Find Academic Supervisors

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +4 more

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

• Regulatory genomics
• Gene regulatory networks
• Computational biology
• +7 more

Stein Aerts is a full professor at the Faculty of Medicine of KU Leuven and head of the Laboratory for Computational Biology (VIB-KU Leuven). He is affiliated with multiple institutes including VIB.AI Center for AI & Computational Biology, the Leuven Brain Institute, Leuven.AI, LIMNI, LISCO, and the Leuven Cancer Institute. He also serves on the Faculty Council of Medicine and departmental boards. Research Focus: Regulatory genomics and gene regulatory networks Single-cell transcriptomics and epigenomics Deep learning for genomics and enhancer design Neurodevelopmental genomics and evolution Cancer genomics and synthetic biology Comparative genomics across species (Drosophila, octopus, mammals, birds) His recent work emphasizes using AI-driven approaches to decode enhancer logic, model cell-type-specific gene regulation, and understand brain evolution. He leads multiple large-scale projects funded through 2029–2030, including SpaceTimeOmics and enhancer-targeted glioblastoma modeling. Scientific Output: Aerts has a prolific publication record with over 15 high-impact papers in 2024–2025 alone, including in Science , Cell Genomics , Nature Reviews Bioengineering , and eLife . His work spans methodological advances (e.g., HyDrop, CREsted, GAME) and biological discoveries in enhancer function, cell-type evolution, and neurodegeneration models. Institutional Roles & Collaborations: Principal investigator in 10+ active grants (2024–2030) Founder and head of the Computational Biology Laboratory at VIB-KU Leuven Member of steering committees for HPC curriculum and bioinformatics POC Active collaborator across European and international consortia

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Daye NamView profile

Assistant Professor

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

Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.

Patrick LamView profile

Associate Professor

• Static Program Analysis
• Verifiable Software Specifications
• Software Engineering
• +8 more

Patrick Lam is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment to the Cheriton School of Computer Science. His research focuses on applications of programming languages and static analysis to software engineering challenges, emphasizing verifiable software specifications and program understanding. Dr. Lam has held significant grants from NSERC and is recognized for his impactful work, including the First Decade High Impact Paper award for his Soot framework. Education: Doctorate in Computer Science, Massachusetts Institute of Technology, 2007 Master's in Computer Science, McGill University, 2000 Bachelor's in Joint Honours Mathematics and Computer Science, McGill University, 1999 Research Interests: His primary areas include static program analysis, verifiable software specifications, and compiler design, with a focus on linking high-level software designs to low-level implementations. He explores techniques like lightweight specifications and domain-specific languages to enhance software reliability and efficiency. Recent work also addresses empirical studies of programming practices and security through modularization. Publications: Dr. Lam's recent publications span advancements in static analysis tools (e.g., WasmWalker for WebAssembly), formal verification of code generated by AI tools like GitHub Copilot, and empirical studies on C++ immutability usage. His work bridges theoretical programming language research with practical software engineering applications, emphasizing tools for developer productivity and code reliability. Awards and Recognition: First Decade High Impact Paper recognition for "Soot – A Java Optimization Framework" (2010) Teaching and Grants: He has taught courses such as CS 447, ECE 453, and ECE 459 on software testing and performance programming. Active in grant-funded research, he secured NSERC Engage Grant (2013) and an ongoing NSERC Discovery Grant (2013–2018). Lam has also advised graduate students and contributed to the Software Engineering Program at Waterloo as its Director (2016–2019). Labs and Teams: His research group explores topics in program analysis and software engineering, with collaborations on projects like abstract debugging tools (GobPie) and static analysis frameworks (Soot). He maintains an open-source repository on GitHub, contributing to educational materials and research tools.

Shrideep PallickaraView profile

Professor

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +12 more

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

Andrew JonesView profile

Senior Lecturer

• Molecular Biology
• Genomics
• Neuroscience
• +3 more

Andrew Jones is a Senior Lecturer in Molecular Biology and Genomics at the School of Biological and Medical Sciences, Oxford Brookes University. He specializes in gene diversity and functional properties of cys-loop ligand-gated ion channels in insects, with applications to pesticide resistance and vector control. Education: BSc in Biochemistry from Imperial College London (1996), PhD in Acetylcholinesterase and Receptor Studies from University of Leeds (2000) Research Interests: His work focuses on insect cys-loop ligand-gated ion channels—including nicotinic acetylcholine, GABA, and glutamate-gated chloride channels—using genome analysis, molecular biology, and electrophysiology. Key areas include pesticide resistance mechanisms, receptor mutations, and neuropharmacological properties. Publication Trends: Recent studies examine pyrethroid resistance in Aedes mosquitoes, neonicotinoid interactions with honeybee receptors, and molecular characterization of lepidopteran GABA receptors. His work bridges insect neurobiology with agricultural pest management and public health vector control. Scientific Awards : Oxford Brookes Research Excellence Award (2017) Nigel Groome PhD Studentship (2015-2018) Leverhulme Trust Grant RPG-2012-602 HEIF Funding (2014) Grants and Projects : Leads BBSRC-funded studentships on insecticide resistance and mosquito receptor characterization. Collaborates on sustainable agroforestry projects in Indonesia.

Fei ChiangView profile

Associate Professor

• Data Management
• Data Quality
• Data Privacy
• +6 more

Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.

• Software Engineering
• Software Testing
• Program Analysis
• +4 more

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

Daphne YaoView profile

Professor

• Cybersecurity
• Program Analysis
• Machine Learning Algorithms
• +3 more

Daphne Yao is a Professor in the Department of Computer Science at Virginia Tech , part of the College of Engineering. She holds the Elizabeth and James E. Turner Jr. '56 Faculty Fellowship and CACI Faculty Fellowship. Her research focuses on cybersecurity, program analysis, machine learning algorithms, and applied cryptography. Yao has contributed to advancing secure coding practices, vulnerability detection in software, and privacy-preserving technologies. Education includes a Ph.D. in Computer Science from Brown University (2007), an M.S. in Computer Science from Indiana University (2002), and earlier degrees in Chemistry from Princeton University (M.A., 2000) and Peking University (B.S., 1998). Her work bridges theoretical computer science with practical applications, emphasizing real-world cybersecurity challenges. Recent research trends include combating ransomware via API profiling, improving privacy in voice assistants, and developing resilient network routing systems. Yao collaborates across disciplines, integrating machine learning into healthcare cybersecurity and epigenomic analysis. Her publications reflect a commitment to measurable, deployable security solutions and ethical AI practices. Key contributions include the CryptoGuard tool for detecting cryptographic vulnerabilities and frameworks like Madeline for low-cost cyber threat monitoring. She has also explored challenges in deploying intrusion detection systems and mitigating data-oriented attacks in cyber-physical systems.

Karim BoutibaView profile

Research Fellow

• 5G Networks
• 6G Networks
• Network Slicing
• +4 more

Karim Boutiba is a Research Fellow at EURECOM's Communication Systems department, specializing in 5G/6G networks and advanced wireless technologies. His work focuses on optimizing network performance through machine learning, resource management, and network slicing. He contributes to both theoretical advancements and practical implementations, such as the 5G INSTRUCT forge pipeline and the GoSimRIS framework for 6G networks. His research interests span dynamic radio resource allocation, energy-efficient communication, and the integration of artificial intelligence in next-generation networks. Notable projects include leveraging deep reinforcement learning (DRL) for optimizing TDD patterns, O-RAN architectures, and edge computing solutions to enhance video streaming and latency-critical applications. Boutiba's publications emphasize the practical deployment of theoretical models, such as network slicing in Radio Access Networks (RAN) and anomaly detection using machine learning. His work bridges gaps between academic research and industry-ready solutions, with active participation in initiatives like the 6G-BRICKS architecture project.

Yousra AaferView profile

Assistant Professor

• Mobile and Smart Device Security
• System and Software Security
• Android Security
• +4 more

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

• Resource Economics
• Aquaculture Economics
• Fisheries Economics
• +3 more

Frank Asche is Professor II in Resource Economics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Security, Economics and Planning. His research focuses on the economic dimensions of aquatic food systems, with emphasis on sustainability, trade dynamics, and policy effectiveness in global fisheries and aquaculture sectors. His primary research interests include: Resource Economics Aquaculture Economics Fisheries Economics Seafood Trade Policy Environmental Economics Sustainability Assessment Asche's recent publications reveal a strong focus on quantifying sustainability through tools like the Aquaculture Performance Indicators (API), analyzing food loss in aquatic systems, and investigating market structures in global seafood trade. His work consistently bridges economic theory with practical industry challenges, particularly in Norwegian and European contexts. He maintains extensive international collaborations with researchers including Taryn Garlock, James Anderson, and Ragnar Tveterås, contributing to major projects on blue food systems and aquaculture sustainability. His work appears in leading journals across ecological economics, fisheries science, and environmental policy domains.

• Watershed Hydrology
• Surface Water Quality
• Hydroinformatics
• +5 more

Jeffrey Horsburgh is a Professor in Civil and Environmental Engineering and a Water Researcher at the Utah Water Research Laboratory (UWRL) at Utah State University. His work focuses on hydroinformatics, environmental sensor networks, and watershed hydrology, integrating data models, cyberinfrastructure, and GIS for water quality and hydrology research. Professor, Utah State University (College of Engineering) Utah Water Research Laboratory Research Interests: Dr. Horsburgh develops technology for environmental observatories, emphasizing high-frequency sensor data, reproducibility, and cyberinfrastructure. His research spans watershed hydrology, surface water quality, human dimensions of water use, and standards-based data sharing through platforms like HydroShare and HydroServer. Notable Awards: 2024 USU Outstanding Researcher 2023 Reproducibility Author Award 2019 Outstanding Teacher 2014 Early Career Excellence Award (International Environmental Modelling & Software Society) Education: Ph.D., MS, and BS in Civil/Environmental Engineering from Utah State University (2009, 2001, 1999).

Michael BaudisView profile

Professor

• Bioinformatics
• Tumorgenomics
• Genomics
• +4 more

Michael Baudis is a Professor of Bioinformatics and Tumorgenomics at the Institute of Molecular Biology , Faculty of Science, University of Zurich. He leads the development of the Progenetix database, a global reference for cancer genomic copy number alterations, and contributes to international standards through his membership in the Global Alliance for Genomics and Health (GA4GH) . Research focuses on genomic data representation , cancer subtype classification , and data sharing protocols Key projects include Beacon networks , Phenopackets , and GA4GH standards Email: michael.baudis@uzh.ch His recent work explores short tandem repeat variations , attention-based deep learning for CNAs , and heterogeneity in cancer classifications . Methodological contributions include segment_liftover , CNARA , and pgxRpi for genomic data calibration and analysis.