Find Academic Supervisors

• Data Science
• Artificial Intelligence
• Legal Technology
• +5 more

Niall McCarroll is a Research Fellow in Data Science at Ulster University's School of Computing, Engineering and Intelligent Systems, based at the Magee Campus in Derry~Londonderry. His research focuses on applying data science and artificial intelligence techniques to legal document analysis and classification, with specific expertise in data augmentation and document classification systems. McCarroll's research interests include: Legal document classification using machine learning Data augmentation techniques for legal text processing Application of shallow and deep learning to non-disclosure agreements Social networking analysis in educational contexts Artificial intelligence for judicial decision-making support His recent publications demonstrate a strong focus on the intersection of artificial intelligence and legal technology, particularly in developing methods for classifying legal documents using both rule-based and generative approaches. McCarroll's work addresses practical challenges in legal text processing, including the classification of clauses in non-disclosure agreements and the application of data augmentation techniques to improve model performance with limited training data. McCarroll is actively involved in significant research projects: "The use of Agentic AI in judicial decision-making" (2025-2026), funded by the Engineering and Physical Sciences Research Council "LFR: Legal Futures Research" (2016-2026) His research has garnered attention, with his 2013 paper on social networking in education accumulating 139 readers on Mendeley and being referenced in 1 Wikipedia page.

• Data Science
• Artificial Intelligence
• Machine Learning
• +7 more

Prof. habil. dr. Gintautas Dzemyda is a leading Lithuanian computer scientist, Professor and Senior Researcher at Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), and Head of the Cognitive Computing Group . He is simultaneously affiliated with the Institute of Mathematics and Informatics (MII) in Vilnius, where he has built an internationally recognized scientific school in visual data analysis. Education & Qualifications 1984 – Candidate of Technical Sciences (PhD equivalent), thesis on “Problem Structure Analysis – a Tool for More Effective Optimization”. 1997 – Habilitation Doctor of Technical Sciences, dissertation on “Isolation of Necessary Knowledge to Improve Optimization Efficiency”. 1992 – Associate Professor, Institute of Mathematics and Informatics, Vilnius. 1998 – Professor, Kaunas University of Technology. Research Interests Prof. Dzemyda’s research integrates data science, artificial intelligence, optimization, and cognitive computing . Core topics include dimensionality reduction, multidimensional data visualization, neural-network–based analytics, parallel and distributed computing, multi-criteria decision support, and advanced AI applications in medicine (ophthalmology, cardiology, oncology). His work has pioneered Lithuanian capabilities in visual analytics and large-scale data exploration. Publication Trends Across 270+ refereed works and 2 Springer monographs (2013, 2023), recent outputs (2021–2025) emphasize geometric multidimensional scaling for big-data visualization, deep learning for pancreatic-cancer detection on CT images, reinforcement learning for autonomous navigation, and fraud-detection techniques for highly imbalanced financial datasets. These contributions appear in Springer LNCS/LNNS, Informatica, Journal of Global Optimization, Engineering Applications of Artificial Intelligence , and other top venues. Awards & Recognition Lithuanian State Science Prize (2001 & 2021) Honorary Doctor of the University of Latvia (2019) Knight's Cross of the Order “For Merit to Lithuania” (2007) Doctoral Supervision & Committees He has mentored 28 doctoral graduates (15 direct, 13 through academic descendants) and currently supervises: Dalia Breskuvienė – Classifier training-set optimization Modestas Motiejauskas – Emotion recognition in photographs Victor Bulava – Machine-learning methods for cyber-incident early detection He also chairs or serves on doctoral and habilitation committees at VU, KTU, VGTU, VMU and MII. Laboratory & Projects As Head of the Cognitive Computing Group , Prof. Dzemyda coordinates several national and EU projects, including the current Lithuanian Research Council grant “Geometric Method for Multidimensional Scaling” (S-MIP-20-19, 2020-2022) and the SMART programme project “CognitiveSTATS” (2021-2023) focused on combating misinformation during pandemics. His team develops open-access tools for large-scale data visualization and contributes to the MIDAS national research-data archive.

Recep Firat CekinelView profile

Researcher

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +7 more

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

Mochen YangView profile

Associate Professor

• Algorithmic Decision-Making
• Machine Learning
• Federated Learning
• +4 more

Mochen Yang is an Associate Professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota. His research bridges algorithmic decision-making with practical organizational challenges. Education: Ph.D. (2018) and B.A. (2013) from Tsinghua University. His research focuses on algorithmic decision-making from both "make" and "use" perspectives. From the "make" angle, he designs robust algorithms for information-intensive environments, while the "use" perspective examines how algorithmic decisions affect fairness, privacy, and user trust. Key areas include federated learning, causal inference, and human-AI collaboration. Recent work explores federated learning partnership dynamics, classifier calibration, and algorithmic governance in short-video platforms. He also investigates user engagement on social media and error impacts in human-AI reliance. Current affiliations include the University of Minnesota, where he contributes to academic and research initiatives in information systems.

Andrzej Stanislaw KosinskiView profile

Professor

• Statistical methodology for evaluation of diagnostic tests
• Adjustment for misclassification
• Missing data
• +2 more

Andrzej Stanislaw Kosinski serves as Professor of Biostatistics & Bioinformatics at Duke University, where he is a Member of the Duke Clinical Research Institute and part of the Division of Biostatistics. Based in Durham, North Carolina, he contributes to statistical methodology development and clinical research applications across cardiovascular and musculoskeletal domains. His educational background includes a Ph.D. from the University of Washington (1990) and an M.S. from the University of Oxford (1984). Professor Kosinski specializes in statistical methodology for diagnostic test evaluation, misclassification adjustment, missing data techniques, and clinical trial design. His research bridges biostatistics with practical clinical applications, particularly in cardiovascular disease analysis and stroke data interpretation, while increasingly addressing biomechanical challenges in orthopedics and pain management. Analysis of his 2025 publications reveals a strong focus on cardiovascular interventions (e.g., transcatheter aortic valve replacement outcomes) and musculoskeletal biomechanics (e.g., meniscus healing, ACL injury mechanisms), frequently utilizing large clinical registries like the STS/ACC TVT Registry. These studies demonstrate interdisciplinary collaboration and methodological innovation in handling real-world clinical data. He currently leads multiple major research initiatives funded by the National Institutes of Health and National Institute of Arthritis and Musculoskeletal and Skin Diseases: Effects of Blood and Blood-Derived Products on Meniscus Healing (NIH, 2025-2030) In vivo ACL mechanics and risk factors for injury (NIAMS, 2024-2029) Biomarkers to Advance Clinical Phenotypes of Low Back Pain (BACk) (NIAMS, 2023-2028) Integrated Biostatistical Training for CVD Research (NCSU, 2022-2027) Mentoring in Musculoskeletal Pain Phenotypes (NIAMS, 2022-2027) In Vivo Knee Motion and ACL Biomechanics During Dynamic Activities (NIAMS, 2019-2026) Mechanical Biomarkers of Chronic Low Back Pain (NIH, 2019-2026) Biomechanical and Biological Predictors of Cartilage Health Following Meniscus Injury (NIAMS, 2021-2026) Making an early diagnosis of talaromycosis (NIH, 2019-2026) BEST-VIVA Registry (vCLI) (NIH, 2019-2025)

Naveen VermaView profile

Associate Professor

• Advanced Sensing Systems
• Machine Learning
• Statistical Signal Processing
• +3 more

Naveen Verma is an Associate Professor at Princeton University's Department of Electrical Engineering, specializing in advanced sensing systems. His research bridges algorithm development with novel sensing and computing technologies. Education: B.A.Sc. from UBC (2003), M.S./Ph.D. from MIT (2005/2009) Key Focus: Large-area flexible sensors, energy-efficient architectures, machine-learning algorithms His work explores how systems for learning and inference can be enhanced through new hardware paradigms, particularly in thin-film electronics and analog computing. Recent publications highlight applications in imaging systems, biomedical sensors, and error-resilient machine learning. His research outputs include: Embedded thin-film classifiers for image sensing Compressive-sensing circuits using flexible electronics Error-adaptive boosting algorithms for hardware resilience Energy-scalable processor designs Scientific awards received: NSF CAREER Award (2013) AFOSR Young Investigator Award (2014) IEEE Best Paper Awards Teaching excellence awards Industry recognition from Intel and IEEE societies

• Sports Medicine
• Exercise Physiology
• Cycling
• +3 more

Cesáreo López Rodríguez serves as External collaborating professor in the Department of Biomedical Sciences at University of Leon and holds Honorary Collaborator status in its Pharmacology Area. Affiliated with Health Service of Castile and León (SACYL), he has taught the Master's Degree in Innovation in Biomedical and Health Sciences since 2009 while directing the Adapted Cycling Classifiers training course. His professional ecosystem integrates academic work with high-level sports governance through concurrent roles at Royal Spanish Cycling Federation (RFEC) since 2008. Research centers on exercise physiology applied to sports medicine , with VALFIS research group collaboration driving publications in cycling medicine, doping control, and adapted sports classification. His 90+ publications in journals like Medicine dello Sport demonstrate translational focus bridging laboratory findings to athletic performance and safety protocols. The Lifesaving School directorship extends this into emergency response applications. Professional engagements form a cohesive triad: RFEC responsibilities (doping committee, adapted cycling classification, 2014 World Championships medical direction) provide elite sports context; Blood Donors Association's Lifesaving School applies first aid research; SACYL affiliation grounds work in public healthcare systems. This integration creates unique opportunities for students in sports medicine innovation and clinical exercise physiology.

• Special Education
• Behavioral Interventions
• Educational Equity
• +3 more

Casey Klinich serves as an Adjunct Professor in Executive & Professional Admissions at Seattle University while maintaining his primary role as a special education teacher in Northshore School District since 2006, following 6 years as classified staff. His 24+ year career focuses exclusively on supporting individuals with severe disabilities across developmental stages. Academic credentials include: M.Ed in Severe/Profound Disabilities, University of Washington Professional expertise spans: K-3 Severe/Profound Disability instruction (18 years) Social/Emotional (EBD) behavioral support Adult Transition Pathways program for 18-21 year olds Positive Behavioral Interventions and Supports (PBIS) Educational equity through community-family partnerships Professional recognition includes: National Board Certified Teacher (Exceptional Needs Specialist, 2011) Professor Klinich actively mentors teaching candidates, co-founded the CanDo Initiative non-profit, and serves on district committees including MTSS and behavioral safety. His current work emphasizes community integration, job training, and life skills development for adults with disabilities through Northshore School District's ATP program.

• Cancer Epigenetics
• Molecular Pathology
• Global Oncology
• +5 more

Demirkan Besim Gursel, PhD, is a Research Associate Professor in the Department of Pathology (Research) at Northwestern University Feinberg School of Medicine and a member of the Robert J. Havey, MD Institute for Global Health—Center for Global Oncology. He conducts translational research spanning cancer epigenetics, chromatin biophysics, and global molecular diagnostics for HPV-related cervical cancer. Education & Training: MS, University of Maryland, 1992 PhD, University of Bordeaux I, France, 2002 MS, Northwestern University, 2018 Post-doctoral Fellow, National Cancer Institute, 2007 Post-doctoral Fellow, Medical College of Cornell University, 2011 Research Focus: Dr. Gursel’s work integrates high-resolution chromatin-packing measurements with DNA-methylation biomarker discovery to understand chemoresistance and to develop low-cost molecular screening tools for cervical cancer in resource-limited settings. Additional interests include single-voxel proteomics technologies that enable spatially resolved, label-free protein profiling of clinical specimens. Recent Publication Trends: Across 48 peer-reviewed articles his 2025 papers exemplify a trajectory toward (i) nano-scale chromatin imaging to overcome tumour chemoevasion, (ii) epigenetic risk classifiers for HIV-positive women, (iii) robust HPV diagnostic assays, and (iv) advanced proteomic sample-handling protocols—together reflecting convergence of fundamental biophysics and practical global-health diagnostics. Professional Integrity: Per institutional policy, Dr. Gursel reported no external industry relationships for 2024, underscoring transparent conflict-of-interest management.

Yun LinView profile

Associate Professor

• Automatic programming
• Software debugging
• Software testing
• +8 more

Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.

• Computational Neuroscience
• Perception
• Decision Making
• +2 more

Ulrik Beierholm is an Associate Professor in the Department of Psychology at Durham University, serving as Department Representative and Fellow of the Durham Research Methods Centre, and holding an Associate Professor position on the Executive Board of the Biophysical Sciences Institute. His research employs computational frameworks to investigate uncertainty processing in perception, decision making, and learning through interdisciplinary methodologies. His core research spans Computational Neuroscience, Perception, Decision Making, Neuroeconomics, and Machine Learning, with emphasis on Bayesian inference and reinforcement learning models validated via psychophysics, fMRI, and pharmacological techniques. Recent work demonstrates how the nervous system optimally handles sensory uncertainty across multisensory contexts, developmental stages, and cognitive tasks. Analysis of his publication trajectory reveals consistent innovation in Bayesian modeling of perceptual processes, with growing emphasis on computational tools development, neuroeconomics applications, and educational initiatives. His work bridges theoretical neuroscience with experimental validation across diverse cognitive domains. Honors include: Facebook Faculty Award for Virtual Reality (2016) Associate Editor for PLoS Computational Biology Editor for Bayesian methods section in Springer's Encyclopedia of Computational Neuroscience Dr. Beierholm supervises postgraduate researchers including Denise Foresteire and has co-organized influential workshops such as the Durham Computational Biology Symposium (2018) and Probabilistic Brain Workshop (2018), establishing himself as a key collaborator in computational neuroscience communities. His dual affiliations with the Biophysical Sciences Institute and Durham Research Methods Centre facilitate cross-disciplinary integration of psychological theory, neural data, and advanced computational modeling to address fundamental questions about human cognition.

Apostolos AmpatzoglouView profile

Associate Professor

• Technical Debt
• Reverse Engineering
• Software Maintainability
• +5 more

Apostolos Ampatzoglou serves as Associate Professor in the Department of Applied Informatics at the University of Macedonia, where he leads research in software engineering with particular emphasis on technical debt management and software quality. Previously, he held an Assistant Professor position at the University of Groningen (2013-2016) and maintains active collaborations across European research initiatives. His academic credentials include a PhD in Software Engineering from Aristotle University of Thessaloniki (2013), MSc in Computer Systems from the University of Macedonia (2005), and BSc in Informatics from the Technological Education Institute of Thessaloniki (2003). Dr. Ampatzoglou's research program centers on technical debt quantification, reverse engineering, and software maintainability. He pioneered financial models for technical debt management and developed metrics for assessing ripple effects in software systems. His work bridges theoretical foundations with industrial applications through projects like SDK4ED and SmartCLIDE, focusing on energy-efficient development and cloud service engineering. Current investigations explore machine learning applications for vulnerability prediction and technical debt prioritization. His extensive publication record reveals strong trends in applying AI to software quality assurance, with recent work emphasizing transformer models for vulnerability detection and explainable AI for technical debt identification. He actively investigates energy-aware development practices and service-oriented architecture patterns in cloud environments. Notable recognitions include: Top-7 reviewer for Journal of Systems and Software (2022) Best paper awards at IGSCC'21, ICSR'18, and EASE'17 18th most active Consolidated Researcher in Software Engineering (2013-2020) 3rd most active Early Stage Researcher (2010-2017) Multiple personal research grants from National Scholarships Association He has successfully supervised PhD candidates including Dr. Areti Ampatzoglou (Best PhD Thesis Award, University of Groningen) and Dr. Elvira-Maria Arvanitou (VERSEN Institute award). His research is funded through major European projects such as SDK4ED, SmartCLIDE, and SKILLAB, with recent grants exceeding €2M in total funding. Current work focuses on FAIR data implementation and skills gap analysis in European labor markets. As leader of the Software Engineering Lab at the University of Macedonia, he directs a 15+ member team working on technical debt management tools, cloud development environments, and XR applications for warehouse management. The lab maintains strong industry partnerships with technology clusters in Thessaloniki and participates in the Eclipse Foundation ecosystem.

Marcin WardaszkoView profile

Assistant Professor

• Serious Game Design
• Simulation Games
• Gamification
• +5 more

Marcin Wardaszko is an Assistant Professor at Kozminski University where he heads the Department of Quantitative Methods and Information Technology and the MBA IT program. He also serves as a Visiting Professor at Vorarlberg University of Applied Sciences. His professional network extends internationally through his roles as a representative of Riva Solutions GmbH in Poland and East Asia, collaborator with HCD Learning Ltd in Shanghai, and Digital Game-Based Learning expert at Poland's NASK National Research Institute. Professor Wardaszko's research focuses on simulation games, serious game design, and gamification applications across educational and business contexts. His work explores how digital game-based learning enhances cognitive outcomes, how VR technologies can develop soft skills (particularly through his Cicero VR public speaking trainer), and how gamification improves workplace environments and organizational development. He has developed models for classifying simulation games based on their strategic application in organizational contexts. His recent publications demonstrate a strong trajectory in serious gaming research, with significant contributions in 2023 (INDCOR White Paper), 2022 (Urban Resilience and Serious Gaming), 2021 (Simulation Gaming Through Times), and 2018-2015 (multiple studies on organizational development, mobile learning effectiveness, and workplace gamification). His work spans interdisciplinary applications from warehouse management optimization to public speaking training in virtual environments. Scientific recognition includes: CEEMAN 2019 Champion in Teaching Award for VR-based learning program President of International Simulation and Gaming Association (2019-2020) Fellow in Association for Business Simulation and Experiential Learning Member of Swiss Austrian German Simulation and Gaming Association Polish representative in COST Action CA18230 on Interactive Narrative Design As head of the Center for Simulation Games and Gamification at Kozminski University, Professor Wardaszko leads development of innovative educational tools including HOTEL STARS business simulation, Cicero VR public speaking trainer, and dcView gamified data presentation system. His work bridges academic research with practical applications in education and business, demonstrating how games and gamification can transform learning and working environments. He has supervised numerous research projects and maintains active international collaborations across Europe and Asia.

Evgeny OsipovView profile

Professor

• Artificial Intelligence
• Vector Symbolic Architectures
• Hyperdimensional Computing
• +3 more

Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.

Chevaleyre YannView profile

Professor

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +3 more

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.