Limin Jia is a Research Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with a courtesy appointment in the Computer Science Department (CSD). Affiliated with CyLab, their work focuses on applying formal techniques to enhance software security through programming language design, formal verification, and information flow analysis. Research interests span Security Programming Languages Formal Verification Information Flow Control Intermittent Computing Rust Programming . Recent publications integrate formal methods with practical security challenges, including Node.js vulnerability detection Rust API testing Secure multi-execution Intermittent computing type systems IFTTT security analysis Browser security frameworks . Limin serves on program committees for conferences like POPL, PLDI, VMCAI, and is actively involved in teaching courses such as Browser Security (18-636) Introduction to Information Security (18-631) .
Mike Kosek (Dr. rer. nat.) is a Research Fellow at the Chair of Connected Mobility (Department of Informatics) at the Technical University of Munich (TUM). His research focuses on transport protocols, congestion control, internet architecture, and internet measurements. Research Interests: Transport protocol design and analysis Congestion control mechanisms Internet architecture and measurement Network performance optimization DNS protocol behavior and privacy Applications in satellite and aerial communication Recent Research Trends: His publications emphasize QUIC protocol analysis, DNS over QUIC investigations, cross-layer protocol interactions, and satellite communication adaptations. Key methodologies include real-world measurements, protocol design extensions, and dataset creation for reproducibility. Contact: E-mail: kosek@in.tum.de Phone: +49 89 289-18665 Office: 01.05.038, Boltzmannstr. 3, 85748 Garching
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.
Christina Youngmi Choi is a Professor in the School of Design at the Royal College of Art (RCA), specializing in emerging technologies, healthcare, and inclusive design. She holds a BFA and MA in Industrial Design from South Korea, followed by a MSc and PhD from the Georgia Institute of Technology (Georgia Tech), where she also served as faculty, earning tenure and holding leadership roles such as Associate Chair and Director of the Graduate Program. Her research focuses on leveraging technology for assistive and inclusive design, emphasizing evidence-based and human-centered approaches. Key areas include usability assessment, assistive product design, and healthcare innovation. She has over 60 publications in journals, conferences, and books, with notable works exploring augmented reality in design and accessibility. Dr. Choi has led or contributed to significant grants, including the Rehabilitation Engineering Research Center (RERC) for Wireless Inclusive Technologies (2016–22), and the NSF-funded Feasibility and Usability Assessment of an Intraoral Inconspicuous Control Surface (2013–16). Her work spans collaborations with industry partners like LG Electronics and Jeju Airlines, addressing challenges in healthcare, education, and consumer technology. Awards: Best Paper Award at AHFE 2022 Georgia Tech Teaching Excellence Recognition (multiple years) National Science Foundation Women of Excellence Award (2016) Her advising and grants highlight interdisciplinary projects, such as integrating traditional wood joinery into CNC manufacturing and developing user-friendly medical devices. She is an editorial board member of the Journal of User Experience and peer reviewer for multiple journals and conferences. Her work also addresses privacy in health technologies and medication adherence systems, reflecting a commitment to societal impact through design.
Dr. Su Ryon Shin is an Assistant Professor in the Division of Engineering in Medicine at Harvard Medical School and Brigham and Women's Hospital (BWH) in Cambridge, MA. She leads an active research laboratory focused on bioengineering, tissue engineering, and regenerative medicine, with particular expertise in 3D bioprinting, biomaterials, and organ-on-a-chip technology. Her research interests span biohybrid robotics, decellularized extracellular matrix, stem cell-based tissue engineering, and volumetric muscle regeneration . Dr. Shin's work integrates advanced biomaterials with cellular systems to create innovative solutions for tissue regeneration and disease modeling. She has pioneered approaches using human stem cell-derived materials for volumetric tissue regeneration and developed biohybrid neuromuscular robots powered by living cardiac muscle cells. Her publication record demonstrates consistent productivity with over 180 publications, including numerous first/senior author papers in high-impact journals like Science Robotics, Advanced Materials, and Nature Reviews Bioengineering . Her work shows a clear progression from fundamental biomaterials development to increasingly complex tissue engineering applications and translational research. Dr. Shin has received significant recognition including being named a 2025 BWH Health & Technology Innovation Awardee , Highly Cited Researcher 2024 by Web of Science, and multiple Stepping Strong Innovator Awards (2015, 2018, 2020). Her research has been featured in Nature Reviews Bioengineering for breakthrough work on biohybrid robots. She actively mentors students and postdocs, with former lab members accepted to prestigious programs like MIT's PhD program in Chemical Engineering. Her collaborative approach is evident through numerous interdisciplinary projects with researchers across Harvard Medical School, BWH, and international institutions.
Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.
Jun Li is a Full Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame's College of Science. He specializes in developing statistical and computational methods for big data, with a focus on interdisciplinary applications in bioinformatics, machine learning, and data mining. His career includes tenure as an Assistant Professor (2012–2017) and promotion to Associate Professor (2017) before becoming Full Professor (2020). Dr. Li holds a Ph.D. in Statistics from Stanford University (2012), supervised by Robert Tibshirani, and earlier degrees from Tsinghua University: a B.E. in Automation (2004) and an M.S. in Pattern Recognition and Intelligent Systems (2007). Research Interests : Dr. Li’s work centers on advancing computational frameworks for handling large-scale datasets, integrating statistical rigor with algorithmic innovation. Recent themes include AI-driven code improvement, ethical LLM applications in HCI, and GUI automation. His methodologies emphasize human-AI collaboration and transparency in algorithmic systems. Publications : His 2025 work explores LLM vulnerabilities in GUI agents, AI-assisted education tools like GLITTER, and ethical challenges in HCI research. Earlier studies (2024–2023) address topics such as natural language database queries, privacy-preserving app promotion analysis, and multimodal task learning. Lab/Teams : Affiliated with Notre Dame’s computational statistics research groups, focusing on interdisciplinary projects bridging statistics, computer science, and applied mathematics. His work often involves collaborations with industry and academic partners to translate theoretical advancements into practical applications.
Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University since 2005. He received his PhD in Information & Computer Science from the Georgia Institute of Technology in 1988. His research spans software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has led significant projects such as the NSF-funded integration of CS into K-5 classrooms and the Google-funded SPARC project for scalable CS1/CS2 instruction. The 15 most recent articles reflect a continued focus on mutation testing cost reduction, model-based testing oracles, educational innovations, and security aspects of web applications. Trends include empirical validation, industrial applicability, and bridging theory with practice in software testing and engineering education. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University’s Alumni Association Faculty Member of the Year (2020) Outstanding Faculty Award from the State Council of Higher Education for Virginia (2019) Best Paper Award at ICST 2021 10-Year Most Influential Paper Award at MODELS 2020 George Mason University Teaching Excellence Award (2013) ACM Notable Article Award (2013) Jeff Offutt has mentored numerous graduate students including Upsorn Praphamontripong, Nan Li, and Yu-Seung Ma, and has led major grant-funded projects such as the SPARC educational model and NSF initiatives on K-5 CS integration. His textbook Introduction to Software Testing (with Paul Ammann) is widely adopted globally. He led the MS in Software Engineering program at GMU and developed several new courses in software testing, web engineering, and usability. He pioneered innovative teaching methods using web technologies and asynchronous learning models. He also co-founded the IEEE International Conference on Software Testing, Verification and Validation (ICST) and served as Editor-in-Chief of Software Testing, Verification and Reliability from 2007 to 2019.
Prof. Dr. Christian Rossow is a faculty member at CISPA – Helmholtz Center for Information Security in Dortmund, Germany. He also serves as a professor at Saarland University and an honorary professor at TU Dortmund. His research focuses on network security , system/software security , and cyber attacks and countermeasures . His recent publications include work on CSS exploitation for browser fingerprinting, Spectre mitigations on ARM, DDoS attack analysis, and hardware-assisted security extensions. Christian Rossow regularly serves as a PC member and track chair for top cybersecurity conferences like ACM CCS and USENIX Security . He advises multiple PhD students and leads a research group exploring foundational and practical security solutions.
Prof. Dr. Michael Bosnjak leads the Department for Psychological Research Methods at the University of Trier, Germany, focusing on research synthesis methods, theory-driven machine learning, survey methodology, and experimental designs applied to personality psychology (HEXACO model), consumer behavior, and public health. His team includes Dr. Henning Silber (GESIS), Dr. Bernd Weiß (GESIS), and international collaborators at Utrecht University. Affiliation: University of Trier (Department of Psychology) Editorial Roles: Zeitschrift für Psychologie (Editorial Board), Social Science Computer Review (Associate Editor), Frontiers in Psychology (Review Editorial Board) Grants: DFG SFB 884 (co-applicant), DFG Project PaCO (2019-2024, principle investigator), BMBF-funded GESIS Panel Campus His recent publications analyze panel conditioning effects in longitudinal studies, e-commerce privacy dynamics, personality-trait interactions, and psychosis proneness measurement. Scientific awards include the 2016 BMBF infrastructure grant, 2011 Isaac Manasseh Meyer Fellowship, and 2003 Karin Islinger Award. Current projects address data quality in probability-based and opt-in panels, with experimental designs across eight groups testing measurement error mechanisms. He serves as a co-editor for multiple special issues on survey methodology and tourism psychology.
Kevin Chenchuan Chang is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the FORWARD Data Lab and the Data and Information Systems Laboratories. His research focuses on bridging structured and unstructured data through natural language processing, data mining, machine learning, and information retrieval, with applications in web search, social media analytics, and knowledge acquisition. He co-founded Cazoodle and developed GrantForward.com, a funding discovery platform used by leading institutions globally. Education: Ph.D. in Electrical Engineering from Stanford University (2001), B.S. from National Taiwan University. Professional roles include service on program committees for SIGMOD, VLDB, KDD, and NeurIPS, as well as editorial roles for PVLDB, TKDE, and the Encyclopedia of Database Systems. His awards include the ICDE 10-Year Test of Time Award (2022), NSF CAREER Award (2002), and multiple UIUC teaching excellence recognitions. He teaches courses such as CS 411 (Database Systems), CS 598 KCC (Understanding LLMs), and CS 511 (Advanced Data Management). Research contributions span graph algorithms (e.g., Geom-GCN, SimRank), social network analysis (ROSE), and NLP (DEER, Open Relation Modeling). The FORWARD Lab emphasizes real-world impact through systems like GrantForward and tools for analyzing large-scale data.
Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
