Find Academic Supervisors

• Transfer Learning
• Recommender Systems
• Medical Imaging
• +3 more

Honghui Du serves as a Research Fellow at the Insight Centre for Data Analytics, a leading Irish research institution specializing in data science with nodes across multiple universities. The role centers within the Decision Making research group, focusing on algorithmic solutions for dynamic environments. Research spans transfer learning in non-stationary data streams , recommender systems (notably news personalization using LLMs and diffusion models), and medical imaging under label scarcity. Key emphases include handling concept drift, optimizing active learning for medical diagnostics, and developing generative approaches for user-item interaction modeling. Emerging work explores gamification for sustainable behavior change and entity resolution via language models. Recent publications (2023-2025) reveal accelerating integration of diffusion models and transformers into recommendation frameworks, while maintaining core expertise in transfer learning for evolving data streams. Medical imaging research increasingly addresses practical constraints like limited annotations through adaptive curriculum strategies. The work operates within the Decision Making research group at the Insight Centre, which investigates algorithmic decision processes under uncertainty and dynamic conditions.

Hugh ChipmanView profile

Professor

• Statistical Learning
• Data Mining
• Bayesian Methods
• +5 more

Hugh Chipman is a Professor in the Department of Mathematics and Statistics at Acadia University , Canada. He holds a BScH (Acadia), MMath and PhD (University of Waterloo), and is a P.Stat. member of the Statistical Society of Canada. Education: BScH, Acadia University MMath, University of Waterloo PhD, University of Waterloo P.Stat. (Professional Statistician) Research Interests: Dr. Chipman specializes in statistical learning and data mining with applications to complex data structures. Key areas include Bayesian ensemble methods , tree-based models , design of experiments , network analysis , and high-dimensional data problems. He has contributed to drug discovery and functional data monitoring through computational statistics. Recent Talks: His 2024 presentation at UNC Chapel Hill focused on simulation studies for experimental design. Earlier work (2015) addressed big data education at Dalhousie University, while 2011-2014 talks covered active learning and network data mining at institutions like Harvard and MITACS. Scientific Awards: Co-organizer of NSERC-funded workshops Contributed to major statistical learning summer schools (e.g., AARMS 2014) Teaching: Courses include Statistics I and Design and Analysis of Experiments . He maintains software tools like BART (Bayesian Additive Regression Trees) and hybrid hierarchical clustering algorithms.

• Graph Analytics
• Parallel Algorithms
• Multicore Computing
• +6 more

Kamesh Madduri is an Associate Professor in the Department of Computer Science and Engineering at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His research focuses on graph analytics, parallel algorithms, and high-performance computing for large-scale data analysis. NSF CAREER Award (2013) His work contributes to the development of scalable graph partitioning algorithms, extreme-scale sparse data analytics, and heterogeneous computing frameworks. Recent projects include multilayer network analysis (NetSplicer) and GPU-accelerated graph processing (Jet). Key research areas include network science, computational biology, and distributed-memory graph algorithms. His publications highlight applications in genomic workflows, advertising keyphrase recommendation (Graphite/BroadGen), and large-scale hydrology data management. Collaborative Research: CCRI (2021-2023) SHF: Medium: NetSplicer (2020-2024) PPoSS: Extreme-scale Sparse Data Analytics (2018-2022) XPS: Genomic Workflows Acceleration (2014-2020) EAGER: SME Manufacturing Integration (2024-2026)

• Description Logics
• Ontologies
• Automated Reasoning
• +7 more

Professor Boris Konev is a faculty member at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He holds the academic rank of Professor in Computer Science. Description Logics Ontologies Automated Reasoning Temporal Logic Formal Verification Encrypted Database Applications His recent research focuses on temporal queries mediated by ontologies, knowledge evaluation agents using large language models, and semantic modularity in description logics. Key sub-fields include LLM applications, encrypted databases, and formal verification techniques. He has contributed to software development projects and industry partnerships, including design of equine simulators and online services with Racewood Limited. Current teaching includes the Foundations of Computer Science module (COMP109). Professional roles include guest editorships for AI Communications and program committee membership for the European Conference on Logics for Artificial Intelligence (JELIA).

Norman KerleView profile

Professor

• Disaster Risk Management
• Remote Sensing
• Geoinformatics
• +6 more

Norman Kerle is a Professor at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, holding the chair of Geoinformatics for Disaster Risk Management within the Earth Systems Analysis department. He earned Masters degrees in geography from the University of Hamburg and Ohio State University, and a PhD in volcano remote sensing from the University of Cambridge (2002). His research spans volcanology, landslide detection, and quantitative geomorphology, with a focus on object-oriented remote sensing methods for disaster risk management. He leads the ITC Object-Based Image Analysis research group and coordinates EU-funded projects like RECONASS and INACHUS , emphasizing UAV-based structural damage mapping. Recent work includes post-disaster recovery assessment using remote sensing and macro-economic modeling. His scientific contributions include over 221 research outputs (peer-reviewed articles, book chapters, conference papers) and datasets such as Evaluating Resilience-Centered Development Interventions with Remote Sensing (2020). He has received the 2011 Lloyd's Science of Risk Prize (Natural Hazards) and served as Associate Editor for journals like Remote Sensing and Natural Hazards and Earth Systems Sciences . Prof. Kerle’s professional affiliations include the European Geosciences Union (EGU), American Geophysical Union (AGU), International Society for Photogrammetry and Remote Sensing (ISPRS), and Remote Sensing and Photogrammetry Society (RSPS). He has examined PhD theses and reviewed proposals for Horizon 2020, STEREO, and UNESCO.

He XuView profile

Visiting Professor

• Biomaterials
• Tissue Engineering
• Biomechanics
• +7 more

Dr. He Xu is a Visiting Professor in the Department of Engineering Science at the University of Oxford, with a focus on Biomaterials , Tissue Engineering , and Biomechanics . She previously worked at Shanghai Normal University, rising from lecturer (2014) to associate professor (2018) and full professor (2024). Education: BEng in Materials Science and Engineering (China University of Geosciences), DPhil in Biomedical Engineering (Shanghai Jiao Tong University, 2014) Her research explores: Biomaterials : Smart hydrogels, piezoelectric systems, and nanogenerators for therapeutic applications. Tissue Engineering : Innovations in intervertebral disc and tendon regeneration. Drug Delivery : Targeted activation, nitric oxide therapy, and bioelectronic systems. Her publications span 2021–2025 , combining Biomaterials , Nanotechnology , and Medical Imaging to address challenges in Diabetes , Cancer , and Cardiovascular Disease . Key collaborations include the 3DMed Interreg 2 Seas Consortium and work on rapid Covid-19 testing .

• Graph Theory
• Combinatorial Optimization
• Operations Research
• +4 more

Cristina Bazgan is a University Professor at Université Paris-Dauphine, affiliated with LAMSADE (Laboratoire d'Analyse et Modélisation de Systèmes pour l'Aide à la Décision) within PSL University. Her office is located at P 409 with contact number 01 44 05 40 90. She maintains an active research profile with numerous publications spanning graph theory, combinatorial optimization, and multi-objective optimization. Professor Bazgan's research primarily focuses on graph theory and combinatorial optimization , with significant contributions to domination theory, network analysis, approximation algorithms, and multi-objective optimization. Her work bridges theoretical computer science and operations research, addressing fundamental problems in computational complexity while developing practical algorithmic solutions. She has made notable contributions to understanding graph partitions, community detection in networks, and the complexity of various optimization problems. Analysis of her recent publications reveals a strong trend in multi-objective optimization and parameterized complexity . Her work often explores the interface between theoretical computer science and operations research, with applications to network analysis and decision support systems. A significant portion of her research addresses the complexity and approximability of graph-theoretic problems, particularly those related to domination, community structure, and anonymization in networks. Professor Bazgan has co-authored the book Combinatorial Algorithms (2022) with H. Fernau and contributed chapters to authoritative works on combinatorial optimization. While specific scientific awards aren't mentioned in the available information, her extensive publication record in top-tier journals demonstrates significant recognition in her field. She maintains an active collaboration network, frequently working with researchers such as Vanderpooten D., Tuza Z., Chlebíková J., and Herzel A. Her research has practical applications in network security, social network analysis, and decision support systems. The LAMSADE laboratory, where she is based, focuses on decision support systems and operations research, providing an interdisciplinary environment for her theoretical and applied work.

Jan MajkutewiczView profile

Lecturer

• Natural Language Processing
• Machine Learning
• Knowledge Representation
• +3 more

Jan Majkutewicz serves as an Assistant lecturer at the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic position focuses on advancing computational methodologies in natural language processing and knowledge representation systems. His research spans Natural Language Processing, Machine Learning, and Knowledge Representation with specific expertise in Large Language Models and graph-based embeddings. Current work investigates human-AI alignment through historical text analysis and neural representations of semantic hierarchies. Methodological innovations include cost-effective preference dataset generation and category graph embedding techniques that preserve structural relationships in knowledge bases. Publication analysis reveals consistent specialization in NLP and knowledge representation. The 2025 EditPrefs framework leverages historical text edits to address dataset scarcity in preference learning, while the 2021 Wikipedia category embedding research demonstrates practical applications for taxonomy navigation and parent category prediction through neural graph representations.

Jürgen PfefferView profile

Professor

• Computational Social Science
• Big Data Analysis
• Social Network Analysis
• +5 more

Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich's School of Social Sciences and Technology, with an additional appointment as Adjunct Professor at Carnegie Mellon University's Institute for Software Research. His interdisciplinary work bridges computer science and social science with a focus on analyzing large-scale socio-technical systems. His research expertise spans computational social science, network analysis, and big data methodologies. Pfeffer's work examines methodological, algorithmic, and theoretical challenges in analyzing dynamic social systems, with current projects focusing on modeling and detecting negative dynamics from social media, particularly online firestorms and hate speech against politically active women. His research combines network science approaches with computational methods to understand complex social phenomena. Pfeffer's publication record demonstrates significant contributions to the field since his 2010 doctorate, with high-impact papers in journals like Science and EPJ Data Science. His work on social media analysis, particularly the influential 2014 Science paper 'Social Media for Large Studies of Behavior' co-authored with Derek Ruths, has shaped methodological approaches in the field. His research shows consistent evolution from foundational network analysis to contemporary applications in political discourse, hate speech detection, and multi-layer network analysis. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying Social Networks. A Guide to Empirical Research Ruths, D., & Pfeffer, J. (2014). Social Media for Large Studies of Behavior Pfeffer, J., Morstatter, F., & Mayer, K. (2018). Tampering with Twitter's Sample API As an advisor and collaborator, Pfeffer has worked extensively with researchers including Raji Ghawi, Mirco Schönfeld, Momin Malik, and Kathleen Carley. His work demonstrates strong connections between theoretical network science and practical applications in social media analysis. His current research continues to address pressing issues in online discourse, with recent work focusing on hate speech classification, lexical change in negative word-of-mouth, and polarization dynamics in social media environments. Pfeffer leads the Pfeffer Lab, which focuses on developing methodological approaches for analyzing complex social systems through computational methods. His work has implications for understanding political legitimacy, social influence, and community dynamics in both online and offline contexts.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Christian WinklerView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +4 more

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

• Machine Learning
• Molecular Modeling
• Drug Discovery
• +4 more

Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.

• Business Analytics
• Operations Research/Management
• Operational Analytics
• +7 more

Mehdi Toloo is a Reader in Business Analytics at the University of Surrey's Surrey Business School. He holds a BSc, MSc, and PhD, and is a docent. Previously, he was a Professor at Technical University of Ostrava (Czech Republic) and Sultan Qaboos University (Oman). His research focuses on Business Analytics, Operations Research, Data Envelopment Analysis (DEA), and Decision Analysis. He has supervised over 40 postgraduate students and contributed to top-tier journals like European Journal of Operational Research and Omega. He is an editor for journals including Computers & Industrial Engineering and Decision Analytics. Recognized globally, he ranks in the top 2% of scientists worldwide in Business Analytics & Operations Research (2020-2024). His research projects include performance evaluation with unclassified factors, economies of scope in network DEA, and selective measures in DEA. He collaborates internationally on projects like robust optimization and supply chain sustainability. His teaching spans undergraduate courses in Operations Research, Mathematics for Business, and Programming, alongside postgraduate modules on Quantitative Methods and Advanced DEA. His work bridges theoretical and applied research, with applications in healthcare, renewable energy, and public policy.

Anuran MakurView profile

Assistant Professor

• Machine Learning Theory
• Information Theory
• Applied Probability
• +4 more

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.

Xiuyuan ChengView profile

Associate Professor

• Applied Mathematics
• High-Dimensional Data Analysis
• Signal Processing
• +4 more

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.