Find Academic Supervisors

Omkant PandeyView profile

Associate Professor

• Cryptography (Applied and Theory)
• Security
• Privacy
• +3 more

Omkant Pandey is an Associate Professor at Stony Brook University, affiliated with the National Security Institute. He holds a PhD from the University of California, Los Angeles (UCLA). His research focuses on Cryptography, Security, and Privacy, with a particular emphasis on post-quantum cryptographic systems, secure computation protocols, and privacy-preserving technologies. Education: PhD in Computer Science, University of California Los Angeles (UCLA) Research interests span theoretical and applied cryptography, including the design of secure multi-party computation protocols, non-malleable codes, and post-quantum cryptographic primitives. His work also addresses challenges in privacy-preserving data sharing and IoT security in enterprise environments. His recent publications highlight advancements in post-quantum secure computation, zero-knowledge proofs, and cryptographic protocol design. Notable projects include grants such as the CAREER award for concurrent security against quantum adversaries and foundational work on black-box constructions in cryptography. Advising and grants: He advises graduate students in cryptography and has secured grants focusing on post-quantum security and cryptographic protocol development. His work is supported by initiatives like the National Science Foundation’s SaTC and CAREER programs. Labs/Teams: His affiliation with the National Security Institute reflects his contributions to applied security research and collaboration with interdisciplinary teams.

• Data Science
• Computational Social Science
• Misinformation
• +3 more

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Theodora VarvarigouView profile

Professor

• Cloud Computing
• Multimedia Content Processing
• Social Networking Technologies
• +3 more

Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.

Daniel M. RoyView profile

Professor

• Machine Learning
• Statistics
• Probabilistic Programming
• +7 more

Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.

• Machine Learning
• Health Informatics
• Public Health
• +3 more

Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.

Ben GreenView profile

Assistant Professor

• AI Ethics
• Algorithmic Fairness
• Human-Algorithm Interactions
• +5 more

Ben Green is an Assistant Professor in the University of Michigan School of Information and a courtesy Assistant Professor in the Gerald R. Ford School of Public Policy. He holds a PhD in Applied Mathematics from Harvard University with a secondary focus on Science, Technology, and Society. His research examines algorithmic ethics, fairness, and governance, aiming to reduce harms and advance social justice. Notable works include The Smart Enough City (2019) and his forthcoming Algorithmic Realism . He is affiliated with the Berkman Klein Center for Internet & Society at Harvard and the Center for Democracy & Technology. Education: PhD in Applied Mathematics, Harvard University (with secondary field in Science, Technology & Society) BS in Mathematics & Physics, Yale University Research Interests: Algorithmic fairness in public policy Human-algorithm interaction dynamics Regulatory frameworks for AI Equity-centered data science practices Urban technology policy His recent publications explore themes like the limitations of human oversight in algorithmic systems, the sociotechnical challenges of implementing ethical AI, and the intersection of legal reasoning with computational systems. His writing emphasizes actionable solutions to systemic biases in algorithmic governance. Ben’s current projects include advancing algorithmic realism – a framework for grounding data science in socially just practices – and analyzing how counterfactual explanations influence judicial decisions. He serves on multiple interdisciplinary advisory boards and frequently collaborates with policymakers to translate research into actionable strategies.

Yonghwi KwonView profile

Visiting Assistant Professor

• Software Systems Security
• Cyber Forensics
• Software Engineering
• +3 more

Yonghwi Kwon is a Visiting Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on software systems security, cyber forensics, and software engineering. He received the CAREER Award for developing dynamic defenses against cyber threats. His work emphasizes securing software from cyber attacks, recovering forensic evidence, and improving software testing and reverse engineering techniques. Key research areas include memory safety mechanisms, automated vulnerability detection in web applications and mobile systems, and forensic analysis of phishing campaigns. He has pioneered frameworks like CMASan for memory allocator-aware sanitization and Racedb for detecting race conditions in database-backed systems. His contributions span cloud security automation, kernel exploitation analysis, and embedded system fuzzing. Notable achievements include the 2025 CAREER Award supporting his dynamic defense research, and impactful publications in areas like Android information leakage detection (DryJIN), Bluetooth protocol fuzzing (BTFuzzer), and autonomous driving bug discovery (Drivefuzz). His work bridges theoretical computer science with practical cybersecurity solutions.

Brad CampbellView profile

Associate Professor

• Ubiquitous Computing
• Internet of Things (IoT)
• Embedded Systems
• +12 more

Brad Campbell is an Associate Professor in the Department of Computer Science and Electrical and Computer Engineering at the University of Virginia, where he is a member of the Link Lab, a cross-disciplinary research group focused on cyber-physical systems. His research centers on designing and building scalable, effective, and unobtrusive embedded systems for the Internet of Things, with applications in smart buildings, smart cities, and personal health. His work spans hardware design, networking, and cloud infrastructure, with a strong emphasis on energy-harvesting systems, low-power wireless communication, and resilient embedded operating systems. He has led projects such as the Living Link Lab, a heavily instrumented smart building testbed, and has developed open-source platforms for self-powered sensing and IoT ecosystems. His recent publications reflect a strong trend toward privacy-preserving federated learning, contactless occupancy sensing using WiFi and light, decentralized edge computing, and sustainable IoT systems. These works are published in top venues including SenSys, BuildSys, MobiCom, and IPSN, indicating a high impact in the systems and networking community. NSF CAREER Award (2022) Best Paper Award at DFHS’19 Multiple graduate fellowships and teaching awards for his students UVA Engineering Endowed Graduate Fellowships Link Lab Seminar Award CPS Rising Star recognition Brad Campbell has advised numerous PhD and master’s students, many of whom have gone on to academic and industry roles. He has secured significant research funding, including from the NSF, and has contributed to curriculum development in cyber-physical systems. He is actively involved in teaching courses on computer networking, IoT, and operating systems, and has co-taught wireless IoT courses across multiple institutions. His lab focuses on real-world deployment of IoT systems, emphasizing scalability, fault tolerance, and long-term sustainability. He continues to push the boundaries of what embedded systems can achieve in everyday environments, from homes to cities.

Irina RishView profile

Professor

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +13 more

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.

Keith W. RossView profile

Professor

• Artificial Intelligence
• Deep Reinforcement Learning
• Deep Learning
• +9 more

Keith W. Ross is a Professor of Computer Science at NYU Abu Dhabi, with affiliated appointments at NYU Tandon School of Engineering, Courant Institute of Mathematical Sciences, NYU Center for Data Science, and NYU Shanghai. He joined NYU Abu Dhabi in September 2023 after serving as Dean of Computer Science, Data Science, and Engineering at NYU Shanghai from 2013 to 2023. His educational background includes a BS from Tufts University, an MS from Columbia University, and a PhD in Computer and Control Engineering from the University of Michigan. BS, Tufts University MS, Columbia University PhD, University of Michigan Professor Ross's research focuses on artificial intelligence, particularly deep reinforcement learning, deep learning, and reasoning in large language models. He has also made significant contributions to Internet privacy, peer-to-peer networking, network measurement, stochastic modeling, queuing theory, and Markov decision processes. His teaching includes courses in Machine Learning and Reinforcement Learning. Although no recent articles are listed in the provided text, his research trajectory emphasizes modern AI, foundational models, and network systems, reflecting a strong interdisciplinary approach bridging theoretical computer science with real-world applications. His scientific honors include: ACM Fellow IEEE Fellow Multiple best paper awards Professor Ross has held leadership roles in academia and industry. He was the Leonard J. Shustek Professor at NYU Tandon (2003–2013), professor at the University of Pennsylvania (1985–1998), and professor at Eurecom Institute (1998–2003). He co-founded Wimba in 1999, serving as CEO and CTO, a company focused on voice and video applications for online learning, later acquired by Blackboard in 2010. He has received media attention for his work in privacy, featured in The New York Times, NPR, Bloomberg Television, and others. He is affiliated with multiple research centers, including the NYU Center for Data Science and the Center for Data Science and Artificial Intelligence at NYU Shanghai. His global academic presence across NYU’s campuses underscores his role in shaping international computer science education and research.

Oliver GasserView profile

Researcher

• Internet Measurement
• IPv6
• BGP
• +9 more

Oliver Gasser is a researcher at the Max Planck Institute for Informatics (MPI-INF) and the Technical University of Munich (TUM), where he has co-lectured courses such as Advanced Computer Networking and Master Course Computer Networks. His research focuses on Internet measurement, security, and privacy, with a strong emphasis on IPv6, DNS, BGP, and web tracking technologies. His research interests include Internet measurement, IPv6 deployment, BGP security, DNS infrastructure, web tracking, privacy technologies, and network resilience. He has made significant contributions to understanding IPv6 hitlists, router fingerprinting, hypergiant content delivery networks, and consent banner manipulation on the web. His work combines large-scale active measurements with data analysis to uncover systemic issues in Internet infrastructure and privacy practices. The recent publications highlight a consistent focus on measurement-driven research across networking, security, and privacy. Trends include analyzing IPv6 adoption patterns, detecting covert tracking mechanisms in web cookies, evaluating security of Internet protocols like DNS and BGP, and improving measurement methodologies for large-scale network studies. His work often involves developing open tools and datasets that advance reproducibility in networking research. PAM 2023 Best Paper Award TMA 2023 Best Paper Award TMA 2023 Fast Track Award PAM 2018 Best Paper Award IRTF Applied Networking Research Prize 2018 IMC 2017 Community Contribution Award TMA 2017 Best Dataset Award CoNEXT 2023 Community Contribution Award Oliver Gasser has advised or co-advised over 40 master’s and bachelor’s theses at MPI-INF and TUM, demonstrating strong mentorship in academic research. He has led several measurement projects that have received external funding, including development of the IPv6 Hitlist Service and tools for analyzing web tracking. His community service includes extensive participation in program committees for major networking conferences such as IMC, CoNEXT, PAM, and TMA. He leads and contributes to several open research initiatives including the IPv6 Hitlist Service, SNMPv3 Measurement Service, MPTCP Measurement Service, DNS Observatory, and the BannerClick tool for automated cookie banner interaction. These platforms provide valuable resources for the global networking research community and support reproducible science.

Chen LiuView profile

Assistant Professor

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +9 more

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

Matteo MontecchiView profile

Senior Lecturer

• Consumer Behaviour
• Digital Marketing
• Artificial Intelligence in Marketing
• +12 more

Dr. Matteo Montecchi is a Senior Lecturer (Associate Professor) in Marketing at King’s Business School, King’s College London, where he also serves as Theme Lead for Sustainable Consumption at the Centre for Sustainable Business and Programme Director for the Strategic Marketing (Open Executive) programme. He teaches core modules such as Consumer Behaviour, Digital Marketing, and Research Methods, and leads executive education initiatives. Previously, he was a Senior Lecturer at the London College of Fashion, University of the Arts London, and has held visiting positions at Milan Politecnico, Università Cattolica del Sacro Cuore, and Università Federico II. Montecchi earned his PhD in Marketing from King’s College London, an MRes in Marketing from Birkbeck College, an Executive Master in Marketing and Sales from SDA Bocconi and ESADE, and a Bachelor’s in Business Economics from the University of Modena and Reggio Emilia. He also holds a PgCert in Academic Practice and is a Fellow of the Higher Education Academy (FHEA). His research focuses on the digital transformation of marketing, particularly the role of artificial intelligence and blockchain in enhancing transparency and trust. He investigates ethical engagement with diverse consumer groups, inclusion, and corporate responsibility, with a special interest in marginalized communities. His work integrates consumer behavior, digital interactions, and decision-making with technological innovation, offering actionable insights for sustainable and responsible business strategies. The most recent 15 publications reflect a strong trend in AI in retail, brand transparency, consumer trust, ethical data use, voice technologies, and social sustainability. His research spans journals in marketing, psychology, interactive marketing, and business ethics, demonstrating interdisciplinary impact. Scientific Awards: KingsCAT: Capture and Analysis Tool for Social Media Research at King’s College London (2024) – Recognized as part of a collaborative team for advancing digital research infrastructure and media analysis. Montecchi is actively involved in PhD supervision and is accepting new students. He has led research projects using social media analytics (KingsCAT) and has contributed to strategic debates on grand challenges in marketing. His work connects academic research with real-world managerial applications, particularly in digital strategy and ethical innovation. He is a key contributor to interdisciplinary research through tools like KingsCAT, which supports collaborative social media analysis, and his publications often involve co-authorship with experts in data science, psychology, and organizational behavior.

Nyteisha BookertView profile

Assistant Professor

• Cybersecurity
• Internet of Things
• Privacy
• +3 more

Dr. Nyteisha Bookert serves as an Assistant Professor of Cybersecurity in the Department of Computer Science & Mathematics at South Carolina State University, where she advances research in emerging technology security. Her academic foundation includes a Ph.D. from North Carolina A&T State University and undergraduate training at South Carolina State University. Education B.S. in Computer Science, South Carolina State University M.S. in Computer Science, North Carolina A&T State University Ph.D. in Computer Science, North Carolina A&T State University Research Focus Her work targets critical vulnerabilities in Internet of Things ecosystems, particularly medical devices (IoMT), employing machine learning to dissect privacy policies and mitigate consumer risks. This research bridges theoretical security frameworks with real-world applications in connected healthcare technologies. Professional Engagement Dr. Bookert contributes to national cybersecurity through prior government agency collaborations while leading community initiatives that introduce underrepresented K-12 students to cybersecurity fundamentals, building pipeline diversity in STEM fields.

Fabio PierazziView profile

Associate Professor

• Adversarial Machine Learning
• Concept Drift Mitigation
• Malware Analysis
• +4 more

Dr Fabio Pierazzi is an Associate Professor in Information Security at the Department of Computer Science, University College London. His research focuses on enhancing systems security through AI, particularly in environments where attackers rapidly adapt to defenses. He investigates adversarial attacks, concept drift mitigation, and explainability of ML-based security systems. Research emphasizes adversarial machine learning in security contexts Works on practical applications in malware analysis and network intrusion detection Explores concept drift robustness and problem-space constraints Collaborates with industry to improve real-world security solutions His publications span top-tier venues like IEEE Security & Privacy, ACM CCS, and USENIX Security. Key themes include adversarial robustness, security evaluation methodologies, and AI's limitations in practice. He supervises research degrees and provides consultancy for security projects.