Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +7 more

Andreas Karwath is an Associate Professor at the University of Birmingham, affiliated with the Cancer and Genomic Sciences Institute, Institute for Interdisciplinary Data Science and AI, and The Centre for Computational Biology. His research spans computational biology, artificial intelligence, and data science applications in healthcare and cancer genomics. His research interests include: Machine Learning and Artificial Intelligence Computational Biology and Bioinformatics Phenotype Analysis and Ontology Cancer Genomics and Radiomics Differential Diagnosis Systems Dr. Karwath's research fingerprint reveals significant contributions to Inductive Logic Programming (100%), Semantic Similarity (89%), Phenotype Profiles (80%), Machine Learning (74%), Learning Systems (74%), Ontology (71%), Differential Diagnosis (66%), and Neural Networks (63%). His work consistently applies computational approaches to biomedical challenges, with recent focus on cancer outcomes prediction, cardiovascular monitoring through wearables, and phenotype library development for health data research. His scientific contributions include 73 research outputs spanning two decades, with notable recent publications in high-impact journals including Nature Medicine, Machine Learning, and Frontiers in Oncology. His work has been cited across multiple platforms with significant attention from news outlets and academic readers. Dr. Karwath serves as Co-Investigator on several major research projects: PETNECK2 - Radiomics in Outcome Predictive Models for Head and Neck Cancer (2025-2027, NIHR) ACORN: Assessment of Chronic Opioid Risk using Neurobiology (2024-2027, Wellcome Leap Inc.) PINK - Provision of Integrated Computational Approaches for Safe-and-Sustainable-by-Design Chemicals and Materials (2024-2027, UKRI) NIHR BRC Theme - Data, Diagnostics & Decision Tools (2022-2027, NIHR) His collaborative network spans multiple UK institutions and international partners, with frequent collaboration with Gkoutos, G.V. across multiple projects. His research has practical clinical applications, particularly in cancer treatment outcomes prediction and digital health monitoring systems.

Fabrizio SilvestriView profile

Professor

• Artificial Intelligence
• Machine Learning
• Web Search
• +7 more

Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Jef PeetersView profile

Professor

• Robotic Disassembly
• Circular Economy
• Materials Recycling
• +3 more

Prof. Jef Peeters is a faculty member at KU Leuven in the Institute for Sustainable Metals and Minerals , specializing in Manufacturing Processes and Systems . His research focuses on robotics, circular economy strategies, and sustainable materials recovery. Academic Rank: Professor Research Areas: Robotic disassembly, WEEE recycling, critical raw materials. Recent Publications highlight interdisciplinary work blending robotics, deep learning, and environmental engineering. Key topics include human-robot collaboration in remanufacturing, advanced sorting techniques for plastics and metals, and life cycle assessments of recycling strategies. Technical Expertise spans computational fluid dynamics (CFD) modeling, laser-induced breakdown spectroscopy (LIBS), and computer vision systems for defect detection and battery dismantling. His work addresses challenges in e-waste management and product reparability. Applications of his research include robotic grasping constraints, unsupervised learning for waste retrieval, and techno-economic assessments of sorting technologies. This reflects a strong commitment to advancing circular economy practices through automation.

• Information Retrieval
• Natural Language Processing
• Artificial Intelligence
• +2 more

Professor Farhad Oroumchian serves as Professor and Program Leader for General Computer Science within the Department of Information Sciences at the School of Computer Science, University of Wollongong in Dubai (UOWD). His career bridges academia and industry across Iran and the United States, with leadership roles including Department Chair at University of Tehran and research positions at TextWise LLC. His educational credentials comprise: BSc from National University of Iran (New York) MSc from Sharif University of Technology (Tehran) PhD from Syracuse University Research expertise centers on information retrieval and natural language processing, with pioneering work in Persian text processing systems and multilingual search engines . He developed an intelligent search engine using human-like reasoning models and created Hamshahri, the standard Persian text corpus. His publication portfolio exceeds 100 works with 1000+ citations, demonstrating significant impact in adaptive ranking algorithms and cross-lingual information retrieval. Professional engagement includes active membership in the Association for Computing Machinery (ACM) and its Special Interest Group on Information Retrieval (SIGIR). While specific grant details are unreported, his extensive publication record and industry collaborations indicate sustained research activity. No dedicated laboratory is mentioned, though his work involves computational linguistics infrastructure for Persian and multilingual systems.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +6 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

• Machine Learning
• Computer Science
• User Behavior
• +7 more

M.I. Gregoriadis is a Researcher at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft) . His work focuses on Machine Learning , Decentralized Systems , and Data Engineering , with notable contributions to federated learning, adaptive ranking algorithms, and blockchain-based data analysis. Key Research Areas : Machine Learning, Ranking Algorithms, User Behavior, Data Deduplication Collaborations : ACM, 4TU.ResearchData Recent work includes decentralized search engines (SwarmSearch), differentiable search indexes (De-DSI), and federated learning datasets. His publications address technical challenges in content-defined chunking, IPFS deduplication, and blockchain analytics. Gregoriadis co-created the Tribler Learning-to-Rank Dataset and collaborates with researchers like Q. Stokkink and J. Pouwelse . His work emphasizes practical implementations in decentralized systems and open-access research outputs.

• Blockchain
• Decentralized Systems
• Federated Learning
• +3 more

J.A. Pouwelse is a Professor at the Data-Intensive Systems department within the Electrical Engineering, Mathematics and Computer Science school at Delft University of Technology . With 125 research outputs and 4 supervised works, his work focuses on Blockchain , Decentralized Systems , Federated Learning , and Peer-to-Peer Networks , particularly for Web3 applications. His recent publications analyze topics such as: Decentralized adaptive ranking Serverless federated learning Green smart contracts Search index optimization Zero-trust frameworks Notable recognition includes the LCN Best Paper Award 2021 . Research spans decentralized infrastructure design, privacy preservation, and scalable system implementation.

Emmanouil AthanasiadisView profile

Research Fellow

• Bioinformatics
• Computational Biology
• Single Cell Genomics
• +3 more

Dr. Emmanouil Athanasiadis is a bioinformatician at the University of Cambridge's Department of Haematology, with dual affiliations at the Wellcome Trust Sanger Institute and Medical Research Council (MRC) Stem Cell Institute. His research integrates computational biology with medical applications, focusing on single-cell genomics, cancer biology, and cardiovascular disease mechanisms since 2016. His educational foundation includes: BSc in Biomedical Engineering from Technological Educational Institution of Athens (2004) MSc in Medical Physics from University of Patras (2006), funded by State Scholarships Foundation of Greece PhD in Medical Physics from University of Patras (2010), supported by National State Scholarship Foundation Athanasiadis specializes in developing computational frameworks for biological data interpretation. His work spans single-cell RNA sequencing analysis in haematopoiesis, medical imaging algorithms for cancer diagnostics, and drug repurposing pipelines. Key contributions include SPNsim for pulmonary nodule simulation and ChemBioServer for chemical compound analysis, demonstrating his dual expertise in algorithm development and biomedical application. Publication analysis reveals a trajectory from medical imaging (2007-2012) to genomic network analysis (2013-2016), culminating in current single-cell and spatial transcriptomics research. His work consistently bridges computational innovation with clinical questions in oncology, haematology, and cardiovascular disease, with strong emphasis on open-source tool development. His scientific recognition includes: Computational award from Greek Research and Technology Network for 'GRAND' project Multiple presentation prizes at national medical conferences Continuous academic distinctions from State Scholarships Foundation of Greece K. Karatheodoris research scholarship He has secured EU funding through FP7 projects including 'PIK3CA Oncogenic Mutations' and 'NOISEPLUS', and maintains active collaborations across Cambridge, UCL, and Greek research institutions. His teaching contributions include lecturing in biomedical engineering programs at Technological Educational Institution of Athens (2010-2016). Current work centers on single-cell RNA sequencing analysis within Cambridge's haematology research ecosystem, particularly investigating transcriptional dynamics in blood cell development and cancer evolution through collaborative projects with Sanger Institute.

Torsten SattlerView profile

Research Professor

• 3D computer vision
• Machine learning
• Image-based localization
• +10 more

Torsten Sattler is a Senior Researcher at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) at the Czech Technical University in Prague (CTU), where he heads the Spatial Intelligence group. Previously, he was a tenured Associate Professor at Chalmers University of Technology in Sweden and spent five years as a PostDoc and Senior Researcher at ETH Zurich in Switzerland. He received his PhD from RWTH Aachen University in Germany. His research focuses on the intersection of 3D computer vision and machine learning, with specific interests in image-based localization, 3D mapping and reconstruction, neural scene representations, and applications in robotics and AR/VR. He aims to make localization and mapping algorithms more robust by incorporating higher-level scene understanding. Dr. Sattler has published extensively at top computer vision conferences including CVPR, ICCV, and ECCV, with recent papers focusing on robust visual localization in changing environments, neural scene representations, and 3D reconstruction. His work has direct applications in autonomous systems and augmented reality. Best Paper Candidate at CVPR 2021 Best Paper Award at Photogrammetric Image Analysis 2019 Multiple Outstanding Reviewer Awards from 2015-2021 Ranked among top-10 computer scientists in Czech Republic by Research.com He currently supervises five PhD students and has served in various leadership roles including program chair for ECCV 2024, general chair for 3DV 2022, and area chair for multiple major conferences. His lab benefits from connections to the RICAIP Centre, one of the largest EU projects in AI and Industry 4.0, providing access to state-of-the-art infrastructure and industrial collaborations.

Vincent ItierView profile

Lecturer

• Multimedia Security
• Digital Forensics
• Machine Learning
• +4 more

Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.

Dominik StögerView profile

Assistant Professor

• Mathematical Foundations of Data Science
• Non-convex Optimization
• Machine Learning Theory
• +4 more

Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +7 more

Jun Wang is a Professor of Information and Data Science in the Department of Computer Science at University College London. He serves as the Founding Director of the MSc Web Science and Big Data Analytics program and is the Co-founder and Chief Scientist of MediaGamma Ltd (acquired), a UCL startup focused on AI for intelligent audience decision-making. His educational background includes: Doctor of Philosophy from Technische Universiteit Delft (2007) Master of Science from National University of Singapore (2003) Bachelor's degree from Southeast University Nanjing (1997) Professor Wang's primary research focuses on AI and intelligent systems, with particular expertise in multiagent reinforcement learning, deep generative models, and their applications across several domains. His work spans information retrieval, recommender systems and personalization, data mining, smart cities, bot planning, and computational advertising. His research has practical implications in healthcare, robotics, and intelligent systems for decision-making, aligning with Sustainable Development Goal 3 (Good Health and Well-Being). His recent publications demonstrate a strong trend toward integrating large language models with reinforcement learning techniques to create more efficient and capable systems. The research spans healthcare applications, robotics, information systems, and strategic decision-making, with an emphasis on privacy preservation, efficient computation, and multi-agent coordination. Professor Wang has received several prestigious awards: Beyond Search – Semantic Computing and Internet Economics award from Microsoft Research Yahoo! FREP Faculty award UCLB One-to-Watch award 2016 for MediaGamma First place in global real-time bidding algorithm contest (80+ participants worldwide) Multiple Best Paper awards With over 15 years of experience, Professor Wang has advised numerous startups including Last.Fm, Passiv Systems, Massive Analytic, Context Scout, and Polecat. He has also collaborated with major industry players such as BT, Microsoft, Yahoo!, Alibaba, and Didi. His knowledge transfer activities bridge academic research with practical industry applications. Professor Wang has developed the Information Retrieval and Data Mining MSc module and the MSc/MRes programme on Web Science and Big Data Analytics. He also teaches a multiagent AI MSc module, sharing his expertise in cutting-edge AI techniques with the next generation of computer scientists.

Qing HuangView profile

Associate Professor

• Software Engineering
• Knowledge Graphs
• Human-Computer Interaction
• +5 more

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.