Find Academic Supervisors

Asta SlotkienėView profile

Associate Professor

• Software quality assurance
• Software development processes
• E-learning systems
• +3 more

Assoc. Prof. Dr. Asta Slotkienė is an Associate Professor and Researcher at the Cybersocial Systems Engineering Group , Institute of Mathematics and Informatics, Vilnius University , Lithuania. She has been a faculty member since at least 2009 and is based at Akademijos St. 4, Vilnius. Education PhD in Computer Science Engineering, Kaunas University of Technology (2009) MSc in Computer Science Engineering, Kaunas University of Technology (2004) BSc in Physics and Computer Science, Šiauliai University (2002) Research Interests Dr. Slotkienė’s research spans software quality assurance , development process improvement , e-learning systems , and cognitive decision-making . She investigates how rigorous evaluation methodologies and AI techniques can enhance the reliability and usability of software products and services. Her recent work explores the intersection of large language models and automated software testing , as well as the modeling of web service quality using fuzzy logic and ontology-based approaches. Research Trends Across her latest publications, a clear trend emerges toward leveraging AI and machine learning , particularly large language models , to automate and improve software testing practices. Simultaneously, she continues to deepen the theoretical foundations of software and service quality modeling , integrating fuzzy logic , ontology engineering , and bibliometric analyses to build robust frameworks for quality evaluation. Scientific Service & Awards Program Committee Member: ACM/SIGAPP SAC (2023–2025) Organizing Committee Member: Baltic DB&IS 2024 Program Committee Member: SS AIEDUMED’25, CompSysTech’25 Reviewer for Informatica, Baltic Journal of Modern Computing, New Trends in Computer Sciences Projects & Funding 2022–2026 Master of Science in Smart, Secure and Interconnected Systems (EU Grant 101083531) 2022–2024 Embracing RAD Skills for Employability and Innovation 2020–2022 AI-Enhanced Accounting and Business Management Systems (Grant 01.2.1-LVPA-K-856-01-0083) 2014–2018 Erasmus+ “Open University for Virtual Mobility” (2014-1-LT01-KA203-000550) 2012–2015 Tempus “High Objectives of National Organizational Reform” (530284-TEMPUS+1-2012-1-ES-TEMPUS-JPHES) International Collaboration & Training Through multiple Erasmus+ teaching and learning visits (Las Palmas de Gran Canaria, Madeira, Azores, Maia), as well as participation in DeepLearn schools and professional training (Devbridge/Cognizant Softvision, Robotic Process Automation, Creative Commons, Contextual Modeling), Dr. Slotkienė actively fosters cross-border academic cooperation and continuous professional development.

• Natural Language Processing
• Semantic Web Technologies
• Machine Learning
• +3 more

Carlos Badenes-Olmedo is an Associate Professor in the Computer Systems Department at Universidad Politécnica de Madrid (UPM) . He is a member of the Ontology Engineering Group (OEG) , AI.nnovation Space , and co-founder of the spin-off company LibrAIry S.L. , which offers AI services for NLP and knowledge management. He also co-chairs the Knowledge Graph Summarization Workshop at ISWC. His research focuses on Natural Language Processing , Semantic Web Technologies , and Knowledge Graphs with applications in multilingual document similarity, topic modeling, and biomedical informatics. He has published extensively on cross-lingual search, scalable hashing algorithms, and AI in public health and procurement. Notable scientific awards include: DAAD Postdoc Funding (2021) Margarita Salas Mobility Grant (2021) ActuaUPM 3rd Prize for Best Startups (2020) Finalist for Margarita Salas Research Awards (2022) Finalist for Innovation Talent Recruitment Award (2021) He contributes to software engineering with tools like librAIry for distributed text mining, Corpus Viewer for NLP pipelines, and r4r for RESTful APIs over RDF data. His work bridges academia and industry through projects like TheyBuyForYou and TBFY Harvester for EU public procurement.

• Mobile Computing
• Vehicular Networks
• Deep Learning
• +7 more

Guohong Cao is a Professor in Computer Science and Engineering . His research focuses on mobile computing , vehicular networks , edge computing , and deep learning . Key Projects Collaborative Research: SHF: Small: Software Hardware Architecture Co-Design for Enabling True Virtual Reality on Mobile Devices (2022–2026) SHF: Small: NPU-Based Architecture for Accelerating Deep Learning on Mobile Devices (2021–2026) CSR: Small: Energy-Aware and QoE-Aware Video Streaming on Mobile Devices (2018–2022) His work spans IoT security , resource optimization , and privacy-preserving systems . Recent publications emphasize 360-degree video streaming , Honeypots with LLMs , and deep learning on mobile devices . Collaborations include NSF grants and international partnerships. Future research directions target edge computing , cyber-physical deception , and health monitoring via wearables .

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +3 more

Jialong Li is an Assistant Professor (non-tenure-track) at the Waseda Institute for Advanced Study. His research focuses on dynamic responsibility allocation in human-software collaboration, with applications in autonomous driving, robotics, and adaptive systems. He employs techniques such as discrete controller synthesis and large language models to address runtime environmental changes. Key research themes: human-robot collaboration, self-adaptive software, autonomous systems Technical approaches: Discrete Controller Synthesis, LLM integration, reinforcement learning Recent publications emphasize multimodal human-AI collaboration, prompt engineering for software development, and ethical considerations in adaptive systems. His work bridges theoretical modeling (e.g., requirements analysis, stochastic control) with real-world applications like warehouse robotics and color-blind accessibility tools. While no formal awards are listed, his research has been presented at top conferences in software engineering and autonomous systems.

Masud RahmanView profile

Assistant Professor

• Software Engineering
• Software Maintenance & Evolution
• Software Debugging
• +13 more

Dr. Masud Rahman is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads the RAISE Lab. He earned his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan under Prof. Chanchal Roy and completed a postdoctoral fellowship at Polytechnique Montreal with Prof. Foutse Khomh. Ph.D., Computer Science/Software Engineering, University of Saskatchewan Postdoctoral Fellow, Polytechnique Montreal M.Sc., Computer Science, University of Saskatchewan B.Sc., Computer Science, Khulna University His research focuses on the intelligent automation of software maintenance and evolution, particularly in debugging, bug localization, code search, and mining software repositories. He integrates Artificial Intelligence with Software Engineering to tackle challenges in modern software systems, including those involving Large Language Models, Deep Learning, and Cloud Computing. His work addresses critical industry problems such as software bugs, crashes, vulnerabilities, and technical debt, aiming to reduce the immense economic cost of software failures. Dr. Rahman’s recent publications show a strong trend in applying AI and machine learning techniques to software engineering problems, especially in deep learning systems, reproducibility of bugs, and automated code analysis. His work frequently appears in top-tier venues like ICSE, FSE, ASE, TOSEM, and EMSE. Scientific Awards: Governor General's Gold Medal U of S Doctoral Thesis Award Best PhD Thesis Award (Computer Science) Dr Keith Geddes Award Dalhousie Belong Research Fellowship President Gold Medal (Bangladesh) TCSE Distinguished Paper Award (2020) Best Reviewer Award (2 times) Best Paper Award (2 times) Dr. Rahman has successfully advised multiple graduate students and secured over $475K as Principal Investigator and $4.3M as Co-PI from competitive grants including NSERC Discovery, Mitacs Accelerate International, NSERC Alliance, and Dalhousie Startup Fund. He actively contributes to the academic community as a Guest Editor for EMSE Special Issue (SANER 2025), PC Chair, PC Member, and journal reviewer. He leads the RAISE Lab, which focuses on R esearch in A rtificial I ntelligence and S oftware E ngineering, fostering innovation in sustainable software and AI systems.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. His research lies at the intersection of programming languages and artificial intelligence, with a current focus on neurosymbolic programming, trustworthy AI for healthcare, and AI-enabled software engineering tools. Education & Career: Ph.D. in Computer Science, Stanford University (2008) – advisor Alex Aiken M.S. in Computer Science, Purdue University (2003) – advisor Jens Palsberg B.E. in Computer Science, BITS Pilani (1999) Former faculty at Georgia Institute of Technology and researcher at Intel Labs, Berkeley Research Interests: Naik’s group develops languages, algorithms, and compilers for neurosymbolic programming, an emerging paradigm that unites symbolic reasoning with data-driven learning. Their flagship system is the open-source Scallop language and toolchain, applied to computer vision, cybersecurity, medicine, and bioinformatics. He also investigates AI-assisted programming tools that boost productivity and software quality by marrying traditional program analysis with modern machine learning. Recent Highlights: In 2024 he was named Misra Family Professor; his former student Elizabeth Dinella received the 2025 ACM SIGSOFT Outstanding Dissertation Award; his team released IRIS , an LLM-assisted static analysis framework for security vulnerabilities, and published the first comprehensive book on Neurosymbolic Programming in Scallop . Teaching: He regularly teaches CIS 5470 (Software Analysis) every Fall and CIS 5500 (Database Systems) every Spring, both of which are also delivered in Penn’s MCIT Online and Georgia Tech’s OMSCS programs. Advising & Service: Naik has graduated 8 Ph.D. students and mentored numerous postdocs and undergraduates; many alumni now hold faculty or research positions worldwide. He has served on organizing, program, and steering committees for premier venues such as PLDI, POPL, OOPSLA, SPLASH, ESEC/FSE, ISSTA, SAS, and others.

Faye WangView profile

Senior Lecturer

• Cyberlaw
• Online Dispute Resolution
• Private International Law
• +8 more

Dr. Faye Wang is a Senior Lecturer in Law at Brunel Law School, College of Business, Arts and Social Sciences, Brunel University. She holds a PhD in Law from the University of Southampton and combines legal expertise with a technical background in Computer Science. Her roles include International Fellow at the University of Hong Kong's Centre for Chinese Law and Advisory Board Member of the European Business Law Review. LLB in Law, Guangdong University of Foreign Studies Diploma in Computer Science & Application, Guangdong University of Foreign Studies LLM in Commercial Law, University of Aberdeen PhD in Law, University of Southampton Dr. Wang's research focuses on cyberlaw, with emphasis on private law challenges from technology. She explores contract law, commercial law, intellectual property (AI-created works, copyright), online dispute resolution (ODR), privacy/data protection, and AI ethics. Her work bridges common and civil law systems, addressing digital transformation of legal processes. Her recent publications highlight AI ethics in arbitration, cybersecurity legislation, and cloud computing jurisdiction. These works span comparative legal analysis, digital policy, and technological innovation in dispute resolution. She integrates empirical studies with normative theory to address regulatory gaps in transnational digital environments. Dr. Wang actively advises on European Commission feasibility studies and contributes to parliamentary AI ethics debates. She supervises PhDs in areas like AI-generated content copyright, video game streaming, and digital consumer rights. Her teaching includes contract law, tort law, and international commercial arbitration, emphasizing hybrid legal-digital skillsets.

Benjamin DelawareView profile

Associate Professor

• Programming Languages
• Program Verification
• Formal Methods
• +2 more

Benjamin Delaware is an active Associate Professor in the Department of Computer Science within Purdue University's College of Science. His research focuses on programming languages and formal verification, with significant contributions to relational verification, proof automation, and oblivious computation. His research interests span Programming Languages , Program Verification , Formal Methods , Relational Verification , and Proof Automation . Delaware develops techniques for verifying program correctness using type systems, automated reasoning, and language-based security approaches. His work bridges theoretical foundations with practical tools for secure and reliable software systems. Analysis of his recent publications shows strong trends in leveraging large language models for verification tasks (2025), advancing relational verification through E-graphs (2023-2025), and developing type-based approaches for security and privacy (2022-2024). His work consistently addresses fundamental challenges in program equivalence, test generation, and oblivious computation. Distinguished Reviewer (PLDI 2023) Delaware actively serves as committee member and session chair across major programming language conferences (POPL, PLDI, SPLASH). His grant activities focus on NSF-funded research in programming language theory and verification tools. He has mentored students through research papers and conference contributions, though specific advisees aren't listed in the source material. His work involves collaboration with research groups focused on formal methods and programming language design, particularly in developing verification frameworks like KestRel and Taypsi. Current projects explore LLM-assisted verification and policy-agnostic oblivious computation.

Jiawei ChenView profile

Research Fellow

• Recommender Systems
• Information Retrieval
• Machine Learning
• +5 more

Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration. His educational background includes: Ph.D. in Computer Science, Zhejiang University (2017-2020) Master's in Computer Science, Zhejiang University (2014-2017) Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014) Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding. His research excellence has been recognized with: Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research. While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.

Amir GholaminejadView profile

Research Fellow

• Machine Learning
• Neural Networks
• Scientific Machine Learning
• +4 more

Amir Gholaminejad is a Research Fellow at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab and Sky Lab, and co-director of the Pallas Lab. He earned his PhD from UT Austin, where his dissertation on large-scale bio-physics-based image segmentation won the university's 2018 Outstanding Dissertation Award. His research spans quantized neural networks , neural ODEs , large-scale agentic systems , and scientific machine learning . Melosh Medal Finalist Amazon Machine Learning Research Award (2020) Best Student Paper Award (SC'17) Gold Medal (ACM Student Research Competition) Research trends focus on Transformer optimization , context length extension , memory-efficient AI , and physics-informed neural networks . He mentors PhD, Masters, and undergraduate students, with alumni now at institutions like Microsoft, Apple, and Google. Recent work includes SqueezeLLM and LLMCompiler for ICML'24, and he teaches AI Systems at Berkeley.

• Natural Language Processing
• Generative AI
• Social Computing
• +5 more

Cagri Toraman is an Assistant Professor at Middle East Technical University's Computer Engineering Department, leading the Applied NLP Group. He holds a PhD in Computer Science from Bilkent University (2017) and has industry experience as Software Engineer (Havelsan 2017-2018), Postdoctoral Research Scientist (University of Central Florida 2018-2019), and NLP Team Lead (Aselsan 2020-2023). PhD in Computer Science (Bilkent University, 2017) Postdoctoral Research Scientist (University of Central Florida, 2018-2019) NLP Team Lead (Aselsan, 2020-2023) His research focuses on Natural Language Processing , Generative AI , and Social Computing , with particular emphasis on low-resource language adaptation, hate speech detection, multimodal analysis, and misinformation identification. Recent work examines dataset quality evaluation, transformer optimization with tweet-specific elements, and pejorative language disambiguation for misogyny detection. Notable achievements include receiving the Google Gemma Academic Research Award 2024 . He teaches core courses in Data Structures and Applied Natural Language Processing , emphasizing both theoretical foundations and practical implementation. Research trends show expertise across: Multilingual dataset creation (MiDe22, JL-Hate) Transformer-based hate speech detection Generative AI adaptation for Turkish Ensemble learning in socio-political event extraction Contextual word embeddings analysis Conversational system robustness

Eduardo Feo FlushingView profile

Teaching Professor

• Robotics
• Multi-Agent Systems
• Machine Learning
• +4 more

Dr. Eduardo Feo Flushing is an Assistant Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science , based in Pittsburgh, PA. His role involves academic instruction and research at the intersection of software engineering, societal computing, and intelligent systems. Research Focus: His work spans robotics, distributed AI, and networked systems, with applications in critical domains. Primary research themes include: Multi-robot coordination for spatially distributed tasks Machine learning in healthcare diagnostics and renewable energy Wireless network optimization for mobile systems Human-robot collaboration under uncertainty Publication Trends: Recent articles (2021-2025) demonstrate a strong emphasis on applied machine learning (LLMs for medical ECG, deep learning for solar panel inspection) and advanced robotics (indoor mapping, task allocation in communication-constrained environments). Earlier work (2016-2020) focused on foundational aspects of multi-robot coordination, optimization, and wireless network resilience.

Daniel VarroView profile

Professor

• Software Engineering
• Artificial Intelligence
• Model-Based Systems
• +4 more

Daniel Varro is a Professor and Head of Unit at the Department of Computer Science (IDA) of Linköping University, Sweden. He leads the Software and Systems (SAS) department, focusing on AI, software engineering, and cyber-physical systems. His research is supported by major grants like the Vinnova 5.6 million SEK project for AI-generated software quality assurance. Affiliation: Department of Computer Science (IDA), Linköping University Department: Software and Systems (SAS) Research Focus: Model-based systems, large language models for code analysis, reinforcement learning, and cyber-physical safety verification. His recent work includes empirical studies on machine learning notebooks, infrastructure code smells, and data leakage in large language models. He collaborates extensively within the Wallenberg Autonomous Systems Program (WASP) and trains doctoral students in software engineering.

Indika KahandaView profile

Assistant Professor

• Bioinformatics
• Biomedical Informatics
• Machine Learning
• +3 more

Dr. Indika Kahanda is an Assistant Professor in the School of Computing at the University of North Florida , where he leads the BioMedInfo Lab focusing on bioinformatics and biomedical informatics. Previously, he held the same position at the Gianforte School of Computing (Montana State University) from 2018 to 2023. Education: PhD in Computer Science (2016), Colorado State University MS in Electrical and Computer Engineering (2010), Purdue University BS in Computer Engineering (2007), University of Peradeniya, Sri Lanka His research applies machine learning and natural language processing to large-scale biomedical data, with key focuses on computational methods for functional genomics and automated text mining tools for biomedical literature. Current projects include Pangenomics , student misconception detection , and inconsistency detection in medical literature . Recent publications highlight his work in LLM evaluation for healthcare applications, protein function prediction , and medical transcription error analysis . He contributes to biomedical datasets like miRNAFinder and MLHCBugs, with technical expertise in deep learning, ensemble methods, and metamorphic testing.

Andreas BothView profile

Professor

• Software Engineering
• Web Engineering
• Artificial Intelligence
• +7 more

Andreas Both is a Professor at the Faculty of Computer Science and Media at Leipzig University of Applied Sciences (HTWK Leipzig), where he leads the Web & Software Engineering (WSE) research group. His work spans multiple domains of computer science with a strong focus on bridging theoretical foundations with practical applications in software engineering, web technologies, and artificial intelligence. His research interests primarily revolve around Software Engineering (particularly test automation with AI and source code analysis), Web Engineering , Applied Artificial Intelligence (including Machine Learning, Deep Learning, and Large Language Models), Question Answering & Chatbots , and Data-driven Applications . He has developed innovative approaches in knowledge graph question answering systems, multilingual NLP applications, and privacy-preserving data sharing technologies using the Solid protocol framework. The analysis of his recent publications reveals a strong trajectory toward leveraging Large Language Models for knowledge graph applications, with particular emphasis on multilingual capabilities, explainability, and quality improvement in question answering systems. His work increasingly integrates privacy considerations with advanced AI techniques, particularly through Solid protocol implementations for data sovereignty. Best Paper Award at ICWE 2024 for AuthApp - a GDPR-compliant access granting system Outstanding Paper Award at ICWI 2024 for LLM-generated explanations in question answering systems Multiple first-place awards at the TEXT2SPARQL Challenge 2025 Best Paper Awards at ICWE 2025 and IEEE ISI 2025 CHI 2015 Honorable Mentions for search interface research Professor Both actively mentors students through the Google Summer of Code program and serves on the leadership board of the Architecture (ARC) working group of the German Computer Science Society (Gesellschaft für Informatik). His teaching portfolio includes Software Engineering, Question Answering & Chatbots, Software Projects, Project Management Practicum, Web Engineering, and Software Engineering & AI courses. His office hours are Thursdays from 11:15-12:15, requiring advance email appointment with topic specification.