Find Academic Supervisors

• Materials Science
• Computational Method Development
• Catalytic Mechanism Study
• +3 more

Dr. Li Lei is an Associate Professor and doctoral supervisor in the Department of Materials Science and Engineering at the Southern University of Science and Technology (SUSTech). He holds a BS from the University of Science and Technology of China and a PhD in Chemistry (2016) from the University of Nebraska-Lincoln. From 2016-2020, he conducted postdoctoral research with Prof. Graeme Henkelman at the University of Texas at Austin, where he developed the machine-learning software package PyAMFF. Education : BS (Materials Science, USTC), PhD (Chemistry, UNL) Affiliation : Southern University of Science and Technology, Shenzhen Peacock Plan Category B Talent Research Focus : His work bridges computational method development and catalytic mechanism discovery through: Machine learning algorithms for force field training Long-timescale dynamics simulations of nanomaterials Saddle-point searching methods for reaction pathways Monte Carlo-based catalyst optimization Development of catalytic reaction databases His recent publications in JACS , Nat. Commun. , and ACS Catalysis demonstrate expertise in: Machine learning-enhanced molecular dynamics Electrochemical catalyst design (ORR/OER) in situ reaction mechanism analysis Atomic-scale electrostatic modeling Scientific Contributions : - Led development of PyAMFF machine learning potential framework - Published 27+ SCI papers in top-tier journals - Collaborated with leading computational chemists (Henkelman, Zeng, Francisco) Awards : Shenzhen Peacock Plan Category B Talent Contact : Office: 5th Floor, North Building, College of Engineering, SUSTech Phone: 0755-88015040 Email: lil33@sustech.edu.cn | lei.li@sustech.edu.cn

• Digital Scholarship
• Online Learning
• Open Education
• +4 more

Katy Jordan is a Lecturer at the School of Social Sciences , Lancaster University. Her work focuses on digital scholarship, educational technology equity, and social media’s role in higher education. She leads research in SMS-based learning solutions and open educational practices. Digital Scholarship Online Learning (formal/informal) Open Education EdTech Equity MOOCs Social Media in Higher Education Her publications examine algorithmic biases in academic searches, SMS learning in low-connectivity contexts, and global EdTech effectiveness. She actively contributes to conferences like OER and Social Media in Higher Education, and serves as an editor for the British Journal of Educational Technology . PhD Students : Xin Liu Laura Riella She is affiliated with the Centre for Technology Enhanced Learning , advancing equitable digital learning solutions.

• Innovation Competition
• Engineering Students
• Facility Layout Problem
• +7 more

Sadan Kulturel-Konak is a Professor at the Division of Engineering, Business & Computing at Penn State University (Berks campus). Her research focuses on Innovation Competition , STEM Education , Transformative Learning , and Facility Layout Problems . She has secured multiple National Science Foundation grants for projects exploring innovation ecosystems, experiential learning, and virtual laboratories in information security. Active Grant : Collaborative Research: Fostering Inclusive Pathways in Innovation Ecosystems (2024–2027) Completed Grant : Cultivating Innovative Thinking Skills in STEM Education (2021–2025) Her recent publications emphasize the role of innovation competitions in student development, validation of transformative learning metrics, and integration of AI in educational frameworks. Collaborations with researchers like Abdullah Konak and D. R. Schneider highlight her interdisciplinary work in engineering education and technology.

• Artificial Intelligence
• Smart and Service Industry
• Mobility and Transport
• +5 more

Marco Schutten is an Associate Professor at the Digital Society Institute of the University of Twente, affiliated with the Industrial Engineering & Business Information Systems department. His work bridges Artificial Intelligence , Transportation , and Operations Research , focusing on optimizing complex systems. Expert in Urban Logistics and Freight Transport Specializes in Mathematical Programming and Optimization Research interests include Vehicle Routing , Machine Scheduling , and Agent-Based Simulation . Key trends in his 15 most recent articles (2015–2025) involve: Dynamic scheduling under time constraints Urban logistics and smart city applications Heuristics for combinatorial optimization Integration of MILP and Simulation models No scientific awards, formal supervisory roles, or part-time appointments are explicitly mentioned.

Anikó Borbála CsébfalviView profile

Professor

• Structural Optimization
• Heuristic Methods
• Stability Analysis of Elastic Structures
• +3 more

Anikó Csébfalvi is a full professor at the Department of Civil Engineering, Faculty of Engineering and Information Technology, University of Pécs. She specializes in structural optimization, heuristic methods, and stability analysis of elastic structures, with a focus on discrete and continuous optimization of space trusses. Her research integrates hybrid metaheuristic algorithms, such as ANGEL, for engineering applications in structural design and project scheduling. Education: MSc (1978), PhD (1996), CSc (1996), Habilitation (2011) from institutions including Budapest University of Technology and Economics and the University of Pécs. Research: Structural optimization (sizing-shaping-topology), heuristic modeling, stability analysis, resource-constrained project scheduling, and elastic-plastic collapse constraints. Her 15 most recent articles (2004–2012) demonstrate expertise in hybrid metaheuristics, discrete-continuous truss optimization, and financial engineering. She serves as a thesis supervisor in the Marcell Breuer Doctoral School and collaborates internationally on structural mechanics topics. Scientific contributions include memberships in CEACM, ISSMO, and editorial roles for journals like Pollack Periodica and Structural and Multidisciplinary Optimization .

Vincent HellendoornView profile

Assistant Professor

• Artificial Intelligence
• Software Engineering
• Programming Languages
• +4 more

Vincent Hellendoorn is a Research Scientist at Google DeepMind and an Assistant Professor at Carnegie Mellon University (currently on leave). He works in the School of Computer Science 's Software and Societal Systems Department , developing intelligent tools that leverage AI to democratize programming expertise through code modeling and LLM research. His research focuses on AI applications in software engineering Code language model analysis and training Multi-modal whiteboard-to-code systems Open-source model releases like PolyCoder Current work examines how to make programming more accessible through LLMs, with recent ICSE’25 research exploring whiteboard sketch translation. He advises PhD students including Nikitha Rao (7 papers, Spring 2025 PhD graduate) Luís F. Gomes (ICSE’25 paper lead) and collaborates with researchers like Jonathan Aldrich and Claire Le Goues. Contact: vhellendoorn@cmu.edu vhellendoorn@google.com GitHub: @VHellendoorn

Xin HuangView profile

Associate Professor

• Graph Data Management
• Big Graph Mining & Visualization
• Social Network Analysis
• +4 more

Xin Huang is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University (HKBU), Faculty of Science. He serves as Coordinator of Research Postgraduate Programme (PhD&MPhil) and Coordinator of Programming Contest Teams. His educational background includes: Ph.D. in Systems Engineering and Engineering Management from The Chinese University of Hong Kong (2014) B.Eng. in Computer Science from Xiamen University (2010) Huang's research focuses on innovative technologies for large-scale graph data management and analysis. His work spans graph data management including keyword search and graph indexing, graph mining techniques for summarization and decomposition, community search algorithms, and graph learning approaches including graph neural networks. His research integrates theoretical foundations with practical applications in social network analysis and privacy-aware computing. His publication record shows consistent output in top-tier venues with a clear evolution from foundational graph algorithms to more complex applications involving dynamic and uncertain graphs, reflecting the growing importance of graph analytics in real-world systems. Notable awards include: Best Student Paper Award at ACM/IEEE IWLS 2023 Best Faculty Article Award from Chinese Communication Association (2022) HKBU President's Award for Outstanding Performance as Young Researcher (2021) RGC Early Career Award (2020) Best Paper Award at WISE 2019 Huang actively mentors PhD students and postdoctoral fellows, currently supervising five PhD candidates and four postdocs. His research has been supported by competitive grants including the RGC Early Career Award. He serves as Associate Editor for Data Science and Engineering and World Wide Web Journal, and participates extensively in program committees for major conferences including VLDB, ICDE, and WWW. As part of HKBU's Database Research Group, Huang contributes to a vibrant research environment that has secured over $10 million in research funding and published more than 200 papers in top venues.

Dake ZhangView profile

Professor

• Information Retrieval
• Health Informatics
• Artificial Intelligence
• +3 more

Dake Zhang is a faculty member at the David R. Cheriton School of Computer Science, University of Waterloo. His research focuses on reducing health misinformation through search systems, leveraging artificial intelligence and information retrieval techniques to improve online health information quality. Key collaborations with Mark D. Smucker, Amir Vakili Tahami, and Mustafa Abualsaud Active participant in TREC Health Misinformation Tracks (2021-2023) Developed ReadProbe system for lateral reading support using OpenAI models and Bing search His work demonstrates expertise in: Information retrieval systems for health domains Mitigating cognitive biases in search Transformer-based document analysis Automated answer prediction from web sources Search engine credibility modeling Human-AI interaction in information verification

David LoView profile

Professor

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +4 more

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

Xiaofei XieView profile

Assistant Professor

• Program Analysis
• Software Testing
• Vulnerability Detection
• +4 more

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Alessio GambiView profile

Researcher

• Software Engineering
• Software Testing
• Self-Driving Cars
• +4 more

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Reyhaneh JabbarvandView profile

Assistant Professor

• Neural Program Analysis
• Software Testing
• Bug Localization
• +4 more

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.

• Information Retrieval
• Data Engineering
• Peer-to-Peer Systems
• +3 more

Dr. Iraklis Klampanos serves as a Senior Lecturer in Data Systems and Data Engineering at the School of Computing Science, University of Glasgow. His academic role centers on advancing research and teaching in data-intensive computing systems within this prestigious institution. His research program spans Information Retrieval , Data Engineering , and Artificial Intelligence , with specialized expertise in peer-to-peer architectures, microblog search optimization, and generative AI interpretability. Key contributions include developing temporal relevance feedback mechanisms for social media search and evaluating distributed retrieval frameworks under realistic conditions. Analysis of his publication trajectory reveals a consistent evolution from foundational work on peer-to-peer testbeds (2003-2007) toward contemporary challenges in AI transparency. His recent 2025 publication on data influence analysis in generative models demonstrates ongoing innovation at the intersection of search technologies and explainable AI. Dr. Klampanos maintains active research collaborations, particularly with Joemon M. Jose, as evidenced by co-authorship across 15+ years of publications in major venues like TREC, TRECVID, and Lecture Notes in Computer Science.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

Christopher F BarnesView profile

Associate Professor

• Synthetic Aperture Radar (SAR)
• Signal Processing
• Data Mining
• +5 more

Christopher F. Barnes is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, with adjunct status as a Principal Research Engineer at the Georgia Tech Research Institute (GTRI). He holds a Ph.D. from Brigham Young University (1989) and has over 27 years of experience in radar signal processing, software engineering, and applied research. His research focuses on synthetic aperture radar (SAR) analysis, data mining, and image/video-driven technologies with applications in remote sensing, medical imaging, and seismology. Dr. Barnes' expertise includes radar imaging algorithms, software architectures for radar systems, and object-oriented programming. He pioneered methods for three-dimensional coherently fused SAR imaging and developed image-driven systems for hurricane damage assessments and bioinformatics. His work in video tracking and content-based search leverages residual vector quantization and machine vision techniques. Notable achievements include the Georgia Tech Outstanding Professional Education Award (2009) and an Interdisciplinary Research Initiative Award (2006). His contributions span over 140 publications and one patent, with research supported by defense and academic collaborations. Dr. Barnes teaches SAR at professional and graduate levels and advises research in video-driven data mining and medical imaging applications. His current projects explore AI-driven SAR analysis and advanced radar system architectures.