Find Academic Supervisors

Wei WangView profile

Assistant Professor

• Leadership
• Business Ethics
• Organizational Behavior
• +3 more

Wei Wang is an Assistant Professor and The Associates Fellow in Leadership and Ethics at the Business Administration Department, Asper School of Business, University of Manitoba. His research focuses on leadership dynamics and ethics in the workplace, employing mixed-method approaches. He has been recognized with the 2019 Society of Business Ethics Emerging Scholar Award and the 2022 Anne Tsui Dare to Care Research Award for impactful work on employee well-being. His teaching expertise spans organizational behavior, negotiation, and business ethics. He leads the GLOW Lab, advancing research in leadership and ethical workplace practices. Key research interests include ethical leadership, workplace mistreatment, and the behavioral consequences of deviant coworker behaviors. His work has been published in leading journals such as Journal of Applied Psychology and Journal of Management . Beyond academia, he enjoys outdoor activities, running, and travel. Awards include the 2019 Emerging Scholar Award for contributions to business ethics and the 2022 Dare to Care Award for research prioritizing employee welfare. His lab, the GLOW Lab, drives innovative studies in leadership and ethical organizational practices.

• Speech Processing
• Machine Learning
• Affective Computing
• +6 more

Professor Julien Epps is the Dean of the Faculty of Engineering at the University of New South Wales (UNSW). He holds a Professorship and previously served as Head of the School of Electrical Engineering and Telecommunications. His academic journey includes roles as Senior Lecturer (2007), Associate Professor (2014), and Head of School (2019). Epps earned his BE (Hons I) and PhD in Electrical Engineering from UNSW in 1997 and 2001, respectively. His research focuses on speech processing, wearable sensor technology, affective computing, and mental health applications. Notable contributions include work on depression detection via speech analysis, multimodal emotion recognition, and the development of tools like the IREye4Task dataset for wearable mental state analysis. He also investigates human-computer interaction through eye-tracking and biometric sensors. Epps' publications (2021–2025) emphasize speech-based health screening, adaptive signal processing for cybersecurity, and the integration of large language models with acoustic landmarks. His work spans engineering education, including a pioneering undergraduate quantum engineering degree program introduced in 2023. His lab and team collaborations include projects on smartphone-based depression screening, real-time task load estimation, and the design of robust spoofing attack detection systems. Epps has led interdisciplinary efforts in healthcare technology and pedagogical innovation in engineering education.

Yutian TangView profile

Assistant Professor

• AI+SE (Large-language Model+SE)
• Program Analysis
• Empirical Software Engineering
• +4 more

Dr. Yutian Tang is an Assistant Professor at the University of Glasgow's School of Computing Science. He holds a Ph.D. from The Hong Kong Polytechnic University and a B.Sc. from Jilin University. His research focuses on AI-driven software engineering, program analysis, and mobile app security, with over 40 publications in top venues like ESEC/FSE and IEEE TSE. He has received awards including the ISSRE'18 Best Industry Paper and Shanghai Pujiang Talent Program. Current research emphasizes automated testing, bug localization, and LLM applications in software engineering. Education: Ph.D., Department of Computing, The Hong Kong Polytechnic University B.Sc., Computer Science, Jilin University Research Interests: His work spans AI+SE (Large Language Models in Software Engineering), automated testing, privacy protection, and malware detection. Notable contributions include XDebloat (app debloating) and tools for analyzing Android vulnerabilities. He actively explores LLM applications for code generation and test suite optimization. Grants & Funding: EPSRC Peer Review College member National Natural Science Foundation of China (NSFC) grant (2023-2025) OpenAI API Researcher Access Program (2024) Google Cloud Research Credits (2024) Service & Leadership: Editorial Board Member of Frontiers in Computer Science , PC member of ICSE, ESEC/FSE, ASE, and other top conferences. Active in reviewing for IEEE Transactions and ACM journals. Future Directions: Tool support for automated bug detection, LLM integration in software lifecycle, and large-scale binary code analysis.

Linda PetzoldView profile

Professor

• Computational Science and Engineering
• Systems Biology
• Bioengineering
• +7 more

Linda Petzold is the Mehrabian Distinguished Professor in Computer Science and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her roles span academic leadership, research, and interdisciplinary collaboration. She holds affiliations with the National Academy of Engineering, National Academy of Sciences, and multiple UCSB centers including the Institute for Collaborative Biotechnologies, Center for Bioengineering, and Center for Control, Dynamical Systems & Computation. Dr. Petzold earned her PhD and BA in Computer Science and Mathematics from the University of Illinois. Her research focuses on modeling, simulation, and software development for multiscale systems in biology, materials, and social networks. Notable projects include the Stochastic Simulation Service (StochSS), a cloud-based platform for biological modeling, and collaborations on topics like circadian rhythms, coagulopathy, and social sentiment analysis. She also leads work in computational neuroscience, including studies of neuronal activity in brain organoids. Her scientific contributions are recognized through prestigious awards, including the Sidney Fernbach Award and ACM Prize in Computational Science. Petzold’s research emphasizes bridging computational methods with experimental data, fostering interdisciplinary partnerships to address complex challenges in healthcare, ecology, and materials science. Her lab develops algorithms for stochastic systems, with applications ranging from trauma patient outcomes to fibrinolytic mechanisms. Publications highlight advancements in numerical methods, machine learning, and systems biology. Key themes include neural ordinary differential equations, prompt tuning for LLMs, and multimodal scientific datasets. Her work integrates computational tools with real-world biomedical and ecological problems, reflecting a commitment to translational research. Dr. Petzold advises students and collaborates extensively, with grants supporting projects like Bayesian neural networks for parameter inference and GAN-based imputation for medical data. Her leadership in academic and professional societies underscores her role as a visionary in computational science and engineering.

Zoey Zhiyu ChenView profile

Assistant Professor

• Artificial Intelligence
• Natural Language Processing
• Healthcare Technology
• +3 more

Zoey Zhiyu Chen is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. Her research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), healthcare technology, financial technology, and dialogue systems. She explores the application of large language models (LLMs) in critical domains such as mental health therapy, cognitive behavior therapy (CBT), and finance, emphasizing robustness and ethical considerations. Key research areas include hypothesis discovery, rule learning, patient simulation for mental health training, and improving medical predictions through multimodal data analysis. Her work also addresses challenges in retrieval-augmented models, agentic search optimization, and zero-shot dialogue state tracking. Chen’s contributions span theoretical advancements in AI reasoning and practical applications in societal domains like law and healthcare. Her articles highlight trends in leveraging LLMs for clinical support, financial analysis, and improving human-AI interaction through preference learning and function calling mechanisms. She advocates for responsible AI development, as seen in studies on energy efficiency benchmarks (Hulk) and domain-specific robustness (RobustFin).

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +4 more

XIE Xiaofei is an Assistant Professor of Computer Science at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the Lee Kong Chian Fellowship and has a PhD from Tianjin University (2018). Previously, he was a postdoctoral researcher at Nanyang Technological University (NTU, 2018–2021). His research focuses on program analysis, software testing, vulnerability detection, and AI system quality assurance. Key areas include adversarial attacks on deep learning systems, autonomous driving testing, and secure software development. He actively advises PhD students and collaborates on projects like the GameRTS framework for video game testing and BehAVExplor for autonomous systems. Education : PhD, Tianjin University, 2018 Postdoctoral Research, NTU Singapore, 2018–2021 Research Interests : Dr. Xie explores cutting-edge topics such as neural network testing , AI security , and large language model (LLM) reliability . He develops tools like DeepHunter for DNN fuzz testing and CAShift for cloud attack detection. His work bridges theory and practice, addressing real-world challenges in software safety and AI robustness. Recent Contributions : His publications span top venues (ICSE, ASE, CVPR) and include innovations like behavior diversity testing for autonomous vehicles and multi-target backdoor attacks on code models. He also leads initiatives to enhance federated learning security and LLM vulnerability detection. Awards & Recognition : ACM Tianjin Doctoral Dissertation Award (2019) ACM SIGSOFT Distinguished Paper Award (ISSTA 2022) Best Paper Award, APSEC 2020 3 rd place in AI Singapore’s Trusted Media Challenge (2022) Advising & Grants : He mentors seven PhD/MSc students and leads research teams funded by initiatives like SMU’s Lee Kong Chian Fellowship. His labs focus on autonomous systems testing and AI-driven security tools .

Vincenzo StoicoView profile

Assistant Professor

• Energy Efficiency
• Machine Learning
• Energy Consumption
• +5 more

Vincenzo Stoico serves as an Assistant Professor in the Department of Computer Science at the Faculty of Science, Vrije Universiteit Amsterdam (VU Amsterdam), with dual affiliation at the university's Network Institute. His academic profile centers on computational sustainability, specifically optimizing energy consumption in emerging AI-driven systems. His research critically examines energy efficiency across machine learning infrastructure, with particular emphasis on large language models (LLMs). Key investigations include energy trade-offs in LLM-generated content delivery (on-device versus cloud), sustainable monitoring of concept drift in ML systems, and energy-aware self-adaptation mechanisms for robotics. This work bridges theoretical performance modeling with empirical validation in green computing contexts, addressing urgent environmental concerns in AI deployment. Recent publications (2024-2025) demonstrate consistent focus on quantifying energy-performance relationships across diverse computing domains. His methodology combines controlled experimentation with architectural analysis, revealing significant energy savings through optimized LLM operations, concept drift detection frameworks, and behavior tree implementations in robotic systems. These contributions advance sustainable AI engineering practices while highlighting critical accuracy-energy tradeoffs. Stoico actively collaborates with researchers including Ivano Malavolta and Patricia Lago across VU Amsterdam's Computer Science department and Network Institute. He contributes to academic instruction through the 'Green Lab' course, though specific advising relationships and grant funding details remain unreported in available sources. His institutional engagement occurs primarily within VU Amsterdam's Network Institute ecosystem, which fosters interdisciplinary research on digital society challenges. Current projects likely integrate with the university's broader sustainability initiatives, though explicit lab affiliations or team structures are not documented in the provided materials.

Dominik SobaniaView profile

Researcher

• Large Language Models in Software Development
• Genetic Programming for Program Synthesis
• Genetic Improvement
• +3 more

Dr. Dominik Sobania is a researcher at the Department of Business Informatics at Johannes Gutenberg University Mainz. His work focuses on the intersection of artificial intelligence and software development, particularly in the areas of Large Language Models (LLM), Genetic Programming (GP), and Genetic Improvement (GI). He explores how evolutionary computation can enhance program synthesis and improve software systems. His research interests include applying genetic algorithms to solve complex programming challenges, integrating LLMs with traditional GP techniques, and optimizing selection methods for better performance in symbolic regression and program analysis. Notable projects include ImageBreeder (combining diffusion models with evolutionary methods) and ComfyGI (automated image workflow improvement). Dr. Sobania's publications emphasize efficient algorithm design, such as down-sampled lexicase selection for GP, and critical assessments of LLM-generated software patches. He actively contributes to advancing AI-driven software development through empirical studies and comparative analyses of machine learning techniques. No scientific awards or formal advising records are mentioned in the provided texts.

• Gamification
• Virtual Reality
• Extended Reality
• +5 more

Nannan Xi is an Associate Professor (tenure track) in the department of Information and Knowledge Management. Her research focuses on gamification, virtual and augmented reality technologies, and their applications in areas such as elderly well-being, consumer behavior, and ethical AI systems. She has collaborated extensively on studies involving the metaverse, service robots, and immersive technologies. Key research areas: Gamification design, extended reality (XR), AI ethics, and sustainable consumption. Notable contributions include experiments on metaverse shopping acceptance, gamification in healthcare, and ethical considerations in AI agents. Her work aligns with UN Sustainable Development Goals related to health and innovation. She has organized academic events such as workshops at the Pacific Asia Conference on Information Systems (2023) and contributed to conferences like ICIS 2024. Publications span journals like Technological Forecasting and Social Change and Internet Research , with a focus on empirical studies and systematic reviews.

Filipa CaladoView profile

Assistant Professor

• Digital Humanities
• Queer Studies
• Natural Language Processing
• +12 more

Filipa Calado is an Assistant Professor in the School of Information at Pratt Institute, where she teaches courses in programming, data science, and critical approaches to technology. Her research lies at the intersection of digital humanities, queer theory, and computational linguistics, focusing on how AI and machine learning encode and reproduce biases related to gender, sexuality, and identity. PhD in English Literature and Certificate in Interactive Technology and Pedagogy, CUNY Graduate Center (2019) MA in English, University of Maryland BA in English with minor in Art History, Catholic University of America Her research critically examines the transformation of linguistic expressions of sex, gender, and sexuality into computable data, with a recent focus on using Large Language Models to analyze transphobic discourse in the U.S. She combines digital methods like text analysis and machine learning with critical frameworks from queer and feminist theory to challenge dominant narratives in technology. Her recent publications explore gender bias in NLP, queer approaches to text encoding (TEI), and the erasure of queer identities in literary texts. Her teaching emphasizes ethical coding practices, algorithmic bias, and feminist data science, equipping students with both technical and critical skills. She has presented her work at major conferences including ADHO, MLA, and ASAP, and delivered keynotes on topics such as impostor syndrome and open knowledge in AI. Most Distinguished Dissertation of the Year, CUNY Graduate Center, 2024 She has secured grants from Pratt Institute, CUNY, and the Digital Ethnic Futures Consortium, and is actively involved in service, including diversity and faculty search committees at Pratt. Her coding projects and teaching materials are openly available on GitHub under gofilipa , reflecting her commitment to open knowledge and digital pedagogy.

• Artificial Intelligence
• Constraint Programming
• Optimization
• +3 more

Mattias Guns is an Associate Professor in the Department of Computer Science at the Faculty of Engineering Science, KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research unit and holds affiliations with Leuven.AI and the KU Leuven Institute for Mobility (LIM). He serves on the Council of the Faculty of Engineering Science and the Programme Committees for Artificial Intelligence and Mobility and Supply Chain. His research centers on bridging Artificial Intelligence with constraint-based optimization, focusing on Explainable AI, Predict-and-Optimize frameworks, and machine learning integration for constraint solving. Key interests include human-centric explainability in decision systems, perceptual reasoning, and energy-efficient optimization models. His work addresses fundamental challenges in program synthesis, scheduling, and trustworthiness of AI planning systems. Recent publications reveal strong trends in fusing machine learning with declarative problem-solving paradigms. Notable themes include LLM-driven constraint modeling, mutational testing for solvers, step-wise explanation generation, and preference learning for unsatisfiable constraints. His research consistently targets real-world applications in supply chain optimization, inventory management, and perceptual reasoning systems. As principal investigator, he leads multiple major projects: TED-AI: Trustworthy Explanations for Decision Making in AI (2025) Towards Human-Centric Explainable Constraint Solving (2025-2028) SAELING: Energy Optimization via Learning (2024-2027) From Natural Language to Constrained Optimization (2023-2027) He has supervised PhD student Mulamba Ke Tchomba on machine learning-enhanced constraint solvers for perceptual reasoning. Within the DTAI research group, he contributes to KU Leuven's leadership in declarative AI through collaborative work on constraint programming foundations and applications. His team actively develops open-source tools like CMPpy for prediction-optimization integration and participates in European AI initiatives through Leuven.AI.

Jessy LiView profile

Associate Professor

• Computational Linguistics
• Natural Language Processing (NLP)
• Discourse Processing
• +4 more

Jessy Li is an Associate Professor in the Department of Linguistics at The University of Texas at Austin, specializing in computational linguistics and natural language processing (NLP). She actively engages in interdisciplinary research across discourse processing, language generation, and NLP applications in social and code-related contexts. Education: Ph.D. in Computer and Information Science, University of Pennsylvania (2017) Jessy’s research spans four core areas: Discourse Processing (analyzing discourse structure, pragmatics, and human/machine comprehension), Natural Language Generation (improving text generation through discourse models), Language and Society (exploring how self-perception and social context influence language use), and Language and Code (applying NLP to software evolution and documentation). Her recent publications align with these themes, emphasizing discourse-level analysis, generation evaluation, and interdisciplinary applications of NLP in software engineering and sociolinguistics. She received recognition for an Outstanding Paper at EMNLP 2024. She serves on the Graduate Studies Committee in the Department of Computer Science and leads AI initiatives at the NSF-Simons AI Institute for Cosmic Origins (CosmicAI) . Her service roles include Senior Area Chair for ACL 2025, Action Editor for Transactions of the Association for Computational Linguistics (TACL) , and co-organizing the Workshop on Computational Approaches to Discourse (CODI).

Marina LuketinaView profile

Professor

• Digital Transformation
• Tax Law
• Predictive Analytics
• +3 more

Marina Luketina is a Professor at the Department of Accounting, Controlling and Financial Management at Fachhochschule Steyr. Her research focuses on digital transformation in tax law, AI applications in public administration, and predictive analytics in financial management. She has published extensively on topics like VAT compliance evaluation using large language models and regulatory challenges in digital platform economies. Her recent work explores the intersection of artificial intelligence and Austrian tax law, including studies on LLM capability assessment for tax cases and predictive analytics frameworks for tax authorities. She actively engages in academic discourse via invited talks on KI in tax auditing and standardization of tax reporting. Luketina has supervised seven scholarly works, contributing to both theoretical and applied research in financial management. Her presentations at international conferences highlight innovative risk management approaches in public finance sectors.

Mark SantolucitoView profile

Assistant Professor

• Program Synthesis
• Computer Music
• DevOps
• +4 more

Mark Santolucito is an Assistant Professor of Computer Science at Barnard College, Columbia University. He holds a PhD in Computer Science from Yale University, where his research focused on program synthesis and computer music. His current work explores program synthesis techniques to enhance programmer productivity, particularly in lowering barriers to entry for underrepresented groups and optimizing workflows for advanced developers. He leads the Barnard PL (Programming Languages) Labs, which develops tools like TSL (Temporal Stream Logic) for synthesizing reactive systems and analyzing infrastructure-as-code (IaC). His research intersects formal methods with creative applications in music and live coding, emphasizing accessibility and usability. Education: PhD in Computer Science (Yale University), focusing on program synthesis and computer music. Research interests include program synthesis, temporal logic specifications, infrastructure configuration analysis, and music technology. He emphasizes human-centered design in his work, aiming to make programming more accessible through tools like TSL and interactive synthesis environments. Projects include TSL Move Cube, Block-based Editor for Temporal Logic, and Spiral Analysis for medical software migration. Notable contributions include developing TSL synthesis pipelines, optimizing Arduino configurations, and exploring static analysis for cost prediction in cloud deployments. His work bridges theoretical foundations with practical applications in both software engineering and creative computing. Labs/Teams: Leads Barnard PL Labs, collaborating on projects like TSL Synthesis Engine and Static Analysis for IaC. Active in workshops such as SEConfig and FMCAD.

Xuan WangView profile

Assistant Professor

• Natural Language Processing
• Data Mining
• AI for Sciences
• +7 more

Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.