Elissaios Sarmas is a researcher in the field of AI and machine learning applications for energy systems and smart cities. He has collaborated extensively with researchers like Vangelis Marinakis, Haris Ch. Doukas, and Ioannis Papias across institutions. Research Interests: AI in Energy Sector Smart Grid Analytics Data-Driven Decision Making Demand Response Programs Energy Poverty Mitigation Climate Change Adaptation Publication Trends: His recent work focuses on ensemble AI models for energy measurement, clustering methodologies for electricity loads, and large language models in energy digital twins, covering 2023-2025. Articles span journals like IEEE Access , Applied Soft Computing , and Information Sciences .
Somayeh Sojoudi is an Associate Professor in the Departments of Electrical Engineering & Computer Sciences and Mechanical Engineering at UC Berkeley, affiliated with the Berkeley Institute for Data Science (BIDS). Her research focuses on Artificial Intelligence, Control Systems, Optimization Theory, and Power and Energy systems. She teaches courses like EECS 127 and EECS 227AT on optimization models in engineering. Education: PhD in Control & Dynamical Systems from the California Institute of Technology (2013). Research Interests : AI and Machine Learning, particularly neural network robustness and generative models Optimization methods for non-convex and low-rank problems Control systems, power grids, and distributed energy resources Game-theoretic approaches in dynamic systems Awards : NSF CAREER Award (2021) ONR Young Investigator Award (2021) INFORMS Optimization Society Prize for Young Researchers (2015) IEEE PES Best-of-the-Best Conference Paper Award (2022) Her work bridges theoretical advancements in optimization with practical challenges in energy systems and AI, emphasizing robustness and safety-critical applications.
Thanassis Tiropanis is a Professor at the University of Southampton's School of Electronics and Computer Science (ECS), specifically within the Web and Internet Science Group. He serves as enterprise champion for ECS and impact champion for computer science. His academic affiliations extend to the Institute for Life Sciences and the Southampton Marine and Maritime Institute, reflecting his interdisciplinary research approach. Professor Tiropanis specializes in decentralised information systems and infrastructures, with research interests spanning decentralised information retrieval, data and web observatories, bias in datasets, inaccessible algorithms, and IoT analytics. His work focuses on the technical and socio-technical aspects of the Web and Internet as evolving socio-technical artifacts, particularly examining how individuals can maintain control over their personal data through decentralised architectures. His recent publications reveal a strong research trajectory in decentralised search technologies, particularly through the ESPRESSO framework for personal online datastores. The publications demonstrate expertise in federated learning, privacy-preserving techniques, and bias detection in machine learning systems. His work bridges theoretical computer science with practical applications in health data, cybersecurity, and social linked data environments. Fellow of BCS Chartered IT professional with BCS Senior member of IEEE Fellow of the Higher Education academy in the UK Member of ACM Member of the Technical Chamber of Greece Professor Tiropanis currently supervises PhD students including Engin Kocak and Wenxuan Huang, while leading active research projects such as ESPRESSO (EPSRC-funded) focused on efficient search over personal repositories. His completed projects include significant EU-funded initiatives like NoBIAS (Artificial Intelligence without Bias) and IoT Lab. He is affiliated with multiple research centers including the Centre for Internet of Things and Pervasive Systems, Centre for Machine Intelligence, and Centre for Digital Finance.
Renaud Vilmart is a researcher at LMF (Laboratoire Méthodes Formelles), part of Inria Saclay, affiliated with Université Paris-Saclay, CNRS, and ENS Paris-Saclay. He holds an Inria Starting Faculty Position (ISFP), placing him in a research-intensive faculty role. He is actively involved in the scientific committee of Inria Saclay and co-supervises the Groupe de Travail Informatique Quantique (GTIQ) under the GdR-IFM. His research focuses on quantum computing, particularly on the ZX-Calculus —a graphical language rooted in category theory that enables visual reasoning about quantum processes. This formalism unifies quantum circuits and measurement-based models, offering intuitive tools for verification and optimization. His work addresses foundational questions such as the completeness of the ZX-Calculus with respect to quantum mechanics. The 15 most recent articles reflect a strong trend in formal methods for quantum computing , with emphasis on diagrammatic reasoning, categorical semantics, and completeness proofs. Key topics include stabilizer theory, Clifford+T circuits, fermionic circuits, and scalable extensions of the ZX-Calculus. These publications demonstrate a deep integration of logic, algebra, and quantum theory. His scientific achievements have been recognized with: Kleene Award for best student paper at LiCS (Logics in Computer Science) Accessit (honorable mention) for the Gilles Kahn Award from the Société Informatique de France He is actively involved in mentoring and academic leadership through co-supervising GTIQ and serving on the Inria Saclay scientific committee. Though no specific grants are listed, his ISFP position is typically grant-funded, indicating sustained research support. He contributes significantly to education through teaching in the QDCS and QMI master’s programs and the ARTeQ year, focusing on advanced complexity and quantum computing topics. His research is conducted within the LMF (Laboratoire Méthodes Formelles), a collaborative lab between Inria, CNRS, and ENS Paris-Saclay, which fosters interdisciplinary work in formal methods and theoretical computer science.
Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.
Ashwin Nayak is a Professor at the University of Waterloo, affiliated with the Department of Combinatorics and Optimization and the Institute for Quantum Computing (IQC). He holds a B.Tech. from IIT Kanpur (1995) and a Ph.D. from UC Berkeley (1999). Before joining Waterloo in 2002, he held postdoctoral positions at DIMACS, AT&T Labs, and Caltech. He was also an associate faculty member at the Perimeter Institute for Theoretical Physics from 2003 to 2011. His research focuses on quantum information and computation, including quantum algorithms, complexity theory, and communication protocols. He has made significant contributions to topics such as quantum walks, quantum error correction, and the theory of quantum communication complexity. His work bridges foundational aspects of quantum mechanics with computational challenges, emphasizing interdisciplinary connections to theoretical computer science. Nayak has advised numerous graduate students and has taught courses ranging from introductory optimization to advanced quantum information theory. His current supervision includes Ph.D. student Pulkit Sinha and M.Math. student Nicholas Allen. Past students have pursued careers in academia and industry, contributing to fields like quantum algorithms and cryptography. He is actively involved in academic activities, including organizing lectures and conferences on quantum information. His lab at IQC collaborates on cutting-edge projects in quantum computing and information theory.
Arka Ghosh is a doctoral student and knowledge engineer at Umeå University , Department of Computing Science. He is affiliated with the AI for Data Management research group led by Prof. Dr. Diego Calvanese. Research focus: Virtual Knowledge Graphs (VKGs), heterogeneous data integration, and management of geospatial/raster data Email: arka.ghosh@umu.se Research Areas: Ontology-based data access (OBDA), semantic querying with SPARQL and NLP, Description Logic formalisms, raster/vector data processing, and temporal-spatial data modeling. Publications: Recent work presented at RuleML+RR 2024 and 2023 conferences includes extending VKG systems to handle large-scale satellite raster data and reformulating queries across heterogeneous data sources.
Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.
Ana Edelmira Pasarella Sanchez is a Professor in the Department of Computer Sciences at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). She is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. Her work bridges theoretical computer science with practical data systems. PhD in Computer Science, Universitat Politècnica de Catalunya Her research centers on logic programming, knowledge representation, and graph databases. She explores how formal methods can enhance data processing, particularly through dynamic pipelines and trust-aware access control. Her work integrates Datalog, semantic reasoning, and big data frameworks to improve scalability and correctness in knowledge systems. She has contributed to foundational semantics of logic programs and their applications in security and data integration. Her recent publications highlight a trend toward efficient, adaptive data processing systems, especially for graph analytics and knowledge graphs. She compares paradigms like MapReduce and pipelining, advocating for dynamic, functional approaches to big data. Her work increasingly addresses real-world challenges in federated knowledge graphs and access control. SACMAT 2017 Best Paper Award Pasarella has been involved in multiple competitive R&D+i projects, such as 'Modelos y Técnicas para el Procesamiento de Información a Gran Escala' and 'Modelos y métodos basados en grafos para la computación en gran escala,' indicating sustained funding and collaborative leadership. She advises on research direction within her group and mentors through collaborative publications. She has served on the scientific committee of the Latin American Informatics Conference (CLEI), contributing to the broader academic community. She is part of the ALBCOM research group and collaborates extensively with researchers like Fernando Orejas, Maria-Esther Vidal, and Elvira Pino, working on logic-based frameworks for data and security systems.
Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs
Adam Perer is an Associate Professor at Carnegie Mellon University, where he is a member of the Human-Computer Interaction Institute within the School of Computer Science. He serves as Co-Director of the Data Interaction Group and holds leadership positions as Area Papers Chair at IEEE VIS and Visualization Subcommittee Papers Chair at ACM CHI. Previously, he worked as a Research Scientist at IBM Research. Ph.D. in Computer Science from the University of Maryland, College Park Perer's research integrates data visualization and machine learning techniques to create visual interactive systems that help users make sense of big data. His work focuses on human-centered data science, extracting insights from clinical data to support data-driven medicine, and facilitating human-AI collaboration. He investigates how people engage with and make decisions using data, designing new interfaces to interact with complex information while assisting impactful domains drowning in data. His recent publications reveal a strong trend toward healthcare applications of AI and visualization, particularly in clinical decision support and overdose prevention. There's also a significant focus on explainable AI (XAI), with multiple papers examining how imperfect explanations affect human-AI collaboration and decision-making in critical contexts like healthcare. His work consistently bridges visualization theory with practical applications in high-stakes domains. Best Paper Honorable Mention for 'Dead or Alive: Continuous Data Profiling for Interactive Data Science' (VIS 2023) Best Paper for 'Neo: Generalizing Confusion Matrix Visualization' (CHI 2022) Most Reproducible Paper Award for 'SQLShare' (SIGMOD 2016) Perer actively mentors students across all levels, with PhD students focusing on human-AI collaboration in healthcare settings, visualization techniques, and clinical decision support systems. His lab receives funding for projects related to human-centered AI, data visualization in healthcare, and explainable machine learning systems. The Data Interaction Group, which he co-directs, focuses on empowering everyone to analyze and communicate data through interactive systems. His research has been supported by collaborations with medical institutions and appears in premier venues for visualization, human-computer interaction, and medical informatics. Current projects include Eye into AI (improving XAI interpretability), Predicting and Visualizing Overdose Risk, and AI applications in intensive care units.
Zhenjie Zhang is a Professor in the Department of Computer Science at East China Normal University's School of Computer Science and Software Engineering, with a distinguished research career spanning nearly two decades. His work bridges theoretical computer science with practical industrial applications, maintaining strong international collaborations with researchers from TU Wien, National University of Singapore, and industry partners including ByteDance. Dr. Zhang's research focuses on the intersection of database systems, machine learning, and industrial applications. His early work centered on database privacy and query processing, evolving toward causal inference, fault diagnosis systems, and industrial AI applications. His recent publications demonstrate a strategic shift toward solving real-world engineering problems using advanced machine learning techniques, particularly in manufacturing, transportation, and cloud systems. The consistent publication trajectory across top venues like IEEE TKDE, VLDB, and ACM Transactions shows sustained research excellence and adaptability to emerging technical challenges. His publication record reveals significant contributions to causal inference methods, evidenced by multiple papers on causal discovery and transfer learning. The research demonstrates practical impact through industrial collaborations, particularly in fault diagnosis systems for mechanical equipment and adaptive control for unmanned vehicles. The recent work shows increasing focus on deploying AI models efficiently in resource-constrained environments, reflecting awareness of practical implementation challenges. Dr. Zhang has mentored numerous junior researchers who have become active contributors in the field, including Ruichu Cai and Zining Zhang. His collaborative network spans multiple continents, indicating strong research leadership and international recognition. The consistent flow of publications in top venues suggests successful grant funding and research group management, though specific grant details aren't provided in the source material.
Muhammad El-Hindi is a researcher at the Technical University of Darmstadt , focusing on database systems , blockchain technology , and secure data management . His work bridges theoretical innovation with practical applications in cloud computing, trusted execution environments, and decentralized systems.
Prateek Jain is a Lecturer at the School of Information, San José State University, where he teaches courses in Big Data Analytics and Python programming. He holds a Ph.D. in Computer Science and Engineering from Wright State University and has over a decade of industrial experience in data science and artificial intelligence at leading institutions such as IBM T.J. Watson Research Center, Nuance Research Labs, and Liveperson. His research interests include Knowledge Graphs, Natural Language Processing, Machine Learning, and Big Data, with over 30 peer-reviewed publications and patents in these areas. His academic and professional background bridges advanced research and practical deployment of AI-driven systems. Ph.D. (Computer Science and Engineering), Wright State University (2007–2012) Ph.D. (Computer Science), University of Georgia (2006–2007, transferred) BS (Information and Communication Technology), Dhirubhai Ambani Institute of ICT (2002–2006) Prateek's research publications focus on the development and application of Knowledge Graphs, particularly in areas like NLP, enterprise search, healthcare, and financial services. His work emphasizes scalable, explainable, and practical AI solutions, with recurring themes in graph embeddings, semantic reasoning, and large-scale data integration. He has served as an Adjunct Instructor at Chabot College and Northwestern Polytechnic University, and currently contributes to academic instruction at SJSU while maintaining an active role in industry as Principal Data Scientist at Liveperson. His industrial roles have spanned: Principal Data Scientist, Knowledge Graphs – Liveperson Inc. (2021–Present) Engineering Manager, Machine Learning – AppZen Inc. (2018–2021) Principal Research Engineer – Nuance Inc. (2016–2018) VP/Data Scientist – BlackRock Inc. (2015–2016) Data Scientist – Ignition One (2015) Research Staff Member – IBM T.J. Watson Research Center (2012–2015) There are no listed scientific awards or advisees in the provided information.
Alexander Brodsky is a Professor in the Department of Computer Science at George Mason University's Volgenau School of Engineering, where he has conducted research since 1993. His work focuses on Decision Support, Guidance, and Optimization (DSGO) systems with applications spanning sustainable manufacturing, energy systems, power networks, and supply chain management. He teaches core database courses and specialized decision-guidance systems at both graduate and undergraduate levels. Brodsky earned his educational credentials from The Hebrew University of Jerusalem, completing a BS and MS in Mathematics and Computer Science followed by a PhD in Computer Science. His academic foundation supports his interdisciplinary research bridging theoretical computer science with practical industrial applications. Current research centers on developing DSGO frameworks for real-world optimization challenges, particularly in sustainable manufacturing and energy infrastructure. His work integrates multi-objective optimization, stochastic modeling, and decision-guidance query languages to create systems that balance competing objectives like cost, efficiency, and environmental impact. Prior research established foundational contributions to constraint databases and secure information disclosure mechanisms. Analysis of his 2022-2025 publications reveals a decisive shift toward virtual manufacturing ecosystems, carbon-neutral investment modeling, and healthcare decision support. Key trends include the development of reusable model repositories for virtual things, Pareto-optimal solutions for energy infrastructure, and interactive recommender systems that incorporate user preference learning and voting mechanisms for group decision-making. Scientific recognition includes: NSF CAREER Award NSF Research Initiation Award Six Best Paper Awards across major conferences Brodsky has mentored 15 PhD graduates (as of 2016) and currently supervises four doctoral candidates, with former students holding professorships at institutions worldwide and research positions at organizations like NIST and Mozilla. His research program is sustained by substantial funding from NSF, ONR, NASA, NIST, and Dominion Virginia Power, supporting projects like the Process Analytics Language for Sustainable Manufacturing and Decision Guidance for Power Optimization. Though not formally named in the source, his research group operates as a hub for developing decision-guidance frameworks and reusable analytics models, collaborating with industry partners on smart manufacturing and energy infrastructure projects while maintaining strong ties to international conferences like ICEIS and ICTAI.
