Find Academic Supervisors

• Schema discovery for Semantic Web data
• Data mining
• Recommendation systems
• +5 more

Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.

Alexey GotsmanView profile

Research Professor

• Distributed Systems
• Software Verification
• Consensus Algorithms
• +5 more

Alexey Gotsman is a tenured Research Professor at the IMDEA Software Institute in Madrid, Spain, where he leads research at the intersection of software verification and distributed computing. He also works part-time as a visiting academic at Amazon Web Services. His educational background includes a Ph.D. from the University of Cambridge, where he was previously a postdoctoral fellow. His research spans theoretical foundations and practical implementations of distributed systems, with particular emphasis on fault tolerance, consensus algorithms, and consistency models. His research interests focus on the theoretical and practical aspects of distributed systems, including Byzantine fault tolerance, state machine replication, distributed transactions, and consistency models. His work bridges the gap between formal verification techniques and real-world distributed systems implementations, with numerous publications in top-tier venues like PODC, DISC, and NSDI. His recent publications show a clear progression from theoretical foundations to practical implementations, with increasing focus on real-world applications, performance considerations, and fault tolerance in large-scale distributed systems. The research demonstrates strong connections between formal methods and practical system design, particularly in the areas of consensus protocols, replication strategies, and transaction processing. Best paper award at OPODIS'23 Best paper award at DISC'18 Best paper award at CONCUR'12 EAPLS Best Dissertation Award Runner-up prize in the BCS Distinguished Dissertation Competition ERC Starting Grant 'RACCOON: A Rigorous Approach to Consistency in Cloud Databases' (2017-2022) Ramón y Cajal Fellowship (2017-2022) Alexey actively mentors PhD students including Alejandro Naser Pastoriza, Fedor Ryabinin, and Antonio José Fernández Pinto, while his alumni have gone on to faculty positions at institutions like University of Paris 7 and University of Colorado Boulder, as well as industry roles at companies like ARM and Informal Systems. He has received significant funding including an ERC Starting Grant and served on program committees for numerous top conferences including POPL, DISC, and PODC. He currently co-organizes the International Symposium on Distributed Computing (DISC) and has been instrumental in organizing workshops focused on consistency in distributed systems. His research group at IMDEA Software Institute focuses on developing rigorous approaches to consistency in cloud databases and distributed systems, with active projects spanning theoretical analysis, protocol design, and practical implementation of fault-tolerant distributed systems.

Christos OuzounisView profile

Professor

• Bioinformatics
• Digital Biology
• Biological Computation
• +5 more

Christos Ouzounis is a Professor of Bioinformatics at the Department of Informatics, Aristotle University of Thessaloniki , with a career spanning institutions including the European Bioinformatics Institute , King's College London , and University of Toronto . His work bridges Computational Biology , Digital Biology , and Metagenomics , focusing on large-scale data analysis, machine learning applications, and functional annotation of proteins. Education : BSc in Biological Sciences (1986), MSc in Biological Computation (1987), and DPhil in Computational Chemistry (1993) Key Roles : Director of the Bioinformatics Centre at King's College London (2007-2010), Research Director at IDEP-EKETA (2014-2020) His research interests include low-complexity protein sequences , Covid-19 seasonality patterns linked to UV radiation, and metagenomic analysis of urban microbiomes in cultural heritage sites. Current projects involve machine learning models for microbial coexistence networks, ontological classification of biomedical literature, and bioinformatics tool development . Publications highlight trends in archaeal genomics , functional dark matter in metagenomics, and epidemiological modelling . Notable collaborations include work on BioTextQuest v2.0 for concept discovery and MjCyc for metabolic pathway analysis.

Florin RusuView profile

Professor

• Database systems
• Large-scale data management
• Query processing and optimization
• +4 more

Florin Rusu is a Professor and Chair of the Department of Computer Science and Engineering at the University of California Merced, School of Engineering. He joined UC Merced in 2010 and has served in multiple administrative positions including as chair of the School of Engineering's Executive Committee and currently as chair of the Department of Computer Science and Engineering. His educational background includes a B.Eng. degree from the Technical University of Cluj-Napoca, Faculty of Automation and Computer Science (2004), and M.Sc. and Ph.D. degrees from the University of Florida in Computer Science (2008 and 2009). Rusu's research focuses on database systems and large-scale data management, with particular emphasis on designing infrastructure for Big Data analytics. His specific research areas include query processing and optimization, approximate and randomized algorithms, scalable machine learning, multi-dimensional array data management, and in-situ data processing. His work bridges theoretical aspects with practical system design issues. His research has been funded by multiple prestigious organizations including the US Department of Energy (DOE), National Science Foundation (NSF), California Department of Education, Hellman Foundation, LogicBlox, and TigerGraph. His recent publications show a continued focus on database query optimization, particularly around cardinality estimation, sketch-based methods, and innovative approaches to query plan generation. His work spans both theoretical contributions and practical implementations, with several projects transitioning into real-world database systems. Scientific Awards: DOE Early Career Award (2014) Hellman Faculty Fellowship (2013) Rusu has advised numerous graduate students through their Ph.D. and Master's programs, with many going on to successful careers at major tech companies (Google, Meta, TigerGraph) and academic positions. His research group has secured substantial funding from NSF (COMPASS project 2020-2025), DOE Early Career Award (2014-2021), TigerGraph, California Department of Education, and Hellman Foundation. His research group maintains active projects in Database Query Optimization, Scalable Gradient Descent Optimization, Array Databases, In-Situ Data Processing, GLADE, Online Aggregation, and Sketches, demonstrating a comprehensive research program spanning multiple aspects of database systems and large-scale data management.

• algorithms
• data structures
• compression
• +2 more

Gonzalo Navarro is a full Professor at the University of Chile where he earned his PhD in Computer Science in 1998. He directs research initiatives including the Millennium Nucleus Center for Web Research and participates in the Center for Biotechnology and Bioengineering (CeBiB) and Millennium Institute for Foundational Research on Data (IMFD). Education PhD in Computer Science, University of Chile, 1998 Research Interests His work focuses on algorithms, data structures, compression techniques, text searching systems, and graph databases. He pioneered compact data structures for highly repetitive sequences, enabling massive data reduction while maintaining efficient search capabilities across applications from bioinformatics to web-scale databases. His research bridges theoretical computer science with practical implementations in real-world systems. Scientific Awards 7 Best Paper Awards in conferences 4 Google Research awards Highest Cited Paper Award from Elsevier Award Scopus Chile ACM Fellow Advising and Grants Navarro has mentored 8 postdocs, 22 PhD candidates, 17 Master's students, and 29 undergraduates. His research receives funding from Google and Yahoo! Research, complemented by 4 Google Research awards. He coauthored two Cambridge University Press books, 25 book chapters, 11 edited conference proceedings, 200 journal papers, and 275 conference papers, establishing him as Latin America's most cited computer scientist. Labs and Teams He founded and directed the Millennium Nucleus Center for Web Research, currently contributes to CeBiB and IMFD, and created the Workshop on Compression, Text, and Algorithms (WCTA) which operates as SPIRE's permanent satellite conference. His leadership extends to steering committees of major algorithms conferences including SPIRE, LATIN, SISAP, and ESA.

Valeriya Nikolaeva SimeonovaView profile

Associate Professor

• Machine Learning
• Metagenomics
• Bioinformatics
• +4 more

Valeriya Nikolaeva Simeonova is an Associate Professor at the Faculty of Mathematics and Informatics, University of Sofia, specializing in Information Technologies . Her research focuses on interdisciplinary applications at the intersection of Bioinformatics , Machine Learning , and Parallel Computing , particularly for error discovery in metagenomics data and QSAR modeling in plant biology. Her work emphasizes Next-Generation Sequencing (NGS) data analysis, where she develops algorithms for error detection and correction. She has contributed to optimizing genome assembly techniques through soft computing approaches and explored distributed computing for financial time series forecasting. Notable publications include studies on Golden Root in vitro culture growth (2013) and metagenomics NGS error detection (2015) in journals like BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT . Her collaborations span institutions in Bulgaria, France, and Belgium.

Nikos GiatrakosView profile

Assistant Professor

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +3 more

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

• Artificial Intelligence
• Machine Learning
• Distributed Learning
• +7 more

Berta María Guijarro Berdiñas is a Researcher in the Department of Computer Science and Artificial Intelligence at the University of A Coruña , Spain. She is affiliated with the Laboratory for Research and Development in Artificial Intelligence and teaches courses like Machine Learning , Development of Intelligent Systems , and Programming at both undergraduate and postgraduate levels. Research Focus: Her work lies at the intersection of Artificial Intelligence , Machine Learning , and Knowledge-Based Systems . Key contributions include frugal learning (limited data), anomaly explanation , and distributed learning for edge devices. She applies these to areas like health informatics , forest fire management , and human-robot interaction . Recent Publications span explainable AI , anomaly detection , multi-agent systems , and low-power machine learning . Her articles appear in top venues like Expert Systems with Applications and IEEE Transactions on Neural Networks and Learning Systems . Grants & Projects include EU-funded initiatives, Spanish Ministry of Science grants, and regional collaborations. She focuses on AI for healthcare , smart systems , and distributed learning .

Juan Carlos Linares CalderónView profile

Professor

• Ecology
• Climate Change
• Forest Management
• +5 more

Juan Carlos Linares Calderón is a Full Professor at Pablo de Olavide University in the Department of Physical, Chemical and Natural Systems. His research focuses on Mediterranean relict forests' response to climate change through multidisciplinary approaches combining dendrochronology , ecophysiology , genomics , and epigenetics . PhD in Science (2008) from University of Jaén Principal Investigator in 10+ national/international projects Research Themes Specializing in climate change impacts on forest ecosystems , Linares leads the Forests Ecology and Global Change Lab . His work spans from individual tree responses to large-scale biogeographical patterns, challenging CO 2 -induced growth paradigms and emphasizing forest management as climate modulator. Scientific Contributions Over 162 publications with 82,704 reads and 9,074 citations Highly Cited Author status (Ioannidis et al., 2020) Developed SAPFLUXNET global transpiration database Email: jclincal@upo.es

• Astronomy
• Astrophysics
• Machine Learning
• +5 more

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

Sandeep ReddivariView profile

Associate Professor

• Software Engineering
• Requirements Engineering
• Visual Analytics
• +7 more

Sandeep Reddivari is an Associate Professor and Interim Graduate Director at the School of Computing, University of North Florida . Holding a Ph.D. in Computer Science and Engineering from Mississippi State University, his work focuses on software engineering , particularly requirements engineering , visual analytics , and software maintenance , with funding from the NSF, US Army Corps of Engineers, NSA, and UNF Foundation. Education : Ph.D. (2014, Mississippi State University), M.S. (2009, Texas A&M University), B.Tech. (2006, JNTU) Research Themes : Requirements Engineering, Visual Analytics, Data Mining, Machine Learning, GenAI, Software Security Teaching : Courses in Software Engineering, Data Science, and Database Systems Awards : NSF Grant Recognition (2024), Best Doctoral Symposium Poster (2013), Outstanding Graduate Teaching Award Nominee (2018) Publications span top venues like IEEE RE, ICSE, and COMPSAC, with a focus on combining visual analytics and machine learning to enhance software decision-making. His lab at UNF mentors students in directed independent studies, emphasizing code navigation , blockchain , and educational software tools .

Santiago FigueiraView profile

Professor

• Theoretical computer science
• Computability theory
• Algorithmic randomness
• +8 more

Santiago Figueira is a Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, and a Researcher at the Institute of Computer Science (ICC), CONICET (Argentine National Council for Scientific and Technical Research) in Buenos Aires, Argentina. He leads the Logic, Language and Computability Research Group (GLyC) and co-directs the Argentinian-French Laboratory SINFIN, a collaborative initiative between Université Paris-CNRS and Universidad de Buenos Aires-CONICET. Additionally, he is a researcher in the Quantum Information, Computation, and Communication team (QuICC). Dr. Figueira's research spans theoretical computer science with a strong emphasis on computability theory, algorithmic randomness, Kolmogorov complexity, and mathematical logic. His work extends to applications in database theory, quantum information, and cognitive modeling. He has made significant contributions to modal logics, model theory, and the theory of databases, particularly in the context of data trees and XPath query languages. His interdisciplinary approach bridges formal methods with practical applications in quantum computing and human cognition. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on modal logic extensions, quantum information theory, cognitive modeling of concept learning, and database query languages. His collaborations span international institutions, particularly with French researchers through the SINFIN laboratory, and with cognitive scientists studying human memory and concept formation. Dr. Figueira has supervised numerous PhD students and postdoctoral researchers, including Gabriel Goren-Roig, Santiago Cifuentes, Edwin Pin Baque, Sergio Romano, Gabriel Senno, and Sergio Abriola. His former postdocs and CONICET researchers include Guido Bellomo, María Emilia Descotte, and Ariel Bendersky, indicating an active and productive research group. He has developed lecture notes in Spanish on Logic and Computability (2020) and Computability Theory (2021), contributing to academic education in his field. His research group GLyC serves as a hub for theoretical computer science research in Argentina, fostering collaborations between Argentine and international researchers.

• Bioinformatics
• Computational Biology
• Algorithms
• +5 more

Travis Wheeler is an Associate Professor in the Department of Pharmacy Practice & Science at the University of Arizona. His work spans bioinformatics, computational biology, and algorithm development for genomic sequence analysis. Developed tools like HMMER and Dfam Research focuses on transposable elements, sequence alignment, and epigenetics Co-author of key works with Robert Finn, Sean Eddy, and colleagues Research Interests include machine learning applications in biological sequence annotation, drug discovery, and evolutionary genomics. His work bridges computational methods with biomedical applications. Notable Contributions : Advancing profile Hidden Markov Model (HMM) methodologies Creating community resources for transposable element research Developing alignment algorithms for biological sequences Collaborations include institutions like Institute for Systems Biology, Harvard University, and Montana State University.

Brian D. AtheyView profile

Professor

• Bioinformatics
• Pharmacogenomics
• Machine Learning
• +8 more

Brian D. Athey is a Professor of Computational Medicine and Bioinformatics and Psychiatry at the University of Michigan Medical School. He serves as Michael Savageau Collegiate Professor & Chair of his department, leading interdisciplinary research in pharmacogenomics, epigenomics, and machine learning applications. His work bridges biomedical informatics with clinical insights in mental health, neurology, and drug response mechanisms. Ph.D. , Biophysics, University of Michigan (1990) B.S. , University of Michigan-Dearborn (1982) Dr. Athey’s research focuses on the pharmacoepigenome, high-throughput 4D imaging, and AI-driven pharmacogenomic pipelines. His lab develops tools for patient-specific drug response prediction, chromatin structure analysis, and next-generation sequencing assays. Collaborations span institutions like Assurex Health, tranSMART Foundation, and Johns Hopkins Medical School. His publications highlight innovations in pharmacogenomics, including AI integration for variant identification, 4D nucleome modeling, and adverse event ontologies. Recent projects address bipolar disorder treatment personalization, hypertension management, and generative AI workflows to enhance academic productivity. Scientific Awards : NIH Postdoctoral Fellowship 1990-1991 NIH Postdoctoral Fellowship 1991-1993 Advisees & Collaborations : His lab mentors PhD and Masters students in bioinformatics and biostatistics, with graduates like Ari Allyn-Feuer (GSK AI Products Director) and Alex Kalinin (Broad Institute). Collaborations include NIH-funded initiatives (O’Brien Kidney Core Center, VIOLIN 2.0) and industry partnerships.

• Radiation Oncology
• Gastrointestinal Cancers
• Head and Neck Cancers
• +3 more

Shane Lloyd, MD, is an Associate Professor in the Department of Radiation Oncology at the University of Utah School of Medicine, affiliated with Huntsman Cancer Institute. He specializes in treating gastrointestinal, head/neck, and genitourinary cancers. His research emphasizes improving care quality through data analysis, clinical trials, and addressing health disparities in cancer survivorship. Education: M.D. & M.H.S. from Yale School of Medicine B.A. in Economics and Zoology from Brigham Young University Residency in Radiation Oncology at Yale New Haven Hospital Internship at University of Utah Affiliated Hospitals Research Focus: Dr. Lloyd investigates radiation therapy optimization, cancer survivorship outcomes, and socio-geographic health disparities. His work integrates population-based data analytics to evaluate treatment efficacy, mental health impacts, and equitable care access. Publications: Recent articles (2022–2025) highlight trends in prostate, bladder, and gastrointestinal oncology, with emphasis on immunotherapy combinations, survivorship disparities, and biomarker-driven recurrence detection. Machine learning applications in treatment decision-making and rural health inequities are recurring themes. Leadership: Chairs the Radiological Drug Research Committee (RDRC) and Human Use Subcommittee of the Radiation Safety Committee (HUS-RSC).