Find Academic Supervisors

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +7 more

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

LING Chun KaiView profile

Assistant Professor

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• +13 more

LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).

Nils StieglitzView profile

Professor

• Strategic Management
• Organizational Behavior
• Strategic Decision-Making
• +3 more

Prof. Dr. Nils Stieglitz is a distinguished Professor of Strategy at Frankfurt School of Finance & Management since 2012 and serves as President & CEO since 2018. He leads the Management Department (2015–2018) and held the Executive Board role as Vice President Corporate Development (2017–2018). His research spans Strategic decision-making Organizational adaptation Human-agent interaction across dynamic environments. His scholarly work appears in top-tier journals like Strategic Management Journal , Management Science , and Organization Science , focusing on complex organizational trade-offs, incentive design, and scenario planning. He also contributes strategic insights to global media outlets such as Harvard Business Review and The Economist . As Senior Editor at the Journal of Organization Design and editorial board member of Organization Science , he shapes discourse on organizational theory. His teaching emphasizes strategy formulation and organizational design principles.

• Causal Inference
• Machine Learning
• Global Health
• +4 more

Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her work focuses on causal inference, machine learning, and addressing methodological challenges in both randomized trials and observational studies, particularly in global health contexts. She leads collaborations in East Africa, focusing on HIV elimination and community health in rural regions. Her research emphasizes translating academic findings into real-world impact. Education: PhD in Biostatistics, UC Berkeley (2015) MPhil in Computational Biology, University of Cambridge (2009) BS in Applied Mathematics, University of Vermont (2008) Research Interests: Dr. Balzer’s work addresses causal inference in complex settings, including semi-parametric methods, measurement challenges, and dependence structures. Her global health projects target HIV prevention, tuberculosis transmission, and hypertension management in sub-Saharan Africa. She designs interventions like the SEARCH Dynamic Choice model, which offers flexible HIV prevention options, and evaluates community health worker programs. Publications highlight her contributions to HIV/AIDS research, including studies on PrEP uptake, viral suppression in adolescents, and tuberculosis-HIV co-infection. Methodologically, she advances causal inference frameworks to handle missing data and clustered designs. Awards: While no specific awards are listed, her work has been funded by initiatives like the SEARCH trials, reflecting its scientific and public health significance. Advising & Grants: Balzer collaborates with multidisciplinary teams in Uganda and Kenya, focusing on translational research. Her grants support interventions linking statistical innovation to healthcare delivery improvements in resource-limited settings. Labs/Teams: Her research is embedded within global health partnerships, particularly within the SEARCH trials network, which integrates biostatistics with clinical and community-based implementation.

• Robotics
• Control Systems
• Multi-Agent Systems
• +6 more

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

Gary MarchioniniView profile

Professor

• Information Interaction
• Human-Computer Interaction
• Digital Libraries
• +6 more

Dr. Gary Marchionini is the Cary C. Boshamer Distinguished Professor and Dean of the School of Information and Library Science (SILS) at the University of North Carolina at Chapel Hill. He previously served as a professor at the University of Maryland's College of Library and Information Services and as a member of the Human-Computer Interaction Laboratory. His expertise spans information interaction, human-computer interaction, digital libraries, and information policy. Marchionini holds a PhD in mathematics education from Wayne State University, emphasizing educational computing. His research focuses on interfaces for information seeking, digital video retrieval, and the impact of data science. Notable projects include the Information in Life Video Series (2007), one of the first academic YouTube channels, and leadership in the Open Video Project . He has secured grants from NSF, NASA, Microsoft, and Google, among others. Marchionini's awards include the ASIS&T Award of Merit (2011) and the LITA Kilgour Award (2000). He has served as editor-in-chief of ACM Transactions on Information Systems (2002–2008) and as president of ASIS&T (2010). Current interests include the societal impact of information, personal health records usability, and digital curation strategies. His leadership roles include serving on the iSchools Board of Directors and directing the Center for Information Impact. He has advised numerous grants, including Mellon Foundation-funded initiatives and EPA research library operations. Marchionini's work bridges academic research with real-world applications, emphasizing human-centered design and interdisciplinary collaboration.

• Database Systems
• Data Management
• Graph Databases
• +5 more

Alin Deutsch is a Professor of Computer Science at the University of California, San Diego (UCSD), specializing in database systems, graph databases, and formal verification. He has contributed significantly to research areas including query optimization, data integration, and privacy-preserving systems. His work spans theoretical foundations and practical implementations, such as the Linked Data Benchmark Council (LDBC) and the TigerGraph database system. He co-authored over 100 papers and has been involved in major conferences like SIGMOD and VLDB. Research interests include graph query processing, parallel computing, data-centric business processes, and automated system verification. Recent work focuses on scalable hybrid analytics and graph databases. Deutsch is also active in database education, co-authoring a paper on UCSD's database curriculum. He has led projects in privacy-aware systems, such as policy-aware location-based services, and contributed to tools like CLIDE for interactive query formulation in service-oriented architectures. His collaborations involve industry partners like TigerGraph and academic institutions globally.

Navid Rekab-sazView profile

Assistant Professor

• Natural Language Processing
• Information Retrieval
• Fairness and Bias in AI
• +7 more

Navid Rekab-saz is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz (JKU), Austria. He is actively involved in research and teaching, offering courses such as Natural Language Processing and Natural Language Processing with Deep Learning . He maintains regular office hours and is accessible via email and a dedicated booking system for meetings. His research focuses on natural language processing , information retrieval , fairness and bias in AI , and recommender systems , with applications in humanitarian action and ethical AI. He employs deep learning and machine learning techniques to address challenges in bias mitigation, explainability, and domain adaptation. His work often bridges technical innovation with societal impact, especially in developing inclusive and fair AI systems. The recent publications of Navid Rekab-saz reflect a strong trend in debiasing strategies , parameter-efficient learning , and evaluation of societal biases in search and recommendation systems. His research spans from foundational work on word embeddings and retrieval models to applied studies in humanitarian NLP and gender bias in user queries. He frequently collaborates with a broad network of researchers and contributes to the development of datasets and benchmarks. Scientific Awards: Best Student Paper Award at ISMIR 2022 for 'Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms' Advising and Grants: Navid Rekab-saz has advised and collaborated with numerous students and researchers, many of whom are co-authors on his publications. While specific grant details are not listed in the provided text, his extensive publication record in top-tier venues suggests active involvement in funded research projects, likely supported by national or European funding bodies. He is also engaged in interdisciplinary research, particularly at the intersection of technical AI and legal or social implications. Labs and Teams: He is a core member of the Institute of Computational Perception at JKU, where he contributes to research projects in computational linguistics and AI. He collaborates closely with the team led by Prof. Markus Schedl and participates in initiatives related to music information retrieval, fairness in AI, and humanitarian applications of NLP.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Polar Science
• Arctic Marine Ecology
• Fisheries Management
• +3 more

Ole Arve Misund serves as a Professor at the Norwegian Fisheries College within UiT The Arctic University of Norway, Tromsø. His research spans polar science, Arctic marine ecology, fisheries management, and international relations in polar regions, with particular focus on Norway-Russia scientific cooperation dynamics following geopolitical shifts in 2022. His research interests center on climate change impacts on Arctic marine ecosystems, acoustic monitoring of marine organisms, and the geopolitical dimensions of polar research. Recent work examines boreal species migration into the Central Arctic Ocean, machine learning applications for backscatter analysis, and management frameworks for the Central Arctic Ocean. Analysis of his recent publications reveals strong interdisciplinary trends, integrating marine ecology with political science through studies of Russian-Norwegian scientific collaboration under sanctions, illegal fishing enforcement, and policy-relevant polar research. His work consistently bridges academic research with practical fisheries management and geopolitical considerations. Misund actively engages in science communication through Norwegian media outlets like Nordlys and Fiskeribladet, addressing topics from climate policy contradictions to Antarctic hydrological changes. His expeditionary work includes North Pole 2022 aboard the "Kronprins Haakon" research vessel. Professional activities demonstrate significant leadership in polar research infrastructure development and international scientific collaboration, particularly through the Norwegian Polar Institute where he contributes to strategic planning and policy advice. His work emphasizes evidence-based approaches to Arctic governance amid evolving geopolitical landscapes.

Romain RaveauxView profile

Associate Professor

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +5 more

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

Erdal ErelView profile

Professor

• Production Operations Management
• Manufacturing Systems Design
• Assembly and Disassembly Line Balancing
• +4 more

Erdal Erel is a Professor of Operations Management (OM) and currently serves as the Vice Rector in charge of administrative and financial affairs at Bilkent University's Faculty of Business Administration. He holds a B.S. in Industrial Engineering from Istanbul Technical University (1981), an M.S. from Stanford University (1983), and a Ph.D. from Virginia Polytechnic Institute and State University (1987). His academic career has been dedicated to advancing research in Production Operations Management, focusing on manufacturing systems design, assembly/disassembly line balancing, scheduling, and project management. Erel's educational background includes: B.S. in Industrial Engineering, Istanbul Technical University (1981) M.S. in Industrial Engineering, Stanford University (1983) Ph.D. in Industrial Engineering and Operations Research, Virginia Polytechnic Institute and State University (1987) His research interests span several critical areas of operations management, including manufacturing systems design, assembly and disassembly line balancing, scheduling and sequencing methodologies, and project management. He has made significant contributions to robust optimization techniques applied to time-cost trade-offs and stochastic modeling in production systems. His work emphasizes practical solutions for complex operational challenges through advanced algorithms like ant colony optimization and beam search methods. Erel's publications reflect a focus on optimization in production systems, scheduling algorithms, and decision models under uncertainty. His research bridges theoretical advancements with real-world applications in manufacturing and service industries, addressing issues such as cost minimization, robust scheduling, and efficiency improvements in assembly line operations. Erel has contributed extensively to his field but no specific scientific awards are mentioned in the provided text. No specific information about advising students or grants is provided in the text. Erel's involvement in collaborative projects, including work with colleagues like I. Sabuncuoglu and J.B. Ghosh, highlights his role in interdisciplinary research teams focused on operational efficiency and advanced manufacturing technologies.

Eamon C ArmstrongView profile

Associate Clinical Professor

• Evidence-Based Medicine
• Family Medicine
• Medical Informatics
• +5 more

Dr. Eamon C Armstrong serves as Clinical Associate Professor and Clerkship Director in the Department of Family and Community Medicine at the University of Arizona College of Medicine. Based at the BUMG Alvernon Family Medicine Clinic and Residency Program, he provides patient care while supervising residents and medical students in Tucson. His career spans institutions across the United States and South Africa, with board certification in Family Medicine through the American Board of Family Medicine. His educational foundation includes: MD from University of Witwatersrand, Johannesburg (1987) MS in International Primary Health Care from University of London (2012) Family Medicine Residency at Highland Hospitals/University of Rochester (1997) Faculty Development Fellowship at University of Wisconsin (1998) Dr. Armstrong's research centers on patient-centered evidence-based medicine (EBM) at the point of care, emphasizing just-in-time clinical decision support using electronic health records. He integrates the biopsychosocial model into resident training to enhance patient-centered clinical methods. His work explores EBM curriculum interventions' impact on physician performance and patient outcomes, with strong focus on organizational change and guideline implementation in primary care settings. Scholarly output reveals consistent EBM innovation since 1998, with publications addressing clinical question formulation, database selection, and practical implementation strategies. His work bridges theoretical evidence with frontline practice, particularly in fracture prevention, cardiovascular care, and vaccine policy. His teaching excellence is recognized through: Graduating Resident Class Faculty Award for Excellence in Teaching (2024) Multiple Faculty Awards for Excellence in Teaching (2024, 2022-2020) Teacher of the Year Award in Family Medicine (2004, 2001) Vice-Chancellor’s team teaching award (2013) Headley White Clinical Teacher of the Year (2002) As Clerkship Director, Dr. Armstrong mentors students through Family Medicine Clerkships (FCM 813C) and Topics courses (FCM 896U), emphasizing real-patient EBM application. His Pool Trust Grant for the EPOC Project demonstrates successful grant acquisition for point-of-care evidence systems. He cultivates resident-as-teacher skills through interactive workshops while maintaining active clinical practice. Dr. Armstrong contributes to the University of Arizona Family Medicine Residency Program's educational framework at Alvernon Clinic, focusing on biopsychosocial integration and evidence-based decision making in diverse clinical scenarios.

Guanghui LanView profile

Professor

• Stochastic Optimization
• Nonlinear Programming
• Machine Learning
• +4 more

Guanghui (George) Lan is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology . He concurrently serves as Associate Director of Machine Learning and Data Science at Georgia Tech’s Center for Machine Learning . Education: Ph.D. in Industrial and Systems Engineering, Georgia Institute of Technology (2009) Research spans three tightly connected pillars: Theory & Algorithms: stochastic optimization, nonlinear programming, first-order and primal-dual methods. Machine Learning & Data Science: reinforcement learning, policy optimization, minimax formulations, robust learning under uncertainty. Applications: healthcare decision-making, sustainable chemical recycling (PET glycolysis), and resilient biorefining. His 2024–2025 articles reveal a clear trend toward model-free and sampling-efficient algorithms for large-scale or constrained problems, with a parallel emphasis on real-world sustainability applications such as waste-plastic recycling and biorefinery control. Scientific Honors: INFORMS Frederick W. Lanchester Prize (2023) INFORMS Computing Society Prize (2022) NSF CAREER Award (2013) Multiple best-paper and finalist distinctions from INFORMS and the Mathematical Optimization Society Grants & Editorial Service: Funding from NSF, ONR, ARO, USDA, AFOSR, AHA Associate Editor: Computational Optimization and Applications (2014–), Mathematical Programming (2016–), SIAM Journal on Optimization (2016–), Operations Research (2023–) Dr. Lan directs algorithmic development within Georgia Tech’s Center for Machine Learning and advises a vibrant group of Ph.D. students and post-docs working at the intersection of optimization and machine learning.

Allen FosterView profile

Academic

• Human Information Behaviour
• Information Needs
• Information Seeking and Searching Behaviour
• +8 more

Allen Foster is a Reader in Information Science at Aberystwyth University, with expertise in information management, information behavior, and interdisciplinary research. He holds degrees in Social History and Information Management and is a Fellow of the Higher Education Academy. Current roles: Senior Tutor, Investigative Officer, Head of Department, Director of Postgraduate Studies, PGR Coordinator, Deputy Dean of Social Science, Senate Member External roles: Joint Editor-in-Chief of the Journal of Information Science , Editorial Board Member for ASLIB: Journal of Information Management , AHRC Peer Review College member, and international conference committee member Research Interests span human information behavior, serendipity, information literacy, knowledge management, mediated communication, and digital transformation. His work bridges theoretical models with practical applications in health, justice systems, and technological adoption. Notable Trends in his 39 research outputs include ORCID integration in academia, music playlist analysis, nonlinear information-seeking models, and informal scholarly communication. His recent work focuses on resistance to monitoring systems and serendipity engineering. Scientific Awards Fellow Higher Education Academy (FHEA) Teaching and Supervision include courses on information services planning, digital literacy, and knowledge architecture. He has supervised over 20 PhD/DProf students and numerous Master’s students. External Contributions involve refereeing for journals, AHRC funding panels, Chilean advisory boards, and consultancy for libraries.