Find Academic Supervisors

Takayuki ItoView profile

Professor

• Multi-agent systems
• Automated negotiation
• Argumentation frameworks
• +7 more

Dr. Takayuki Ito is Professor at Nagoya Institute of Technology in the Computer Science & Engineering school. He earned his Doctor of Engineering from Nagoya Institute of Technology in 2000. His academic journey includes positions as a JSPS research fellow, associate professor at JAIST, and visiting scholar at prestigious institutions including USC/ISI, Harvard University, and MIT (visited twice). He has served as a board member of IFAAMAS (International Foundation for Autonomous Agents and Multiagent Systems). Dr. Ito's research primarily focuses on multi-agent systems, automated negotiation, argumentation frameworks, and AI-mediated discussion platforms. His work spans theoretical foundations of argumentation semantics to practical applications in sustainable development, urban planning, and online citizen engagement. He has developed the D-Agree platform for facilitating large-scale online discussions, with notable implementations in Afghanistan for municipal policy-making and SDG implementation. His publication record demonstrates significant contributions to understanding how AI can mediate human discussions, with experiments involving thousands of participants in countries like Afghanistan. His research shows how argumentative agents can improve responsiveness in discussions while also potentially polarizing debates by reinforcing initial stances. His work bridges theoretical computer science with practical social applications, particularly in contexts with challenging participation constraints. Board member of IFAAMAS Developer of D-Agree discussion support system Conducted large-scale experiments in Afghanistan with over 1,000 participants Expert in multi-agent negotiation protocols Dr. Ito's research has substantial implications for democratic processes, particularly in contexts where traditional face-to-face meetings are problematic due to security concerns, cultural restrictions, or pandemic conditions. His work demonstrates how AI mediation can overcome barriers to equal participation, especially for women and religious minorities in restrictive societies.

Deniz YuretView profile

Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Deniz Yuret is a Professor in the Department of Computer Engineering at Koç University , Istanbul, and the founding director of the KUIS AI Center . Previously, he spent 12 years at the MIT AI Lab and co-founded Inquira, Inc. His research focuses on Natural Language Processing and Machine Learning , with significant contributions in dependency parsing , language modeling , grounded language learning , and character-level NLP . He has pioneered frameworks like Knet , a deep learning library in Julia, and AutoGrad.jl for automatic differentiation. Deniz's academic work spans neural architectures for language-robot interaction, transfer learning in low-resource NMT, and context embeddings for grammatical category acquisition. His recent publications emphasize transformer models , multimodal systems , and efficient language modeling . He has supervised multiple graduate students, including Emre Can Açıkgöz (PhD, UIUC), Onur Kuru (M.S. 2016), Saman Zia (M.S. 2016), and Osman Baskaya (M.S. 2015). His projects include the TUBITAK 1001 (2016-2018) and ReGROUND (2015-2018) in collaboration with international institutions.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +2 more

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

• Computer Science Education
• Algorithms
• Graph Theory
• +4 more

Eleni Akrida is an Associate Professor in the Department of Computer Science at Durham University and serves as Deputy Executive Dean (Academic Student Experience) in the Faculty of Science. She joined Durham in 2019 after studying Mathematics at Patra, Greece, and Computer Science at Liverpool, UK. Between 2020-2025, she held the role of Director of Undergraduate Studies for Computer Science. Research Focus Her research bridges theoretical computer science and education, with primary interests in: Computer Science Education : Pedagogical frameworks, neurodiversity inclusion, and abstraction skill development Algorithms & Complexity : Temporal networks, graph optimization, and stochastic processes Applied AI : Paraphrase generation/identification using deep learning She leads the Pedagogical Innovations in Computer Science and Algorithms & Complexity research groups. Publication Trends Recent works (2022-2025) demonstrate a dual focus: education research (developing pedagogical frameworks and addressing multi-track programming challenges) and technical innovations (temporal graph algorithms and NLP applications for plagiarism detection). Earlier work (2016-2021) established expertise in temporal network optimization and stochastic graph theory. Grants & Supervision Awarded grants include: CPHC Special Project (2025/26): Exploring Neurodivergent Student Experiences in UK CS Education CPHC Special Project (2022/23): Building a Theoretical CS Commons for hybrid learning She currently supervises postgraduate students Arwa Al saqaabi and Saira Richardson.

Sameh Al NatourView profile

Associate Professor

• Electronic Commerce
• Decision Support Systems
• Information Systems Adoption
• +1 more

Dr. Sameh Al Natour is an Associate Professor and Director of the Information Technology Management program at the Ted Rogers School of Management, Toronto Metropolitan University. His academic credentials include a PhD and MSc from the University of British Columbia, and an MBA and BS from Simon Fraser University. His research explores: Design of human-computer interfaces and decision support systems Adoption patterns in information systems and e-commerce platforms Trust dynamics in computer-mediated communication Virtual advisor effectiveness and user disclosure behaviors Recent publications (2020-2021) focus on privacy in mobile apps, sentiment analysis applications, and methodologies to enhance decision-aid relevance. His work consistently appears in FT50-listed and A*-rated journals. Major Awards: Dean’s Scholarly Award (2021-2022) TRSM Teaching Innovation Award (2021) Best Reviewer Award, ICIS (2016) Multiple TRSM Research Recognition grants (2020-2021) He oversees undergraduate and graduate courses in information systems foundations, strategy, and global technological trends.

William J. BowmanView profile

Assistant Professor

• Secure and Verified Compilation
• Dependently Typed Programming
• Verification
• +4 more

William J. Bowman is an Assistant Professor in the Department of Computer Science at the University of British Columbia. He focuses on secure and verified compilation, dependently typed programming, and meta-programming systems. His work bridges high-level language design with low-level code generation to maintain correctness and security invariants throughout compilation. Education: PhD in Computer Science from Northeastern University Research spans type-preserving compilation, including: Typed closure conversion for dependently typed languages Allocation-aware type universes Hybrid embedding techniques Secure interoperability via multi-language semantics Recent publications examine: Heap allocation modeling through type universes Flat closure representations WebAssembly extensions with indexed types Control-effect semantics

Ying WangView profile

Associate Professor

• Dependency management
• Software ecosystem governance
• Software refactoring
• +5 more

Dr. Ying Wang is an Associate Professor and Assistant Dean at the Software College of Northeastern University in China, where she also serves as a doctoral supervisor. She received her PhD in Software Engineering from Northeastern University in January 2019 and joined the faculty in February 2019. Her academic career includes a postdoctoral fellowship at the Hong Kong University of Science and Technology (2022-2023) and a visiting scholar position at Microsoft Research Asia (2021) through the StarTrack Program. Her research focuses on dependency management, software ecosystem governance, software refactoring, and software supply chain security. She has made significant contributions to understanding cross-language dependencies, vulnerability propagation across ecosystems, and developing tools for dependency conflict detection and resolution. Her work spans multiple programming language ecosystems including Java, C#, Python, Go, JavaScript, Android, and Rust. Dr. Wang's publication record shows a consistent trajectory of high-impact research in top software engineering conferences (CCF-A level) including ASE, ICSE, ESEC/FSE, and ISSTA. Her recent work increasingly integrates large language models with traditional software engineering techniques, particularly in dependency analysis and software refactoring. The publications demonstrate a strong emphasis on practical tool development with industry applications. Microsoft Research Asia Star Program Scholar (2020) CCF Outstanding Doctoral Dissertation Award nomination (2020) Liaoning Province Outstanding Doctoral Dissertation Award (2021) ACM SIGSOFT Distinguished Paper Award (ICSE 2021 and ESEC/FSE 2023) Multiple CCF ChinaSOFT Software Prototype Competition awards (2020, 2023) OpenHarmony Community Security Governance Contributions (2024, 2025) Dr. Wang actively mentors a large group of graduate students working on various aspects of software engineering, with many graduates securing positions at major technology companies including Huawei, Microsoft, Alibaba, and Tencent. She serves on the editorial board of IEEE Transactions on Software Engineering and has held numerous program committee positions at top software engineering conferences. Her research has strong industry connections, with several tools developed by her team being integrated into commercial platforms at Huawei and Microsoft.

Karol ChlastaView profile

Assistant Professor

• Artificial Intelligence
• Artificial Neural Networks
• Machine Learning
• +7 more

Dr. Karol Chlasta serves as an Assistant Professor in the Faculty of Management in Networked and Digital Societies at Kozminski University in Warsaw. He earned his doctoral degree in engineering and technical sciences from the Polish-Japanese Academy of Information Technology in 2023, defending his dissertation "Neural Simulation Pipeline for Liquid State Machines" with distinction (summa cum laude). His academic journey includes a Master's degree in Economic Computer Science from the University of Economics in Krakow (2008) and postgraduate studies in Business Analytics at Warsaw University of Technology (2015). Dr. Chlasta's research spans artificial intelligence, particularly neural networks and machine learning, human-computer interaction, and information management. His recent publications demonstrate expertise in AI applications for mental health screening through eye-tracking and speech analysis, sentiment analysis of social media data for migration studies, and innovative virtual reality interaction techniques. He has published consistently from 2020-2024 across multiple high-impact venues, with his work appearing in journals like Frontiers in Psychology, European Psychiatry, and Telematics and Informatics Reports, as well as conferences including ACM DIS and IEEE VR. His scientific contributions form several interconnected research threads: AI-based diagnostic systems for depression, anxiety, and dementia through biometric analysis Computational neuroscience tools including the Neural Simulation Pipeline deployable in cloud environments Sentiment analysis of social media data to understand migrant experiences during the pandemic Novel interaction techniques like VXSlate for virtual reality environments Dr. Chlasta has received notable recognition including the Scholarship of the Minister of National Education and Sport (2006) and the "Outstanding Leader" award from Aviva's global CIO (2017). His interdisciplinary approach bridges technical AI expertise with practical applications in healthcare, migration studies, and human-computer interaction. With over fifteen years of industry experience at major organizations including HP, IBM, and Aviva, Dr. Chlasta brings substantial real-world perspective to his academic work. Since 2022, he has served as Head of Technicus Poland, where he established and manages the Polish branch while developing the company's global IT and cybersecurity functions. He is also a certified specialist across multiple enterprise technologies and a member of several professional organizations including ACM and IEEE. His research approach integrates academic rigor with practical problem-solving, focusing on developing tools that address real-world challenges in mental health screening, migration support, and virtual interaction environments.

• Animal Communication
• Bioacoustics
• Behavioral Ecology
• +2 more

Sabrina Engesser is a Guest Researcher and Postdoc in the Department of Biology at the University of Copenhagen, specializing in Ecology and Evolution. Her work investigates complex communication systems in birds, particularly Western Australian magpies, and their implications for understanding language evolution. Her research focuses on animal communication, bioacoustics, and behavioral ecology, examining how social environments shape combinatorial structures in avian vocalizations. She explores whether birds exhibit language-like generativity through call combinations, using field studies and acoustic analysis to bridge zoology and linguistic theory. Recent publications reveal consistent themes in magpie vocal behavior across 2023-2024, demonstrating multi-level combinatoriality and social context dependencies. Her work in journals like Philosophical Transactions of the Royal Society B highlights how acoustic communication influences group dynamics, suggesting sophisticated non-human communication systems relevant to evolutionary linguistics.

Ali MesbahView profile

Professor

• Software Engineering
• AI-driven Software Analysis
• Software Testing
• +5 more

Ali Mesbah is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he leads the SALT lab. His research focuses on software engineering with emphasis on AI-driven software analysis, software testing, and software evolution. Previously, he was a Visiting Research Scientist at Google during 2017-2018. Dr. Mesbah received his BSc/MSc (2003) and PhD (2009) degree cum laude in Computer Science from the Delft University of Technology (TUDelft). After completing a postdoctoral fellowship with the Software Engineering Research Group at TUDelft and a Visiting Researcher position at Fujitsu Laboratories of America, he joined UBC in 2011. His research interests span software engineering with particular focus on AI-driven software analysis, software testing, software evolution, program comprehension, fault localization and repair. His work has significant applications in web application testing, JavaScript analysis, and automated program repair. He has pioneered techniques for testing modern web applications, analyzing JavaScript code, and leveraging AI for software maintenance tasks. His recent publications demonstrate a clear evolution toward integrating large language models with traditional program analysis techniques, focusing on test generation, bug repair, and understanding multi-hunk patches. His work bridges theoretical software engineering concepts with practical applications, particularly in web technologies and AI-assisted development. Amazon Research Award (2023) Killam Accelerator Research Fellowship (KARF) (2020) Killam Faculty Research Prize (2019) NSERC Discovery Accelerator (DAS) award (2016) ACM Distinguished Paper Awards at ICSE (2009, 2014) IEEE Distinguished Paper Award at ICST (2018) Best Paper Award at ESEM (2015) Best Paper Award at ICWE (2013) Dr. Mesbah has advised numerous PhD and MASc students, many of whom have gone on to positions at leading technology companies including Google, Amazon, Apple, Microsoft, and SAP. His research has been supported by various grants including the Amazon Research Award and NSERC funding. He leads the SALT lab at UBC, which focuses on software analysis, testing, and learning, with current research directions including AI-driven software engineering, web application testing, and program repair. The lab maintains active collaborations with industry partners and academic institutions worldwide.

Xiao YuView profile

Researcher

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +2 more

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.

Jie LiuView profile

Professor

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +3 more

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Zhiyun QianView profile

Professor

• System Security
• Network Security
• Vulnerability Analysis
• +5 more

Zhiyun Qian is the Everett and Imogene Ross Professor in the Department of Computer Science and Engineering at the University of California Riverside. His research bridges academic security research with practical hacking techniques, focusing on vulnerability discovery and analysis across operating systems, networks, and mobile platforms. His primary research interests include: System security: Automated cyber attacks/defenses, kernel vulnerability discovery, and security tool development Network security: TCP side channels, multi-path TCP flaws, and firewall evasion techniques AI/ML applications for security: LLM-integrated static analysis and reinforcement-learning-based fuzzing His work has led to critical discoveries including unfixable TCP side channel vulnerabilities (CVE-2016-5696) recognized with GeekPwn awards. His research methodology combines program analysis, reverse engineering, fuzzing, model checking, and machine learning to build practical security systems. Notable scientific awards include: GeekPwn 2016 most creative idea award Geekpwn 2017 winner award Applied Networking Research Prize Professor Qian actively mentors students in security competitions including Pwn2Own and GeekPwn. He serves on prestigious program committees including IEEE Security and Privacy (Oakland), ACM CCS, and USENIX Security. His teaching portfolio includes graduate courses CS 254 (Network Security) and CS 255 (Computer Security), along with undergraduate courses CS 153 (Operating Systems) and CS 165 (Computer Security). He leads the SecLab research group at UCR (GitHub: seclab-ucr, 3824★) developing security tools for kernel and Android ecosystems. Current projects focus on LLM-enhanced static analysis, precise vulnerability detection, and automated patch testing.

Lars GrunskeView profile

Professor

• Software Engineering
• Automated Software Engineering
• Systems Verification
• +4 more

Lars Grunske is a Professor in the Department of Computer Science at Humboldt University of Berlin, Germany. His academic career spans multiple institutions across Germany, Australia, and internationally, with a strong focus on research and conference participation in software engineering. His educational background includes a PhD in computer science from the University of Potsdam (Hasso-Plattner-Institute for Software Systems Engineering) in 2004. Professor Grunske's research interests center on modeling and verification of systems and software, with particular emphasis on automated analysis techniques. His work primarily focuses on probabilistic and timed model checking and model-based dependability evaluation of complex software intensive systems. He has made significant contributions to software testing, program repair, formal methods, and the application of machine learning techniques to software engineering problems. His publication record shows consistent contributions to top software engineering conferences over the past decade, with recent work exploring the intersection of AI/ML with traditional software engineering challenges. His research demonstrates an evolution from foundational model checking techniques toward more practical applications in software testing and repair. Boeing Postdoctoral Research Fellow Professor Grunske actively mentors through conference activities including chairing mentoring circles at ICSE 2021. He serves on numerous program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and others, demonstrating his standing in the academic community. His involvement spans multiple roles from committee member to track chair and award committee positions. He maintains an active research laboratory focused on software verification and testing, as indicated by his departmental affiliation and research website.