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Viktoria SpaiserView profile

Professor

• Climate Politics
• Sustainability Research
• Computational Social Science
• +5 more

Professor Viktoria Spaiser is a Professor of Climate Politics and Computational Social Science at the University of Leeds' School of Politics and International Studies (POLIS), holding this position since March 2025. Previously, she served as an Associate Professor (2020–2025) and a University Academic Fellow (2015–2020). She is affiliated with the Priestley Centre for Climate Futures and the Leeds Institute for Data Analytics (LIDA). Her research focuses on societal transitions to sustainability, climate governance, and computational social science methods. Education: PhD in Sociology (Bielefeld University, 2012), MA in Conflict, Security and Development (King’s College London, 2008), German Diploma in Computer Science (University of Applied Sciences Trier, 2013). Postdoctoral research included roles at ETH Zurich, the Institute for Futures Studies Stockholm, and Uppsala University. Research interests emphasize rapid, fair transitions to zero-emissions societies, leveraging mathematical and computational tools. Current projects include a UKRI Future Leaders Fellowship on 'Understanding normative change to address the climate emergency.' She explores topics like climate narratives, behavioral change, tipping points governance, and AI applications in social science. Methodological expertise spans agent-based modeling, big data analysis, and Bayesian statistics. Teaching focuses on quantitative and computational methods, including modules on programming for social science, statistical analysis, and climate justice. She supervises PhD students in climate politics, AI ethics, and advanced quantitative methods. Her work bridges academia and policy, addressing global challenges such as disinformation, democratic engagement, and socio-ecological system dynamics. Recent projects include studies on climate protests' impact on policy, smartphone-based behavioral interventions, and EU governance of tipping points.

• Computational Chemistry
• Computer-Aided Drug Design
• Cheminformatics
• +12 more

Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.

Erik QuaeghebeurView profile

Assistant Professor

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +4 more

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Eric F. LockView profile

Associate Professor

• Statistical genomics
• High-dimensional data analysis
• Machine learning
• +7 more

Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.

Patrick JailletView profile

Professor

• Online Optimization
• Machine Learning
• Sequential Decision Making Under Uncertainty
• +4 more

Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.

Neil LawrenceView profile

Professor

• Machine Learning
• Artificial Intelligence
• Uncertainty Quantification
• +3 more

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.

Jonathan L. AuerbachView profile

Assistant Professor

• Data Science
• Causal Inference
• Survey Methods
• +6 more

Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .

Robert MoserView profile

Professor

• Turbulence physics
• Direct numerical simulation and spectral methods
• Large-eddy simulation
• +2 more

Dr. Robert D. Moser is a Professor at the University of Texas at Austin and holds the W.A. "Tex" Moncrief, Jr. Chair in Computational Engineering and Sciences I. He is affiliated with the Thermal and Fluid Systems program, the Institute for Computational Engineering and Sciences (ICES), and serves as Director of the DOE-funded Center for Predictive Engineering and Computational Sciences (PECOS). Ph.D. in Mechanical Engineering from Stanford University (1984) His research focuses on computational methods for turbulence modeling, cardiovascular fluid mechanics, and uncertainty quantification in complex physical simulations. He develops large-eddy simulation techniques for aerospace applications and biological flow analysis, while pioneering methods to characterize uncertainties in reentry vehicle simulations and turbulence modeling. Dr. Moser leads interdisciplinary research at PECOS and ICES, combining computational engineering with biomedical applications. His work spans theoretical turbulence physics, numerical methods for Navier-Stokes equations, and practical implementations for aerodynamic and medical device design.

Andreas NieklerView profile

Researcher

• Natural Language Processing
• Computational Social Science
• Digital Humanities
• +7 more

Andreas Niekler is a research associate and lecturer in Computational Humanities at the Institute of Computer Science, Leipzig University, Faculty of Mathematics and Computer Science. He develops computational methods for semantic language analysis and applies them to computational social science and humanities research, with focus on machine learning and data management methodologies. His research interests span Natural Language Processing , Computational Social Science , Digital Humanities , Text Mining , Machine Learning , and Conversational AI . Niekler specializes in developing algorithms including Bayesian Models, Topic Models, Deep Learning, and Support Vector Machines for text analysis applications. He is actively involved in the development of the interactive Leipzig Corpus Miner platform and researches how semantic representations can be applied to literary studies and scientometrics. Niekler's recent publications demonstrate expertise across computational linguistics, social science methodology, and practical applications of text mining. His work shows strong interdisciplinary connections between computer science, linguistics, and social sciences, with particular emphasis on developing robust methodologies for automated content analysis. As an educator, Niekler has extensive teaching experience in computational methods for humanities and social sciences, offering courses on text mining, computational linguistics, and programming in R and Python. He serves as a scientific staff member in the Computational Humanities research group led by Prof. Dr. Manuel Burghardt and represents scientific staff in the Faculty Council of Mathematics and Computer Science at Leipzig University.

• AGN
• Galaxy surveys
• Radio interferometry
• +4 more

Professor Leah Morabito is a Professor (Research) - UKRI Future Leaders Fellow at Durham University, affiliated with the Department of Physics and the Institute for Computational Cosmology. She specializes in high-resolution imaging at low frequencies using the LOFAR telescope to study how supermassive black holes co-evolve with their host galaxies. As leader of the LOFAR Imaging of Resolved AGN (LIRA) group, she has made significant contributions to our understanding of active galactic nuclei and galaxy evolution through numerous high-impact publications. Professor Morabito's research primarily focuses on AGN physics, galaxy surveys, and radio interferometry. She has pioneered techniques for sub-arcsecond imaging at low radio frequencies, which has opened new windows for studying radio jets, galaxy evolution, and the interstellar medium. Her work reveals critical insights about AGN feedback mechanisms and the connection between supermassive black holes and their host galaxies across cosmic time. The analysis of her recent publications shows a strong emphasis on utilizing LOFAR's unique capabilities to study radio sources with unprecedented resolution at low frequencies. Scientific Recognition: UKRI Future Leaders Fellowship Professor Morabito actively mentors the next generation of astronomers, currently supervising PhD students Benite Tantely, Ciera Sargent, and Emmy Escott. Her research is supported by significant funding through her UKRI Future Leaders Fellowship and her role as co-Principal Investigator of the new LOFAR2.0 Large Programme, which extends her work on high-resolution low-frequency radio surveys. She has secured substantial research funding that enables cutting-edge observations and supports her research team. As leader of the LOFAR Imaging of Resolved AGN (LIRA) group, Professor Morabito oversees a collaborative research effort focused on advancing our understanding of how AGN help shape galaxy evolution. Her team utilizes unique high-resolution, low-frequency observations to study radio jets, AGN feedback mechanisms, and the connection between supermassive black holes and their host galaxies, contributing significantly to one of the most fundamental questions in modern astrophysics.

• Machine Learning
• Statistical Methodology
• Computational Biology
• +5 more

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

Matt KoslovskyView profile

Assistant Professor

• Bayesian modeling
• Variable selection
• Graphical models
• +10 more

Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)

Giorgia RamponiView profile

Assistant Professor

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Eythan LevyView profile

Research Fellow

• Computational Archaeology
• Ancient Chronology
• Northwest Semitic Epigraphy
• +3 more

Eythan Levy serves as Senior Assistant in Digital Archaeology at the University of Zurich's Institute of Classical Archaeology within the Faculty of Arts and Social Sciences. Previously, he led an SNSF SPARK project at the University of Bern (2024) and conducted postdoctoral research on stamp seals from the Southern Levant (2022-2023). His research interests focus on computational approaches to archaeological problems, particularly: Computer applications and quantitative methods in archaeology Ancient chronology of the Iron Age Levant Northwest Semitic epigraphy and paleography Archaeology of the Southern Levant Ancient Egyptian archaeology and epigraphy His work bridges computer science and archaeology through innovative methodological frameworks. Levy's publication trends demonstrate consistent interdisciplinary output combining computational methods with archaeological analysis. Recent work focuses on chronological modeling tools, epigraphic analysis of Hebrew seals, multispectral imaging of ostraca, and computational approaches to ceramic typology. His research shows strong emphasis on developing formalized schemes for synchronizing archaeological data and creating specialized software solutions. Levy has developed several significant archaeological software tools : ChronoLog : For computer-assisted chronological modeling Scrypt : Web application for computer-assisted decipherment of ancient inscriptions TPQ Composer : For displaying stratigraphic termini post quem Artifacts Analyzer : For analyzing archaeological artifact datasets These tools represent his commitment to creating practical computational solutions for archaeological challenges. His academic background uniquely combines computer science and archaeology: PhD in Archaeology (Tel Aviv University, 2017-2021) PhD in Computer Science (Université Libre de Bruxelles, 2003-2009) Multiple MA degrees in Archaeology and Ancient Oriental Languages Teaching certificate for higher education This dual expertise enables his innovative approach to digital archaeology.

• Physical geography
• Environmental geoscience
• Glaciology
• +5 more

Summer Rupper is a Professor at the School of Environment, Society & Sustainability at the University of Utah, where she has held her position since July 2019. Her research focuses on understanding the interactions between climate, glaciers, and water resources, with particular emphasis on high mountain regions including High Mountain Asia, the Himalayas, and polar regions. She leads multiple research projects examining glacier dynamics, hydrological processes, and climate change impacts on water security for downstream populations. BS in Geology from Brigham Young University (2001) MS in Geology from University of Washington (2004) PhD in Earth and Space Sciences from University of Washington (2007) Professor Rupper's research spans physical geography, environmental geoscience, and climate change science, with specific expertise in glaciology, hydrology, and atmospheric sciences. Her work integrates field measurements, remote sensing, and numerical modeling to understand glacier dynamics, snow processes, and water resource availability in mountainous regions. She has particular expertise in High Mountain Asia, where glaciers provide critical water resources for over a billion people. Her research addresses fundamental questions about glacier response to climate change, hydrological partitioning, and the implications for water security in vulnerable regions. Her recent publications demonstrate a consistent focus on understanding glacier dynamics, hydrological processes, and climate interactions in mountainous regions. The work spans multiple methodologies including remote sensing analysis, numerical modeling, statistical approaches, and field-based measurements. Key themes include glacier melt contributions to river systems, precipitation patterns in complex terrain, snow density modeling, and the impacts of climate change on water resources in High Mountain Asia and polar regions. Her research often integrates multiple data sources and approaches to address complex questions about cryospheric processes and their societal implications. Superior Research Award (2024, CSBS, University of Utah) G.K. Gilbert Award for Excellence in Geomorphic Research (2022) Outstanding Utah Higher Education Science Teacher (2021) Top Researcher Award, Celebrate U showcase (2017) Antarctic Service Medal (2010, USAF) Professor Rupper actively mentors graduate students through thesis research courses at both the PhD and Master's levels, as well as individual projects. She has secured significant research funding from multiple federal agencies including NSF, NASA, and USAID, with current projects examining climatic controls on Antarctic ice sheets, glacier dynamics in High Mountain Asia, and historical glacier changes. Her collaborative work extends across international boundaries, working with scientists in Pakistan, Bhutan, and other regions to address shared water security challenges. She also engages in community outreach through workshops with school districts and science teacher associations to communicate climate science to broader audiences. Professor Rupper participates in multiple collaborative research teams including the NASA High Mountain Asia Team (HiMAT), where she contributes expertise in glacier dynamics and hydrology. She serves on several scientific committees including the NSF Ice Core Facility Sample Allocation Committee and the American Geophysical Union Cryosphere Section Fellows Committee. Her research often involves interdisciplinary teams combining expertise in glaciology, hydrology, remote sensing, and climate modeling to address complex questions about mountain water systems under changing climate conditions.