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Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.

• Human-Computer Interaction
• Ubiquitous Computing
• Sustainability
• +7 more

Blase Ur is an Associate Professor of Computer Science at the University of Chicago, leading the UChicago SUPERgroup (Security, Usability, & Privacy Education & Research group). His research focuses on data-driven methods to improve online security, privacy, and usability of complex systems. He holds a PhD from Carnegie Mellon University and an AB from Harvard University. Education: PhD in Societal Computing, Carnegie Mellon University (2016) AB in Computer Science, Harvard University Research Interests: Blase's work spans computer security, privacy, HCI, and ethical AI. He develops tools like password meters, privacy transparency systems, and IoT security solutions. His SUPERgroup emphasizes interdisciplinary collaboration, with projects addressing data-driven decision-making and usability challenges in AI and IoT. Articles Trends: Recent work includes studies on ad transparency systems, user perceptions of privacy in AI, and barriers to passwordless authentication. Research often combines empirical user studies with technical implementations to bridge gaps between security mechanisms and user needs. Awards: NSF CAREER Award (2021) Quantrell Award for Teaching (2021) SIGCHI Outstanding Dissertation Award (2018) Advising & Grants: Advisor to PhD students like Kevin Bryson. Secured grants from NSF, Mozilla, and Meta. Active in outreach to broaden CS participation, including K-12 programs. Labs & Teams: Co-director of the SUPERgroup and affiliated with the Systems Group and CERES Center for Unstoppable Computing at UChicago.

• Computer Security
• Privacy
• Human-Computer Interaction (HCI)
• +4 more

Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.

• Data Mining
• Big Data
• Privacy-Preserving Data Publishing
• +9 more

Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.

• Human-Computer Interaction
• Data Science
• Health Informatics (eHealth/mHealth)
• +3 more

Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.

• Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +4 more

Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.

• Human-Computer Interaction
• Artificial Intelligence
• Data Mining
• +7 more

Frank Li is an Assistant Professor at Georgia Tech, affiliated with the School of Cybersecurity and Privacy and School of Electrical & Computer Engineering . His research focuses on computer security and privacy , particularly internet, network, and web security, with a data-driven approach. Education : PhD in Computer Science from UC Berkeley, BS in Computer Science from MIT (Course 6-3) Experience : Previously a visiting researcher at Facebook, with internships at Google and Microsoft Research Research interests include empirical computer security , internet measurement, password policies, IoT security, browser extensions, and DeFi vulnerabilities. His work combines large-scale data analysis, network traffic studies, and user behavior experiments. His publications highlight trends in IPv6 adoption , security notifications, password masking, third-party web content, and PLC security evaluation. Teaching awards include the Student Recognition of Excellence in Teaching (Annual CIOS Award and Semester Honor Roll) at Georgia Tech. He leads the BEES Lab (Better Empirically Established Security), which investigates security and privacy issues through internet-wide measurements, network traffic analysis, and data mining. Lab members include PhD and MS students in computer science.

• Computer Security
• Network Security
• Web Security
• +5 more

Deian Stefan is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research focuses on building secure systems through interdisciplinary approaches spanning security , programming languages , and systems . Key areas include WebAssembly security , JavaScript JITs , constant-time programming , memory safety , information flow control , and program analysis tools. He co-founded Intrinsic , a web-security startup later acquired by VMWare, and contributed to standards like W3C WebAppSec and Node.js Security Working Groups. Education: PhD in Computer Science from Stanford University (2015), advised by David Mazières, John C. Mitchell, and Alejandro Russo BE and ME in Electrical Engineering from Cooper Union (2011) Research Trends: His work targets secure systems (Web frameworks, sandboxing, runtime systems), language-based security (constant-time programming, memory safety), and verification for security . Recent publications focus on mitigating speculative execution attacks, hardware-assisted sandboxing, and WebAssembly security. Awards & Recognition: Distinguished Paper Award at POPL 2021 and IEEE MICRO Top Picks 2024 Best Paper Award at CollaborateCom 2010 2010.01 Prize by Daniel J. Bernstein (DJB) Most Influential Paper Award at ICFP 2022 Teaching: He has taught graduate and undergraduate courses including Graduate Computer Security (CSE 227) , Building Secure Systems with Rust , and Programming Languages: Principles and Paradigms at UCSD since 2018. Prior teaching experience includes Stanford and Cooper Union.

• Security
• Programming Languages
• Systems
• +6 more

Zizi Papacharissi is a UIC Distinguished Professor of Communication and Political Science at the University of Illinois at Chicago (UIC), serving as Department Head of Communication. She is also a University Scholar and affiliate faculty at the Discovery Partners Institute (University of Illinois System). Her research explores the social and political implications of digital media, with a focus on affective publics, networked identities, and democratic processes. She holds a PhD from the University of Texas at Austin and has authored over 10 books and 80+ journal articles. Education: PhD, University of Texas at Austin (2000) in New Media Technologies and Political Communication MA, Kent State University (1997) in Communication Studies BA, Mount Holyoke College (1995) in Media Studies and Economics Research Interests: Dr. Papacharissi investigates how digital media shape political participation, identity formation, and public discourse. Key areas include affective publics (emotional dimensions of online activism), the role of social media in democracy, and the ethical implications of AI and connective technologies. Her work bridges communication studies, political theory, and sociology. Recent Contributions: Her 2021 book After Democracy explores post-democratic futures in the digital age. She edits Social Media & Society and collaborates with tech firms like Facebook/Meta and Tencent on media ethics and policy. Her research on #Egypt and #OWS movements highlights social media’s role in mobilizing protests. Awards & Recognition: 2018 Wayne Danielson Award (National Communication Association) 2015 Best Book Award (NCA Human Communication and Technology Division) High-Impact Scholar (University of Texas at Austin) Advising & Grants: While no student names are listed, her courses (e.g., Democracy in a Digital Age ) reflect her mentorship in critical media studies. She has secured grants from the National Academies of Science and the Institute of Medicine for projects on youth well-being and digital media. Labs & Teams: Her research lab focuses on digital media’s societal impact, collaborating with global institutions like the Oxford Internet Institute and the Harvard Kennedy School. She chairs UIC’s interdisciplinary Digital Media and Democracy initiative.

• Social Media Studies
• Political Communication
• Digital Democracy
• +5 more

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +3 more

Alexandra Boldyreva is a Professor at the Georgia Institute of Technology, holding joint appointments in the School of Cybersecurity and Privacy and the School of Computer Science. She serves as Associate Chair for Graduate Studies in the School of Cybersecurity and Privacy and coordinates the Information Security Master’s program in the College of Computing. Her affiliations include the Institute for Information Security & Privacy (IISP), the Algorithms, Combinatorics and Optimization (ACO) program, and the Algorithms and Randomness Center (ARC). She earned her Ph.D. in Computer Science from the University of California, San Diego, and holds bachelor’s and master’s degrees in applied mathematics from St. Petersburg State Technical University, Russia. Her research focuses on cryptography and information security, with notable contributions to encryption methods, authentication protocols, and privacy-preserving systems. Boldyreva’s work emphasizes provable security analysis of protocols like FIDO2 and TLS 1.3, as well as searchable encryption and data privacy techniques. Her recent studies include secure communication channel establishment, leakage quantification in encryption systems, and applications of fuzzy search in encrypted databases. She has received Test of Time Awards for foundational contributions to cryptography. Boldyreva leads initiatives in cybersecurity education and has secured grants for research in secure communication protocols and human-computing approaches to key exchange. Her interdisciplinary collaborations span computer science, mathematics, and privacy engineering.

• Cryptography
• Information Security
• Searchable Encryption
• +3 more

Arvind Narayanan is a Professor of Computer Science at Princeton University and Director of the Center for Information Technology Policy (CITP). His research focuses on the societal impact of digital technologies, particularly artificial intelligence, with emphasis on policy implications, fairness, and privacy. He leads interdisciplinary efforts connecting technical research with real-world policy challenges. Dr. Narayanan earned his Ph.D. from the University of Texas, Austin in 2009. His academic journey has established him as a leading voice in the critical examination of AI systems and their societal consequences. Narayanan's research spans multiple domains where technology intersects with society. His work on AI includes critical analysis of AI capabilities versus marketing claims (AI Snake Oil), fairness in machine learning systems, and the reproducibility crisis in ML-based science. In privacy research, he led the Princeton Web Transparency and Accountability Project which uncovered how companies track users online, developing the OpenWPM tool used in over 100 studies. His early work demonstrated fundamental limits of de-identification techniques and how machine learning reflects cultural stereotypes. His recent publications reveal a consistent focus on demystifying AI capabilities while identifying genuine opportunities and risks. Narayanan's work bridges technical computer science with policy relevance, emphasizing the importance of evidence-based approaches to AI governance. His research increasingly addresses the limitations of prediction systems, the challenges of evaluating AI systems, and the need for transparency in foundation models. Presidential Early Career Award for Scientists and Engineers (PECASE) Privacy Enhancing Technologies Award (twice recipient) Privacy Papers for Policy Makers Award (three-time recipient) TIME's inaugural list of 100 most influential people in AI 2025 Graduate Mentoring Award Narayanan is recognized as an exceptional mentor, receiving Princeton's Graduate Mentoring Award in 2025. His policy engagement extends to congressional testimony, advisory roles, and frequent media commentary. He has secured significant research funding supporting his work on web transparency, AI policy, and cryptocurrency analysis. His research group has produced influential tools like OpenWPM for web privacy studies and contributed to foundational textbooks on cryptocurrencies and fairness in machine learning. At Princeton, Narayanan leads the Web Transparency and Accountability Project, a major research initiative that has conducted large-scale measurements of online tracking across millions of websites. He also co-founded and directs the CITP's AI Policy Initiative, which brings together researchers from multiple disciplines to address pressing AI governance questions. His work frequently involves collaboration with social scientists, legal scholars, and policymakers to develop practical solutions to technology governance challenges.

• AI Policy
• Societal Impact of Digital Technologies
• Fairness in Machine Learning
• +6 more

Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.

• Data Management
• Big Data
• Decision Support Systems
• +4 more

Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.

• Security and Privacy
• Cryptography
• Blockchain
• +4 more

Owen R. White is a Professor in the Department of Epidemiology & Public Health at the University of Maryland School of Medicine, serving as Associate Director of the Institute for Genome Sciences and Associate Director of Research Collaboration & Development. He leads a team of 25 scientists and engineers developing genomic annotation pipelines and data analysis tools for state-of-the-art research in microbiome and multi-omic studies. His academic background includes: BS in Biotechnology from the University of Massachusetts (1985) PhD in Molecular Biology from New Mexico State University (1992) Postdoctoral Fellowship in Genome Informatics at the Institute for Genomic Research (TIGR) (1994) Dr. White's research spans bioinformatics, genomics, transcriptomics, and metagenomics with emphasis on data management, metadata standards, ontologies, and cloud systems. His work has been foundational for large-scale initiatives like the Human Microbiome Project (HMP) and Integrative Human Microbiome Project (iHMP), generating over 50,000 datasets totaling 10 terabytes of multi-omic data. Analysis of his recent publications reveals a strong trend toward neuroscience multi-omics (BRAIN Initiative), cloud-based data infrastructure, and ethical data sharing frameworks. His work consistently bridges microbiome research with emerging fields like single-cell analysis and Alzheimer's disease biomarker discovery through integrated data platforms. Notable awards include: Benjamin Franklin Award for Open Access in the Life Sciences (2015) Kumho Science International Award in Plant Molecular Biology and Biotechnology (2001) As Principal Investigator for major NIH-funded centers, he has secured sustained support for the HMP Data Analysis and Coordination Center and iHMP Data Coordination Center. His team's work combines fee-for-service models with collaborative research funding to maintain cutting-edge genomic analysis capabilities. The Institute for Genome Sciences houses his computational team responsible for developing production annotation pipelines, database systems, and visualization tools that serve researchers across the University of Maryland School of Medicine and national consortia.

• Bioinformatics
• Genomics
• Transcriptomics
• +6 more