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Thomas W. RepsView profile

Professor

• Program analysis
• Abstract interpretation
• Model checking
• +7 more

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair in the Computer Sciences Department of the University of Wisconsin-Madison, where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., which he co-founded in 1988 to commercialize research on programming environments. Reps received his Ph.D. in Computer Science from Cornell University in 1982, winning the ACM Doctoral Dissertation Award. His research spans program analysis, abstract interpretation, model checking, programming languages, and computer security. Reps's most influential work includes pioneering program slicing techniques with Susan Horwitz, development of the Cornell Program Synthesizer and Synthesizer Generator with Tim Teitelbaum, and significant contributions to interprocedural dataflow analysis. His recent work extends into quantum computing and advanced program synthesis techniques. The analysis of his publications reveals consistent focus on program analysis techniques, with recent papers exploring quantum circuit verification, abstract transformer synthesis, and program synthesis. His work bridges theoretical foundations with practical applications, often addressing scalability challenges in program analysis. NSF Presidential Young Investigator Award (1986) Packard Fellowship (1988) ACM Fellow (2005) ACM SIGPLAN Programming Languages Achievement Award (2017) Elected foreign member of Academia Europaea (2013) Multiple best paper awards including ETAPS (2004, 2008) Reps has advised numerous students, several of whom have received departmental and national recognition. His work on the Wisconsin Program-Slicing Tool led to commercial products through GrammaTech. He has served on advisory boards for significant projects including Year 2000 remediation for the Department of Defense and F/A-22 avionics software integration.

• Process Mining
• Data Privacy
• Event Log Analysis
• +3 more

Martin Kabierski serves as a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin, specializing in process mining and data privacy. His work bridges theoretical frameworks and practical applications in event log analysis and privacy preservation. Education: Master of Science (M.Sc.) Kabierski's research centers on advancing process mining methodologies through species estimation techniques for data quality assessment, privacy-preserving mechanisms, and conformance checking. He has pioneered approaches for log representativeness evaluation, control-flow anonymization, and stream processing optimization. His work consistently addresses critical challenges in balancing analytical utility with data privacy requirements in business process analysis. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research trajectories: (1) Species estimation applications for quantifying process representation completeness and diversity in event logs; (2) Privacy-preserving frameworks for process performance indicators and control-flow anonymization; (3) Stream mining innovations for window sizing and real-time conformance checking. These themes demonstrate his systematic approach to solving data quality and privacy challenges in process mining. No scientific awards are documented in the available information. Kabierski has not publicly listed any doctoral or master's students under his supervision. His research appears to be conducted collaboratively within institutional frameworks without mention of specific grant funding in the provided materials. He operates within the Databases and Information Systems research group at Humboldt University, contributing to the institute's focus on foundational data management and analysis techniques with applications in process mining.

David SpielerView profile

Professor

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +3 more

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

Sarah DepaoliView profile

Associate Professor

• Bayesian Statistics
• Structural Equation Modeling
• Latent Variable Modeling
• +5 more

Sarah Depaoli is Associate Professor of Quantitative Psychology at the University of California, Merced and Visiting Distinguished Professor at the Methods Center of the Eberhard Karls University of Tübingen . She specializes in Bayesian statistics, structural equation modeling, and mixture modeling with applications in psychological and health-related research. PhD in Quantitative Methods (minor in Mathematical Statistics), University of Wisconsin (2010) MA in Quantitative Psychology, California State University-Sacramento (2007) BA in Psychology, California State University-Sacramento (2003) Her research focuses on: Bayesian estimation of latent variable and growth models Prior distribution selection in small-sample research Measurement invariance testing across populations Development of guidelines for model transparency (WAMBS-checklist) Psychological and health outcome modeling with Bayesian methods The most recent articles demonstrate expertise in: Bayesian longitudinal analysis (CushingQoL interpretation differences) Latent class enumeration in growth mixture models ML vs Bayesian estimation comparisons Health psychological outcomes (PTSD, Quality of Life) Cross-cultural measurement invariance Statistical software implementation (JAGS, Mplus) Scientific awards include: 2015 Association for Psychological Science Rising Star 2013 Hellman Fellow 2011 APA Division 5 Distinguished Dissertation Multiple travel awards from Psychometric Society and SMEP She has advised numerous graduate students and served as Associate Editor for Psychological Methods , Multivariate Behavioral Research , and Journal of Royal Statistical Society, Series A .

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +3 more

Yang Liu is a researcher at the Institute of Computing Technology (ICT), Tsinghua University , and affiliated with the Beijing Academy of Artificial Intelligence . His work spans computational linguistics, machine translation, and dialogue systems, with additional collaborations in medical NLP and cross-lingual knowledge representation. Doctorate from Purdue University Collaborations with Microsoft, Amazon, and Facebook researchers Research focuses on Neural Machine Translation (NMT) advancements through: Self-supervised alignment and regularization Document-level context integration Robust translation frameworks for low-resource scenarios Adapter-based pluggable models Recent works extend to conversational AI, including: Subjective knowledge integration in task-oriented dialogue Context representation analysis for open-domain dialogue Synthetic conversation dataset generation Key contributions include: THUMT: Open-source NMT toolkit Earth Mover's Distance for lexicon induction Context Gates for translation fluency Publications demonstrate continuous engagement with top-tier conferences including ACL, EMNLP, and COLING, collaborating with prominent figures like Maosong Sun and Qun Liu.

• Information Retrieval
• Search Engine Technologies
• Data Compression
• +4 more

Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.

• Database Systems
• Natural Language Processing
• Data Mining
• +4 more

Hongrae Lee is a researcher specializing in database systems, natural language processing, and data mining. His work bridges structured data management with language models, focusing on tasks like natural language to SQL translation, similarity joins, and efficient data processing. His research interests include: Database query optimization and similarity search Language model applications for text generation and hallucination correction Web data curation and structured data ecosystems Cloud storage optimization and distributed systems Hongrae Lee's recent publications (2022-2023) highlight trends in large language models (LLMs) for dialogue applications, attributed text generation, and acronym disambiguation with weak supervision. Earlier work (2016-2007) established foundational techniques in database scalability, LSH-based similarity estimation, and geographical data thinning. He has collaborated extensively with researchers at institutions like Google, Seoul National University, and University of British Columbia on projects such as WebTables, LaMDA, and Google Fusion Tables. His contributions span both theoretical advancements (e.g., variance-aware query optimization) and practical systems (e.g., CloudRAMSort, T5-based disambiguation).

Carina da SilvaView profile

Assistant Professor

• Statistical Model Checking
• Monte Carlo Simulation
• Stochastic Hybrid Systems
• +7 more

Dr. Carina da Silva is an academic researcher and educator at the Institute of Computer Science, University of Münster, within the Department of Mathematics and Computer Science. She holds the position of Akademische Rätin, a tenured academic role combining research and teaching responsibilities, equivalent to Assistant Professor. Her work focuses on formal methods and probabilistic verification in hybrid systems. Institution: University of Münster School: Department of Mathematics and Computer Science Department: Department of Computer Science Email: carina.dasilva@uni-muenster.de Office: Room 215, Einsteinstraße 62, 48149 Münster, Germany Dr. da Silva's research centers on the development and application of statistical model checking and Monte Carlo simulation techniques for analyzing stochastic hybrid systems, particularly hybrid Petri nets and stochastic hybrid automata. Her work enables rigorous dependability evaluation of complex systems such as battery-powered and critical infrastructures. She actively supervises bachelor’s and master’s theses and teaches courses including Simulation and Statistical Model Checking, as well as an advanced seminar on scientific work for computer science students. Her research is frequently published in top-tier venues like ACM Transactions on Modeling and Computer Simulation, NASA Formal Methods, and QEST. The most recent publications demonstrate a consistent trajectory in optimizing reachability analysis, rare event simulation, and model transformation, with a strong emphasis on formal correctness and computational efficiency. She is also involved in collaborative benchmarking efforts such as the ARCH competition, contributing to the evaluation and advancement of stochastic verification tools. Ursula von Euch Grant (2024) Recognized in Ausgezeichnete Informatikdissertationen 2021 Dr. da Silva is actively involved in academic governance, serving as a deputy member of the Department Council for Mathematics and Computer Science, representing academic staff. She returned from parental leave in March 2024 and continues to supervise theses and offer seminars. While specific advisee names are not listed, she regularly invites students to work on topics such as simulation frameworks, uncertainty quantification, convergence analysis, and integration of empirical data in model checking. She is not part of a named lab but collaborates closely with researchers like Prof. Anne Remke and contributes to the broader formal methods and embedded systems research groups at Münster.

Dominik WiedView profile

Professor

• Statistics
• Financial Econometrics
• Portfolio Management
• +5 more

Dominik Wied is a Professor of Statistics and Econometrics at the Faculty of Management, Economics and Social Sciences at the University of Cologne. Previously, he held positions as Assistant Professor (2011–2016) and Visiting Professor (2015–2016) at TU Dortmund. His research focuses on Financial Econometrics , Structural Breaks , and Microeconometrics , with applications in portfolio management and risk assessment. Education: Diploma in Statistics (TU Dortmund, 2008), Ph.D. in Statistics (TU Dortmund, 2009). Affiliations: Institute for Statistics and Econometrics (University of Cologne), ECONtribute Cluster of Excellence (Markets & Public Policy). His recent publications emphasize conditional distribution modeling , structural break detection , and copula-based dependence analysis , particularly in financial and economic contexts. He has contributed to advanced statistical methodologies in time series and spatial modeling, including applications to banking risk assessment and health insurance data. Notable trends in his work include: Developing robust tests for nonlinear endogeneity corrections and quantile regression stability. Advancing factor copula models with exogenous covariates for financial dependence structures. Improving spatial dependence and time series cointegration monitoring techniques. Contact: dwied@uni-koeln.de | Phone: +49 221 470 4514

• Database Systems
• Data Science
• Information Systems
• +2 more

Hermann Stolte is a scientific collaborator at the Institute of Computer Science , Humboldt University of Berlin . His work focuses on data science, database systems, and information systems, with applications in astrophysics, educational technologies, and mobile app analysis. Institution: Humboldt University of Berlin Department: Databases and Information Systems His research includes developing validation frameworks for data analysis workflows, plausibility checks in exploratory data analysis, and predictive modeling for user feedback. Recent work explores astrophysical data analysis and educational robotics. Hermann's publications span data validation, star rating prediction from reviews, and transformative educational technologies. His work frequently addresses data quality, interpretability in machine learning, and interdisciplinary applications.

• Hybrid Systems
• Stochastic Modeling
• Formal Methods
• +4 more

Mathis Niehage is a Researcher and PhD Student at the University of Münster, affiliated with the Safety-Critical Systems group led by Prof. Dr. Anne Remke. His research focuses on hybrid systems analysis, simulation tools for stochastic hybrid Petri nets (HYPEG), and reinforcement learning applications. He holds a position in Room 201 and can be reached at mathis.niehage@uni-münster.de. Research Interests : Development of HYPEG, a Java library for simulating hybrid systems with random variables Extensions of hybrid Petri nets with general transitions Integration of reinforcement learning in stochastic hybrid systems Nondeterminism resolution using optimizing schedulers Teaching Contributions : Winter 2024/25: Programming in Java Summer 2024: Modeling with Automata and Probabilities Winter 2023/24: Capstone Project on Malware and Botnets Previously taught courses on functional programming, model checking, and compiler construction Key Publications (2018–2025): Niehage has contributed to seminal works on hybrid system simulation, stochastic modeling, and formal verification. His research bridges symbolic state-space exploration with statistical model checking, advancing methodologies for optimal decision-making in uncertain environments. Awards : 2023 Best Paper Award from the European Alliance for Innovation (EAI) Labs/Teams : Member of the Safety-Critical Systems group, collaborating on projects involving hybrid systems dependability and formal verification techniques.

Robert FischbachView profile

Researcher

• 3D Integration
• Heterogeneous Systems
• Layout Design
• +3 more

Dr. Robert Fischbach is a researcher at the Institute of Micro and Electronic Systems (FET) at Dresden University of Technology. His work focuses on 3D integration, heterogeneous system design, layout verification, and thermal analysis of advanced electronic packaging solutions. Key research areas: 3D Integrated Circuits Thermal and Reliability Engineering Design Automation & Optimization Interposer-Based Systems He has contributed extensively to co-design methodologies, micro-transfer printing assembly, and XML/XSLT-based simulation frameworks. Collaborative work includes automotive mission profiles and standardized formats for electronics reliability. Notable publications span topics in layout verification, thermal modeling, and heterogeneous packaging. His collaborations include researchers like J. Lienig, A. Krinke, and T. Horst.

Jan KretinskyView profile

Professor

• Verification
• Synthesis
• Probabilistic Model Checking
• +7 more

Jan Kretinsky is a full professor at the Faculty of Informatics, Masaryk University, Brno, Czech Republic, and holds an affiliated professorship at the Chair for Foundations of Software Reliability and Theoretical Computer Science at Technical University of Munich (TUM), Germany. He specializes in formal methods, focusing on verification and synthesis of probabilistic systems, applications of machine learning in verification, and explainable AI. His research bridges theoretical foundations with practical tools, including the Automata Tutor teaching platform and the Rabinizer tool for LTL-to-automata translation. Education: PhD from TU Munich (advisor: Javier Esparza) and Masaryk University (advisor: Antonín Kučera), both with distinction. Previously an IST Fellow at IST Austria and a tenure-track assistant professor at TUM. Research interests span verification of neural networks, probabilistic model checking, temporal logics, and automata theory. He leads the LiVe Lab and collaborates on EU projects like the ERC grant InOVationCS. Active in organizing workshops (LiVe series, Dagstuhl seminars) and serves on program committees for conferences like CONCUR, LICS, and TACAS. Teaching includes courses on complexity, automata theory, and quantitative verification at both institutions. Supervised numerous PhD and master's students, with notable projects in decision tree controllers, runtime monitoring, and attack-defense analysis. Key grants include DFG projects (e.g., GOPro, ConVeY) and EU initiatives. Tools developed: Automata Tutor, Rabinizer series, dtControl, SeQuaiA, and QUADTool. Research highlights include PAC guarantees for MDPs, semantic abstraction for motion planning, and formal methods for cybersecurity.

Vera SchmittView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Human-Computer Interaction
• +6 more

Dr. Vera Schmitt is an Assistant Professor and leads the XplaiNLP research group at the Quality and Usability Lab (Q&U) of Technische Universität Berlin. She established the XplaiNLP group in 2023 through third-party funding and focuses on NLP, AI explainability, HCI, and legal aspects of AI systems, particularly in disinformation detection and medical data processing. She holds a BA in Political and Administrative Sciences from the University of Konstanz, a Master's in Data Science from Leuphana University Lüneburg, and a PhD from TU Berlin on data protection economics. Her research spans NLP (misinformation detection, text anonymization), data synthesis (medical records), XAI (natural language explanations), HCI (collaborative AI systems), and usable privacy (economic perspectives of privacy). She has secured over €4.1 million in third-party funding, including the €1.4 million BMBF grant for her AI junior research group. Current projects include VERANDA (medical data anonymization), NEWS-POLYGRAPH (disinformation), and FakeXplain (AI fact-checking). Her work emphasizes ethical AI alignment with regulations like the AI Act and GDPR. Teaching includes seminars on usable privacy, AI ethics, and advanced statistics. She collaborates with institutions like DFKI’s SLT group and has published extensively on topics ranging from privacy nudges to multimodal fake news detection.

Li ZhangView profile

Researcher

• Geodesy
• Construction Quality Assurance
• GNSS Technology
• +3 more

Li Zhang is a Senior Academic Councillor and Group Leader at the Institute of Engineering Geodesy (IIGS) within the University of Stuttgart's Faculty 6: Aerospace Engineering and Geodesy. Her work focuses on holistic quality models for construction processes, low-cost GNSS monitoring systems , and multi-sensor fusion in infrastructure projects. She leads the Quality Modeling and Sensor Fusion group within the Cluster of Excellence IntCDC, which emphasizes integrative computational design for architecture. PhD in Geodesy (2016, University of Stuttgart) Specializes in GNSS error mitigation , construction process digitization , and digital map applications for transport security Her recent publications analyze geospatial data fusion for rock cliff monitoring, real-time quality assurance in residential construction, and cost-effective positioning for mobile manipulators. She supervises students on topics ranging from terrestrial laser scanning to indoor robot localization , balancing technical precision with sustainability. Li Zhang actively contributes to the FIG Working Group 5.6 "Cost Effective Positioning" and leads the DVW working group "Quality Assurance" . Her teaching portfolio includes courses on deformation analysis , multi-sensor systems , and statistical methods for engineering students.