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Jonathan CullenView profile

Professor

• Resource Efficiency
• Circular Economy
• Decarbonization
• +3 more

Jonathan Cullen is Professor of Sustainable Engineering at the University of Cambridge and President of Fitzwilliam College, specializing in resource efficiency and decarbonization through top-down analysis of industrial material and energy systems. His work bridges academic research with industry applications across energy-intensive sectors. Education: Bachelor's in Chemical and Process Engineering, University of Canterbury, New Zealand MPhil in Engineering for Sustainable Development, University of Cambridge PhD in Engineering Fundamentals of Energy Efficiency, University of Cambridge His research develops metrics for quantifying energy and material consequences of production systems, focusing on circular economy implementation, minimum energy requirements, and zero-carbon transition pathways. Key applications target cement, steel, plastics, and petrochemicals where he pioneers methods like exergetic analysis and material flow accounting to expose carbon lock-ins and circularity opportunities. Recent publications reveal three dominant trends: (1) Frameworks for theoretical minimum energy requirements across industrial processes, (2) Geopolitical analysis of critical mineral flows and ownership structures, and (3) Circular economy metrics for plastics and construction materials. These consistently employ system-scale modeling validated through industry partnerships. Research Funding: Lead: C-THRU ($4M, VKRF) - carbon clarity in petrochemical supply chains Co-I: UK FIRES (£5.2M, EPSRC) - industrial decarbonization program Co-I: CirPlas (£1.25M, UKRI) - plastic waste elimination 7+ projects (EPSRC, Innovate UK, Horizon 2020) Academic Leadership: Teaching: Energy Systems and Policy (MPhil in Energy Technologies) Undergraduate supervision in Materials/Mathematics Graduate Tutor at Fitzwilliam College IPCC AR6 Lead Author (Industry Chapter) He directs the Resource Efficiency Collective, which develops open-source tools like Mat-dp for material demand projections and Starter Data Kits for energy planning. Current work focuses on scaling circular business models for construction retrofitting and quantifying geopolitical risks in critical mineral supply chains.

Ken OnoView profile

Professor

• Number Theory
• Combinatorics
• Algebra
• +6 more

Ken Ono is the STEM Advisor to the Provost and the Marvin Rosenblum Professor of Mathematics at the University of Virginia. He also holds courtesy appointments as Professor of Electrical and Computer Engineering, Professor of Data Science, and Professor of Statistics. Additionally, he is a Fellow of the Shannon Center for Advanced Studies and serves on the Institute for Advanced Study board of trustees and the National Security Agency Advisory Board. His educational background includes a Ph.D. in Mathematics from UCLA (1993) and a B.A. in Mathematics from the University of Chicago (1989). Throughout his distinguished career, Ono has held leadership roles including Vice President of the American Mathematical Society, Chair of the Mathematics Section of the American Association for the Advancement of Science, and Chairman of the Department of Mathematics at UVa. Ken Ono specializes in Number Theory, Combinatorics, Algebra, and Arithmetic Geometry, with recent work expanding into sports analytics and swimming optimization. His research has produced numerous publications on partition theory, modular forms, and related areas, continuing the mathematical legacy of Srinivasa Ramanujan. His recent publications demonstrate a continued focus on partition theory, modular forms, and their connections to prime numbers and combinatorial structures. The work shows sophisticated connections between classical number theory and modern mathematical physics, with applications in cryptography and data science. Guggenheim Fellowship David and Lucile Packard Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Presidential Early Career Award (2000) National Science Foundation Director's Distinguished Teaching Scholar (2005) University of Chicago Alumni Medal for Professional Achievement (2023) Fellow of the American Mathematical Society Ono has advised 34 PhD students and 17 postdocs, mentoring numerous award winners including 2 Breakthrough Mirzakhani Prize winners, 9 Morgan Prize winners, and 9 Schafer Prize winners. He founded and directs the Spirit of Ramanujan Global STEM Talent Search, which has awarded 125 budding scientists from 23 countries. His research grants have supported extensive REU programs, with continuous undergraduate research opportunities for 25 years. Beyond academia, Ono serves as a technical consultant for elite swimmers, has worked as an Associate Producer on the film The Man Who Knew Infinity , and is actively involved with the Infinity Arts Foundation. His interdisciplinary work spans from pure mathematics to practical applications in sports science, where he has developed data-driven approaches to optimize swimming performance and served as a consultant for Olympic medalists. He co-founded the Velocity Swim Camp with Olympic coach Todd DeSorbo, combining mathematical analytics with elite swimming training.

David HongView profile

Assistant Professor

• Big Data Analysis
• Low-Rank Matrix and Tensor Methods
• Unsupervised Learning
• +5 more

David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.

Pinar AkmanView profile

Professor

• Competition Law
• Digital Economy
• Market Power
• +6 more

Professor Pinar Akman is a Professor of Law at the University of Leeds, specializing in competition law, particularly in digital markets. She holds positions as a judge at the UK's Competition Appeal Tribunal and a member of the Financial Conduct Authority's Innovation Advisory Group. Her roles include former directorships of the Jean Monnet Centre of Excellence on Digital Governance (2019-2023) and the Centre for Business Law and Practice (2017-2020). She was promoted to full Professor within five years of her PhD. Education: Holds LLB and LLM from the University of Ankara Law School (ranked first overall undergraduate), and a PhD from UEA Law School/ESRC Centre for Competition Policy, funded by ORSAS and UEA Scholarships. Her doctoral work focused on abuse of dominance under EU competition law. Research interests include competition law's application to digital markets, market power, online platforms, and the interplay of competition law with consumer and contract law. She emphasizes legal and economic foundations of competition policy, including historical origins and modern challenges in regulating tech giants. Awards: Philip Leverhulme Prize (£100k), Women@Competition 5-Star Award, and recognition as a top global competition scholar by Global Competition Review. Her research on digital markets and antitrust policy has been funded by UKRI, ESRC, and international bodies. Advising & Grants: Led a £4M ESRC grant for the Centre for Competition Policy (2009), and a UKRI-funded empirical study on digital platforms. Provides policy advice to the IMF, World Economic Forum, and international organizations like UNCTAD and OECD. Engages in global policymaking through roles with the Canadian Chamber of Commerce, ASEAN, and Bhutan. Labs & Teams: Senior Fellow at George Washington University's Competition & Innovation Lab, Affiliated Scholar at Berkeley's Dynamic Competition Initiative, and member of Stanford Law School's Computational Antitrust Advisory Board. Editorial roles include World Competition: Law and Economics Review and Oxford Competition Law.

David Lydon-StaleyView profile

Associate Professor

• Health Communication
• Substance Use
• Media Effects
• +7 more

David Lydon-Staley is an Associate Professor at the Annenberg School for Communication, University of Pennsylvania, where he serves as Principal Investigator of the Addiction, Health, & Adolescence (AHA!) Lab. His research integrates neuroscience with communication science to examine substance use, media effects, and curiosity using fMRI, ecological momentary assessment, and network analysis. Education includes a Ph.D. in Human Development & Family Studies from The Pennsylvania State University, an M.S. from Penn State, an M.F.A. in Creative Writing from Drexel University (2025), and a B.A. in Psychology and English Literature from Trinity College Dublin. Research focuses on three interconnected areas: curiosity in media environments and health communication, media engagement in emotion dynamics, and substance use through dynamic network perspectives. Work emphasizes intensive longitudinal measurement of brain-behavior interactions during daily life. Recent publications (2023-2025) predominantly explore tobacco behavior, neural mechanisms of addiction, curiosity modulation, and social media's emotional impacts. Articles demonstrate consistent themes: fMRI analysis of inhibitory control, real-time geospatial tracking of smoking triggers, curiosity-based health messaging, and emotion regulation networks. Research has been supported by the National Institute on Drug Abuse, Jacobs Foundation, International Society for Behavioral Development, Center for Curiosity, and Brain & Behavior Research Foundation. Leads the Addiction, Health, & Adolescence (AHA!) Lab investigating substance use through network science approaches. Collaborates with the Complex Systems Lab at Penn's Department of Bioengineering.

• Computer Vision
• Computer Graphics
• Machine Learning
• +3 more

Gerard Pons-Moll is a Professor at the University of Tübingen, endowed by the Carl Zeiss Foundation, and heads the Emmy Noether independent research group 'Real Virtual Humans'. He is a core faculty member at the Tübingen AI Center, a senior researcher at the Max Planck Institute for Informatics (MPII), and faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the Saarland Informatics Campus. His research focuses on computer vision, graphics, and machine learning, particularly in creating virtual human models and analyzing human motion from video and sensor data. Education: PhD (with distinction) in 2014 from Leibniz University of Hannover, Master's in Telecommunications Engineering (Northeastern University, 2008), and B.S./M.Sc. in Telecommunications Engineering from the Technical University of Catalonia (2002–2008). Research Interests: 3D human modeling, pose estimation, human-object interaction, and applications in industry and research. His work emphasizes real-world applications like virtual avatars and motion capture systems. Awards: Emmy Noether Grant (2018), German Pattern Recognition Award (2019), Google Faculty Research Award (2019), and multiple best paper awards at top conferences (BMVC’13, Eurographics’17, 3DV'18, CVPR'20). Advising & Grants: Served as program chair of 3DV 2021, area chair for ECCV, CVPR, and IJCAI. Active in reviewing for DFG, ANR, and ISF. Supervises research in areas like neural rendering frameworks (Blendify) and synthetic data generation (STAGE). Labs/Teams: Leads the Emmy Noether group and collaborates with MPII, Tübingen AI Center, and IMPRS-IS on projects like XNect (real-time 3D motion capture) and Human 3Diffusion (avatar creation).

Yunan YangView profile

Assistant Professor

• Applied Mathematics
• Inverse Problems
• Optimal Transportation
• +3 more

Yunan Yang is the Goenka Family Assistant Professor in Mathematics at Cornell University, within the Department of Mathematics, College of Arts and Sciences. He holds a Ph.D. from the University of Texas at Austin (2018), supervised by Prof. Björn Engquist. Previously, he was a Courant Instructor at NYU (2018–2021), Simons-Berkeley Research Fellow (2021), and Advanced Fellow at ETH Zürich (2022–2023). His research focuses on computational mathematics, including inverse problems, optimal transport, machine learning, and nonconvex optimization. Notable contributions include applications of optimal transport to seismic inversion and PDE-constrained optimization. He has advised numerous students, including undergraduates and Ph.D. candidates at Cornell and other institutions. Yang teaches courses such as MATH 6220 (Applied Functional Analysis) and has published extensively in journals like SIAM Journal on Scientific Computing and Communications on Pure and Applied Mathematics. His work bridges theoretical foundations with practical applications in geophysics and computational science.

Rainer GemullaView profile

Professor

• Machine Learning
• Data Processing
• Knowledge Graphs
• +3 more

Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.

Stuart KurtzView profile

Professor

• Theoretical Computer Science
• Computational Complexity Theory
• Type Theory
• +4 more

Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.

Robert GorwaView profile

Research Fellow

• Technology Policy
• Transnational Regulation
• International Political Economy
• +4 more

Robert Gorwa is a Research Fellow at the WZB Berlin Social Science Center, specializing in technology policy, transnational regulation, and international political economy. His work focuses on platform governance, digital policy, and AI governance, with a particular interest in how governments and institutions shape online content moderation and platform ecosystems. He holds a DPhil in Politics and International Relations from the University of Oxford, an MSc in Media and Communication from the Oxford Internet Institute, and a BA from the University of British Columbia. Education: Doctorate (DPhil) in Politics and International Relations, University of Oxford MSc in Media and Communication (Internet Studies), Oxford Internet Institute Bachelor's degree from the University of British Columbia Research Interests: Interdisciplinary studies on platform governance, AI intermediaries, automated content moderation, transnational digital policy, and the intersection of law, technology, and political economy. His 2024 book, The Politics of Platform Regulation , explores how governments influence online content moderation globally. Key Contributions: Founded and coordinates the Platform Governance Research Network (PlatGovNet) Organizes the WZB's Platform Politics and Policy Seminar series Authored influential works on AI governance, platform lobbying, and live-streaming content moderation Awards: Top departmental honors for his doctoral dissertation at the University of Oxford. Grants & Collaborations: Holds fellowships at the Centre for International Governance Innovation (CIGI), Weizenbaum Institute, and has collaborated with institutions like the Center for Democracy and Technology (CDT) and Stanford's Center for Philanthropy and Civil Society. Labs & Teams: Active in the Platform Governance Research Network and affiliated with the WZB's research group on Globalization, Work, and Production.

Paolo PapottiView profile

Associate Professor

• Data Management
• Data Integration
• Data Cleaning
• +3 more

Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.

Richard BellamyView profile

Professor

• Democratic Theory
• Comparative Politics
• EU Studies
• +11 more

Professor Richard Bellamy is a leading scholar in political science and constitutional theory at University College London (UCL) and serves as a Senior Fellow at the Hertie School, Berlin. He holds a PhD from the University of Cambridge and has held visiting positions across Europe and Australia. Doctor of Philosophy , University of Cambridge (1983) MA Cantab , University of Cambridge (1979) His research spans democratic theory , republicanism , constitutionalism , and EU studies , with a focus on Italian political thought and global ethics . His recent articles explore rule of law , data ethics , and EU differentiated integration . He has authored over 150 publications and 11 monographs, including the acclaimed Political Constitutionalism . Key awards include: Fellow of the British Academy (2022) 2016 PADEMIA Research Award Serena Medal (2012) David and Elaine Spitz Prize (2009) Fellow of the Academy of Social Sciences (2008) Member, Academia Europaea (2024) Bellamy has supervised 24 PhD students and led major Leverhulme , ESRC , and European Commission projects. His current work includes Defending the Political Constitution (Oxford UP) and The Democratic Prince (political leadership study). He co-edits The Cambridge Companion to Constitutional Theory and The Cambridge Dictionary of Political Thought .

• Machine Learning
• Coding Theory
• Optimization
• +5 more

Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.

Hubert WagnerView profile

Assistant Professor

• Computational Geometry
• Topological Data Analysis
• Machine Learning
• +3 more

Hubert Wagner is an Assistant Professor in Data Science at the University of Florida's Department of Mathematics, part of the College of Liberal Arts and Sciences. He teaches courses such as Computational Applied Topology and Linear Algebra for Data Science. Prior to joining UF, he completed a postdoctoral fellowship at IST Austria under Herbert Edelsbrunner and earned his PhD from Jagiellonian University under Marian Mrozek. His research focuses on developing topological algorithms and tools for practical applications in fields like astrophysics and biomedicine. Notably, he received the 2022 Google Research Scholar Award in Algorithms & Optimization for his work on Bregman divergences and topological methods in high-dimensional data analysis. His research interests span computational geometry, topological data analysis, machine learning, and algorithm engineering. Recent projects include optimizing topological computations for large-scale imaging data (e.g., cosmic microwave background analysis) and detecting adversarial attacks on neural networks using persistent homology. He emphasizes practical applications through collaborations with industry and interdisciplinary research. Hubert is actively involved in academic service, including course development and mentoring. His work has been published in leading venues such as SoCG and NeurIPS, with a focus on bridging theoretical foundations and real-world computational challenges.

Anthony D. JosephView profile

Professor

• Computer and Network Security
• Distributed Systems
• Mobile Computing
• +6 more

Anthony D. Joseph is a Chancellor's Professor in the Department of Computer Science at the University of California, Berkeley, within the College of Engineering. He is a faculty member in the Computer Science Division and part of the RISE Lab and AMP Lab at UC Berkeley. His research spans multiple domains in computer science with a focus on security, distributed systems, and networking. Education: 1998, Ph.D., Computer Science, MIT 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Professor Joseph's primary research interests include Computer and Network Security, Distributed Systems, Mobile Computing, Wireless Networking, Software Engineering, Operating Systems, Genomics, Secure Machine Learning, and Datacenters. His work has significant implications for both theoretical computer science and practical applications in industry. He leads multiple research projects including Mesos, SecML (Secure Machine Learning), D-Trigger, DETER, and Tapestry/Brochure. His research has been instrumental in advancing the fields of distributed systems and security, particularly in the context of machine learning applications. His publications reflect a strong focus on the intersection of security and distributed systems, with recent work emphasizing secure machine learning techniques and resource management in data centers. Professor Joseph's research has evolved from foundational work in networking and distributed systems to addressing contemporary challenges in cloud computing and AI security. Scientific Awards: Diane S. McEntyre Award for Excellence in Teaching Computer Science (2007) NSF Faculty Early Career Development Award (CAREER) (2000) Okawa Research Grant (1999) Professor Joseph has advised numerous graduate and undergraduate students, many of whom have gone on to make significant contributions in academia and industry. His research has been supported by various grants, including the NSF CAREER award. He has been actively involved in teaching core computer science courses including CS162: Operating Systems and Systems Programming and CS262: Advanced Topics in Computer Systems. He leads several research groups including the AMP Lab (which focuses on data analytics) and has been instrumental in projects like Mesos (for resource sharing in data centers) and SecML (focusing on the security of machine learning systems). His labs work on cutting-edge problems at the intersection of systems, networking, and security, with applications ranging from cloud computing to critical infrastructure protection.