Marten Wegkamp is a Professor of Mathematics and Professor of Statistics & Data Science at Cornell University, located in Ithaca, NY. He holds dual affiliations within the College of Arts and Sciences, contributing to both the Department of Mathematics and the Department of Statistics & Data Science. His research focuses on applied mathematics, probability, and statistics, with a strong emphasis on high-dimensional statistics, statistical learning theory, and empirical process theory. He has developed methodologies in latent factor regression, sparse topic models, and interpretable statistical frameworks. His work frequently addresses challenges in high-dimensional data analysis and machine learning. Education: PhD in Mathematics from Leiden University (1996). Research Interests: Wegkamp’s research spans mathematical statistics, empirical process theory, and the development of novel statistical learning techniques. He explores areas such as latent factor models, high-dimensional inference, and the theoretical foundations of machine learning algorithms. His contributions include advancements in prediction methods, discriminant analysis, and optimal estimation strategies for complex data structures. Publications: His recent work includes studies on latent factor regression, sparse topic models, and interdisciplinary applications in genomics and multi-omic data analysis. Key themes across his publications involve high-dimensional data analysis, latent structure discovery, and algorithmic optimization for statistical models. Professional Contributions: He is affiliated with the Statistical Learning and High Dimensional Inference Group at Cornell, and his research has led to software packages like STRS, LOVE, and LoveER, which implement his methodologies. He teaches advanced courses such as Statistical Learning Theory (MATH 7740) and supervises research projects.
Benjamin D. Wandelt is a Research Professor at Johns Hopkins University with joint appointments in the Department of Physics and Astronomy and the Department of Applied Mathematics and Statistics. As a cosmologist and data scientist, he studies fundamental physics through astronomical observations using AI/ML and statistical inference. His research connects theoretical astrophysics with large-scale astronomical data to explore the cosmological framework. Research Focuses: Cosmological parameter estimation from supernova and CMB data Dark energy constraints through large-scale structure Machine learning applications in astrophysical inference Statistical methods for cosmological data analysis Major Honors: Gruber Prize in Cosmology (2018) Friedrich Wilhelm Bessel Prize Fellow of the American Physical Society Fellow of the International Association of Astrostatisticians
Prof. Christoph Kinkeldey is a Lecturer at Hamburg University of Applied Sciences, affiliated with the Department of Information, Media and Communication within the Faculty of Design, Media and Information. He holds a doctorate in Geoinformatics from HafenCity University Hamburg (2015) and has conducted research globally, including at PennState University, University of Melbourne, and Inria. His work focuses on data visualization, visual analytics, and uncertainty visualization, emphasizing how visual tools aid decision-making in complex data environments. Education: PhD in Geoinformatics, HafenCity University Hamburg (2015) Research Interests: Interactive visual data analysis Uncertainty communication in visualizations Blockchain data exploration (e.g., Bitcoin network analysis) Evaluation of visualization techniques His research bridges cartography, computer science, and human-centered design to empower diverse stakeholders in understanding complex information. Publications: Recent work emphasizes uncertainty visualization for data analysts, machine learning interpretability, and blockchain analytics. Key contributions include the BitConduite tool for Bitcoin network analysis and participatory design methods for non-technical users. Awards: 2016 VAST Mini Challenge 2: Honorable Mention for Clear Analysis Strategy Advising & Collaboration: Currently on parental leave until August 2024, he collaborates with the gicentre (City, University of London) and Monash University’s Department of Human-Centered Computing. His research teams focus on interdisciplinary projects merging visualization theory with practical applications. Labs/Teams: Active in the gicentre’s visualization initiatives and Monash’s Human-Centered Computing group, contributing to open-source tools and international research networks.
Felix Schneider is a Researcher at the Chair of Structural Mechanics at Technical University of Munich since 2018. He holds an M.Sc. in Civil Engineering from TU Munich (2018) and completed a semester abroad at the Norwegian University of Science and Technology. His research focuses on uncertainty analysis in structural and acoustic models, Bayesian updating techniques, and stochastic reduction methods in the frequency domain. He has contributed to advancements in rational polynomial chaos expansions and sparse Bayesian learning for structural dynamics applications. Education: 2011-2018: Study of Civil Engineering at Technical University of Munich 2018: Master of Science (M.Sc.) in Civil Engineering 2016: Semester abroad at Norwegian University of Science and Technology 2009: Abitur at Humboldtgymnasium Solingen Research emphasizes computational methods for structural dynamics, including: Bayesian parameter estimation for linear systems Rational surrogate models for uncertainty quantification Frequency-domain stochastic reduction techniques Applications in seismic risk assessment and vibroacoustic simulations Key contributions include a Maurer Söhne Prize for his Master's thesis and a published MATLAB toolbox for rational polynomial chaos expansions on the LRZ Git repository. Teaching responsibilities include courses on random vibrations, structural dynamics, and continuum mechanics. He has participated in research stays at ETH Zurich (2023) and collaborates with international institutions on uncertainty quantification projects.
Dr Eleanor Graham is a Lecturer in Forensic Science at Northumbria University, specializing in DNA analysis and biomolecular applications. She holds a PhD in Genetics from the University of Leicester (2003) and has academic affiliations with the Forensic Science Society, the International Society of Forensic Genetics, and the British Association for Human Identification. Education: BSc in Biochemistry (UMIST), MSc in Biomolecular Archaeology (UMIST/University of Sheffield), PhD in Genetics (University of Leicester) Her research focuses on forensic genetics, DNA profiling, and post-mortem analysis. She has contributed to studies on DNA stabilization techniques, alternative funerary practices in Roman archaeology, and interdisciplinary forensic ethics. Her recent publications highlight collaborations across Europe in mRNA-based body fluid typing. Key scientific award: 2008 Da Vinci Health Technology Awards - Highly Commended for 'The University Biopsy Tool' Dr Graham supervises postgraduate research, including Blake Kesic's work on forensic profiling of smokeless powders. She previously served as a Post-Doctoral Research Associate at the University of Leicester under Professor Guy Rutty.
Olivier Hekster is a Professor in Ancient History at Radboud University Nijmegen, where he has been a faculty member since 2004. He serves as Chair of Ancient and Medieval History and is affiliated with the Radboud Institute for Culture and History. Hekster is an elected member of the Royal Netherlands Academy of Arts and Sciences (KNAW) since 2020 and has held prestigious fellowships including the Humboldt Fellowship (2012-2013) and WWU Fellowship at Münster (2020-2021). He previously served as Director of the Institute for Historical, Cultural and Literary Studies (2015-2019) and Head of the History Department (2010-2012). Hekster's educational background includes: PhD in Roman History (cum laude), Radboud University Nijmegen, 2002 MA in Roman History (with distinction), University of Nottingham, 1998 MA in Ancient History (cum laude), Radboud University Nijmegen, 1997 Erasmus student at Terza Università di Roma, 1995 Hekster's research focuses on Roman history, particularly the impact of the Roman empire in its wider world. His primary interests include the power and imagery of Roman emperors, dynastic power and self-representation, Roman emperorship, Late Antiquity, the city of Rome, Roman numismatics, and theater and gladiatorial games in antiquity. He approaches these topics through interdisciplinary methods, examining how traditions influence new systems of rule and how power is communicated, contested, and accepted in changing societies. His work bridges historical analysis with contemporary questions about leadership, tradition versus innovation, and ancient globalization. Analysis of Hekster's publications reveals a consistent focus on Roman imperial power structures, with particular attention to how emperors established legitimacy through tradition while navigating political change. His work spans from early imperial periods through Late Antiquity, with increasing attention to methodological questions about historical contingency and scale. The interdisciplinary nature of his research is evident in collaborations with computer scientists on facial recognition of emperors and with numismatists on coinage analysis. His scholarship demonstrates a progression from specific case studies of individual emperors toward broader theoretical frameworks for understanding imperial transformation. Hekster has received numerous prestigious awards and grants: 2017 Ammodo KNAW Award for Humanities 2016-2021 NWO VICI grant: 'Constraints and Tradition. Roman power in changing societies (50 BC – AD 565)' 2017 GRAVITATION grant for the program Anchoring Innovation 2012-2013 Alexander von Humboldt Fellowship 2009-2014 NWO Open competition Humanities grant 2003 Keetje Hodshon Prize for best historical dissertation Hekster has led significant research initiatives including the GRAVITATION program 'Anchoring Innovation' and the NWO VICI project 'Constraints and Tradition.' As chair of the international Impact of Empire network since 2006, he has fostered collaborative research across institutions. His grant success demonstrates the high regard for his methodological innovations in connecting ancient history with contemporary theoretical questions. While specific student advisees aren't listed in the provided materials, his leadership roles suggest extensive mentorship of junior scholars through the Impact of Empire network and departmental responsibilities. Hekster chairs the international Impact of Empire network, which connects scholars studying the Roman Empire's influence. He has also been instrumental in establishing interdisciplinary research frameworks through the 'Anchoring Innovation' program, which examines how societies integrate novelty while maintaining tradition. His work bridges traditional historical scholarship with digital humanities approaches, as evidenced by his collaboration on facial recognition methodologies for identifying Roman emperors.
Ravinder Bhavnani is a Professor of International Relations/Political Science at the Graduate Institute of International and Development Studies in Geneva, Switzerland. His research focuses on the micro-foundations of violence, utilizing agent-based computational modeling and empirical analysis to explore actor behavior, social mechanisms, and emergent structures. He holds affiliations with the Centre for Finance and Development and the Albert Hirschman Centre on Democracy. Educated at the University of Michigan, Ann Arbor (PhD), Bhavnani specializes in armed conflicts, terrorism, urban-rural dynamics, and regional expertise in Africa, the Middle East, and South Asia. Key themes in his work include surveillance effects on collective action, urban conflict morphology, and resilience to socio-economic crises. His research outputs span journals like American Journal of Political Science and Journal of Peace Research , with a focus on computational methods and policy-relevant insights. He has co-edited volumes on global conflict trends and serves on editorial boards such as the Journal of Conflict Resolution . Advising and grants include collaborations on projects analyzing protest dynamics, malnutrition resilience, and segregation effects. His work bridges theoretical frameworks with applied conflict analysis, emphasizing interdisciplinary approaches to peacebuilding and violence prevention.
Matthew Kopec is a Program Director and Lecturer at Harvard University's Embedded EthiCS program, which integrates ethics into computer science education. He holds an MA from Virginia Tech (supervised by Joe Pitt) and a PhD from the University of Wisconsin-Madison (supervised by Elliott Sober). Previously, he taught at Northeastern, Australian National University, Northwestern, and the University of Colorado-Boulder. His research focuses on the ethical implications of computing technologies, including content moderation on social media, algorithmic predictions' societal impacts, and tech ethics education efficacy. Education: PhD in Philosophy, University of Wisconsin-Madison MA in Philosophy, Virginia Tech Research Interests: Kopec explores normative dimensions of technology, such as misinformation governance, big tech regulation, and ethical design in computational systems. His work bridges philosophy and computer science, emphasizing practical applications of ethical frameworks in education and policy. Recent Research Trends: Kopec's publications (2021–2023) highlight empirical studies on ethics education in CS curricula, experimental approaches to moral psychology, and the governance of social media platforms. His work underscores interdisciplinary collaboration between ethics, technology, and policy. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No specific details on advisees or grants are provided here. Labs/Teams: Central to his role is the Embedded EthiCS program, which collaborates across Harvard's CS departments to embed ethical modules into technical coursework.
Nan Yang is a University Researcher in the Mathematics and Computer Science department at Eindhoven University of Technology, specializing in empirical software engineering with focus on embedded systems and log analysis methodologies. Her work bridges theoretical models with industrial software development practices through collaborations with leading technology companies. Her research expertise centers on Software Engineering , Embedded Systems , and Log Analysis , employing rigorous empirical methods including developer interviews and case studies. She investigates how execution logs are utilized in real-world embedded software engineering contexts, developing model-driven approaches for protocol inference and system maintenance. Her work reveals critical insights into developer workflows and industrial software practices. Analysis of her publication record (2018-2023) demonstrates a progressive research trajectory from foundational protocol inference techniques to comprehensive embedded systems engineering frameworks. Her work consistently integrates log analysis with model-driven approaches, yielding practical tools for software maintenance in resource-constrained environments. This research has significant implications for improving reliability in industrial embedded software development. Nan Yang has supervised academic work as evidenced by her documented supervised projects, contributing to the development of next-generation software engineering researchers.
Kyunghee Lee serves as an Assistant Professor of Information Systems at McGill University's Desautels Faculty of Management, teaching core analytics courses including Data Handling and Coding for Analytics (INSY 336) and Data Mining for Business Analytics (INSY 446). Previously, he held an Assistant Professor position at Wayne State University's Mike Ilitch School of Business. Dr. Lee earned all his academic credentials from the Korea Advanced Institute of Science and Technology (KAIST), completing a PhD and MS in Managerial Engineering with Information Systems specialization, and a BS in Electrical Engineering. His research program critically examines digital platform ecosystems through three interconnected lenses: the business value of IT in organizational contexts, IT-enabled business model innovation , and societal transformations driven by technology. He employs advanced computational methodologies to investigate how information systems reshape interorganizational relationships and strategic outcomes, with particular attention to platform-mediated economic activities. Analysis of Dr. Lee's recent publications reveals consistent focus on real-world business transformations, with studies spanning cross-border acquisition dynamics, urban transportation disruption by ride-hailing services, and social commerce mechanics. His work in premier journals like MIS Quarterly demonstrates rigorous empirical approaches to understanding how digital platforms create value and reshape industry structures across diverse economic sectors. While specific advising activities and grant funding details are not documented in available materials, Dr. Lee's research trajectory indicates active engagement with pressing business challenges through methodologically sophisticated investigations of information systems phenomena.
Gabriele Sicuro holds a Researcher position, with a focus on theoretical physics and statistical mechanics. He earned a Doctor of Science in Physics from Università di Pisa (2012–2015), a Master of Science from University of Salento (2011), and a Bachelor of Science from the same institution (2009). His research interests include high-dimensional systems, random graphs, and machine learning applications. Notable contributions address superstatistical features, dimer models, and hypergraph matching problems. He collaborates internationally, with work published in journals like Physical Review E and Journal of Statistical Mechanics . His publications explore topics such as regularization in high dimensions, graph alignment algorithms, and asymptotic analysis of Gaussian mixtures. With 220 citations, his work bridges theoretical frameworks and computational methods, contributing to advancements in statistical physics and data science.
Sergio López-Sancio is a psycholinguist specializing in human language processing and Natural Language Processing (NLP). He holds a PhD from the University of the Basque Country (UPV/EHU), an MA in Linguistics from the same institution, and a BA in English Philology from the University of Oviedo, where he earned the National Award for Excellence in Academic Performance and an Outstanding Degree Award for his honors thesis on the subjunctive mood in English and Spanish. His research focuses on real-time dependency processing, combining EEG and behavioral methods to study syntactic structures across languages. He has presented at prestigious conferences such as the Linguistic Society of America and AMLaP, addressing topics like dependency locality, island effects, and cross-linguistic variation in Basque, Spanish, and Italian. López-Sancio has contributed to research projects including OVO: Originating Variation from Order (MINECO-funded), The Bilingual Mind (Basque Government), and AThEME (EU-funded), totaling over €5M in funding. His current role at Amazon involves enhancing Alexa's Spanish language understanding through NLP tools. Key Awards: National Award for Excellence in Academic Performance, Outstanding Degree Award, Outstanding Cum Laude Dissertation Research Themes: Dependency Parsing, Locality Effects, Cognitive Modeling, Cross-Linguistic Syntax
Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.
Andrea Mocci is a Lecturer at the Faculty of Informatics of the Università della Svizzera italiana (USI). His work focuses on software engineering methodologies, developer productivity, and IDE interaction analysis. He is affiliated with the Software Institute and actively contributes to academic events such as the IEEE International Workshop on Mining and Analyzing Interaction Histories (MAINT). His research explores empirical software engineering techniques, including developer behavior analysis, code documentation improvement, and the application of natural language processing to software artifacts. Key areas of investigation include: IDE interaction and navigation efficiency Code redundancy and quality metrics Video tutorial analysis for educational content Runtime systems and annotation APIs Defect prediction and software maintenance Publications from 2016-2020 highlight trends in developer-centric tools, holistic recommender systems, and visualization techniques for software evolution. His work often bridges theoretical formal methods with practical developer workflows, aiming to improve both software quality and developer productivity through empirical studies and tool development.
Prof. Dr. Oliver Dickhäuser holds the Chair of Educational Psychology at the University of Mannheim within the Faculty of Social Sciences A . His research focuses on motivational and cognitive processes in learning and teaching, emphasizing the role of social norms, achievement goals, and psychological need satisfaction in educational contexts. He leads international studies on faculty well-being, error climates in mathematics education, and feedback mechanisms in e-learning environments. Key Research Areas: Motivational Psychology Self-Regulated Learning Teacher-Student Dynamics Quality Assurance in Education Projects: 12th Annual Conference of the Society for Empirical Educational Research (2025) CHEAT Project (2020–2024) on performance goals and academic dishonesty Formation and Impact of Expectations (funded by German Research Foundation) Contact: Room B312, A5,6 Tel.: +49 621 181-2208 Email: oliver.dickhaeuser@uni-mannheim.de Web: Chair Website Team: Dipl.-Psych. Joschi Kratzer (Lecturer) Stefan Janke (PhD) Marc Philipp Janson (PhD, Barbara Hopf Foundation awardee) Martin Daumiller (PD Dr.) Academic staff: Paula Schmelzer, Sophie von der Mülbe, Julia Hilpert Student assistants: Anna Boch, Lilly Bongartz, Marie Dimmer, Jule Helfer, Katharina Nepscha, Radina Slavova, Charly Weiß, Sarah Wörner
