Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.
Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases
Debmalya Panigrahi is a Professor of Computer Science at Duke University and serves as Associate Chair in the Department of Computer Science since 2025. He received his Ph.D. in Theoretical Computer Science from the Massachusetts Institute of Technology (MIT), advised by David Karger, and also studied at the Indian Institute of Science and Jadavpur University. Ph.D., MIT, 2012 Indian Institute of Science Jadavpur University His research focuses on algorithms design and analysis, particularly in graph algorithms (minimum cuts, vertex connectivity, max-flows) and algorithms under uncertainty (online algorithms, learning-augmented frameworks). He also works on approximation algorithms, algorithmic game theory, and practical applications in advertising, AI, and network design. Recent publications address problems like network unreliability estimation, convex paging with fairness constraints, and hypergraph reliability. His work combines theoretical rigor with practical impact, including patents and prototypes. NSF CAREER Award He has received grants from the National Science Foundation (including multi-objective optimization projects), Google Inc., and the Indo-US Science and Technology Forum. He mentors graduate and undergraduate students, including current PhD candidates Ruoxu Cen and Anish Hebbar. Panigrahi is affiliated with Duke's theory group and collaborates with CS-econ, AI/ML, and database groups. He recently returned from a sabbatical at Berkeley (Simons Institute and UC Berkeley) and maintains strong ties with industry through roles at Google Research and Microsoft Research.
Jolanta Kolbuszewska is an Associate Professor in the Department of History of Historiography and Auxiliary Sciences of History at the Faculty of Philosophy and History, University of Lodz. Her research focuses on the history of science, social history, and historiography, with particular emphasis on 19th-century Polish history and cultural history. University: University of Lodz School: Faculty of Philosophy and History Department: History of Historiography and Auxiliary Sciences of History Her scientific profile includes 61 publications documented in institutional databases, reflecting expertise in fields such as the history of women, historiography of the PRL (Polish People's Republic), and biographical studies. Despite extensive contributions to academic output, no specific awards or grants are highlighted in the provided data.
Professor Simon F Deakin is a leading academic in Labour Law, Tort Law, and Corporate Governance at the University of Cambridge and holds the Professor of Law title. As Director of the Centre for Business Research (CBR) and a Fellow of Peterhouse , he bridges empirical legal studies with socio-economic analysis. His work spans comparative law, legal history, and the interplay between law and capitalism, with significant contributions to understanding labor regulation in global contexts. M.A., Ph.D. (Cambridge), Honorary Ph.D. (Louvain-la-Neuve) Director, Centre for Business Research Fellow, Peterhouse College His research program integrates law and economics , institutional theory , and computational legal studies , focusing on labor law evolution, corporate governance, and legal institutionalism. Key projects include computational analysis of historical poor law cases and cross-national studies of labor regulation. His empirical work uses the Cambridge Leximetric Database to quantify legal frameworks' economic impacts. Scientific recognition includes: Fellow of the British Academy (2005) Honorary Doctorate, Catholic University of Louvain (2012) ECGI and Allen & Overy Prizes for Corporate Governance As editor of the Industrial Law Journal and contributor to the Cambridge Journal of Economics , he shapes academic discourse. His advisory roles include Omron Visiting Fellow at Doshisha University (2004-2016) and Francqui Visiting Professor (2012-13). Research collaborations span Japan, China, and Europe, examining labor-market deregulation and legal origins theory.
Nathan Johnson is an Associate Professor at Arizona State University (ASU) within the Ira A. Fulton Schools of Engineering's Polytechnic School. He directs the Laboratory for Energy And Power Solutions and the ASU-Starbucks Center for the Future of People and the Planet, while serving as Assistant Director of Research for the Global Futures Laboratory. His work focuses on sustainable development through energy decarbonization, microgrid innovation, and public-private partnerships. Ph.D. Mechanical Engineering, Iowa State University (2012) M.S. International Development & Mechanical Engineering, Iowa State University (2008, 2005) B.S. Mechanical Engineering, Iowa State University (2004) Research interests span microgrid resilience , energy-water-food nexus , grid modernization , and resource circularity , with applications in defense energy security, global energy access, and climate adaptation. His recent publications examine cascading infrastructure failures, hydrogen's role in decarbonization, and AI-driven solar panel diagnostics, reflecting interdisciplinary work in energy economics , control systems , and climate-resilient infrastructure . Over $70 million in funding includes projects like: DoD-funded microgrid resilience programs ($1.05M, 2022) World Bank climate-adaptive energy solutions in West Africa ($39.95M, 2021) NSF grants for urban resilience modeling ($3.6M, 2019) As leader of ASU's LEAPS initiative, he develops deployable energy solutions for disaster relief and underserved communities, combining hardware innovation with workforce development programs for veterans and civilians.
Professor Richard Durbin (FRS) is a computational biologist at the Department of Genetics , University of Cambridge, and Associate Faculty member at the Wellcome Trust Sanger Institute . His work spans computational methods development, large-scale genomics projects, and evolutionary studies. Academic Affiliation: Professor of Genetics (University of Cambridge) Research Institute: Associate Faculty (Wellcome Sanger Institute) Key Projects: 1000 Genomes Project, UK10K Project, Gorilla Genome Sequencing Research Interests Durbin's group focuses on: Evolutionary Genomics: Human population history through modern and ancient DNA, Malawi cichlid fish speciation with adaptive introgression Computational Methods: Burrows-Wheeler transform algorithms (BWA), variant call format (VCF), variation graph mapping (vg package) Genome Assembly: Long-read sequencing techniques for high-contiguity reference genomes across vertebrates Scientific Contributions Co-author of Biological Sequence Analysis (HMM methods for gene finding) Co-developer of ACeDB software and founding contributor to WormBase, Pfam, TreeFam, Ensembl Scientific Awards Fellow of the Royal Society (FRS) - Recognized for outstanding contributions to computational biology
Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish
Giomara Lárraga Maldonado is a Postdoctoral Researcher at the Faculty of Information Technology within the University of Jyväskylä , Finland. She contributes to the Multiobjective Optimization Group and is affiliated with the Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) thematic research area. Research Focus: Interactive Multiobjective Optimization, Evolutionary Computation, Explainable AI Key Areas: Preference integration, Decomposition-based methods, Human-Computer Interaction for decision support Her recent work explores explainability frameworks (e.g., LIME integration), phase-specific algorithm configuration, and semantic distance studies for visualization. She collaborates with researchers like Kaisa Miettinen and Giovanni Misitano. She has contributed to conferences such as GECCO, PPSN, and AAMAS, with publications emphasizing open-access availability. The R-XIMO framework (2022) highlights her work on explainable systems.
Prof. Dr. Axel-Cyrille Ngonga Ngomo is a Professor at the University of Paderborn , affiliated with the Faculty of Electrical Engineering, Computer Science and Mathematics and the Institute of Computer Science . He leads the Data Science group at the Heinz Nixdorf Institute and is a member of the Sonderforschungsbereich Transregio 318 (Constructing Explainability). His roles include heading the Informatik Rechnerbetrieb (IRB) team. Research Focus : Knowledge graphs, semantic web technologies, explainable AI, and distributed systems. Selected Projects : SAIL (Sustainable Life Cycle of Intelligent Sociotechnical Systems), TRR 318 (Constructing Explainability), Colide (Co-training for Industrial Data), 3DFed (Dynamic Data Distribution), and SFB 901 (On-The-Fly Computing). Contact : Email axel.ngonga@uni-paderborn.de , Office F1.225 (Fürstenallee 11) and TP6.3.106 (Technologiepark 6), Paderborn. Teaching : Courses include Seminar on Recent Advances in Knowledge Graphs, Project Groups on SPARQL Query Processing, Large Language Model Training, Retrieval Augmented Generation, and Foundations of Knowledge Graphs.
Walter Jetz is a Professor of Ecology and Evolutionary Biology and the School of the Environment at Yale University, where he directs the Center for Biodiversity and Global Change. He chairs the E.O. Wilson Biodiversity Foundation and co-chairs the GEO BON Species Population Working Group. His work focuses on biodiversity science, conservation, and global change ecology. Education: D.Phil. in Zoology (University of Oxford, 2002), M.Sc. in Integrative Bioscience (Oxford, 1997) Research interests include macroecology, species distribution modeling, and conservation science across spatial scales. His group develops tools like Map of Life , Wildlife Insights , and Half-Earth Project to address biodiversity monitoring and area-based conservation. Current projects explore climate change impacts on tropical ecosystems, movement ecology, and machine learning applications in biodiversity science. Recent publications focus on niche scaling, climate change vulnerability, deep learning for species distribution, and mountain biodiversity monitoring. Awards include being an ISI Highly Cited Researcher since 2014. Over 30 former students hold faculty positions globally. The lab promotes diversity, equity, and inclusion in science and collaborates with NASA, Microsoft, and the Gordon and Betty Moore Foundation.
Ben Domingue is an Associate Professor at Stanford University's Graduate School of Education and, by courtesy, in the Department of Sociology. His research bridges psychometrics, quantitative methods, and interdisciplinary applications in education, psychology, and social sciences. He leads the development of the Item Response Warehouse, a data resource for psychometrics research, and explores how statistical tools can better measure complex educational and psychological outcomes like reading ability and treatment effects. PhD in Education from the University of Colorado at Boulder (2012). MA and BS in Mathematics from the University of Texas at Austin (2006, 2001). His work focuses on advancing psychometric methodologies, including response time analysis, item-level treatment effects, and predictive accuracy metrics (e.g., InterModel Vigorish). He investigates how genetic and environmental factors interact with educational outcomes and social mobility, using large-scale datasets like the Health and Retirement Study and Add Health. Recent articles emphasize AI-driven psychometric tools, cross-cultural validation of medical assessments, and equity in educational testing. 2024–2025: Associate Professor, Stanford GSE. 2015–2022: Assistant Professor, Stanford GSE. Affiliated: Stanford Center for Longevity, Bio-X, Population Health Sciences. Scientific awards include the Jacobs Foundation Research Fellowship (2022–2024) and AERA Open Outstanding Reviewer (2018, 2019). His advising roles span doctoral and master’s students, with a focus on psychometrics and social-genomic research.
Paul Leadley is a Professor at the University of Paris-Saclay, France, where he directs the Population and Community Ecology group within the Ecology, Society and Evolution Laboratory (IDEES). His research focuses on global change impacts on terrestrial ecosystems, biodiversity, and ecosystem functioning through field experiments and mathematical modeling. His educational background includes a B.S. in Science from Pennsylvania State University (1981), an M.S. in Botany from North Carolina State University (1985), and a Ph.D. in Ecology from San Diego State University and UC Davis (1993). Prior to his professorship, he worked as a research technician at San Diego State University, Smithsonian Environmental Research Center, and New Mexico State University, followed by post-doctoral research at the University of Basel. Leadley investigates climate change and rising CO2 impacts on plant diversity, biodiversity-ecosystem functioning relationships, and nutrient competition between plants and soil microorganisms. His experimental work centers on California and temperate grasslands, examining fire, temperature, CO2, nitrogen deposition, and precipitation interactions. He develops multi-scale models from rhizosphere nutrient fluxes to regional climate change projections, collaborating with institutions like INRA, Stanford University, and Northern Arizona University. His research integrates field experiments with mathematical modeling to quantify uncertainties in global change projections. His recent publications (2008-2012) reveal consistent themes: climate change impacts on biodiversity via species distribution modeling, interactive effects of multiple global change drivers on soil nitrogen cycling, and development of biodiversity scenarios for policy. Key methodological approaches include multi-model comparisons, experimental manipulations of grassland ecosystems, and trait-based biodiversity assessments. Scientific Awards: No specific awards listed in the provided text. Leadley has directed five Ph.D. students: Alexandra Gastine, Romain Barnard, Xavier Raynaud, Audrey Niboyet, and Sandrine Fontaine. He has secured major research grants including QDiv (770 k€, 2005-2009), SCION (470 k€, 2010-2012), and HumboldtCES (450 k€, 2010-2012). Current projects focus on biodiversity modeling (MOBILIS), biome boundary shifts, and climate change impacts on forests. He participates in international assessment processes including IPBES and IPCC. He leads the Population and Community Ecology team at IDEES Laboratory, comprising approximately 30 researchers, engineers, technicians, and graduate students. The team operates experimental sites in California grasslands and collaborates with national networks including FRB, AllEnvi, and GIS Climat, Environnement, Société. Current initiatives include eco-evolutionary approaches to climate change impacts and development of adaptive forest management strategies.
Julian Shun is an Associate Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Previously, he was a Miller Research Fellow at UC Berkeley and earned his Ph.D. from Carnegie Mellon University under Guy Blelloch. His research focuses on parallel and high-performance computing, with emphasis on graph analytics, spatial/graph clustering, and dynamic algorithms. He designs algorithms with theoretical guarantees and empirical efficiency, along with high-level programming frameworks to simplify parallel code development. His work spans cache-oblivious, external-memory, and streaming graph algorithms, addressing scalability and performance across diverse computational architectures. Julien's recent publications highlight advancements in parallel graph traversal, dynamic connectivity, and approximation algorithms for centrality metrics. His research also explores domain-specific languages like GraphIt for graph analytics and frameworks such as Julienne for work-efficient bucketing. Scientific Awards: Miller Research Fellow at UC Berkeley He has taught graduate-level courses at MIT, including 6.506 (Algorithm Engineering) and 6.886 (Graph Analytics), emphasizing theoretical foundations, experimental analysis, and open-ended research projects.
Michael J. Cafarella is an Associate Professor in the Computer Science and Engineering department at the University of Michigan . His research focuses on databases, information extraction, data integration, and data mining, with applications in economics, social media analysis, and combating human trafficking. He leads the Software Systems Lab and Michigan Database Group . Scientific Awards NSF CAREER award Sloan Research Fellowship (2016) 2018 VLDB Ten-Year Best Paper award Research Impact : Cafarella co-founded the Hadoop open-source project and Lattice Data (acquired by Apple). His work on DeepDive and DARPA MEMEX was featured on 60 Minutes and in Scientific American . Funding from The Census Bureau, DARPA, Google, NSF, Yahoo!, General Electric, and Dow.
