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Sharma V. ThankachanView profile

Associate Professor

• String Algorithms
• Compressed Data Structures
• Computational Biology
• +1 more

Sharma V. Thankachan is an Associate Professor in the Department of Computer Science at North Carolina State University since 2022, previously serving as an Assistant Professor at the University of Central Florida (2017-2022). His academic journey includes postdoctoral research at the University of Waterloo with Ian Munro and Georgia Institute of Technology with Srinivas Aluru. His educational credentials feature: Ph.D. in Computer Science from Louisiana State University (2014) B.Tech in Electrical and Electronics Engineering from National Institute of Technology Calicut, India (2006) Dr. Thankachan's research centers on string algorithms and compressed data structures with critical applications in computational biology, particularly addressing pan-genomic data modeling and gapped pattern matching challenges. His theoretical work bridges algorithmic design with practical genomic analysis, focusing on space-efficient representations for repetitive biological sequences. Analysis of his 2022-2025 publications reveals dominant themes in BWT-based indexing, non-overlapping pattern constraints, and external memory optimization. These contributions advance core areas of data compression, bioinformatics algorithms, and scalable text processing systems, demonstrating consistent innovation in handling genomic-scale data. His distinguished recognition includes the NSF CAREER award: National Science Foundation Faculty Early CAREER Development Award (2022) Dr. Thankachan mentors multiple doctoral students including Mano Prakash Parthasarathi, Paul Macnichol, and Oliver Chubet (co-advised with Donald Sheehy), with alumni Paniz Abedin, Daniel Gibney, and Sahar Hooshmand now holding faculty positions. His research is supported by two major NSF grants: CAREER: Algorithmic Aspects of Pan-genomic Data Modeling, Indexing and Querying (2023-2027, $513,686) AF: Small: Theoretical Aspects of Repetition-Aware Text Compression and Indexing (2023-2026, $414,034)

Adrián García GutiérrezView profile

Professor

• Aerospace Engineering
• Uncertainty Quantification
• Computational Fluid Dynamics
• +3 more

Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.

Kim JelfsView profile

Professor

• Computational Materials Chemistry
• Physical Chemistry
• Materials Engineering
• +4 more

Prof. Kim Jelfs is a Professor in Computational Materials Chemistry at Imperial College London's Department of Chemistry, part of the Faculty of Natural Sciences. Her research focuses on computational design of functional molecular materials, particularly porous organic cages, polymer membranes, and materials for energy storage. She leads the Jelfs Group, collaborating with synthetic teams to bridge theory and experiment. Key affiliations include the Institute for Digital Molecular Design and Fabrication (DigiFAB), the Artificial Intelligence Network, and the Thomas Young Centre. Her work is funded by the Royal Society, EPSRC, ERC, and industry partners. Research interests span materials engineering, theoretical chemistry, and AI-driven discovery. Notable projects involve developing porous liquids, ion-selective membranes for batteries, and computational workflows for material screening. She co-directs DigiFAB and contributes to Imperial's Data Science Institute. Her group emphasizes interdisciplinary approaches to accelerate material innovation.

• Machine Learning
• Artificial Intelligence
• Catalysis
• +6 more

Gabriel Gomes is an Assistant Professor in the Departments of Chemistry and Chemical Engineering at Carnegie Mellon University, where he leads the Gomes Group research program focused on the intersection of machine learning and chemistry. His work centers on developing new chemical reactions, catalysts, and materials using state-of-the-art machine learning and automated synthesis techniques. Dr. Gomes received his Ph.D. in Chemistry from Florida State University in 2018 under Professor Igor V. Alabugin, where he researched stereoelectronic effects. Prior to joining CMU in 2022, he was a Banting Postdoctoral Fellow at the University of Toronto in the Matter Lab led by Professor Alán Aspuru-Guzik. He earned his B.S. in Chemistry from Federal University of Rio de Janeiro, Brazil in 2013. His research program focuses on developing autonomous platforms for reaction discovery, with emphasis on catalysis. Gomes aims to create inverse design frameworks where algorithms can propose new functional catalysts from scratch based on desired properties. His work integrates quantum mechanics with machine learning to teach computers chemical intuition, enabling novel approaches to catalyst design and chemical synthesis. He strongly advocates for the integration of AI with human creativity to transform chemical research. Gomes' recent publications demonstrate a clear trend toward integrating large language models with chemical discovery, developing transferable machine learning potentials for catalysis, and advancing molecular representations with quantum chemical insights. His work spans from fundamental quantum chemistry to practical applications in biocatalysis and materials design. Scialog Fellow (2023) C&EN Talented Twelve (2022) NSERC Banting Postdoctoral Fellowship (2020-2021) CAS SciFinder Future Leaders Program (2018) ACS COMP Chemical Computing Group Excellence Award (2018) FSU's Graduate Student Research and Creativity Award (2018) IBM Ph.D. Scholarship (2017) Dr. Gomes mentors several graduate students including Robert MacKnight and Daniil Boiko, who have contributed to projects like the Catnip web application for biocatalyst prediction. His research has received funding through prestigious programs like the Scialog Automating Chemical Laboratories awards. Gomes actively promotes open science initiatives and has contributed to the Open Reaction Database to improve data accessibility for machine learning in chemistry. The Gomes Group operates at the cutting edge of AI-driven chemistry research, developing tools like Coscientist that bridge the gap between natural language prompts and actual chemical reactions. Their work on autonomous reaction discovery systems represents a significant step toward fully automated chemical laboratories where machines can 'dream of molecules' with specific desired properties.

Zachary SickmannView profile

Assistant Professor

• Sedimentology
• Stratigraphy
• Sediment Provenance
• +9 more

Zachary Sickmann is an Assistant Professor in the School of Natural Sciences and Mathematics at the University of Texas at Dallas. His research focuses on sedimentology, stratigraphy, and sediment provenance, with applications in environmental monitoring, construction materials, and climate change studies. His research interests include: Sediment transport in coastal and estuarine systems Detrital records of glaciation and sea-level change Fingerprinting sand sources for traceable construction material sourcing Machine learning applications in geological image analysis Tectonic controls on deep-water sediment dispersal His recent publications highlight a strong trend in integrating traditional geological field and laboratory methods with data science and machine learning, particularly through the development of the sandID project, which aims to create a global database for sand provenance using image-based classification. This work bridges academic research with practical applications in sustainability and resource management. Scientific contributions and projects: sandID : A machine learning–driven tool for identifying sand composition and provenance via image analysis Citizen science initiative for global sand sample imaging Collaborative research on sediment supply networks and environmental change Dr. Sickmann actively seeks contributions from citizen scientists and researchers to expand the sandID dataset. He is involved in advising and mentoring through research participation, though no formal students are listed. His work has implications for sustainable construction, coastal management, and paleoenvironmental reconstruction.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +7 more

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

• Machine Learning
• Case-Based Reasoning
• Natural Language Processing
• +3 more

Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .

Gyunam ParkView profile

Researcher

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +12 more

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Mirel CosulschiView profile

Associate Professor

• Algorithms and Complexity
• Software Engineering
• Web Data Management
• +13 more

Mirel Cosulschi is an Associate Professor in the Department of Computer Science at the University of Craiova, Romania, where he has been a faculty member since 1997. He is affiliated with the Faculty of Automation, Computers and Electronics and teaches courses in Graph Algorithms, Software Engineering, Web Data Management, and Fundamentals of Programming at both undergraduate and master's levels. PhD in Computer Science, University of Craiova, 2006 MSc in Artificial Intelligence, University of Craiova, 1998 BSc in Computer Science, University of Craiova, 1997 His research interests include algorithms and complexity, software engineering, web data management, semantic web, distributed programming, and cloud computing. He has supervised numerous BSc and Master’s theses focusing on web applications, mobile development, data processing, and software modeling. His teaching emphasizes algorithmic thinking, structured programming, and modern software development practices. While no formal scientific publications are listed, his academic contributions include authored textbooks such as Algoritmica grafurilor si aplicatii (2014) and Practica programarii in C (2014), indicating active engagement in curriculum development and educational materials. He is involved with the MSDN Academic Alliance and utilizes tools like Windows Installer MSI and Advanced Web Ranking. Mirel Cosulschi has advised over 100 students in thesis projects related to software development, data management, and web technologies. Although no external grants or awards are mentioned, his long-standing academic career and consistent supervision reflect sustained scholarly activity. He maintains a professional web presence at http://inf.ucv.ro/~mirel and can be contacted via email at mirelc@central.ucv.ro . He is currently active in teaching and academic supervision, with no indication of retirement or part-time status.

Daniel N JacksonView profile

Professor

• Software Design and Specification
• Design Methods
• Tools and Analysis
• +9 more

Prof. Daniel N. Jackson is a Professor of Electrical Engineering and Computer Science at MIT, serving as Associate Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Director of the Middle East Education Through Technology (MISTI MIT-MEET) initiative. His research focuses on software dependability, formal methods, and design analysis, particularly through the Alloy framework. Jackson emphasizes lightweight formal methods to reduce development costs and enhance software safety, with applications in cybersecurity, autonomous systems, and safety-critical software. His work includes developing tools like Bluefish for declarative diagram composition and Riffle for reactive systems. He explores ethical software design frameworks to address dark patterns and advocates for concept-centric development. Jackson's contributions span academic publications, educational initiatives, and industrial collaborations, aiming to bridge formal methods with practical software engineering challenges. Recent projects involve certified control systems for autonomous vehicles, end-to-end dependability cases, and model checking for security flaws. His research integrates interdisciplinary approaches, combining programming languages, static analysis, and human-centered design principles.

• Information Retrieval
• Digital Libraries
• Informetrics
• +5 more

Philipp Mayr-Schlegel is a Professor at the University of Göttingen’s Institute of Computer Science, leading the team 'Information & Data Retrieval' at GESIS - Leibniz-Institute for the Social Sciences. His research focuses on interactive information retrieval systems, scholarly recommendation mechanisms, and the integration of bibliometric methods with digital library technologies. University of Göttingen - Institute of Computer Science GESIS - Leibniz-Institute for the Social Sciences His work spans information retrieval , digital libraries , and applied informetrics , with particular emphasis on: Interactive search systems Non-textual ranking algorithms Scholarly document processing Knowledge representation Semantic search technologies User behavior analysis Recent research outputs (2025) include studies on LLMs for scholarly search, bibliometric reproducibility tools, scientific uncertainty annotation, and preprint adoption disparities. He has published extensively in Scientometrics , International Journal on Digital Libraries , and top conference proceedings like ACL and ECIR . As organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing (SDP) series, he has shaped academic discourse through editorial roles at ISSI , JCDL , and SocInfo conferences. His projects have attracted significant national and European funding.

• Cloud Computing
• Cybersecurity
• Software Engineering
• +6 more

Per Lauvås is an Associate Professor at the Department of Computer Science, Faculty of Technology, Art and Design, Oslo Metropolitan University. His research and teaching focus on IT education, software engineering, and e-learning technologies. Key research areas include Software testing pedagogy Gamification in data modeling education Interactive web development tools Cloud computing and cybersecurity Assessment innovations in database courses Work-integrated learning for IT students Recent publications explore AI chatbots in education, employer prioritization of IT graduates, and gamified learning tools. His work emphasizes student-centered approaches and practical skill development. Contact: pelau8806@oslomet.no

• Data Mining
• Database Systems
• Spatial Databases
• +5 more

Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.

• 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.