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Jingbo LiuView profile

Assistant Professor

• Statistical Inference
• Information Theory
• Signal Processing
• +4 more

Jingbo Liu is an Assistant Professor in the Department of Statistics at the University of Illinois, Urbana-Champaign, with an affiliate appointment in Electrical and Computer Engineering. He received his B.E. (2012) from Tsinghua University, M.A. (2014) and Ph.D. (2018) from Princeton University, all in Electrical Engineering, followed by a postdoc at MIT IDSS. Education Ph.D. in Electrical Engineering, Princeton University (2018) M.A. in Electrical Engineering, Princeton University (2014) B.E. in Electronic Engineering, Tsinghua University (2012) His research focuses on statistical inference under systems constraints, information-theoretic inequalities, graphical models, and applications of high-dimensional probability to information sciences. Key areas include mutual covering bounds, hypercontractivity, Brascamp-Lieb inequalities, and their connections to machine learning and communication systems. Recent work applies information theory to generative AI, analyzing diffusion models' utility, privacy enhancements, and computational efficiency. He also investigates statistical physics techniques for high-dimensional problems like Lasso distributional limits and tensor model free energy, with applications in variable selection and PCA. Scientific awards include the Thomas M. Cover Dissertation Award (2018) and Princeton's Wallace Memorial Fellowship (2016). Courses taught include STAT 578 (High-Dimensional Statistics), STAT 430 (Nonparametric Statistics), and STAT 542 (Statistical Learning).

Jingrui HeView profile

Professor

• heterogeneous machine learning
• active learning
• neural bandits
• +9 more

Jingrui He is a Professor and MSIM Program Director at the School of Information Sciences, University of Illinois Urbana-Champaign. She holds multiple faculty affiliate positions including with the Department of Computer Science, National Center for Supercomputing Applications (NCSA), Illinois Informatics, Center for Digital Agriculture (CDA), and Mayo Clinic Arizona. Her research spans machine learning with applications in diverse domains including healthcare, agriculture, security, and finance. Dr. He received her PhD in Machine Learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning. She addresses complex data challenges where multiple types of heterogeneity coexist, developing methods for exploring, understanding, characterizing, and predicting real-world data through statistical machine learning techniques. Her recent publications demonstrate a strong focus on graph learning, federated learning, fairness in AI, and neural bandit algorithms. She has developed innovative approaches for class-imbalanced graph learning, Byzantine-robust federated learning, and privacy-preserving graph machine learning. Her work bridges theoretical foundations with practical applications across multiple domains. Her scientific awards include the Amazon Research Award (2025), ACM Distinguished Member (2023), AAAI Senior Member (2023), FAccT Distinguished Paper Award (2022), NSF CAREER award (2016), and multiple IBM Faculty Awards. She has been recognized as an excellent teacher and received Best Paper awards at major conferences including ICDM and SDM. Dr. He directs the iSAIL Lab and leads several major research projects including the AI Institute for Future Agricultural Resilience Management and Sustainability (AIFARMS). She has successfully mentored numerous doctoral students who have become co-authors on her publications. Her research has been funded through prestigious grants including the NSF CAREER award and IBM Faculty Awards.

Sherif KhattabView profile

Academic

• Cybersecurity
• Cloud Computing
• Internet of Things
• +3 more

Sherif Khattab is a Teaching Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. With a Ph.D. in Computer Science from the University of Pittsburgh (2008), he brings extensive expertise in cybersecurity systems with applications across cloud computing, Internet of Things, electronic voting, and Big Data security. His research focuses on the systems aspects of cybersecurity, maintaining an h-index of 16 (Google Scholar) and 10 (Scopus) with over 60 publications. Khattab has successfully supervised more than 15 graduate students throughout his academic career. Prior to his position at Pitt, he served as an Associate Professor at Cairo University's Department of Computer Science, Faculty of Computers and Information. Professor Khattab teaches numerous undergraduate and graduate courses, with particular emphasis on hands-on ethical hacking and security education. His current teaching portfolio includes Algorithms and Data Structures (CS 0445) and Network Security (CS 1653), with extensive experience teaching operating systems, formal methods, and computer networks across multiple semesters. His research publications reveal consistent focus on practical security solutions for emerging technologies, with recent work addressing IoT security frameworks, blockchain-based voting systems, and cloud security challenges. The publication trend shows increasing emphasis on practical implementation aspects alongside theoretical security models. With industry experience from internships at Google Inc., Ericsson Data Networks, and Bosch Research, Khattab bridges academic research with real-world security challenges. His educational background includes a Bachelor's in Computer Engineering from Cairo University (1998) and both M.Sc. and Ph.D. in Computer Science from the University of Pittsburgh (2004 and 2008).

• Cybersecurity
• Cryptography
• Data and information privacy
• +2 more

Dr. Joonsang Baek is an Associate Professor at the School of Computing and Information Technology, University of Wollongong (since 2021). His research focuses on Cybersecurity , Cryptography , and Network Security , with notable contributions to digital signature revocation, attribute-based encryption, and privacy-preserving protocols. Member, Institute of Cybersecurity and Cryptology (since 2017) Supervision interests: Cybersecurity, Applied Cryptography, Network Security Research Highlights: Innovations in withdrawable signatures, secure cloud data sharing, and 5G authentication protocols. His work combines theoretical cryptography with practical applications in edge computing and AI-driven security systems. Grants: Led projects on ransomware datasets, dynamic access control, and TLS optimization. Collaborated with Data61, Discovery Projects, and industry partners on cybersecurity resilience initiatives. Collaborations: Frequent co-author with researchers like Willy Susilo, Cao Cao, and Xinyu Liu. Active in ACM Asia CCS, IEEE Transactions, and LNCS publications.

Peng GaoView profile

Assistant Professor

• Cybersecurity
• Network Security
• AI for Security
• +7 more

Peng Gao is an Assistant Professor in the Department of Computer Science at Virginia Tech. He is affiliated with the Virginia Tech Security & Intelligence Lab and holds a Ph.D. in Electrical Engineering from Princeton University. Prior to his faculty position, he was a postdoctoral researcher at UC Berkeley and held research internships at Microsoft Research, Facebook, and NEC Laboratories America. Education includes a B.Eng. from Shanghai Jiao Tong University (2009-2013), M.A. and Ph.D. from Princeton (2013-2019), and an exchange program at the University of Hong Kong (2012). Key roles include Technical Program Committee memberships for conferences like IEEE S&P, USENIX Security, and CCS. Research focuses on Cybersecurity (APT prevention, network security with P4/eBPF) AI applications (LLMs for security, AI safety) Systems security (attack investigation, threat intelligence) AI for science (molecular/materials prediction) Notable awards include the 2022 Amazon-VT Initiative Faculty Award, CCI Fellowships, and multiple best paper nominations. His lab has received grants from CCI, NSF, and industry partners like Google Cloud and Cisco. Teaching includes courses on Principles of Computer Security (CS 4264) and Blockchain Technologies (CS 5594). He advises ~20 students across Ph.D., MS, and undergraduate levels.

• Cybersecurity
• Privacy-Preserving Computation
• Cryptography
• +2 more

Dr. YANG Guomin is an Associate Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he coordinates the BSc Cybersecurity Track. His research focuses on privacy-preserving cryptography, authentication systems, and secure IoT frameworks. Research spans cryptographic protocols for cloud security, blockchain applications, and federated learning with emphases on efficiency and practical implementation. Recent publications demonstrate innovations in threshold authentication, redactable blockchains, and privacy-aware communication protocols. Advisees include LI Huilin and WANG Jiaheng, with research projects examining hardware-enhanced encryption, biometric authentication policies, and space network security. Work consistently addresses tension between security guarantees and computational efficiency in distributed systems.

• Machine Learning
• Graph Neural Networks
• Data Attribution
• +4 more

Jiaqi Ma is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding dual appointments in the School of Information Sciences and the Siebel School of Computing and Data Science. Their research focuses on machine learning, graph neural networks, and data attribution, with particular emphasis on fairness in AI, large language models, and scalable algorithms. Ma has contributed to frameworks like dattri for efficient data attribution and GraSS for scalable influence functions. Research interests include graph learning (e.g., structural information analysis in text-attributed graphs), fairness in ML models (e.g., mitigating disparities in unlearning processes), and ethical AI applications. Collaborations span topics like data curation (DCA-Bench benchmark) and reinforcement learning transparency (A Snapshot of Influence). Key contributions include improving data removal while maintaining fairness (Fair Machine Unlearning), analyzing LLMs' graph structural utilization, and developing open-source tools like OpenHexAI for explainable ML evaluation. No scientific awards are explicitly listed, but their work reflects significant contributions to trustworthy AI and machine learning systems. Advising and grants: While specific students/grants are unlisted, Ma leads research teams advancing graph learning (e.g., Graph Learning Indexer platform) and safety-critical AI (e.g., unsafe data detection via attribution). Their work bridges theoretical foundations (e.g., influence functions) with practical applications (e.g., MNL model rankings).

Narges MahyarView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Information Visualization
• Digital Civics
• +4 more

Narges Mahyar is an Associate Professor at the University of Massachusetts Amherst in the Manning College of Information and Computer Sciences (CICS) . She is currently on sabbatical with the Aviz team at the Inria Center, University of Paris-Saclay . Her research focuses on Human-Computer Interaction (HCI) , Information Visualization , and Digital Civics , aiming to empower communities through technology. Education: She holds a PhD in Computer Science from the University of Victoria, an MS in Information Technology from the University of Malaya, and a BS in Electrical Engineering from Tehran Azad University. She completed postdoctoral fellowships at the University of British Columbia (2014–2016) and the University of California San Diego (2016–2018). Research Interests: Her work addresses complex societal challenges like climate change, urban planning, and healthcare by designing inclusive technologies. Key areas include civic engagement, data visualization for equity, and integrating AR/VR for public participation. Notable projects include CommunityClick and RisingEMOTIONS , which enhance public input in decision-making. Publications & Awards: With over 50 publications, her work has received prestigious awards including Best Paper Awards at CHI 2023 , Eurovis 2022 , and CSCW 2020 . Her research emphasizes ethical design and inclusivity, particularly involving marginalized communities. Grants & Advising: She has secured grants totaling over $1.4 million, including NSF funding for projects like "Mapping Instability" . Advises PhD student Mahmood Jasim and collaborates with Ali Sarvghad and Pari Riahi on interdisciplinary research. Labs & Teams: Leads the HCI-VIS Lab at UMass, focusing on social computing and visualization. Collaborates internationally with teams like Aviz and Inria on civic tech initiatives.

Gerome MiklauView profile

Professor

• Data Privacy
• Database Systems
• Differential Privacy
• +3 more

Gerome Miklau is a Professor of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences (CICS). He leads the DREAM Lab and focuses on privacy, security, and equitable data management, particularly in differential privacy and fair data analysis. His work includes designing algorithms for private data synthesis, privacy-preserving SQL engines, and auditing systems like AuditGuard. He co-founded Tumult Labs to commercialize privacy technology and advised the U.S. Census Bureau on privacy for the 2020 decennial census. Education: Ph.D. in Computer Science from the University of Washington (2005), B.S. in Mathematics and Rhetoric from UC Berkeley (1995). Research Interests: Differential privacy, secure data management, fairness in algorithms, privacy-preserving data synthesis, and forensic database analysis. His lab develops tools like Ektelo and PrivateSQL, addressing challenges in privacy-accurate tradeoffs and scalable private data processing. Awards: 2006 ACM SIGMOD Dissertation Award, 2007 NSF CAREER Award, 2013 ICDT Best Paper Award, and two ACM PODS Test-of-Time Awards (2020 and 2012). Grants & Service: Co-chair of OpenDP Advisory Board, steering committee member for TPDP workshops, and organizer of the 'Data, Responsibly' Dagstuhl workshop. His service includes program committees for SIGMOD, ICML, and FAT*. He teaches courses on databases, privacy, and programming. Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and collaborations with the Center for Data Science and Cybersecurity Institute at UMass Amherst.

• Cybersecurity
• Data Encryption
• Access Control Systems
• +3 more

Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.

Sohail AhmadView profile

Senior Lecturer

• Sustainable Urban Development
• Climate Change Mitigation
• Housing Inequality
• +3 more

Sohail Ahmad is a Senior Lecturer in Town Planning and Programme Leader of the BSc (Hons) Town Planning Programme at the School of Computing, Engineering and Physical Sciences, University of the West of Scotland. He holds a PhD in City Planning from Seoul National University and is a Chartered Member of the Royal Town Planning Institute (MRTPI), Fellow of the Higher Education Academy (FHEA), and Fellow of the Royal Geographical Society (FRGS). His career includes roles at the University of York (Lecturer in Sustainable Cities), University of Glasgow (contributor to SHLC and CaCHE research initiatives), and postdoctoral fellowships at the Mercator Research Institute on Global Commons and Climate Change (Berlin) and UNU-IAS (Tokyo). He previously served as an Assistant Professor at Vijayawada’s School of Planning and Architecture. Research focuses on four themes: climate change mitigation in cities (e.g., heat pumps in Scotland), housing inequality in Indian subcontinent cities, urbanisation impacts on well-being, and neighbourhood governance in Asia/Africa. Over 35 peer-reviewed articles and book chapters highlight his work, funded by organisations like the Japan Society for the Promotion of Science and Scottish Funding Council. Teaching emphasizes student-centred learning with problem-based approaches. Current research directions include heating/cooling strategies for Global North/South cities. Collaborations target housing developers, energy companies, and think tanks like ICLEI and C40. He serves as External Examiner at Heriot-Watt University (2023–2027).

Daniel J. WeitznerView profile

Researcher

• Privacy
• Data Governance
• Cybersecurity
• +5 more

Daniel J. Weitzner serves as Founding Director of the MIT Internet Policy Research Initiative (IPRI) and 3Com Founders Senior Research Scientist at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His work bridges technical research and public policy across critical domains including privacy frameworks, data accountability systems, cybersecurity risk assessment, and trustworthy AI governance, with significant influence on national and international policy development. Weitzner's research spans multiple interconnected domains: Privacy and Data Governance: Leading the MIT Future of Data Initiative to develop responsible data stewardship frameworks Cybersecurity Risk Measurement: Directing the SCRAM project for quantifiable security assessment methodologies Pandemic Response Technology: Co-leading the PACT project whose privacy-preserving contact tracing architecture directly informed the Apple-Google Exposure Notification system deployed globally Accountability Systems: Developing cryptographic techniques for Practical Accountability of Secret Processes Encryption Policy: Authoring the seminal "Keys Under Doormats" analysis of exceptional access risks Election Security: Conducting the influential security analysis of Voatz internet voting systems that prompted state-level policy changes As an academic mentor, Weitzner advises current graduate students Kevin Liao (EECS PhD/Harvard JD), Nicola Lawfard, Lisa Baer, and Chelsea Conard across MIT's Technology and Policy Program and EECS department. His alumni include Mike Specter (EECS PhD 2022, Georgia Tech faculty), Jonathan Frankle (EECS PhD 2023), and numerous practitioners shaping cybersecurity policy. He teaches foundational courses including "Foundations of Internet Policy" and the MIT-Georgetown joint course "Privacy Legislation Law and Technology". Weitzner directs the MIT Internet Policy Research Initiative as a cross-disciplinary hub connecting computer scientists, legal scholars, and policy experts. His leadership extends to major initiatives including the SCRAM cybersecurity project, the EU-US Privacy Bridges collaboration, and the National Academy of Sciences study committees on authentication technologies. His work consistently translates technical research into real-world policy frameworks through partnerships with government agencies, civil society organizations, and industry stakeholders.

M. Hadi AminiView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Interdependent Networks
• +7 more

M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.

Pedro FonsecaView profile

Assistant Professor

• Computer Security
• Operating Systems
• Distributed Systems
• +4 more

Pedro Fonseca is an Assistant Professor at the Department of Computer Science, Purdue University. He leads the Reliable and Secure Systems Lab, focusing on building reliable and secure core software systems such as operating systems, hypervisors, and distributed systems. His research has been recognized with awards including the NSF CAREER Award and Google Faculty Research Awards. Before Purdue, he completed a postdoc at the University of Washington, working with Arvind Krishnamurthy, Hank Levy, and Xi Wang. He earned his PhD from MPI-SWS and the University of Saarland under Rodrigo Rodrigues. His academic contributions span over 30 peer-reviewed publications in top-tier conferences like SOSP, OSDI, EuroSys, and ASPLOS. He teaches courses including CS503 (Operating Systems), CS592 (Reliable and Secure Systems), and CS408 (Software Testing). He actively serves on program committees for major systems conferences including SOSP, OSDI, EuroSys, and ASPLOS.

Alan MisloveView profile

Professor

• Algorithmic Auditing
• Fairness
• Privacy
• +4 more

Alan Mislove is a Professor of Computer Science at Northeastern University and serves as Senior Associate Dean for Academic Affairs and Interim Executive Director of the Institute of Experiential Artificial Intelligence (EAI). He is also a core faculty member of the Cybersecurity and Privacy Institute and EAI at Northeastern. His research focuses on algorithmic auditing, fairness, and privacy in online systems. Notably, he has testified before Congress on algorithmic discrimination, challenged the CFAA in ACLU litigation, and contributed to landmark FTC and DOJ cases against Meta. His work has been funded by the NSF, Army Research Office, Google, and Amazon Web Services. Mislove’s academic leadership includes roles in Khoury College, where he supports over 2,000 students and faculty. He has advised numerous PhD students, including current research scientist Piotr Sapieżyński. His lab explores algorithmic transparency, PKI security, and middlebox traffic differentiation. Key awards include the 2017 ACM SIGCOMM Test of Time Award and recognition for work on QUIC protocol evaluation. He has authored over 150 publications, including seminal studies on Facebook ad discrimination and Uber’s surge pricing. His teaching spans systems, networks, and social computing courses, emphasizing experiential learning. Mislove actively serves on program committees for IMC, PETS, and other top conferences, and contributed to policy as Deputy U.S. CTO for Privacy (2023–2024).