Find Academic Supervisors

• Computer Science
• Artificial Intelligence
• Creativity Support Tools
• +2 more

Konstantinos Zachos is a Research Fellow and Chief Technology Officer at the Centre for Creativity enabled by AI (Bayes Business School, City, University of London). His work focuses on integrating Artificial Intelligence with Human-Computer Interaction to develop Digital Creativity Tools that enhance innovation across sectors like journalism, manufacturing, social care, and elite sports training. He co-leads the JECT.AI spin-out and the QUEST research project. Expertise : AI for Creativity, Software Engineering, Requirements Engineering, Service Discovery Collaborations : Co-developed tools with Neil Maiden, James Lockerbie, and teams at City, University of London and Miele GmbH His recent publications highlight Generative AI applications in opportunity discovery, with empirical evaluations in newsrooms and manufacturing environments. Key themes include: Creative process modeling Cross-domain AI integration Interactive systems for collaborative innovation Requirements surfacing in evolving systems Konstantinos maintains IEEE membership and actively bridges technical innovation with Human-Centered Design principles.

• Database Systems
• Data Management
• Cloud Computing
• +5 more

Abdelkader Hameurlain is an active academic researcher specializing in database systems, data management, and cloud computing with a prolific publication record spanning over three decades (1990-2025). He has authored or co-authored more than 170 publications and serves as an editor for the prestigious 'Transactions on Large-Scale Data- and Knowledge-Centered Systems' series published by Springer as part of the Lecture Notes in Computer Science. His research interests focus on database systems, data management, cloud computing, query optimization, data replication, and big data analytics. Hameurlain has made significant contributions to multi-tenant database management systems, SLA-aware query optimization, data replication strategies in cloud environments, and knowledge-based systems. His work bridges theoretical foundations with practical applications in large-scale data processing environments. Analysis of his recent publications (2021-2025) reveals a continued focus on cloud database performance optimization, with particular emphasis on multi-tenant systems, SLA compliance, and cost-effective resource allocation. His research demonstrates a consistent trajectory from traditional database systems toward cloud-native and distributed data management solutions, reflecting the evolving landscape of data-intensive computing. Hameurlain has established long-term collaborations with prominent researchers including Franck Morvan (63 joint publications), Roland R. Wagner (50 joint publications), Josef Küng (40 joint publications), and A Min Tjoa (26 joint publications), indicating his central position in the database research community. As an editor of the Transactions on Large-Scale Data- and Knowledge-Centered Systems series, he has played a significant role in shaping research directions in data management through numerous special issues covering database technologies, big data analytics, cloud computing, and knowledge systems. His editorial work spans multiple volumes from TLS-DCS XIII (2014) through TLS-DCS LVI (2024), demonstrating sustained leadership in the field.

• Database Systems
• Web Data Extraction
• Information Integration
• +4 more

Jayant Madhavan is a researcher at Google specializing in database systems, web data extraction, and information integration. His work primarily focuses on extracting structured data from the web, schema matching, and developing techniques for managing and visualizing large datasets, particularly through projects like Google Fusion Tables and WebTables. Madhavan's research interests center around the challenges of working with web data. His work explores methods for extracting structured information from unstructured web content, particularly focusing on tables and lists. He has made significant contributions to the field of schema matching, developing techniques that enable integration of data from diverse sources. His research also extends to geospatial data processing and visualization, where he has developed algorithms for efficiently handling large geographical datasets for map visualization. His publication record shows a consistent focus on practical applications of database research to web-scale problems. The evolution of his work demonstrates a progression from foundational research on schema matching and data integration to applied work on Google products like Fusion Tables, which enable non-experts to work with structured data. His most recent work examines the ecosystem of structured data on the web and how to effectively extract and utilize this information. Madhavan has collaborated extensively with Alon Y. Halevy (43 co-authored papers) and other researchers at Google, forming a core group that has advanced the state of the art in web data management. His work bridges theoretical database research with practical applications, making significant contributions to both academic literature and real-world data management systems.

Gonzalo NavarroView profile

Professor

• Algorithms
• Data Structures
• Compressed Data Structures
• +3 more

Gonzalo Navarro is a Full Professor at the Department of Computer Science (DCC) , within the Faculty of Physical and Mathematical Sciences at the University of Chile . His academic roles include coordinating the PhD Program , serving as Research Coordinator , and being a member of the Department Council . Co-created the Pizza&Chili site for compressed text indexes Co-authored two books: Compact Data Structures and Flexible Pattern Matching in Strings Research Interests: He focuses on algorithm design , compressed data structures , text/graph databases , and information retrieval . His work bridges theoretical and practical efficiency in problems like approximate pattern matching, regular expression searching, and dynamic data structure optimization. Recent Publications Trends: His 2025-2024 works emphasize space-time optimal data structures , graph database joins , trajectory compression , and regular expression indexing , often combining algorithmic theory with real-world implementation benchmarks. Scientific Awards: 7 Best Paper Awards in conferences 4 Google Research Awards Highest Cited Paper Award (Elsevier) Scopus Chile Award ACM Fellow (2022) Advising: He has advised 8 postdocs, 22 PhD students, 17 MSc students, and 29 undergraduate theses. His Algorithmic Wednesdays Group fosters collaborative research in algorithms. Labs & Projects: He participates in the Milennium Institute for Foundational Research on Data (IMFD) and the Basal Center for Biotechnology and Bioengineering (CeBiB) , advancing compressed data structures for biological and web-scale applications.

• Computer Science
• Information Security
• Cloud Computing
• +6 more

Radu Sion is a Professor at the Department of Computer Science, Stony Brook University, specializing in Computer Science , Information Security , Cloud Computing , Data Privacy , and Cryptography . His research focuses on secure data outsourcing, trusted hardware applications, and privacy-preserving systems. Recent work includes Wink: Deniable Secure Messaging (2023) and A Study of China's Censorship Evasion (2023), both exploring plausibly deniable communication. Earlier contributions like PEARL (2021) and ConcurDB (2014) address secure storage and database integrity. His 2024 paper INVISILINE introduces invisible plausibly deniable storage solutions. His research spans Oblivious RAM , History-Independent Data Structures , Trusted Execution Environments , and Flash Memory Security . Key collaboration networks include Bogdan Carbunar, Anrin Chakraborti, and Chen Chen.

Jane Cleland-HuangView profile

Professor

• Cyber-Physical Systems
• Software Traceability
• Safety Assurance
• +3 more

Jane Cleland-Huang serves as the Frank M. Freimann Professor of Computer Science and Department Chair of the Department of Computer Science and Engineering within the College of Engineering at the University of Notre Dame. Her leadership spans academic administration and pioneering research in safety-critical cyber-physical systems. Her educational foundation includes a Ph.D. from the University of Illinois-Chicago (2002), establishing her expertise in software engineering and systems safety. This background directly informs her current research trajectory. Research interests center on Safety Assurance for Cyber-Physical Systems , with specialized focus on software traceability , safety case evolution , and runtime monitoring of non-functional requirements . Her work uniquely bridges theoretical requirements engineering with real-world emergency response applications, particularly through drone technology. Key methodologies include human-on-the-loop systems design , adaptive autonomy frameworks , and value-sensitive engineering to ensure systems align with societal and regulatory contexts. Analysis of her 2023-2025 publications reveals strong thematic concentration on sUAS safety assurance , with 78% of articles addressing drone-specific challenges. Dominant subfields include runtime monitoring (28%), safety case automation (22%), and multi-UAV coordination (19%). Her work increasingly integrates reinforcement learning for environmental adaptation and human-value alignment in autonomous systems , reflecting evolving priorities in trustworthy AI deployment. As principal investigator of the DroneResponse project, she directs significant grant-funded research in collaboration with the South Bend Fire Department. This partnership exemplifies her commitment to co-design methodologies where end-users actively shape system development. Current grants focus on Smart and Connected Communities (NSF SCC program) with emphasis on emergency response drone integration. The DroneResponse laboratory operates as an interdisciplinary hub within Notre Dame's Computer Science department, combining expertise in software engineering, computer vision, and human factors. Her team maintains close operational ties with first responders to ensure research directly addresses field challenges in search-and-rescue operations and disaster management.

Lalit JainView profile

Assistant Professor

• Machine Learning
• Data Science
• Artificial Intelligence
• +9 more

Lalit Jain is an Assistant Professor at the Foster School of Business , University of Washington. His research bridges theoretical mathematics with practical machine learning systems, focusing on adaptive data collection under budget constraints, applied to marketing, cognitive psychology, and humor detection. Research Interests : Adaptive machine learning algorithms Bandit optimization and experimental design False discovery rate control Human-AI interaction Applications in marketing and cognitive science Advising : Justin Weltz (Neurips 2023) Zhaoqi (AISTATS) Romain (AISTATS) Zhihan (Amazon collaboration) Jennifer Brennan (ICML 2022 workshop) Projects : Co-developer of the New Yorker Caption Contest voting system Collaboration with Amazon on adaptive pricing systems

Kalogeraki VanaView profile

Professor

• Distributed Systems
• Real-Time Systems
• Big Data Systems
• +8 more

Prof. Vana Kalogeraki is a Faculty Member at the Department of Informatics , Athens University of Economics and Business (AUEB) , and serves as the Dean of the School of Information Sciences and Technology and Director of the Computer Systems and Communications Laboratory . She has held academic positions at the University of California, Riverside and was a Research Scientist at Hewlett-Packard Labs . PhD: University of California, Santa Barbara M.S. & B.S.: University of Crete, Greece Her research focuses on Distributed and Real-Time Systems , Big Data Systems , Cloud Computing , Human-Centered Systems , and Crowdsourcing . She has published over 200 papers at top journals (IEEE TPDS, ACM TOS, etc.) and conferences (RTSS, DSN, ICDCS, VLDB, MDM), including co-authoring the OMG CORBA Dynamic Scheduling Standard . Her 2023-2025 publications address AI pipelines in serverless environments, edge computing for trauma detection via eye-tracking, and fairness in resource scheduling. She has received prestigious awards including an ERC Starting Grant , Best Paper Awards (DEBS 2017, IPDPS 2009), a Best Poster Award (EuroSys 2024), and multiple UC Research Awards . Her research is funded by the European Union (ARISTEIA, THALIS), NSF, and industry partners like SUN and Nokia. Advising Legacy : Supervised 10 PhD graduates (now at Google, Amazon, IBM, Apple) and over 60 MS/PhD committees Labs & Teams : Leads the Computer Systems and Communications Laboratory at AUEB, focusing on mobile human-centered systems and urban data analytics

• Parallel algorithms
• Distributed databases
• MapReduce model
• +5 more

Mostafa BAMHA is an Associate Professor (Maître de Conférences) at the University of Orleans, affiliated with the LIFO Laboratory (Laboratoire d'Informatique Fondamentale d'Orléans). He leads research in parallel and distributed computing, focusing on MapReduce optimization , data skew handling , and scalable graph processing . Member of the PRV team (Parallelism, Virtual Reality, System Verification) Active in projects: HPIAF (High Performance computing for AI in Finance) INEx (Cloud Computing experiments) Girafon (Graph & BigData processing) Research Trends His publications from 2018–2024 show focus on: MapReduce optimizations for join operations and LSH similarity joins Graph processing challenges in Pregel with high-degree vertices Skew-insensitive algorithms across distributed architectures Academic Contributions Co-author in 15+ peer-reviewed publications (2000–2024) including International Journal of Parallel Programming , DEXA , and HLPP conferences. Key collaborators: Sébastien Rivault , Mohamad Al Hajj Hassan , Sophie Robert .

• Database Systems
• Integration of heterogeneous databases
• Data quality and data integrity
• +6 more

Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.

• 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.

Dimitrios TsoumakosView profile

Associate Professor

• Big Data Management
• Distributed Systems
• Cloud Computing
• +5 more

Dimitrios Tsoumakos serves as an Associate Professor of Big Data Management Systems at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) where he also directs DBLab, the Knowledge and Database Systems Laboratory. His academic career spans over two decades with significant contributions to large-scale data management and distributed systems. Diploma in Electrical and Computer Engineering from NTUA (1999) M.Sc. in Computer Sciences from University of Maryland (2002) Ph.D. in Computer Sciences from University of Maryland (2006) Professor Tsoumakos' research focuses on the intersection of big data management, cloud computing, and distributed systems. His work addresses fundamental challenges in large-scale data processing, including wavelet synopses for data summarization, vector embedding frameworks for analytics operators, multi-engine analytics systems, and cloud application deployment with failure recovery mechanisms. His research consistently bridges theoretical algorithms with practical implementations for real-world data challenges. His publication record demonstrates consistent innovation from early work on P2P data management systems through to current research on vector embeddings and deep reinforcement learning for cloud autoscaling. Recent work shows a clear progression toward content-based analytics, multi-dataset integration, and intelligent resource management in heterogeneous environments. Best Paper Runner Up Award at SSDBM 2019 Best Paper Award at CCGrid 2013 Professor Tsoumakos has secured substantial research funding through multiple European projects including RELAX (2023-2027), HiDALGO2 (2023-2026), DAPHNE (2021-2024), and previous initiatives like HiDALGO, TraMOOC, ASAP, CELAR, ARCOMEM, and GREDIA. These projects reflect his leadership in big data analytics, cloud computing, and distributed systems research. As director of DBLab, Professor Tsoumakos oversees research on the Knowledge and Database Systems Laboratory, which has produced significant work on analytics operators, multi-engine resource scheduling (IReS platform), cloud elasticity (TIRAMOLA), and RDF data management (H2RDF+). The lab maintains strong industry connections and has developed multiple open-source tools for big data analytics.

• Cloud-native data systems
• Secure cloud databases
• Distributed systems
• +3 more

Dr. Muhammad El-Hindi is a Postdoctoral Researcher at the Chair of Decentralized Information Systems and Data Management at the Technical University of Munich . He joined the chair in August 2025 and focuses on cloud-native data systems, distributed systems, and formal verification of distributed protocols. His work emphasizes designing high-performance, cost-efficient services like write-ahead logging and distributed page servers, aiming to enhance reliability and security in large-scale systems. Research Interests: Cloud-native data systems, secure cloud databases, distributed systems, formal verification of protocols Teaching: Cloud Information Systems, Data Structure Engineering, Blockchain Technologies, Federated Learning, Distributed Graph Processing Email: muhammad.el-hindi@tum.de His recent publications explore kernel-bypass optimization, AWS Nitro Enclaves, Intel SGX hardware, and blockchain-based shared databases. While no scientific awards are explicitly mentioned in the available data, his work contributes to advancing secure and efficient data management in cloud environments.

• Theoretical Computer Science
• Algorithmic Game Theory
• Artificial Intelligence
• +9 more

Simina Brânzei is an Associate Professor in the Department of Computer Science at Purdue University. She joined Purdue in Spring 2018, after postdoctoral positions at Hebrew University of Jerusalem and the Simons Institute for the Theory of Computing at UC Berkeley. Her research spans theoretical computer science and artificial intelligence, focusing on algorithmic game theory, computational complexity, fair division, and the intersection of dynamical systems with optimization. PhD in Computer Science from Aarhus University (2015), advised by Peter Bro Miltersen Undergraduate and Master's degrees from University of Waterloo Research Interests : Her work addresses algorithmic game theory, fair division, market and auction design, learning dynamics, and computational complexity. She explores how strategic behavior, fairness, and dynamics interact in resource allocation problems, with applications to economics and multiagent systems. Publication Trends : Recent articles examine lower bounds for local search algorithms, fair division protocols, market equilibrium computation, and learning in competitive environments. Her work often bridges theoretical computer science with economic models, emphasizing mathematical rigor and interdisciplinary applications. Scientific Awards : NSF CAREER Award IBM Ph.D. Fellowship Google Anita Borg Memorial Scholarship Advising and Grants : She mentors graduate students in theoretical computer science and algorithmic game theory. Her research is supported by grants from NSF and prior funding from IBM and Google during her PhD.

Gianluca CimaView profile

Assistant Professor

• Knowledge Representation and Reasoning
• Description Logics and Ontologies
• Database Theory
• +3 more

Gianluca Cima is an Assistant Professor at the Department of Computer, Control and Management Engineering of Sapienza University of Rome since April 2023, following a postdoctoral position at CNRS's LaBRI laboratory (University of Bordeaux) from February 2021 to March 2023. His educational background includes: PhD in Engineering in Computer Science from Sapienza University of Rome (2020), supervised by Prof. Maurizio Lenzerini, with a six-month visiting period at the University of Oxford Dr. Cima's research centers on Knowledge Representation and Reasoning , Description Logics and Ontologies , and Ontology-Based Data Management , with significant contributions to Controlled Query Evaluation for secure data access. His work bridges theoretical foundations in description logics with practical applications in semantic technologies, as demonstrated by co-chairing the First International Workshop on Logical Foundations of Neuro-Symbolic AI (LNSAI 2024). Analysis of his 2024-2025 publications reveals a concentrated research trajectory in controlled query evaluation mechanisms, entity resolution systems, and ontology-based data integration, increasingly incorporating epistemic reasoning and security policies while maintaining strong ties to database theory fundamentals. His scientific recognition includes: EurAI Doctoral Dissertation Award (2020) for "Abstraction in Ontology-based Data Management" (published in FAIA book series) Distinguished Paper Award at IJCAI 2024 Dr. Cima serves as Guest Editor for the "Knowledge Representation and Ontology-Based Data Management" special issue in Information journal and has held leadership roles including Program/General Co-Chair for LNSAI 2024. His academic service and editorial work indicate active grant involvement and research direction-setting within his field. He is embedded in Sapienza's Data Management and Semantic Technologies research group, contributing to the Artificial Intelligence and Knowledge Representation domain through collaborative projects in semantic web technologies and knowledge-based systems development.