Find Academic Supervisors

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +2 more

Dr. Ge Liu is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. Her research focuses on Artificial Intelligence, Bioinformatics, and Ethical AI, with notable contributions to LLM policy frameworks, adversarial AI defenses, and social AI modeling. She teaches courses including CS 582 (ML for Bioinformatics) and CS 598 GBL (Genetic Models for Biomedical Sciences). Recent work emphasizes balancing AI safety and utility, particularly through policy design for large language models and multi-agent systems. Liu's publications address topics like content moderation, uncertainty expression in AI, and cross-modal video-language models. Education: Not explicitly listed in provided text Her research interests span AI ethics, LLM behavior analysis, and computational biology applications. Key projects include MOSAIC (social AI simulations) and SciCode (scientific coding benchmarks). Collaborative efforts focus on multi-agent systems and adversarial attack defenses. Publications highlight trends in AI safety, LLM governance, and multimodal learning. No scientific awards are listed, but her record includes notable contributions to Grainger Engineering's faculty expansion.

Mohammad MoshirpourView profile

Professor

• Applied Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Mohammad Moshirpour is a faculty member at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences and the Department of Computer Science. As Director of the Software Engineering Practice and Education (SEPE) Lab, his work spans applied software engineering, artificial intelligence, machine learning, and educational methodologies. He emphasizes practical applications, collaborating with healthcare institutions (e.g., University of Calgary nursing faculty), industry partners, and academic institutions to address real-world challenges such as pipeline monitoring, video restoration, and dementia therapy through virtual reality. His research interests include software requirements engineering, AI-driven software development, and innovative education models like project-based learning (PBL) and hackathon-based assessment. He advocates for hands-on learning, comparing software engineering education to medical training. His SEPE Lab focuses on creating scalable, maintainable software solutions with a strong emphasis on AI integration. Key collaborations include work on parent-child interaction analysis via machine learning and video super-resolution techniques. Professor Moshirpour’s recent projects highlight cross-disciplinary innovation, such as leveraging federated learning for video enhancement and applying Monte Carlo methods to pipeline monitoring. His educational contributions include frameworks for agile teaching, automated feedback systems, and curriculum modernization. While no specific awards are listed, his work reflects a commitment to bridging theoretical research with practical implementation.

• Constitutional Law
• Property Law
• Environmental Law
• +4 more

Professor Rachael Walsh is an Associate Professor and Fellow at Trinity College Dublin's School of Law, specializing in constitutional and property law. She serves as the Law School's Director of Global Engagement and Erasmus Coordinator. Previously, she held roles as a Lecturer at King's College London and an Adjunct Professor at Trinity College Dublin. Her research focuses on the intersection of constitutional law, property rights, and environmental policy, addressing challenges like climate change and housing affordability. Walsh's work bridges legal theory and practice, emphasizing equitable responses to global issues. Notably, she contributed to the Irish Citizens' Assembly as a constitutional law advisor, exploring citizen participation in democratic processes. Education: She earned a PhD from Trinity College Dublin (Ussher Scholarship recipient) and an LLM from Harvard Law School (Fulbright Scholar). Key publications include Property Rights and Social Justice: Progressive Property in Action (2021) and Kelly: The Irish Constitution (2018), a seminal text in Irish constitutional law. Her research also examines constitutional amendment processes and the role of deliberative democracy in fostering political legitimacy. Research Interests: Constitutional law, property law, environmental law, democratic governance, and climate policy. Grants & Projects: Lead PI for the ASHA project (€400,000 TRDA funding), Co-PI of the H2020 RESILENS project. Walsh has received numerous accolades, including the TCD Research Supervision Award (2023) and the Provost's Teaching Award (2018). Her work impacts both academia and policy, with her scholarship cited by the Irish Supreme Court and influencing legal frameworks globally.

• Quantum Computing
• Software Engineering
• Artificial Intelligence
• +4 more

Dr. Aakash Ahmad is a Lecturer in Computing and Communications at Lancaster University, UK. His research focuses on quantum software systems, AI-driven software engineering practices, and microservices architecture. He leads empirical studies on developer experiences with tools like GitHub Copilot and has contributed to foundational work in quantum architecture description languages (QADL). His work bridges practical software development challenges with cutting-edge AI and quantum computing innovations. Affiliations: Department of Computing at Lancaster University, UK. Research Interests: Quantum software architecture, AI in software engineering, microservices challenges, empirical studies on developer tools, and blockchain system development. His datasets include empirical analyses of Stack Exchange and GitHub data, exploring architecture decisions in quantum systems and challenges in Copilot usage. He has received awards for impactful studies on GitHub Copilot practices and quantum software development processes. Ahmad is actively involved in organizing workshops such as the Engineering Processes and Practices for Quantum Software (PPQS’24) and presents at international conferences. Key Projects: Quantum architecture frameworks, AI ethics in development, and blockchain system design.

Katja TumaView profile

Research Fellow

• Cybersecurity
• Threat Analysis
• Security Design
• +7 more

Katja Tuma is a Visiting Research Fellow at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Computer Systems department and the Network Institute. Her primary research focuses on cybersecurity, threat analysis, and secure software design. She explores the intersection of human factors, artificial intelligence, and traditional security practices to enhance system resilience. Affiliations: Faculty of Science, Network Institute, Computer Systems Key Research Areas: Threat Modeling, Security Compliance, Large Language Models in Security, Gender Dynamics in Security Training Her work emphasizes practical applications such as automated security repair, validation of security designs using data flow diagrams, and addressing human biases in security decision-making. Tuma has contributed to projects like a training framework for industrial cybersecurity skills and certification mechanisms for software supply chains. Recent publications highlight challenges in using LLMs for code generation, the role of gender in security evaluations, and improving threat analysis replication methodologies. She teaches courses on data structures for AI and security experiments, bridging theoretical concepts with real-world applications. Notable Projects: Personalized Cybersecurity Training Framework (2023), Assurance in Open Software (2020-2023) Her research also addresses emerging areas like AI-augmented security pipelines and uncertainty management in AI-driven threat intelligence, reflecting a commitment to both technical innovation and socio-technical security challenges.

Yangfeng JiView profile

Associate Professor

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

Yangfeng Ji is an Associate Professor in the Department of Computer Science at the University of Virginia, where he has been since 2018. Previously, he held a postdoctoral position at the Paul G. Allen School of Computer Science & Engineering, University of Washington, from 2016 to 2018. He earned his PhD in Computer Science from the Georgia Institute of Technology in 2016. His research focuses on Natural Language Processing (NLP) and Machine Learning, with an emphasis on ethical AI, bias mitigation, and model interpretability. Education: PhD in Computer Science, Georgia Institute of Technology, 2016 Postdoctoral Researcher, Paul G. Allen School of Computer Science & Engineering, University of Washington, 2016–2018 Research Interests: Dr. Ji’s work addresses critical challenges in NLP and Machine Learning, including fairness and bias in Large Language Models (LLMs), model interpretability through techniques like saliency estimation and rationale evaluation, and the development of robust evaluation frameworks. His contributions span topics such as gender representation vectors in LLMs, allocational harms, and improving temporal awareness in recommendation systems. He also explores data selection methods for model fine-tuning and secure data appraisal techniques. Recent Research Trends: His publications emphasize addressing biases and fairness in LLMs, enhancing model interpretability via contrastive activation analysis, and improving robustness through adversarial training and data selection. Key themes include mitigating vulnerabilities in model explanations and optimizing latent spaces for out-of-distribution detection. Scientific Awards: No awards explicitly mentioned in the provided information. Advising and Grants: No advisees or grant details provided in the text. Dr. Ji’s research group focuses on collaborative projects in ethical AI and NLP applications. Labs/Teams: No specific lab or team affiliations explicitly stated in the text.

• Software Testing
• Compiler Testing
• Software Engineering
• +3 more

Yongqiang Tian is a Lecturer in the Department of Software Systems & Cybersecurity at Monash University. His research focuses on software testing, compiler testing, and software engineering. He holds a Ph.D. in Computer Science from the University of Waterloo (Canada) and another Ph.D. in Computer Science from the Hong Kong University of Science and Technology, alongside a BEng in Information Engineering from City University of Hong Kong. His research interests emphasize compiler testing methodologies, program reduction techniques, and deep learning system reliability. Recent work includes leveraging large language models (LLMs) for enhancing compiler testing and program analysis. He has explored probabilistic delta debugging, program reduction algorithms, and evaluating DNN model behaviors under compression. Key contributions include frameworks like T-Rec for language-agnostic program reduction, Lampr for LLM-aided optimization, and tools such as EvalDNN for deep learning model evaluation. His articles highlight trends in compiler bug analysis, testing deep learning libraries, and improving fault localization through algorithmic innovations. No scientific awards are explicitly mentioned. Advising and grants details are not provided in the text. His work often intersects with program analysis, compiler validation, and applying AI techniques to software engineering challenges.

• Learning Analytics
• Artificial Intelligence in Education
• Educational Technology
• +3 more

Xinyu Li is a Research Fellow at the Department of Human Centred Computing, Monash University. Their work focuses on multimodal learning analytics, AI in education, and collaborative learning dynamics, contributing to UN Sustainable Development Goals through educational technology advancements. Key research areas include generative AI's impact on learning processes, self-regulated learning strategies, and spatial data analysis for educational systems. Over 27 peer-reviewed publications since 2021 demonstrate expertise in learning analytics, educational psychology, and computer science intersections. Recent studies examine metacognitive effects of AI tools, comparative analysis between ChatGPT and human experts in learning support, and temporal dynamics of collaborative learning through sensor data. Work also extends to spatial computing with road network query optimization and ethical challenges of large language models in education. Collaborations span global institutions, reflecting interdisciplinary reach in education technology. Publications have been widely disseminated via news outlets, social platforms, and academic networks, achieving significant readership on Mendeley. Research bridges technical innovations (e.g., FLoRA Engine, VizChat systems) with educational outcomes, emphasizing practical deployments and real-world impact through field trials and randomized controlled trials.

• Distributed Systems
• Context-Aware Computing
• Software Engineering
• +2 more

Pedro Alves is an Associate Professor in Computer Engineering at the Lusófona University of Humanities and Technologies, where he previously served as Director of the Computer Engineering undergraduate program (2016-2022). He holds a PhD in Computer Engineering from the University of Lisbon's Instituto Superior Técnico (2014). His research focuses on distributed context-aware systems, mobile computing, automated assessment tools, and educational technology. He leads projects developing practical applications including Drop Project (automatic grading system), App4SHM (structural health monitoring), and mobile solutions for healthcare/education. Recent investigations explore AI integration in programming education, analyzing student interactions with LLMs and developing assessment strategies for AI-augmented coding. Pedro teaches courses in Mobile Computing, Programming Fundamentals, Algorithms, and Programming Languages. He mentors student projects spanning healthcare applications, civil engineering tools, and educational platforms, fostering industry-academia collaboration.

Chongle PanView profile

Associate Professor

• Bioinformatics
• Machine Learning
• Proteomics
• +5 more

Chongle Pan is an Associate Professor of Microbiology and Computer Science at The University of Oklahoma. He holds a Ph.D. from the University of Tennessee (2006) and a B.S. from East China Normal University (2001). His research bridges computational and biological sciences, focusing on explainable machine learning, parallel computing for biomedical informatics, human microbiome dynamics, and environmental microbiome modeling. Key projects include studying probiotic/prebiotic impacts on human health and developing mechanistic models for wetland sediment communities' greenhouse gas emissions. His lab employs proteomic stable isotope probing, integrated -omics, and synthetic microbiome approaches. Research interests include computational biology, proteomics, and environmental microbiology. Notable contributions include genome-resolved proteomic studies of soil microbial communities and cloud computing-based proteomics analysis tools like CloudProteoAnalyzer. Collaborations involve institutions like Lawrence Berkeley National Lab and Pacific Northwest National Lab. Publications highlight advancements in metaproteomics, microbial community modeling, and biomedical imaging technologies. His work addresses environmental bioremediation challenges and computational solutions for genomic data analysis. The Pan Lab's research emphasizes interdisciplinary innovation at the interface of computer science and life sciences. Grants and funding sources support his work in computational biology and microbial ecology. No specific awards are listed, but his extensive publication record reflects sustained academic impact. Students are advised through collaborative interdisciplinary programs combining computer science and microbiology disciplines.

• Applied Machine Learning
• Large Language Models
• Generative ML
• +4 more

Dr. Andrei Kucharavy is an Assistant Professor at HES-SO Valais-Wallis in the Faculty of Economics and Services, where he teaches Business Economics courses including Fundamentals of Algorithms and Computational Thinking. He co-founded the GenLearning Center, an applied research entity focused on safety and deployment challenges of Generative AI technologies. His research centers on machine learning safety, privacy, and distribution from cybersecurity and human factors perspectives. PhD from Paris Sorbonne University (research at Johns Hopkins University and Stowers Institute for Medical Research) Engineer degree from Ecole Polytechnique Former postdoctoral researcher at EPFL's Distributed Computing Lab Dr. Kucharavy's research spans Applied Machine Learning, Large Language Models, and Cybersecurity with particular expertise in generative ML since 2018 and its cybersecurity applications since 2020. His work bridges theoretical machine learning with practical security implementations in Swiss defense contexts. His publications reveal strong focus on LLM vulnerabilities, privacy leakage detection, and developing bibliometric methods resilient to AI-generated content. His recent publications demonstrate expertise in LLM security vulnerabilities, code generation risks, and developing frameworks for technology forecasting using iterative self-prompting. The research shows consistent progression from foundational LLM understanding to specialized security applications, particularly in code vulnerability injection through dataset poisoning and malicious fine-tuning. Distinguished Cyber-Defence (CYD) Postdoctoral Fellowship recipient Dr. Kucharavy leads multiple Cyber-Defence Campus-funded projects totaling over CHF 430,000, including work on characterizing LLM attacks in code generation, developing vulnerability characterization frameworks, and secure privacy leakage detection protocols. His research team includes Percia David Dimitri and Vallez Cyril, with collaborations extending to Cyber-Defence Campus experts like Dr. Ljiljana Dolamic. He actively develops applied solutions through the GenLearning Center, focusing on on-premises deployment of generative models for healthcare applications and knowledge transfer tools that maintain data privacy while enhancing educational outcomes. His work directly informs Swiss defense technology standards for LLM-based solutions in cyber-physical systems.

Todd MowryView profile

Professor

• Computer Architecture
• Systems
• Databases
• +3 more

Todd Mowry is a Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on enhancing microprocessor-based system performance through parallelism exploitation, including single-chip multiprocessing and latency mitigation strategies. He leads initiatives like the STAMPede project, addressing challenges in compiler-driven parallelization and hardware-software co-design. His work spans computer architecture, systems, and databases, with a strong emphasis on compiler optimization, memory management, and scalable computing. Mowry has advised students including Sam Arch, Patrick Coppock, Hongyi Jin, Ruihang Lai, and Eliot Solomon. He teaches advanced courses such as 15745 (Fall 2025) and 15418 (Spring 2025), contributing to graduate-level education in computer systems. Recent research highlights include innovations in machine learning optimization (e.g., Relax framework), efficient LLM microservices, and architectural solutions for thread-safe metadata management. His publications address topics like UDF optimization, auto-batching in neural networks, and energy-minimal dataflow systems. Mowry's projects often involve collaboration across academic and industrial partners, driving advancements in both theoretical and applied computer systems. He has contributed to frameworks like CORA for tensor compilation and NVOverlay for non-volatile memory systems, emphasizing practical scalability and efficiency.

• Deep Learning
• Hardware Architecture
• Computer Networks
• +3 more

Brad McDanel is an Assistant Professor of Computer Science specializing in the intersection of deep learning, hardware architecture, and computer networks. His research focuses on optimizing deep neural networks (DNNs) through techniques like systolic array implementations, sparse architectures, and quantization. He has contributed to accelerating DNN inference on edge devices and distributed systems, with recent work addressing challenges in Large Language Models (LLMs) and multi-modal architectures. Education: Ph.D., Computer Science, Harvard University (2019) M.Sc., Computer Science, Wake Forest University (2012) B.Sc., Computer Science, Wake Forest University (2010) Research interests include speculative decoding, transformer optimization, and efficient deployment of AI systems. His work bridges theoretical frameworks and practical implementations, emphasizing computational efficiency and scalability. Notable contributions include innovations in systolic array optimization and distributed DNN architectures. Publications span conferences like ASPLOS, ICS, and ICDCS, with a focus on algorithmic and hardware-level optimizations for AI systems. He has pioneered techniques such as BranchyNet and Embedded Binarized Neural Networks, which enhance inference speed and resource utilization.

Jingnan XieView profile

Associate Professor

• Formal Languages
• Automata Theory
• Computational Complexity
• +5 more

Dr. Jingnan Xie is an Associate Professor of Computer Science at Millersville University, specializing in theoretical computer science. His research spans formal languages, automata theory, computational complexity, computable analysis, and quantum computing. Millersville University of Pennsylvania Ph.D. in Computer Science (2017) from University at Albany, SUNY His work explores computational parallels among automata theory, real function analysis, and quantum models, with a focus on bridging classical complexity theory and modern applications in AI/ML. Key contributions include foundational studies on multi-pattern languages, L-systems, and cellular automata. Recent research trends highlight collaborations at the intersection of vulnerability detection, large language models (LLMs), and quantum computing. Papers in Theoretical Computer Science , Acta Informatica , and Cluster Computing underscore his interdisciplinary impact. Awards include the 2025 PACISE Best Faculty Paper Award and the 2025–2026 PASSHE FPDC Grant for quantum computing research. He serves as a reviewer for journals like Natural Computing and Information and Computation . Dr. Xie mentors students like Emily Riley, who joined NC State’s Ph.D. program. He leads the TheoryX Lab, which investigates foundational questions in theoretical computer science, with funding from PASSHE and collaborations with Dr. Harry Hunt, Dr. Richard Stearns, and Dr. Ching-Sheng Lin.

Michael KirkpatrickView profile

Professor

• Computing & Professional Ethics
• Information Security & Privacy
• CS Education & Pedagogy
• +1 more

Michael Kirkpatrick is a Professor of Computer Science at James Madison University (JMU), part of the College of Integrated Science & Engineering. He holds a Ph.D. from Purdue University (2011), M.S. from Michigan State University (2007), and B.A. from Indiana University (2001). His academic journey includes roles as Assistant Professor (2011–2017) and Associate Professor (2017–2024) before his current professorship. Kirkpatrick's research focuses on computing ethics, information security, CS education pedagogy, and computer systems. He teaches courses such as Computer Systems I-II, Societal and Ethical Issues in Computing, and Operating Systems. His scholarly work emphasizes integrating ethics into computing education, modern web development pedagogy, and security challenges in genetic data. Recent publications (2024) explore AI's impact on research ethics and transparent classroom practices. He has contributed to ACM ethics guidelines and pioneered interactive educational tools like OpenCSF. While no explicit awards are listed, his extensive contributions to curriculum design and ethics education highlight his academic influence. Kirkpatrick's teaching philosophy prioritizes active learning and interdisciplinary ethics integration. His courses use team-based learning and readiness assurance processes, with a focus on preparing students for professional conduct challenges. Though specific grants or labs aren't detailed, his work reflects a commitment to advancing both technical and ethical dimensions of computer science education.