Henry Corrigan-Gibbs is an Assistant Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads research in computer security, cryptography, and privacy-preserving systems. His work focuses on practical cryptographic systems that empower users while maintaining strong security guarantees. Notable contributions include the Tiptoe private search engine, Prio for privacy-preserving data aggregation, and Larch for secure authentication. His research has influenced industry standards at Apple, Google, and Mozilla, and has been recognized with awards such as the Best Young Researcher Paper at Eurocrypt and the Caspar Bowden Award. Education: PhD in Computer Science (Stanford University, advised by Dan Boneh), Postdoc at EPFL (hosted by Bryan Ford). B.S. in Computer Science from Yale University. Research Interests: Private Information Retrieval, Secure Authentication, Cryptographic Systems, Privacy-Preserving Analytics, and Hardware Security. His lab collaborates with PDOS and CSS research groups at MIT and co-hosts the MIT Security Seminar series. Grants and Funding: Supported by industry and government agencies (details in paper acknowledgments). Teaching roles include co-instructor for Applied Cryptography (6.5610) and Foundations of Computer Security (6.1600). Key Projects: Prio (used in iOS/Android), Tiptoe (private web search), Larch (backdoor-resistant authentication), and Whisper/Poplar systems for private data aggregation. His team includes postdocs, PhD students, and undergrad researchers working on cutting-edge cryptographic protocols.
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Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.
Mykola Oleksiyovych Zhuravel serves as a Senior Lecturer at Zaporizhia Polytechnic National University within the Department of Occupational Safety and Environmental Protection, part of the Faculty of Construction, Architecture and Design. With over 37 years of service since 1987, he has established himself as a dedicated educator and researcher in occupational safety, civil defense, and environmental protection fields. His research interests span occupational safety, civil defense, environmental safety, life safety, industrial safety, ergonomics, and risk management. Zhuravel's scholarly work consistently addresses practical safety concerns in Ukrainian industrial and educational settings, with a particular focus on regional environmental challenges in Zaporizhzhia. His publications demonstrate a progression from foundational safety principles to contemporary concerns like ecological energy security and digital workplace safety. Analysis of his 15 most recent publications reveals a strong emphasis on practical applications of safety science, with recurring themes in environmental protection, workplace hazard mitigation, and emergency preparedness. His work bridges theoretical safety frameworks with practical implementation strategies, particularly relevant to Ukrainian industrial contexts. Zhuravel maintains active professional development, with numerous certifications through 2024 in occupational safety, civil defense, and academic integrity. He is an associated member of the European Community for Occupational Safety and Health (EUOP register number 13823000211, registered June 12, 2023). As an educator, he teaches multiple safety-related disciplines including Safety of Life of a Specialist with Basics of Labor Protection, Protection of Human Living Environment and Occupational Safety, and Civil Protection and Occupational Safety in Industry. His teaching approach integrates theoretical knowledge with practical safety applications relevant to Ukrainian industrial contexts.
Professor Fernando Lusa Bordin is a College Associate Professor at Sidney Sussex College and a Fellow at the Lauterpacht Centre for International Law, University of Cambridge. He holds a PhD (Cantab), LL.M. (NYU), LL.B. (UFRGS), and a Diploma of Public International Law (Hague Academy). His research focuses on Public International Law, Jurisprudence, and the Law of Torts, with emphasis on international legal theory, law-making, international organizations, and dispute settlement. His key academic roles include: College Associate Professor, Sidney Sussex College Professor of Institutional Law (Schermers Chair), Leiden University Fellow, Lauterpacht Centre for International Law Research Interests: Fernando’s work explores the applicability of general international law to international organizations, treaty law, and state responsibility. His monograph The Analogy between States and International Organizations (2018) received the 2020 ASIL award. His recent articles address corporate legal status, regional organizations, and immunity frameworks. Professional Experience: He has served as a Judicial Fellow at the International Court of Justice, advisor to the UN International Law Commission, and counsel in major cases like the Chagos Marine Protected Area Arbitration . Awards: 2020 Certificate of Merit (American Society of International Law) Yorke Prize (University of Cambridge, for doctoral thesis) Labs/Tems: Active contributor to the Network of Empirical Labour Law Scholars (NELLS) and editor of the Oxford International Organizations (OXIO) database.
Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.
Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.
Professor Louis Appleby is a distinguished Professor of Psychiatry at the University of Manchester, where he serves in the Division of Psychology & Mental Health. He holds the prestigious position of Director of the National Confidential Inquiry into Suicide and Safety in Mental Health and leads England's National Suicide Prevention Strategy. His work has positioned him as one of the UK's foremost experts in suicide prevention and mental health safety. Professor Appleby's research focuses on suicide prevention across diverse populations including mental health patients, children and young people, individuals with prior self-harm, and offenders. He leads the Centre for Mental Health and Safety, recognized as the largest research unit in suicide prevention globally. The Centre has developed a UK-wide database of over 100,000 suicides that informs evidence-based policy and practice. His research examines risk factors that increase suicide vulnerability and identifies protective factors and effective intervention points across different populations. Recent publications demonstrate his continued leadership in the field, with studies examining suicide in veterans, self-harm patterns in children and women, and suicide risk among patients with psychosis. These works reflect his commitment to translating research into practical interventions that save lives across multiple demographic groups. CBE (2006) RCPsych Lifetime Achievement Award (2021) Knighted in the 2023 New Year Honours Making a Difference Award, University of Manchester (May 2019) Outstanding Benefit to Society Through Research (2015) Professor Appleby has secured substantial research funding totaling £25 million from organizations including the Wellcome Trust, Medical Research Council, and Department of Health. His leadership extends to numerous doctoral students and early-career researchers in suicide prevention. He has held significant government roles including National Director for Mental Health (2000-2010) and National Clinical Director for Health and Criminal Justice (2010-2014), demonstrating his impact on national mental health policy. As Director of the Centre for Mental Health and Safety, Professor Appleby oversees multiple research programs including the National Confidential Inquiry, the Manchester Self-Harm Project (MASH), bereavement support research, offender health initiatives, and international epidemiological collaborations with Danish colleagues. His work continues to shape suicide prevention strategies at local, national, and international levels.
Mathias Lécuyerمشاهده پروفایل
استادیار
Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.
Istvan Albertمشاهده پروفایل
استاد پژوهش
- Bioinformatics
- Genomics
- Computational Biology
- +۵ مورد دیگر
Istvan Albert is a Research Professor of Bioinformatics at Pennsylvania State University , affiliated with the Department of Biochemistry and Molecular Biology . He leads the Bioinformatics Consulting Center and teaches BMMB 852: Applied Bioinformatics . Research Interests: Specializing in bioinformatics, large-scale biological data analysis, microarray and sequence analysis, scientific programming, algorithm development, and database-driven web development. His work spans gene ontology visualization , RNA-Seq analysis , and coronavirus research . Software Development: Created GeneScape for gene function visualization and bio for bioinformatics workflows. Maintains the Biostar Handbook series and the Biostars Q&A Forum , a leading bioinformatics resource.
Natacha Crooksمشاهده پروفایل
استادیار
Natacha Crooks Dr. Natacha Crooks is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her research focuses on distributed systems, databases, and security, with a particular emphasis on consistency models, BFT protocols, and cloud computing. She is a founding member of the SkyLab and a core contributor to the Data Systems and Foundations Group at Berkeley. She holds a Ph.D. in Distributed Systems from the University of Texas at Austin (2019) and a BA in Computer Science and Law from the University of Cambridge (2012). Affiliations & Roles: Assistant Professor, UC Berkeley EECS Visiting Researcher at Azure Research, Security & Privacy Founding Member of SkyLab Member of Berkeley Center for Decentralized Intelligence Former Scientific Advisor at Improbable and Astronomer Research Interests: Her work bridges distributed systems and database research, addressing challenges in transactional consistency, fault-tolerant protocols, and cloud resource optimization. Key themes include: Designing scalable BFT consensus algorithms Secure multi-cloud storage solutions Optimizing distributed transaction processing Privacy-preserving distributed systems Awards & Recognition: Sloan Research Fellow (2025) NSF CAREER Award ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2020) IEEE CS TCDE Early Career Award (2024) Teaching: Fall 2025: CS 294-282 (Research Culture and Community Norms), Wheeler 130. Industrial Collaboration: Prior roles include visiting researcher at Cornell University, Microsoft Research (DMX group), and Imperial College London. Industrial partnerships include work with Materialize, Astronomer, and Improbable.
Tianxi Liمشاهده پروفایل
استادیار
Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.
Emmanuel Saez is a Professor of Economics and Director of the Stone Center on Wealth and Income Inequality at the University of California, Berkeley , where he has been a leading figure in the Department of Economics within the College of Letters and Science. His research is foundational in the study of income and wealth inequality, optimal taxation, and empirical public policy. His academic journey began with a PhD from MIT in 1999. His work, particularly in collaboration with Thomas Piketty and Gabriel Zucman, has revolutionized how economists measure and understand economic inequality. He is best known for constructing long-term top income share series, which reveal the dramatic rise in U.S. inequality since the 1980s. His influential 2019 book, The Triumph of Injustice , co-authored with Gabriel Zucman, presents a compelling critique of the decline in U.S. progressive taxation and proposes bold policy reforms for the 21st century. Saez's recent research spans a wide range of topics including real-time inequality measurement, wealth tax enforcement, labor market dynamics for older workers, and macroeconomic models of unemployment. His publications, often appearing in top journals like the Quarterly Journal of Economics , American Economic Review , and Brookings Papers on Economic Activity , consistently focus on empirical analysis with direct policy relevance. His work shows a strong trend toward integrating macroeconomic models with distributional data and evaluating the real-world impact of fiscal and social policies. John Bates Clark Medal, American Economic Association, 2009 MacArthur 'Genius' Fellowship, 2010 Honorary degree from Harvard University, 2019 Saez is deeply involved in mentoring and advising, though specific student names are not listed in the provided texts. He has been instrumental in major data initiatives such as the World Inequality Database (WID.world) and Real-Time Inequality , which provide open-access, high-quality data on economic distribution. He has also secured significant research funding from foundations like the Bill & Melinda Gates Foundation, the Russell Sage Foundation, and the Alfred P. Sloan Foundation. He leads the Stone Center on Wealth and Income Inequality, fostering interdisciplinary research on economic disparity.
Bonnie Berger is the Simons Professor of Mathematics at the Massachusetts Institute of Technology and head of the Computation and Biology group at MIT's Computer Science and AI Lab. She holds additional appointments as an Associate Member of the Broad Institute, Faculty member of Harvard/MIT Health Science & Technology, and Affiliated Faculty of Harvard Medical School. Her career has been dedicated to pioneering computational approaches in molecular biology, where she has been instrumental in defining the field. Professor Berger's research focuses on designing algorithms to extract biological insights from large-scale data sets. Her work spans Compressive Genomics, Network Inference, Structural Bioinformatics, Genomic Privacy, and Medical Genomics. She actively collaborates with experimental biologists to maximize the power of computation for biological discovery, developing methods that address the challenges of modern high-throughput biological data. Her recent publications demonstrate a strong trend toward integrating machine learning with structural biology and genomic privacy. The articles show increasing sophistication in using deep learning for protein structure prediction, developing privacy-preserving techniques for genomic data sharing, and creating efficient algorithms for massive biological data sets. Her work bridges theoretical computer science with practical biological applications. Professor Berger's scientific recognition includes: Election to the National Academy of Sciences (2021) ISCB Accomplishments by a Senior Scientist Award SIAM Sonya Kovalevsky Lecture Prize Fellowships in ACM, ISCB, AMS, and other prestigious societies Multiple RECOMB Test of Time Awards NIH Margaret Pittman Director's Award She has mentored numerous students who have gone on to make significant contributions in computational biology, including Ellen Zhong, Yun William Yu, and Hyunghoon Cho. Her lab receives substantial research funding supporting projects in genomic privacy, structural bioinformatics, and compressive algorithms for biological data. Professor Berger serves on the Executive Editorial Board of the Journal of Computational Biology and multiple other editorial boards. The Computation and Biology group at MIT CSAIL, which she leads, is at the forefront of developing computational methods for biological discovery. The group combines expertise in algorithms, machine learning, and biology to tackle fundamental challenges in genomics and structural biology. They are currently organizing the Machine Learning in Structural Biology workshop at NeurIPS 2025, highlighting their leadership in this rapidly evolving interdisciplinary field.
Bo Liuمشاهده پروفایل
دانشیار
Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.
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