Lilyan Fulginiti is a Roy Frederick Professor of Agricultural Economics at the University of Nebraska-Lincoln. Her work focuses on agricultural productivity, climate change impacts, irrigation policies, and sustainable resource management, particularly in regions like the Ogallala Aquifer and South America. Research areas include agricultural economics, environmental economics, and climate policy. Her recent publications address gene-edited crops, carbon farming, and productivity disparities linked to trade and climate. Her scholarly output spans topics such as irrigation efficiency, biofuel economics, and conflict impacts on agriculture, reflecting a commitment to integrating economic theory with pressing environmental challenges. While no specific awards or student advisement details are listed, her extensive publication record underscores her contributions to advancing sustainable agricultural practices and policy analysis.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Jonathan Cullen is Professor of Sustainable Engineering at the University of Cambridge and President of Fitzwilliam College, specializing in resource efficiency and decarbonization through top-down analysis of industrial material and energy systems. His work bridges academic research with industry applications across energy-intensive sectors. Education: Bachelor's in Chemical and Process Engineering, University of Canterbury, New Zealand MPhil in Engineering for Sustainable Development, University of Cambridge PhD in Engineering Fundamentals of Energy Efficiency, University of Cambridge His research develops metrics for quantifying energy and material consequences of production systems, focusing on circular economy implementation, minimum energy requirements, and zero-carbon transition pathways. Key applications target cement, steel, plastics, and petrochemicals where he pioneers methods like exergetic analysis and material flow accounting to expose carbon lock-ins and circularity opportunities. Recent publications reveal three dominant trends: (1) Frameworks for theoretical minimum energy requirements across industrial processes, (2) Geopolitical analysis of critical mineral flows and ownership structures, and (3) Circular economy metrics for plastics and construction materials. These consistently employ system-scale modeling validated through industry partnerships. Research Funding: Lead: C-THRU ($4M, VKRF) - carbon clarity in petrochemical supply chains Co-I: UK FIRES (£5.2M, EPSRC) - industrial decarbonization program Co-I: CirPlas (£1.25M, UKRI) - plastic waste elimination 7+ projects (EPSRC, Innovate UK, Horizon 2020) Academic Leadership: Teaching: Energy Systems and Policy (MPhil in Energy Technologies) Undergraduate supervision in Materials/Mathematics Graduate Tutor at Fitzwilliam College IPCC AR6 Lead Author (Industry Chapter) He directs the Resource Efficiency Collective, which develops open-source tools like Mat-dp for material demand projections and Starter Data Kits for energy planning. Current work focuses on scaling circular business models for construction retrofitting and quantifying geopolitical risks in critical mineral supply chains.
Scott Fraundorf is an Associate Professor in the Department of Psychology at the University of Pittsburgh , where he leads the MAPLE (Memory And Psycholinguistics in Learning & Education) Lab . He combines cognitive science and data science to study human behavior prediction, educational program evaluation, and psycholinguistics . His research focuses on student learning and metacognition language processing and educational technology interventions statistical modeling using regression , machine learning , and mixed-effects models as well as open-source tool development for cognitive science. Key technical skills include Python , R , and SQL programming, with 3 patents for intelligent tutoring systems in English grammar. He has mentored over 70 graduate students and faculty in quantitative methods.
Thomas Streinz serves as Full-time Professor - Joint Chair at the European University Institute's Department of Law and Robert Schuman Centre for Advanced Studies since January 2025. His interdisciplinary work bridges law, technology studies, and global governance frameworks. His research focuses on digital governance and global law, particularly examining regulatory frameworks for data economies, digital infrastructures, and the interplay between European law and global tech regulation. Key projects investigate software regulation, AI/cloud computing governance, and infrastructure-as-regulation mechanisms. He integrates perspectives from science and technology studies, infrastructure studies, and critical data studies to analyze socio-techno-legal systems across public/private and jurisdictional boundaries. Professor Streinz previously served as Adjunct Professor of Law at New York University School of Law, where he directed the Guarini Global Law and Tech initiative and participated in MegaReg/InfraReg research projects through NYU's Institute for International Law and Justice. Supervises PhD candidates on global law and technology topics Active in International Thinking and Planetary Futures research cluster Administrative support provided by Alice Pineschi
Ben Martin is Professor of Science and Technology Policy Studies at the University of Sussex Business School, leading SPRU (Science Policy Research Unit). With 40+ years of research experience, he specializes in science policy, innovation studies, and research evaluation. His work spans foresight methodologies, university-industry collaboration, and research integrity. Recent publications explore evolutionary economics, academic publishing ethics, and research misconduct frameworks. Key contributions include developing techniques for evaluating scientific performance, analyzing technological spin-offs, and pioneering studies on foresight. He has supervised 30+ doctoral students and secured funding from EU, ESRC, and DFID for projects like NETGENESIS and PRIME.
Cengiz Zopluoglu is an Associate Professor in the Department of Special Education and Clinical Sciences at the University of Oregon's College of Education. His research focuses on quantitative methods in education, item response theory, computational psychometrics, and educational data science. He teaches advanced courses on psychometrics, statistical methodology, and data analysis using R. Education: PhD, 2013: University of Minnesota (Educational Psychology, Quantitative Methods) MA, 2009: University of Minnesota (Educational Psychology, Quantitative Methods) BA, 2005: Abant Izzet Baysal University (Mathematics Education, K-8) Research Interests: Zopluoglu's work emphasizes integrating machine learning and statistical models into educational measurement. He develops methods to detect test misconduct (e.g., item preknowledge) using response time and accuracy data, and explores automated scoring of open-ended responses using AI (e.g., transformers). His contributions include advancements in continuous response models, multidimensional IRT, and DETECT analysis for dimensionality assessment. Awards: 2023 Runner-up Prize in NAEP Math Automated Scoring Challenge (NCES) 2021 3rd Place in NIJ Recidivism Forecasting Challenge 2013 Graduate Student Research Award (University of Minnesota) Advising & Grants: Zopluoglu has advised on projects related to test security, automated scoring, and machine learning applications. His work often involves open-source tools like R and Stan, with a focus on reproducible research. Labs & Collaborations: He collaborates on initiatives like the Deterministic Gated Models for Test Security and the WrightRightNow automated scoring platform. His research leverages interdisciplinary approaches, blending psychometrics with computer science and data science.
Robert P. Anderson is a Professor of Biology in the Division of Science at City College of New York (CCNY), part of the City University of New York (CUNY) system. His research laboratory is located in Marshak Science Building (Room 810), with additional affiliation as a Research Associate at the American Museum of Natural History (AMNH) Mammalogy Department. As a Highly Cited Researcher (2019-2023) and AAAS Fellow (2023), he leads an interdisciplinary biogeography research program focused on modeling species niches and distributions. Dr. Anderson's research spans biodiversity modeling, biogeography, and ecology with specialization in mammals. His lab develops ecological modeling software widely applied in conservation biology, invasive species management, zoonotic disease studies, and climate change impact assessments. Key research themes include: Characterizing spatial configuration of environmental suitability for species Developing machine learning approaches (particularly Maxent) for species distribution modeling Studying climate change effects on biodiversity Conservation applications of biogeographic models Neotropical mammal systematics and ecology His work has resulted in significant software contributions including Wallace, ENMeval, and spThin, with recent publications emphasizing methodological improvements in species distribution modeling and conservation applications. The lab maintains active projects funded by NASA and the National Science Foundation, focusing on small mammals of North and South America. Scientific recognition includes: AAAS Fellow (2023) Web of Science Highly Cited Researcher (2019-2023) Blavatnik Science Scholar (New York Academy of Sciences) Most Downloaded Paper in Ecography (2023-2024) Most Cited Paper in Ecography (2023) Dr. Anderson mentors graduate students through the CUNY Graduate Center and CCNY Master's programs, with recent advisees receiving prestigious awards including the ASM Horner Award and NASA FINESST Fellowship. His lab trains students in environmental biology through interdisciplinary research combining fieldwork, morphology, climatology, remote sensing, physiology, and genetics. Current lab members include Andrew Gaier (NASA Fellow), Mariano Soley-Guardia, and Kass (lead author on highly cited Wallace v2 paper). The Anderson Lab operates from CCNY's Marshak Science Building as part of the university's biodiversity group studying ecology, evolution, and geography of life on Earth. The lab emphasizes software co-design between end-users and developers to enhance conservation utility, with recent work focusing on neighborhood approaches for range estimation and operationalizing expert knowledge in species assessments.
Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.
Gert Helgesson is a Professor of Medical Ethics at the Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, since 2015. He previously held an associate professorship (docent) at KI from 2008. His academic background includes a PhD in ethics from Uppsala University (2002), focusing on value assumptions in microeconomics. His research spans clinical and research ethics, emphasizing interdisciplinary collaboration. Key interests include authorship issues, patient-centered care, ethical dilemmas in psychiatry, and the ethics of compulsory care for patients with borderline personality disorder. He leads the Medical Ethics group at the Stockholm Centre for Healthcare Ethics (CHE), addressing topics like healthcare prioritization, patient rights, and research integrity. Education: PhD in Practical Philosophy (Ethics), Uppsala University (2002); Docent in Medical Ethics, Karolinska Institutet (2008). Research Interests: Medical ethics (clinical and research), authorship ethics, palliative care ethics, compulsory treatment ethics, and interdisciplinary ethics. Current projects include studying the 'best interest of the child' in social care decisions and analyzing non-standard plagiarism cases. Teaching: Research ethics for master’s and doctoral students, physician training in clinical ethics, and ethics for doctoral supervisors. Grants: Includes a Swedish Research Council grant (2024–2026) on child welfare decisions and a VINNOVA grant (2014–2018) on person-centered care in psychiatry and pediatrics. Labs/Teams: Co-founder of the Stockholm Centre for Healthcare Ethics (CHE), collaborating with KTH and Stockholm University. His group includes researchers like Niklas Juth, Manne Sjöstrand, and Antoinette Lundahl.
Gerard Pons-Moll is a Professor at the University of Tübingen, endowed by the Carl Zeiss Foundation, and heads the Emmy Noether independent research group 'Real Virtual Humans'. He is a core faculty member at the Tübingen AI Center, a senior researcher at the Max Planck Institute for Informatics (MPII), and faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the Saarland Informatics Campus. His research focuses on computer vision, graphics, and machine learning, particularly in creating virtual human models and analyzing human motion from video and sensor data. Education: PhD (with distinction) in 2014 from Leibniz University of Hannover, Master's in Telecommunications Engineering (Northeastern University, 2008), and B.S./M.Sc. in Telecommunications Engineering from the Technical University of Catalonia (2002–2008). Research Interests: 3D human modeling, pose estimation, human-object interaction, and applications in industry and research. His work emphasizes real-world applications like virtual avatars and motion capture systems. Awards: Emmy Noether Grant (2018), German Pattern Recognition Award (2019), Google Faculty Research Award (2019), and multiple best paper awards at top conferences (BMVC’13, Eurographics’17, 3DV'18, CVPR'20). Advising & Grants: Served as program chair of 3DV 2021, area chair for ECCV, CVPR, and IJCAI. Active in reviewing for DFG, ANR, and ISF. Supervises research in areas like neural rendering frameworks (Blendify) and synthetic data generation (STAGE). Labs/Teams: Leads the Emmy Noether group and collaborates with MPII, Tübingen AI Center, and IMPRS-IS on projects like XNect (real-time 3D motion capture) and Human 3Diffusion (avatar creation).
Jennifer Hicks is the Executive Director of the Wu Tsai Human Performance Alliance at Stanford University, focusing on collaborative research to advance understanding of human performance through biomechanical modeling and machine learning. She also serves as Director of Research for the NIH-funded Mobilize Center and Restore Center, integrating engineering tools into rehabilitation science. Her work emphasizes predictive modeling of surgical outcomes, mobile health data analysis, and exoskeleton design. Dr. Hicks leads software development for the OpenSim project, guiding its user-centric evolution and promoting open-source biomedical tools. Her research spans musculoskeletal dynamics, wearable technology, and clinical applications of AI. Key contributions include smartphone-based motion capture (OpenCap) and foundational datasets like AddBiomechanics. She co-develops training programs for interdisciplinary teams and advocates for large-scale health data utilization. Dr. Hicks' efforts bridge academia and industry, supporting translational research in neurorehabilitation, sports performance, and chronic disease management.
Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.
Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.
