and serving as Secretary for the IEICE Special Research Committee on 'Agreement and Co-creation'.

Dimitris Fassouliotisمشاهده پروفایل

استاد

• Particle Physics
• High-Energy Physics
• Collider Experiments
• +۷ مورد دیگر

Prof. Dimitris Fassouliotis is a Professor in the Department of Physics at the National and Kapodistrian University of Athens. He specializes in Experimental Elementary Particle Physics, with a focus on collider experiments at CERN. He has been a member of the DELPHI collaboration at LEP and the ATLAS collaboration at LHC since 1998. His research includes precise Standard Model (SM) validation, Higgs boson discovery and property measurements, and BSM searches. He has contributed to the construction of the ATLAS muon spectrometer and developed gas micro-pattern detectors. Education: Bachelor's degree in Electronic Engineering (National Technical University of Athens, 1989), PhD in Experimental Particle Physics (National Technical University of Athens, 1994), and didactics qualification (PA.TE.S, 1995). His postdoctoral work at INFN Trieste and NCSR Demokritos focused on LEP results, including W mass measurements and gauge boson coupling studies. Research interests span collider-based studies of SM and BSM physics, detector development, and precision measurements of Higgs properties. He has supervised 4 PhD, 7 MSc, and 21 undergraduate theses. His recent work includes Higgs boson cross-section analyses, searches for heavy BSM bosons, and R&D on resistive Micromegas detectors for LHC upgrades. Lab affiliations include the ATLAS collaboration, where he contributed to detector construction, software development, and physics analysis. His work emphasizes experimental techniques and data-driven discoveries in high-energy physics.

Jimmy Linمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

Nitin Naikمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Nitin Naik is a Senior Lecturer in Software Engineering & Cybersecurity at Aston University's School of Computer Science and Digital Technologies, part of the College of Engineering and Physical Sciences. He holds a PhD in Computer Science from Aberystwyth University and has diverse qualifications including an MBA in Finance, PGCTHE in Higher Education Teaching, and multiple degrees from Devi Ahilya University. His research focuses on cybersecurity, artificial intelligence, data science, and IoT. He teaches courses like CS21DE (Data Encryption) and CS22DS (Distributed Systems). Research emphasizes threat modeling (STRIDE, DREAD, etc.), attack tree analysis with fuzzy logic, federated learning, and malware detection using fuzzy hashing/YARA rules. He has published 125+ works including analyses of OT system defenses, privacy-preserving techniques in tourism data fusion, and AI chatbot applications. Professional memberships include SFHEA and IEEE Computational Intelligence Society. He supervises PhD students in areas like secure LLMs, quantum search-based malware detection, and AI-based cyberattack modeling. His work integrates computational intelligence with cybersecurity solutions, including honeypot systems and blockchain-based identity management.

• Neural Computation
• Spiking Neural Networks
• Auditory System Processing
• +۵ مورد دیگر

Dr. Dan Goodman is a Senior Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. He leads the Neural Reckoning Group, focusing on uncovering principles of neural computation using precisely timed spikes. His work bridges computational neuroscience and artificial intelligence, emphasizing spiking neural networks and sensory processing, particularly in auditory systems. Key tools include the Brian simulator, a widely used spiking neural network framework. Research affiliations include the Artificial Intelligence Network, Centre for Neurotechnology, and Georgina Mace Centre for the Living Planet. His interests span neural computation, neuromorphic engineering, and interdisciplinary applications of neuroscience. Recent work explores multisensory fusion, neural modularity under resource constraints, and adaptive temporal coding. Publications highlight advancements in spiking network models, sound localization, and neural simulation techniques. Collaborations emphasize open science and online conference formats, as seen in initiatives like Neuromatch. No explicit awards or grant details are provided, though his tool development and theoretical contributions suggest significant academic impact.

Tom Van Cutsemمشاهده پروفایل

دانشیار

• Distributed Systems
• Software Security
• Blockchain
• +۵ مورد دیگر

Tom Van Cutsem is an Associate Professor of computer science at KU Leuven in Belgium, where he is affiliated with the Distributed and Secure Software (DistriNet) research group within the Faculty of Engineering Science and Department of Computer Science. He also maintains an affiliation with Nokia Bell Labs, where he previously served as a Research Department Head with research interests in decentralized systems, IoT, stream processing and machine learning for software engineering. His academic career includes a previous position as Assistant Professor at Vrije Universiteit Brussel from 2010 to 2014. Dr. Van Cutsem holds a PhD in Computer Science from Vrije Universiteit Brussel. His educational background has provided the foundation for his expertise in distributed systems, programming languages, and software security. His primary research interests span Distributed Systems , Software Security , Blockchain , and Programming Language Design . With the DistriNet research group, he pursues work on decentralized systems, IoT, stream processing, and machine learning for software engineering. His research focuses on making distributed systems more secure, reliable, and accessible, with particular attention to blockchain technology and Web3 infrastructure. He is a founding member of IFIP WG 2.16 on Programming Language Design. His recent publications demonstrate a strong focus on blockchain security and infrastructure, with numerous papers on Web3, smart contracts, and decentralized systems from 2023-2025. His work addresses critical challenges in blockchain validation, smart contract security, and Web3 infrastructure, reflecting his commitment to making decentralized systems more secure and accessible to low-resource participants. Several of his current projects focus on secure middleware for smart contracts and scalable infrastructure for self-sovereign applications. Dr. Van Cutsem supervises PhD students including Weihong Wang, with whom he has co-authored multiple recent publications. He is currently leading several research projects including SODISA (Scalable Software Development and Infrastructure for Self-sovereign Applications), Secure Sandboxing for WebAssembly-based Smart Contracts, and Programming languages for privacy-preserving and verifiable computing, with funding extending through 2028-2029. He leads research within the Distributed and Secure Software (DistriNet) group at KU Leuven, which focuses on creating secure and dependable distributed systems. His team works on cutting-edge challenges in blockchain infrastructure, smart contract security, and decentralized application development, with connections to both academic and industrial partners including Nokia Bell Labs.

Robert Loftinمشاهده پروفایل

مدرس

• Ad Hoc Human-AI Cooperation
• Human-AI Alignment
• Human-in-the-loop Machine Learning
• +۶ مورد دیگر

Dr. Robert Loftin is a Lecturer in Machine Learning at the University of Sheffield’s School of Computer Science. His research focuses on Ad Hoc Human-AI Cooperation, Human-AI Alignment, and Human-in-the-loop Machine Learning, with expertise in Deep Reinforcement Learning and Multi-Agent Systems. He holds a PhD from North Carolina State University (2019) and a Bachelor’s from Georgia Tech (2011). Postdoctoral work includes roles at Microsoft Research Cambridge and TU Delft, exploring AI applications in gaming and multi-agent cooperation. His research interests span AI ethics, human-robot interaction, and games, with a strong emphasis on practical applications of interactive learning. Recent work includes advancements in entropy-based coordination for multi-agent systems (2023–2025) and theoretical studies on cooperation in socially rational agent populations. He contributes to the Machine Learning research group and has published extensively in top venues like NeurIPS, AAMAS, and AAAI. Publications highlight innovations in curriculum design, exploration strategies, and adaptive feedback mechanisms. His work bridges theoretical foundations and real-world applications, particularly in cooperative and competitive multi-agent environments. Current projects explore safe team coordination and scalable strategies for high-dimensional state spaces.

• Cybersecurity
• Artificial Intelligence in Education
• Information Systems Security
• +۳ مورد دیگر

Dr. Bernard Vukelić is a Professor of Professional Studies at Veleučilište u Zagrebu (Collegium Carolinum), specializing in cybersecurity, AI applications in education, and information systems security. He holds a Doctorate in Sociology (Dr. Sc.) and focuses on topics like digital transformation, phishing detection, and lifelong learning strategies. His research bridges theoretical frameworks with practical applications in office automation, web application security, and AI-driven solutions for educational assessment. Education: PhD in Sociology (Dr. Sc.) His research interests include: Cybersecurity measures for critical infrastructure AI ethics and implementation in higher education Automated office processes using ChatGPT Phishing attack recognition and mitigation Content management system vulnerabilities Indoor positioning system analysis Recent Research Trends: His work from 2024 emphasizes AI's role in office automation and cybersecurity resilience, while 2023 papers address AI-driven examination proctoring and phishing detection methodologies. Earlier studies explored backend web frameworks and biometric algorithms. He teaches courses like Tools for Digitalization of Business and Security of Information Systems , reflecting his practical focus on industry-relevant technical skills.

Miriam Leeserمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Accelerators
• Wireless Communications
• +۴ مورد دیگر

Miriam Leeser is a Professor and Associate Chair of Research in the Department of Electrical and Computer Engineering at Northeastern University. She leads the Reconfigurable and GPU Computing Lab, focusing on heterogeneous architectures, FPGA accelerators, and their applications in wireless communications, machine learning, and data privacy. Her work emphasizes practical implementations of theoretical advancements, such as cloud-edge computing frameworks and secure multi-party computation using FPGAs. Leeser holds a PhD in Computer Science from the University of Cambridge and a BS in Electrical Engineering from Cornell University. She has been at Northeastern since 1996 and is a Senior Member of both the IEEE and ACM. Notable recognitions include the National Science Foundation Young Investigator Award and her status as a Charter Member of the IEEE Computer Society Distinguished Contributor Program. Her research portfolio includes projects funded by the NSF on topics like resilient cloud infrastructure (CAREFREE), cross-layer wireless coexistence, and FPGA-enabled secure computation. The Reconfigurable Computing Lab develops tools and libraries to simplify accelerator usage, particularly for emerging applications in IoT, medical imaging, and 5G/6G systems. Key Projects: CAREFREE, P4-based FPGA Testbeds, Secure Function Evaluation via FPGAs, MIMO Systems Acceleration Grants: Over $2M in NSF awards for cloud infrastructure and cybersecurity Labs: Reconfigurable and GPU Computing Lab (RCL) Leeser’s publications span FPGA optimization, hardware-software co-design, and privacy-preserving technologies. Recent work highlights include accelerating large language models with token-adaptive quantization and low-latency optical wireless transceivers.

Guido Tackمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Constraint Programming
• Programming Languages
• +۳ مورد دیگر

Guido Tack is an Associate Professor in the Department of Data Science & Artificial Intelligence at Monash University's Faculty of Information Technology. He leads development of the MiniZinc constraint modeling language and is a core developer of Gecode, a leading constraint programming library. His research focuses on combinatorial optimization, constraint solver architecture, and industrial applications, extending to programming languages and computational logic. Education: PhD (Dr.-Ing.) in Computer Science from Saarland University (2009), Diplom Informatiker (MSc equivalent) from Saarland University (2003). Professional experience includes postdoctoral roles at NICTA, Saarland University, and KU Leuven before joining Monash in 2012 as a Lecturer and Monash Larkins Fellow. Research interests span constraint programming, optimization algorithms, and their applications. Recent projects include improving the MiniZinc ecosystem, optimizing metering systems for smart water networks, and quantum information technology. He has secured over 13 major research grants, including leadership in the ARC Training Centre for Optimization Technologies. Teaching: Chief Examiner for FIT5170, FIT9135, FIT5216; Lecturer for FIT1047, FIT4010, and others. Editorial Roles: Co-Editor-in-Chief of Constraints journal (2015), active in ACM and Association for Constraint Programming. Awards: 2024 Eureka Prize finalist, 2017 FIT Dean's Award, 2010 Doctoral Research Award. Publications span constraint solving techniques, optimization algorithms, and educational tools. Recent work emphasizes real-time path planning, bi-objective search, and energy-efficient systems. He contributes to both academic conferences and industry-focused projects, bridging theoretical research with practical applications.

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +۲ مورد دیگر

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

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

Charles E. Thorpe is a Professor at Carnegie Mellon University, affiliated with the Robotics Department within the College of Engineering. His work focuses on robotics, autonomous systems, computer vision, and sensor-based navigation. He has contributed significantly to projects like the NavLab, an early autonomous vehicle initiative, and has pioneered research in simultaneous localization and mapping (SLAM), obstacle detection, and machine learning applications in robotics. His collaborations span across academia and industry, with notable partnerships in autonomous vehicle development and robotics education. Key Research Areas: Autonomous Vehicles, SLAM, Computer Vision, Robotics Algorithms, Human-Robot Interaction. Major Projects: NavLab, Urban Navigation Systems, Laser-Based Perception. Thorpe's publications emphasize practical robotics solutions, from theoretical algorithms to real-world implementations in urban environments. His work bridges robotics theory with applications in healthcare, transportation, and education.

Alvin Cheungمشاهده پروفایل

دانشیار

• Database Management Systems
• Programming Languages
• Data Management
• +۵ مورد دیگر

Alvin Cheung is an Associate Professor in the Department of Computer Science at the University of California, Berkeley, where he is a member of the Data Systems and Foundations group and Programming Systems group. He also participates in the Sky Computing Lab and SpeciaLIzed Computing Ecosystems (SLICE) Lab, and serves as a faculty affiliate at the Berkeley Institute for Data Science. His research spans database systems, programming languages, and software engineering with applications across various domains. Professor Cheung's research focuses on creating systems that bridge the gap between data management and programming languages. His work centers on three main themes: verified lifting techniques for inferring program properties; designing new data processing and programming language techniques; and improving end-user data programming experiences through novel interfaces and code generators. His research integrates formal methods, deep learning, and program synthesis to solve practical challenges in data-intensive applications. His publications reveal a strong trend toward leveraging machine learning, particularly large language models, to enhance code generation, optimization, and understanding. Recent work increasingly focuses on verified approaches that combine formal reasoning with neural techniques, addressing challenges in database systems, compiler design, and programming language theory while maintaining correctness guarantees. ACSIC Rock Star Award (2025) AITO Dahl Nygaard Junior Prize (2024) VLDB Early Career Research Contributions Award (2023) Army Research Office Young Investigator Award (2022) Office of Naval Research Young Investigator Award (2021) Sloan Research Fellowship (2019) DOE Presidential Early Career Award for Scientists & Engineers (2019) Professor Cheung has advised numerous doctoral and master's students who have gone on to positions at leading technology companies including AWS, OpenAI, Microsoft Research, and Adobe Research. His research has been generously supported by multiple federal agencies including the National Science Foundation, Department of Energy, Office of Naval Research, Army Research Office, and Intel Corporation, reflecting the significance and impact of his work across both academic and industrial settings. He leads research efforts in the EPIC Data Lab, Sky Computing Lab, and SLICE Lab, where his team develops innovative approaches to data management, programming systems, and specialized computing ecosystems. Current projects focus on applying verified lifting techniques, developing new data processing frameworks, and creating user-friendly interfaces for data programming across diverse application domains.

• Machine Learning
• Computer Vision
• Data Analytics
• +۱۲ مورد دیگر

Michael Dittenbach is a researcher at TU Wien's Institute of Software Technology and Interactive Systems (E188). He contributes to interdisciplinary work combining machine learning, computer vision, and tourism informatics. His career spans over 15 years of publications in semantic web technologies, data analytics, and 3D virtual environments. Current research focuses on computer vision applications for cultural heritage, tourism recommender systems, and self-organizing map visualization Key projects include Media Square, SOIRE, and Web 3D environments Collaborates extensively with Helmut Berger, Andreas Rauber, and Robert Sablatnig His recent work (2022) explores computer vision techniques for digital library preservation, while earlier studies (2005-2015) focused on tourism informatics, data analytics frameworks, and semantic web applications. Publications demonstrate a consistent trajectory toward integrating machine learning with user-centric digital systems. As part of TU Wien's research infrastructure, he contributes to technical reports, conference proceedings, and multimedia system development without explicit mentions of scientific awards in available records.