Find Academic Supervisors

• Theoretical Computer Science
• Algorithms
• Robotics
• +3 more

Dr Duncan Adamson is a Lecturer at the School of Computer Science, University of St Andrews. His research focuses on theoretical computer science, algorithms, robotics, and discrete mathematics. He has contributed to areas such as robot scheduling, temporal graphs, and combinatorial optimization. Recent work includes collision-free path planning for robots and harmonious colourings of temporal networks. He received the Best Paper Award at SAND 2024 for his contributions to algorithmic foundations. His publications span conferences like LATIN, ALGOWIN, and RP, addressing topics from graph theory to distributed systems. His research combines algorithm design with applications in robotics and computational chemistry. Education details are not explicitly stated in the provided texts. His research interests emphasize algorithmic solutions for complex systems, with a focus on graph structures and combinatorial problems. Collaborations include work on crystal structure prediction with chemists and computer scientists, demonstrating interdisciplinary impact. Awards: Best Paper Award SAND 2024 Advising/Grants: No specific grants or advisees listed in the texts provided. Labs/Teams: Affiliated with the School of Computer Science research groups, though specific lab names are not mentioned.

• Psycholinguistics
• Socio-psycho-linguistics
• Children's pronoun processing
• +5 more

Juhani Järvikivi is a Professor and Director of Undergraduate Studies in the Department of Linguistics at the University of Alberta. His research focuses on psycholinguistics, socio-psycho-linguistics, and developmental aspects of language processing. He employs methods like eye-tracking and pupillometry to study pronoun resolution, accented speech comprehension, and individual differences in language processing. Järvikivi's work often intersects with cognitive science, exploring how factors like political ideology, personality traits, and foreign accent influence real-time language comprehension. He teaches courses in psycholinguistics, neurolinguistics, and directed research, emphasizing experimental methodologies. His research has been published in top journals such as Cognitive Science , Applied Psycholinguistics , and Frontiers in Psychology . Key contributions include studies on children's pronoun processing, the role of prosody in focus marking, and the impact of accent on speech perception. Research Interests: Psycholinguistics, socio-psycholinguistics, developmental language processing, accented speech, pupil dilation as a cognitive measure, and visual world paradigms. Awards: None explicitly listed in the provided materials. Notable collaborations involve investigating the ENglish Reading Online (ENRO) project and developing tools like the PupilPre and VWPre packages for preprocessing psycholinguistic data. His work bridges theoretical linguistics with cognitive and experimental approaches, contributing to our understanding of language processing in both monolingual and multilingual contexts.

Pål Grønås DrangeView profile

Associate Professor

• Parameterized Complexity
• Graph Algorithms
• Sparse Network Analysis
• +7 more

Pål Grønås Drange is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity of graph algorithms, experimental algorithmics, and applications in machine learning and network interdiction. He leads projects on trustworthy AI and energy transition optimization. Dr. Drange holds a diploma in electronics, bachelor's in cognitive science, master's in logic for AI, and a PhD in algorithms. His work bridges computer science, mathematics, and engineering. Education: PhD in Algorithms (University of Bergen) Master's in Logic for AI Bachelor's in Cognitive Science Electronics Diploma His research interests include parameterized complexity, sparse network algorithms, and applied machine learning. Recent projects involve correlation clustering with overlapping data and autonomous drone inspection algorithms. He is affiliated with the Center for Data Science (CEDAS) and leads the Algorithmic Foundations for Trustworthy AI project funded by the Trond Mohn Foundation. Dr. Drange has received the 2024 outreach award for science communication. His lectures on AI ethics and societal impacts have reached diverse audiences, including policymakers and industry leaders. He is active in program committees for major conferences like ESA, COCOON, and SWAT. Key Achievements: PACE 2024: 1st in parameterized track, 2nd in heuristics track Lead software advisor for Equinor's energy transition projects Open-source contributions to algorithmic toolkits Teaching includes courses on algorithms, machine learning, and software engineering. He advises industry workshops and has conducted over 30 outreach lectures on AI ethics and technology governance in 2023–2025.

• Parameterized Algorithms
• Graph Theory
• Algorithm Design
• +3 more

Daniel Lokshtanov is a Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized algorithms, graph theory, and computational complexity. He has collaborated extensively with leading researchers such as Fedor Fomin and Saket Saurabh. Notable contributions include foundational work on kernelization techniques, fixed-parameter tractability, and algorithm design for graph problems. His work often addresses structural graph properties and combinatorial optimization challenges. Lokshtanov has authored influential papers on topics like Odd Cycle Transversal, cograph completions, and algorithms for planar graphs. He co-authored the textbook *Kernelization: Theory of Parameterized Preprocessing* (2019), a key reference in the field. His research spans theoretical breakthroughs and practical algorithmic advancements, with over 150 publications in top venues like ACM Transactions on Algorithms and SIAM Journal on Computing.

• American Politics
• Public Opinion
• Media and Politics
• +2 more

Jonathan Ladd is an Associate Professor at Georgetown University with joint appointments in the McCourt School of Public Policy and Department of Government. He holds a PhD in Political Science from Princeton University and is affiliated with the Massive Data Institute and the Lab for Globalization and Shared Prosperity. His research specializes in American politics, public opinion dynamics, media-politics interactions, and electoral behavior. Ladd's work examines how media distrust influences political polarization and voting patterns, utilizing survey research and experimental methodologies. His publications demonstrate consistent focus on media effects in political systems, evolving from foundational studies on media distrust to contemporary investigations into social media's electoral impacts. Recent work emphasizes quantitative analysis of social media data and experimental approaches to understanding political communication. Goldsmith Book Prize from Harvard's Shorenstein Center McGannon Award for Social and Ethical Relevance in Communications Policy Research He currently serves on the American Association for Public Opinion Research's Task Force on 2024 Pre-Election Polling and previously served as a senior fellow at the Brookings Institution.

Ki Hwan YumView profile

Senior Lecturer

• Computer Architecture
• Parallel/Distributed Systems
• Computer Networks
• +5 more

Ki Hwan Yum is a Senior Lecturer in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on computer architecture, parallel/distributed systems, and energy-efficient computing. He holds a Ph.D. from The Pennsylvania State University (2002), M.S. from Pohang University of Science and Technology (1994), and B.S. in Mathematics from Seoul National University (1989). His research interests include computer networks, cluster computing, fault-tolerant systems, and wireless sensor networks. Notable contributions include work on QoS-aware clusters, on-chip network design, and power-efficient architectures. He has been recognized with the Best Graduate Research Assistant Award (2002) from Penn State University. Recent publications emphasize innovations in network-on-chip (NoC) architectures, energy-efficient designs, and distributed deep learning systems. His work bridges theoretical models with practical implementations, addressing challenges in scalability, latency, and power consumption in modern computing systems. Awards include the 2002 Best Graduate Research Assistant Award. His research has been published in top-tier journals like IEEE Transactions on Computers and Journal of Parallel and Distributed Systems. He advises no current students, but his work has influenced graduate research in computer architecture and distributed systems.

Eun Jung KimView profile

Professor

• Computer Architecture
• Power Efficient Systems
• Parallel/Distributed Systems
• +3 more

Eun Jung Kim is a Professor in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. Her research focuses on computer architecture, power-efficient systems, parallel/distributed systems, cluster computing, performance evaluation, and fault-tolerant computing. Ph.D.: Computer Science and Engineering, Pennsylvania State University (2003) M.S.: Computer Science and Engineering, Pohang University of Science and Technology (1994) B.S.: Computer Science, Korea Advanced Institute of Science and Technology (1992) Her work explores innovative solutions for optimizing hardware and software systems, including energy-efficient interconnects, security enhancements, and adaptive network-on-chip designs. Notable achievements include the NSF Early CAREER Award (2009) and contributions to publications like IEEE Transactions on Parallel and Distributed Systems . Recent research trends include advancements in near-data processing, secure computing abstractions (e.g., WHISTLE), and mitigating hardware vulnerabilities (e.g., cache timing attacks). She also integrates IoT and low-cost cluster systems for environmental research applications.

Chengchun ShiView profile

Associate Professor

• Statistical Learning
• Reinforcement Learning
• Precision Medicine
• +3 more

Dr. Chengchun Shi is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical learning methods for individualized decision-making and analyzing complex data, with applications in precision medicine, finance, and ride-sharing. He joined LSE in 2019 after completing his PhD in Statistics at North Carolina State University, supervised by Dr. Wenbin Lu and Dr. Rui Song. His work emphasizes developing computationally efficient algorithms with statistical guarantees for real-world challenges such as personalized treatment regimes, optimal investment strategies, and dynamic resource allocation. He has contributed to reinforcement learning, causal inference, and off-policy evaluation methodologies, addressing issues like interference in spatial experiments and handling high-dimensional, heterogeneous data. Key research areas include reinforcement learning algorithms, causal mediation analysis, and robust policy evaluation. His publications span topics like off-policy evaluation in confounded environments, spatial experimental designs, and deep learning approaches for distributional modeling. Dr. Shi’s research bridges theoretical statistics and practical applications, aiming to improve decision-making across diverse domains. He holds a strong academic background with a focus on high-dimensional statistical models and their applications in dynamic treatment regimes. His work has been recognized for its innovative approaches to tackling complex data challenges through interdisciplinary methods combining machine learning and rigorous statistical theory.

Thomas RobinsonView profile

Assistant Professor

• Voter behavior
• Money in politics
• Machine learning
• +3 more

Thomas Robinson is an Assistant Professor in the Department of Methodology at the London School of Economics (LSE). Previously, he held positions at Durham University's Centre for Institutions and Political Behavior and collaborated with Meta's Core Data Science team. He earned his DPhil in Politics from the University of Oxford in 2020. His research focuses on machine learning applications in political methodology, including neural network modeling, causal inference, and experimental design. Key themes include voter behavior, money in politics, and pandemic-related policy preferences. Collaborations span education policy and tech sectors, with ongoing projects on synthetic data and missing-data imputation using deep learning. Notable works include analyzing voter responses to campaign finance disclosures and studying pandemic impacts on global economic preferences. He develops open-source tools like MIDASpy and rMIDAS for data imputation. Affiliations include LSE's Data Science Institute.

• Deductive Verification
• Interactive Theorem Proving
• Formal Methods
• +7 more

Yong Kiam Tan is a research scientist at the Institute for Infocomm Research (I²R), A*STAR, Singapore, and holds a joint appointment as a Nanyang Assistant Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science (Pure and Applied Logic) from Carnegie Mellon University, advised by Prof. André Platzer, under the A*STAR National Science Scholarship. His research lies at the intersection of formal methods, interactive theorem proving, and automated reasoning, with applications in hybrid systems, compiler verification, and cybersecurity. He has made significant contributions to the CakeML and KeYmaera X projects, focusing on verified compilation, deductive verification of differential equations, and formalized mathematics. The 15 most recent publications reflect a strong trend in formally verified automated reasoning, certified algorithms, and applications in both classical and emerging domains such as neural networks and cryptography. His work consistently appears in top-tier venues like POPL, PLDI, CAV, and ITP, emphasizing end-to-end verification from logic to executable code. Distinguished Paper Award at CAV 2024 Best Paper and Best Repeatability Evaluation Award at HSCC 2022 Best Tool Paper Award at FM 2019 Peter Landin Prize at IFL 2015 CMU SCS Distinguished Dissertation Award 2022 Singapore NRF Fellowship Class of 2024 He actively advises students and researchers at NTU and A*STAR, collaborates internationally with experts such as Magnus Myreen, André Platzer, and Jakob Nordström, and has secured competitive funding including the NRF Fellowship. He has served on program committees for CPP, ITP, TACAS, and PLDI Artifact Evaluation, demonstrating active engagement in the research community. He leads a research team working on verified tools for automated reasoning and hybrid systems, with current members including Wei-Lin Wu, Joe Watt, Shuhan He, and several student researchers. His group maintains verified proof checkers in CakeML and contributes extensively to the Archive of Formal Proofs (AFP) in Isabelle/HOL.

• Fuzzy Logic
• Computational Intelligence
• Multiple-Criteria Decision Making (MCDM)
• +7 more

Pasi Luukka is a Full Professor (tenured) at the LUT Business School, Lappeenranta University of Technology (LUT University), Finland, where he is affiliated with the Department of Business Studies. His research bridges computational intelligence, fuzzy systems, and business decision-making, with a strong emphasis on practical applications in finance, innovation, and industrial performance. His research interests include: Fuzzy Logic and Soft Computing Multiple-Criteria Decision Making (MCDM) Machine Learning and Feature Selection Real Option Valuation under Uncertainty Financial Forecasting and Trading Strategies Innovation and Knowledge Management His recent publications (2022–2025) show a strong trend in enhancing fuzzy classification methods (e.g., fuzzy k-NN), developing aggregation operators (OWA, Bonferroni), and applying fuzzy logic to real-world problems such as stock market prediction, R&D investment analysis, and water-energy nexus modeling. His work frequently appears in top-tier journals like Expert Systems with Applications , Information Sciences , and Fuzzy Sets and Systems . He has received recognition through a substantial body of peer-reviewed publications and active collaboration with leading researchers in fuzzy systems and decision science. Pasi Luukka advises several graduate students, including Mahinda Mailagaha Kumbure and Christoph Lohrmann, and has contributed to numerous research projects involving real options, innovation evaluation, and intelligent systems. He has served as a peer reviewer for journals such as Decisions in Economics and Finance , Group Decision and Negotiation , and Soft Computing . He is a key member of a research group focused on intelligent decision support systems, fuzzy logic applications, and computational methods in business and finance. The team actively publishes and collaborates on interdisciplinary projects involving energy, finance, and industrial systems.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +5 more

Professor José Manuel Lanza Gutiérrez is affiliated with the Department of Computer Science at the University of Alcalá. He leads research in artificial intelligence, machine learning, and optimization algorithms, focusing on applications in wireless sensor networks, edge computing, and affective computing. He is part of the INTELIA (Interaction Technologies and Artificial Intelligence Lab), TIFyC (Information Technologies for Training and Knowledge), and PMI (Intelligent Mobile Platforms) research groups. He earned his PhD from the University of Extremadura in 2015, with a thesis on multiobjective metaheuristics for wireless sensor network optimization, supervised by Dr. Juan Antonio Gómez Pulido. His research interests span emotion recognition, IoT security, and optimization techniques for cryptography and sensor networks. Recent work includes the WEMAC dataset for emotion recognition, the Bindi wearable system to combat gender-based violence, and edge computing for healthcare monitoring. Publications highlight trends in multimodal affective computing, metaheuristic-based cryptographic solutions, and IoT-driven healthcare systems. His work bridges theoretical optimization with practical applications in safety, healthcare, and social justice. Lanza Gutiérrez has contributed to multiple collaborative projects and integrates machine learning with embedded systems for real-world impact. His labs and teams focus on interdisciplinary solutions for smart technologies and societal challenges.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +7 more

Luis de Marcos Ortega is a Full Professor at the Department of Computer Science, University of Alcalá. He belongs to the TIFyC research group (Information Technologies for Training and Knowledge), focusing on interdisciplinary intersections of computer science, education technology, and cybersecurity. He holds a PhD from the University of Alcalá (2009), with a thesis on adaptive educational content sequencing using AI agents. His research interests span Artificial Intelligence applications (e.g., ChatGPT for automated analysis), Cybersecurity (dark web threat patterns, malware detection), Education Technology (gamification, mobile learning usability), and Interdisciplinary studies (gender representation in academia, editorial board dynamics). Recent work emphasizes AI-driven solutions for content analysis, dark web monitoring, and healthcare IT security. Key contributions include methodological frameworks for gender detection via AI, optimization of dark web datasets for OpenAI processing, and UML profiles for accessible web design. His publications also address usability guidelines for mobile apps, continuous authentication systems (e.g., JBCA architecture), and gamification in MOOCs and classroom environments. He has collaborated on projects like the μDevOps platform for cyber ranges, the ProTego healthcare security initiative, and the ECO iMOOC gamification experiment. His work frequently bridges technical innovation with societal impact, emphasizing accessibility, ethical AI, and academic equity.

• Cybersecurity
• Machine Learning
• Educational Technology
• +5 more

José Javier Martínez Herraiz is an Associate Professor at the Department of Computer Science, Universidad de Alcalá. He is part of the TIFyC research group (Technologies of Information for Training and Knowledge). His doctoral thesis (2004) focused on integrating intelligent tutoring modules into internet-based learning systems, supervised by Dr. Luis López Corral and Dr. Paltin Sturdza. His research spans cybersecurity, machine learning, educational technology, and AI applications. Notable areas include dark web content analysis, malware detection, continuous authentication systems, and gamification in e-learning. He has authored over 80 publications, with recent work addressing cybersecurity factors, zero-shot classification with GPT models, and IoT healthcare device security. His contributions include frameworks for web protection analysis, resilient authentication systems (C-Lock), and methodologies for malware analysis in Linux environments. He actively participates in cybersecurity education initiatives, developing gamified platforms for teaching security practices. His research integrates interdisciplinary approaches, combining technical cybersecurity solutions with pedagogical innovations. Current projects emphasize AI-driven vulnerability repair, dark web dataset optimization, and ethical considerations in GPT model reliability.

• Natural Language Processing
• Data Science
• Deep Learning
• +5 more

Savaş Yıldırım is an Associate Professor in the Department of Computer Engineering at Istanbul Bilgi University, College of Engineering. He holds a PhD in Computer Engineering from Trakya University and completed his postdoctoral studies at Eberhard Karls Universität Tübingen, Germany. His academic work is centered on data science and computational linguistics, with a focus on Turkish language technologies. His research interests include Natural Language Processing, Deep Learning, Big Data Analytics, and Open Source software. He actively contributes to advancing Turkish language processing through text normalization, synonym detection, and meronym extraction. His work bridges theoretical NLP with practical applications in public policy and industry. The recent publications of Savaş Yıldırım are primarily in the domain of computational linguistics, with a strong emphasis on Turkish-specific challenges. The articles reflect a consistent trend in unsupervised and knowledge-poor methods for text analysis, focusing on morphological richness and resource scarcity in low-resource languages. Key themes include lexical semantics, text preprocessing, and pattern-based information extraction. Notable scientific contributions include projects funded by TÜBİTAK and EU programs such as: Ağlandırılmış Türk Kamu Alanının Haritalanması (TÜBİTAK 2219) Metin Madenciliği Yöntemlerinin Uygulanması (TÜBİTAK 1001) Akıllı Hazine Nakit Yönetimi (TÜBİTAK TEYDEB) İnsan Kaynakları Süreçlerinde Öngören Mekanizmalar (TÜBİTAK TEYDEB) Engelleri Kaldırarak Bir Araya Gelelim (EU Urban Net – TÜBİTAK 1001) Savaş Yıldırım has served as an academic coordinator and researcher in multiple nationally and internationally funded projects. He has also worked as a Data Science Trainer for The Banks Association of Turkey and as a trainer for IBM’s Academy of Technology. While no formal advisees are listed, his leadership in research projects indicates mentoring and collaborative supervision roles. He maintains an active research blog at openanalitik.wordpress.com . He is associated with research initiatives in data analytics and natural language processing, particularly through project-based labs and interdisciplinary teams focused on public sector data and intelligent systems. These teams often involve collaboration between academia and industry, especially in finance and human resources technology.