Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Baishakhi Ray is an Associate Professor at Columbia University, specializing in improving software reliability and developers' productivity for both traditional and AI-driven systems. She leads the ARiSE Lab, focusing on interdisciplinary research at the intersection of software engineering and artificial intelligence. Her research interests include software testing for AI systems, adversarial robustness, automated testing of autonomous systems, and leveraging AI techniques such as neural networks for dynamic analysis and fuzzing. Notable projects include DeepTest for autonomous car testing and NEUZZ for efficient fuzzing. Awards: VMware Early Career Faculty Award (2020), IBM Faculty Award (2019), NSF CAREER Award (2019), and multiple best paper awards including EAPLS FASE (2020) and ACM Distinguished Papers (FSE 2017, MSR 2017). Grants: NSF CAREER grant (2019-2024) for deep learning testing, NSF grants for workshops and security bug detection, and collaborative grants on persistent memory and SSL/TLS implementations. Her recent work emphasizes advancing code generation with large language models (LLMs), evaluating model robustness under data contamination, and developing tools like CodeSense and CrashFixer for code semantics and kernel debugging. The ARiSE Lab also explores causal performance debugging and transfer learning for configurable systems.
Nickolai Zeldovich is the Joan and Irwin M. Jacobs Professor of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from Stanford University in 2008 and focuses on building practical secure systems, including encrypted databases, undefined behavior detection tools, formally verified file systems, and cryptocurrency protocols. His work spans both theoretical and applied aspects of computer security and distributed systems. Research interests include: Secure system design Distributed consensus mechanisms Formal verification of software/hardware Cryptographic protocols Privacy-preserving web technologies Publications & Verification Tools Recent work focuses on modular verification of complex systems, including: Shipwright (2025): Byzantine-fault-tolerant distributed system verification PoWER (2025): Crash consistency verification framework K2 Architecture (2023): Trustworthy hardware security modules Grove (2023): Separation-logic-based verification library Tiptoe (2023): Private web search protocols Major Awards Best paper award, ACM SOSP (2011, 2015, 2017) Sloan Research Fellowship (2010) NSF CAREER award (2011) MIT Jamieson Award for Teaching (2024) Active in multiple startup ventures including Algorand (cryptocurrency), MokaFive (virtualization), and PreVeil (end-to-end encryption).
Dr. Sebastian Hellmann is a senior researcher at the Institute of Computer Science , University of Leipzig , affiliated with the Business Information Systems department. He leads the Knowledge Integration and Language Technologies (KILT) Competence Center at InfAI and serves as executive director and board member of the DBpedia Association . His work spans semantic technologies, linked data, and knowledge graphs, with significant contributions to data curation, FAIR data principles, and natural language processing. PhD in Computer Science (2014) at University of Leipzig Contributed to open-source projects like DBpedia, NLP2RDF, and OWLG Author of over 80 peer-reviewed publications (h-index 21, 4300+ citations) Sebastian's research focuses on Knowledge Graphs , Ontology Management , and Data Interoperability , as evidenced by his publications and projects. Recent work includes ClassRank for knowledge graph summarization, DBpedia Databus for dataset management, and the Open Energy Ontology for energy systems analysis. His projects often bridge semantic web technologies with practical applications in data quality, integration, and user-centric tools. Selected publications highlight his expertise in Linked Data , Ontology Archiving , and Agile Knowledge Engineering . He actively participates in academic-industry collaborations through EU H2020 projects like ALIGNED and FREME , as well as the Smart Data Web initiative. His work with DBpedia, Wikidata, and the Semantic Web community underscores his commitment to advancing machine-readable knowledge representation.
Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .
Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.
Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).
Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.
Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.
Hannes Hick is a Professor at Graz University of Technology , affiliated with the Institute of Machine Elements and Development Methodology . His research focuses on mechanical development, tribology, and systems engineering for automotive and industrial applications. He actively contributes to engineering education and methodology standardization. Research Interests Hydrogen internal combustion engines System modeling and digital twins Tribology in electric drivetrains Sustainable engineering practices MBSE (Model-Based Systems Engineering) Friction and wear analysis Article Trends His recent work emphasizes hydrogen propulsion systems, model-based approaches for interdisciplinary engineering challenges, tribological optimization for sustainable mobility, and integrating AI with mechanical design workflows. Labs and Teams He leads research at the Institute of Machine Elements, focusing on mechanical validation and development methodologies for advanced powertrain systems.
Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.
Gad Allon is the Jeffrey A. Keswin Professor and Professor of Operations, Information and Decisions at the University of Pennsylvania’s Wharton School. He directs the Management and Technology Program and teaches in the Education Entrepreneurship program. His research focuses on operations strategy, service systems, gig economy dynamics, and education technology. A co-founder of ForClass, a platform enhancing classroom engagement, he advises firms on service and operations strategy. Allon holds a Ph.D. from Columbia Business School and degrees from the Israeli Institute of Technology. Recognized as one of the ‘World’s Top 40 B-School professors under 40,’ he has pioneered work on behavioral drivers in service systems and gig economies. His recent research explores multihoming in gig work, machine learning in causal inference, and agile product development. Academic contributions span over 50 publications in top journals, emphasizing real-world applications of operations management theories. Education: Ph.D. in Management Science, Columbia Business School (New York) Bachelor’s and Master’s Degrees, Israeli Institute of Technology Research Interests: Professor Allon’s work bridges theoretical operations research with practical challenges in service systems, digital platforms, and educational technology. Key themes include optimizing customer service through behavioral insights, analyzing labor dynamics in gig economies, and leveraging machine learning for causal inference in business decisions. Notable Contributions: Co-founder of ForClass, addressing classroom engagement Leading studies on worker behavior in gig economies Groundbreaking work on call center retrials and service quality trade-offs Labs/Initiatives: Active in educational technology innovation through ForClass and advises on large-scale service marketplace design through Wharton’s Management and Technology Program.
Reza Curtmola is a Professor in the Department of Computer Science at NJIT. His research focuses on cybersecurity, distributed systems, and network security with an emphasis on secure routing, cloud computing, and privacy-preserving technologies. He holds a Ph.D. in Computer Science from Johns Hopkins University (2007), an M.S. from the same institution (2003), and a B.S. from the Politehnica University of Bucharest (2001). Dr. Curtmola’s work addresses challenges in wireless mesh networks, vehicular communication systems, and mobile-cloud integration. His contributions include innovative solutions for secure network coding, distributed resource management (e.g., parking assignment systems), and auditable data storage mechanisms. He has developed middleware frameworks like Moitree for mobile-cloud applications and has explored defenses against side-channel attacks, cache leaks, and entropy-based network vulnerabilities. His research also extends to privacy in vehicular DSRC protocols, dynamic traffic optimization, and verifiable code review systems. He has published extensively on topics ranging from cryptographic defenses in distributed systems to practical implementations of remote data checking in untrusted clouds. Current research activities include advancing secure cloud infrastructure, improving mobile crowdsensing reliability, and mitigating threats in IoT-enabled urban environments. His work often bridges theoretical foundations with practical system implementations, emphasizing real-world applicability in smart cities and critical infrastructure systems.
Annie Antón is a Professor in the School of Interactive Computing at Georgia Institute of Technology. Former Chair of her school, she specializes in privacy, security, software engineering, and public policy. Dr. Antón advises national defense and intelligence entities, including roles in the IDA/DARPA Defense Science Study Group (2005-2006) and the Obama-appointed 2016 Commission on Enhancing Cybersecurity for the Nation. She serves on numerous boards, including NIST Information Security & Privacy Advisory Board, Future of Privacy Forum, and former roles in U.S. DHS Data Privacy Committee and NSF Advisory Council. Her research focuses on aligning software systems with federal privacy/security regulations, emphasizing legal compliance, secure development practices, and IoT privacy challenges. Notable projects include UCON_LEGAL for HIPAA compliance and frameworks for analyzing regulatory requirements. She has contributed to policy implementations via empirical studies on user privacy perceptions and legal text mining. Dr. Antón's work bridges technical and policy domains, addressing ethical AI education, cross-border data governance, and cybersecurity preparedness. Her leadership spans academia, government, and industry through advisory roles at Intel, Microsoft Research, and the Georgia Tech Alumni Board.
Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.
