جستجوی استاد راهنمای دانشگاهی

Lijing Zhuمشاهده پروفایل

استادیار

• Machine Learning
• Graph-based Deep Learning
• Continual Learning
• +۳ مورد دیگر

Dr. Lijing Zhu serves as an Assistant Professor of Data Science within the College of Science and Engineering at the University of Houston-Clear Lake, where she teaches foundational data science courses and conducts research in artificial intelligence. Academic Background Ph.D. in Data Science, Bowling Green State University (August 2025) Research Focus Dr. Zhu's research spans machine learning, graph-based deep learning, continual graph learning, and computer vision. Her work addresses critical challenges in knowledge representation through continual knowledge graph learning, human-object interaction detection, and graph representation learning. She develops innovative algorithms that enhance the robustness and efficiency of deep learning models for complex structured data, with particular emphasis on overcoming catastrophic forgetting in dynamic knowledge graphs. Publication Trends Her active 2024-2025 publication record in venues like ECML PKDD, CIKM, and IEEE Big Data demonstrates a cohesive research trajectory across three interconnected domains: (1) advancing graph neural network robustness against adversarial attacks, (2) developing continual learning frameworks for evolving knowledge graphs, and (3) applying multimodal deep learning to drug discovery and computer vision problems. This cross-cutting work positions her at the intersection of theoretical machine learning and practical applications. Teaching and Mentorship Dr. Zhu teaches DASC 5133 (Introduction to Data Science), DASC 5333 (Database Systems for Data Science), and DASC 5431 (Data Analytics and Machine Learning). As an early-career faculty member building her research program, she offers graduate students opportunities to contribute to high-impact publications while developing expertise in graph-based AI systems and multimodal learning.

• Combinatorial Optimisation
• Explainable AI
• Metaheuristics
• +۷ مورد دیگر

Dr. Lee Christie is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research in optimization and artificial intelligence. He is affiliated with the Complex Optimisation Research Group and maintains connections with the National Subsea Centre through his research on net-zero operations. His educational background includes: BSc (Hons) in Computer Science from Robert Gordon University (2003-2007) MSc in Information Engineering (Distinction) from Robert Gordon University (2009-2011, part-time) PhD in Computational Intelligence from Robert Gordon University (2011-2016) Dr. Christie's research primarily focuses on combinatorial optimization, structure learning, and blockchain technologies. He investigates how to make non-deterministic solvers more transparent through trajectory mining and feature extraction. His work bridges theoretical optimization techniques with practical applications in transportation systems (particularly connected autonomous vehicles), renewable energy (wind farm optimization), and supply chain management. He has published extensively on explainable metaheuristics, with a growing emphasis on making optimization algorithms interpretable for end-users while maintaining effectiveness. His recent publications (2021-2025) demonstrate a clear research trajectory toward explainable AI for optimization algorithms, with applications spanning transportation systems, renewable energy infrastructure, and complex supply chains. These works showcase his ability to translate theoretical advances into practical solutions for real-world problems with societal impact. Dr. Christie has secured research funding for projects including the A.R.T. Forum NSR (2019-2022), which developed implementation roadmaps for automated road transport in the North Sea Region. He teaches programming for business analytics courses and actively contributes to the Aberdeen Python User Group as a steering committee member. His academic service includes supervision of PhD students, with Dr. Martin Fyvie recently completing a dissertation on 'Explainability of Non-Deterministic Solvers' under his guidance as second supervisor. Dr. Christie maintains active collaborations with researchers including John McCall, A.-C. Zăvoianu, and A.E.I. Brownlee, resulting in consistent publication output across reputable venues in evolutionary computation and artificial intelligence.

• Medical image processing
• Deep Learning
• Computer Vision
• +۵ مورد دیگر

Nicolás Vila Blanco is an Associate Professor at the University of Santiago de Compostela (USC), Spain, conducting research through CiTIUS (Centro de Investigación en Tecnoloxías da Información e as Comunicacións). His academic journey includes a PhD in Information Technology from USC and the University of Coruña (2022), an M.Sc. in Data Science and Computer Engineering from the University of Granada, and a B.Sc. in Computer Science from the University of Vigo. Dr. Vila Blanco's research focuses on medical image processing using Deep Learning techniques, with particular expertise in dental panoramic radiograph analysis, age and sex estimation, oral microbiome research, and medical imaging applications for conditions like COVID-19. His work bridges computer science and clinical dentistry/medicine, developing practical tools that address real clinical challenges. His publication record demonstrates consistent output in high-impact journals and conferences, with numerous Q1 publications across medical imaging, dentistry, and bioinformatics fields. His research shows a clear trajectory from theoretical AI development to practical clinical implementation, with emphasis on tools that can be readily adopted in medical practice. Dr. Vila Blanco has developed several important research tools including DenTiUS Deep Plaque software for automated dental plaque quantification, IDALib for efficient image data augmentation, and has contributed to PrimerEvalPy for microbiome primer evaluation. His clinical research has produced evidence on antimicrobial efficacy, dental plaque analysis methods, and diagnostic systems with real-world applicability. His collaborative approach is evident in his numerous co-authored publications spanning dentistry, microbiology, cardiology, and computer science departments. This interdisciplinary work positions him as a bridge between technical AI development and clinical healthcare applications.

Chevaleyre Yannمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +۳ مورد دیگر

Chevaleyre Yann is a Professor of Computer Science at LAMSADE, Paris-Dauphine PSL University, where he has been working since 2017. Previously, he served as Professor of Computer Science at Paris-Nord University from 2009 to 2017 and as Director of the "data science" team at the LIPN laboratory. His academic journey includes a Lecturer position at LAMSADE, Paris-Dauphine University from 2002 to 2009, a Habilitation thesis at Paris-Dauphine University in 2009, and a Doctorate at Pierre and Marie Curie University under the supervision of Jean-Daniel Zucker from 1998 to 2001. Professor Chevaleyre's research spans multiple areas within artificial intelligence and computer science, with particular expertise in multi-agent systems, computational social choice, and machine learning. His work on preference modeling, voting theory, and resource allocation has significantly contributed to the field of computational social choice. More recently, his research has expanded into adversarial machine learning, robust classification, and generative models, reflecting the evolving landscape of AI research. His publication record demonstrates consistent contributions across multiple domains, with recent work focusing on precision-recall optimization in generative models, the role of randomization in adversarial robustness, and novel approaches to graphical bilinear bandits. His research shows a clear trajectory from foundational work in multi-agent systems toward more contemporary challenges in machine learning security and evaluation. Professor Chevaleyre has maintained active collaborations with researchers across France and internationally, evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in areas ranging from robotics to bioinformatics.

• Interpretable Machine Learning
• Explainable AI
• High-Stakes Decision Systems
• +۵ مورد دیگر

Cynthia Diane Rudin is a Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics at Duke University, where she directs the Interpretable Machine Learning Lab. Previously, she held faculty positions at MIT Sloan School of Management and research roles at Columbia University and NYU. PhD in Applied and Computational Mathematics, Princeton University BS in Mathematical Physics and Music Theory, University of Chicago Her research pioneers interpretable machine learning for high-stakes domains where transparency is non-negotiable. She challenges the accuracy-interpretability tradeoff myth, proving that transparent models can match black-box performance in healthcare, criminal justice, and power grid reliability applications. Her work has produced deployable systems like the 2HELPS2B ICU seizure predictor and NYPD's Patternizr crime detection tool. Rudin's publication record shows consistent focus on interpretable algorithms since 2007, with recent work exploring the Rashomon set of equally accurate models and causal inference frameworks. Her highly cited 2018 Nature Machine Intelligence paper fundamentally shifted industry practices toward transparent AI. Squirrel AI Award for AI Benefiting Humanity (2022) Guggenheim Fellowship (2022) Triple INFORMS Innovative Applications Award winner (2013, 2016, 2019) Fellow of AAAI, ASA, and IMS She actively mentors students through Duke's Data+ program and has advised teams winning international competitions. Her lab maintains strong industry partnerships focused on ethical AI deployment, with current grants from NSF, NIH, and DARPA supporting healthcare and criminal justice applications. Rudin serves on National Academies committees shaping federal AI policy and has testified before Congress on algorithmic transparency. The Interpretable Machine Learning Lab maintains an open-science ethos with all code publicly available. Current projects focus on sparse decision trees for medical diagnostics, causal inference frameworks for high-stakes decisions, and extending the Rashomon effect theory to new application domains.

Olga Kalininaمشاهده پروفایل

استاد

• Bioinformatics
• Drug Discovery
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Olga Kalinina leads the Drug Bioinformatics research group at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). She holds an M.Sc. in Mathematics from Moscow State University (2003) and a Ph.D. in Bioinformatics from the Russian Academy of Sciences (2007). Her academic journey includes postdoctoral positions at the European Molecular Biology Laboratory (EMBL) and University of Heidelberg, followed by establishing her independent research group at the Max Planck Institute for Informatics (2012-2018). Her research program integrates cutting-edge bioinformatics and computational approaches to address challenges in drug discovery and resistance mechanisms. Primary research domains include: Development of machine learning tools for predicting functional consequences of genetic variants and drug resistance phenotypes Structural bioinformatics approaches for protein-drug interaction analysis Graph theory-based investigation of drug-binding pockets Protein language models enhanced with 3D structural information Explainable AI for drug-target interaction prediction Her group maintains active research projects on: Predicting molecular mechanisms of genetic variants in cardiac/renal diseases (BMBF-funded) Investigating off-target drug binding mechanisms (BMBF-funded) Antimicrobial resistance discovery using explainable AI (HelmholtzAI-funded) Protein structure-informed drug affinity prediction Prof. Kalinina has received the EMBO Long Term Fellowship and currently supervises seven doctoral students. Her laboratory develops computational resources including novel methods for solvent accessibility estimation (SphereCon), variant annotation in structural context (StructMAn), and protein interaction analysis (DIGGER).

Ajitha Rajanمشاهده پروفایل

استاد

• Software Testing
• Software Verification
• Test Automation
• +۶ مورد دیگر

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

Maxime Folschetteمشاهده پروفایل

دانشیار

• Computer Science
• Formal Methods
• Bioinformatics
• +۳ مورد دیگر

Maxime Folschette is an Associate Professor at Centrale Lille Institut, affiliated with the CRIStAL research laboratory (UMR 9189) and the BioComputing research group. His position combines teaching responsibilities at Centrale Lille and its internal school IG2I with active research in computational biology and formal methods. His primary research focuses on the analysis and learning of dynamical biological models, with particular emphasis on formal methods applied to biological regulatory networks. His work spans multiple domains including Parameters Inference, Static Analysis, Polyadic μ-calculus, and Hoare Logic, all applied to biological systems modeling. Folschette's publications demonstrate a strong trend toward developing computational frameworks that bridge theoretical computer science with practical biological applications. His recent work shows increasing focus on hybrid modeling approaches that combine discrete and continuous dynamics, logical learning techniques for network inference, and applications in medical domains such as diabetes modeling and cancer research. ILP best paper award during IJCLR'2021 Best Paper Award for BIOINFORMATICS'2024 As an advisor, Folschette currently mentors two PhD students (Madeleine Eyraud and Karim Raqbi) and has successfully guided two PhD students to defense (Honglu Sun in 2023 and Danilo Dursoniah in 2024). His research is supported through multiple collaborations across France and Japan, particularly with the BioComputing team at CRIStAL. His laboratory work focuses on developing computational tools like Pint, Hybrid Hoare Logic tool, key-pipline, INEX-MED, and pyBRAvo for biological network analysis.