Amrita Roy Chowdhury is an Assistant Professor in the Department of Computer Science at the University of Michigan, Ann Arbor. Her research focuses on developing systems that enable safe, decentralized data analytics while ensuring provable privacy guarantees through the synergy of differential privacy and cryptography. Key research areas: Data Privacy, Cryptography, Secure Data Analytics, and Privacy-Preserving Machine Learning. Recent work explores prompt sanitization for LLMs (NDSS 2026), robust graph analysis (ASIACCS 2025), and metric differential privacy (CCS 2024). She has received awards including Best Paper at Private ML@ICLR'24 and Best Poster at ITA'23. Current Ph.D. advisees include Mushtari Sadia, Yiyi Sun, and Samanway Sadhu. Her work spans conferences like IEEE S&P, CCS, USENIX Security, and ICML.
Brent Waters is a Professor at the Department of Computer Science, University of Texas at Austin , where he has been since 2008. He received his Ph.D. in Computer Science from Princeton University (2004) and held a postdoctoral position at Stanford University (2004-2005). His research focuses on cryptography and computer security, with groundbreaking work in Identity-Based Encryption, Functional Encryption, Attribute-Based Encryption, and code obfuscation. He is a founder of Functional Encryption and Attribute-Based Encryption. Education Ph.D., Computer Science, Princeton University (2004) Research Interests Cryptography, Security Protocols Functional Encryption, Attribute-Based Encryption Indistinguishability Obfuscation, LWE-Based Systems Zero-Knowledge Proofs, Key-Dependent Message Security Selected Publications Trends Recent work (2025) addresses adaptive security in broadcast encryption, SNARGs, and multi-authority ABE systems using LWE and bilinear maps. Key themes include collusion resistance, witness encryption, and optimizing cryptographic assumptions like CRS size reduction. Scientific Awards IEEE Fellow (2025), IACR Fellow (2024), ACM Fellow (2021) Simons Investigator (2019), Grace Murray Hopper Award (2015) Presidential Early Career Award (2011), Packard Fellowship (2011) Advising Current Ph.D. students: Shafik Nassar, George Lu Past Ph.D. students: Rachit Garg (2024), Satya Vusirikala (2021), Rishab Goyal (2019), Venkata Koppula (2018), Yannis Rouselakis (2013), Allison Bishop (2012) Contact Email: bwaters@cs.utexas.edu Phone: (512) 232-7464 | Office: GDC 6.810
Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.
Rogério de Lemos is a Senior Lecturer in Computing Science and Director of Postgraduate Research (PGR) at the School of Computing, University of Kent. He previously served as an invited assistant professor at the University of Coimbra, Portugal, and as a Senior Research Associate at the Centre for Software Reliability (CSR) at the University of Newcastle upon Tyne, UK. Dr. de Lemos' research focuses on architecting resilient systems, particularly in resilient AI, self-adaptive software systems, and authorization infrastructures. He belongs to both the Programming Languages and Systems Group and the Cyber Security Group at the University of Kent. His specific research interests include: Software engineering for self-adaptive systems assurances and resilience evaluation Dynamic generation of processes Handling insider threats using self-adaptive authorization Architectural abstractions for fault tolerance Verification and validation of dependable software architectures Software development for safety-critical systems Dependability and bioinspired computing His publication trends show increasing emphasis on practical applications of self-adaptive systems in cyber security contexts, with recent work spanning network traffic analysis, cryptographic function detection, and cloud-edge security architectures. His research bridges theoretical foundations with practical implementations, particularly in cyber security and resilient systems architecture. Dr. de Lemos currently leads the "Collaborative and Confidential Information Sharing and Analysis for Cyber Protection" project funded by the European Union's Horizon 2020 Programme. His past projects include "ADAAS: Assuring Dependability in Architecture-based Adaptive Systems" and multiple collaborations with NCR on sensor fusion and fault tolerance. As Director of Postgraduate Research, he oversees the School of Computing's research degree programs and likely supervises PhD students in resilient systems and cyber security, though specific student names are not listed in the available information.
Tuuli Toivonen is a Professor of Geoinformatics at the Department of Geosciences and Geography, University of Helsinki. She leads the transdisciplinary Digital Geography Lab , which addresses human-scale spatial analytics for sustainable societies. She serves as Vice-Director of Geography Degree Programs (post-2022) and previously held the Director role (2020-2022). After receiving the ERC Consolidator Grant in 2022, she continues advancing open science through active memberships in HELSUS and URBARIA . PhD in Geography (University of Turku, 2006) Specialist Vocational Qualification in Leadership (2023) Life Member, Clare Hall, University of Cambridge (since 2021) Her research focuses on Human-Place Interactions through accessibility/mobility lenses, combining Open Data , Machine Learning , and Spatial Analytics . Key application areas include Urban Geography , Conservation Science , and Governance Policy . Recent publications address Dynamic Cities (2018), Social Media for Conservation (2019), and Environmental Exposure During Travel (2021). Her lab produces datasets like the Helsinki Region Travel Time Matrix series (2014-2023). Scientific Awards: European Open Data Champion (2017) Open Science Price (2017) University of Helsinki Geography Award (2018) She supervises Master's thesis work and serves as Opponent for doctoral defenses across Europe. Her teaching portfolio includes Advanced Geoinformatics and Digital Geographies courses.
Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.
Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.
Hanna Halaburda is an Associate Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where she joined in 2019. Her research lies at the intersection of economics, technology, and digital platforms, with a strong focus on blockchain, cryptocurrencies, and platform competition. She has published extensively in top academic journals and co-authored the seminal book Beyond Bitcoin: The Economics of Digital Currencies . PhD in Economics, Northwestern University MA in Economics, Warsaw School of Economics MA in Philosophy, Warsaw University Her research interests center on the economic implications of digital transformation. She investigates how blockchain technology reshapes trust, governance, and competition in digital markets. Her work explores token design, consensus mechanisms, smart contracts, and the strategic use of decentralization in platforms. She also studies platform competition under network effects, consumer choice, and omnichannel marketing. A recurring theme is how digital technologies alter traditional economic forces and business models. The most recent articles show a strong trend toward analyzing the governance, security, and economic design of blockchain systems. Her work combines rigorous theoretical modeling with empirical insights, often applying game theory and industrial organization frameworks. Topics include permissioned vs. permissionless blockchains, the role of cryptographic tokens in coordination, and the macroeconomic implications of digital currencies. She also contributes to debates on Web3, AI, and the future of digital platforms. Scientific awards and recognitions include: ISR Best Paper Published in 2022 Runner-Up Lead article in RAND Journal of Economics Best Paper Award at Tokenomics 2023 Best Paper Award at WISE 2023 Best Paper Finalist at WISE 2022 and WISE 2021 Hanna Halaburda has advised and collaborated with numerous researchers and institutions. Her co-authors include leading scholars from Harvard, NYU, and international universities. She has received research recognition through best paper awards and invitations to contribute to high-impact journals and policy discussions. Her work has been supported by academic and policy institutions, including the Bank of Canada, where she previously served as a senior economist. She frequently publishes in both academic and practitioner outlets, including Harvard Business Review and Nature Human Behavior , indicating strong translational impact. She is actively involved in research teams focused on digital assets, blockchain governance, and platform economics. While no formal lab is mentioned, her extensive list of working papers and collaborations suggests leadership in a dynamic research group at NYU Stern. Her recent work on DAOs, public crypto mining firms, and CBDCs indicates ongoing, forward-looking research programs with real-world policy and business implications.
Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne, previously affiliated with the University of Innsbruck. He is actively involved in research and leadership in formal methods, automated reasoning, and machine learning for theorem proving. Research Interests: Automated Reasoning and Interactive Theorem Proving Formalized Mathematics and Proof Automation Machine Learning for Logic and Theorem Proving Integration of AI with Proof Assistants (Coq, Isabelle) Dependent Type Theory and Higher-Order Logic His recent publications (2023–2025) span topics in dependently-typed logic, learning for proof guidance, formalization of surreal numbers, and blockchain-based formal methods. The works consistently bridge formal logic with machine learning, emphasizing automation, explainability, and cross-system integration. Scientific Leadership and Projects: Principal Investigator, ERC project FormalWeb3 Lead Developer, CoqHammer , Tactician , ProofWeb WG5 Leader, COST Action EuroProofNet (until 2024) Contributor to HOL(y)Hammer , Isabelle Enigma He supervises multiple PhD students and has mentored several graduates in formal methods and AI. He teaches courses in theoretical computer science, logic, and machine learning. There are no listed awards in the provided data, but his extensive publication record and project leadership indicate significant recognition in the field. Labs and Research Groups: He leads a research group focused on formal methods and learning-based reasoning, collaborating internationally on projects involving proof automation, formal libraries, and semantic technologies.
Dominique Foray is a Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) and holds the Chair of Economics and Management of Innovation (CEMI). He is a member of the Swiss Council for Science, chairman of the KOF Swiss Economic Research Institute's Advisory Board, and a foreign member of Columbia University's Center of Capitalism and Society. His career includes extensive advisory roles, such as with the European Commission's 'Knowledge for Growth' group and Germany's Expert Commission for Research and Innovation. Education: Ph.D. in Economics, University Lumière of Lyon (1984) Habilitation à diriger des recherches, University Lumière of Lyon (1992) Foray's research focuses on the economics of innovation, knowledge-based economy dynamics, technology policy, smart specialization strategies, and institutional frameworks for innovation. He has authored seminal works including The Economics of Knowledge (MIT Press, 2004) and Smart Specialisation (Routledge, 2015). His recent publications explore mission-oriented innovation policies, smart specialization in EU contexts, and knowledge diffusion mechanisms. Foray's work bridges academic theory with practical policy design, influencing regional and national innovation strategies globally. Advising: Past EPFL PhD students: Aouinait Camille Marguerite, Ayoubi Charles Chadi, Baruffaldi Stefano Horst, Bednyagin Denis, Bogers Marcellus, Coffano Monica, Conti Annamaria, Cristelli Gabriele, Gaulé Patrick, Hamdan Livramento Intan Maizurah, Marino Marianna, Muñoz Tellez de Kieffer Viviana Carolina, Pellegrin Claudia, Simeth Markus Foray's institutional roles include leadership in European policy initiatives and contributions to the World Bank's studies on innovation's role in economic growth. His interdisciplinary approach integrates economics, policy analysis, and technology management to address grand societal challenges through innovation.
Scott Hopkins is a Professor in the Department of Chemistry at the University of Waterloo, specializing in Physical Chemistry. His research integrates machine learning with experimental techniques to study ion mobility, mass spectrometry, and spectroscopic analysis. He directs the Hopkins Laboratory, focusing on computational predictions of chemical behaviors and molecular interactions. His work addresses fundamental questions in gas-phase chemistry, cluster formation, and analytical method development. Research interests span physical chemistry, computational modeling, and analytical instrumentation, with a strong emphasis on developing predictive tools for complex chemical systems. Recent investigations explore ion-solvent dynamics, fragmentation mechanisms, and machine-learning applications for spectral interpretation.
Gunnar Kusch is a Senior Research Associate at the Department of Materials Science & Metallurgy, University of Cambridge. His research focuses on defects in semiconductors, porous AlGaN materials, and advanced characterization techniques like cathodoluminescence (CL) and atom probe tomography (APT). He holds a PhD from the University of Strathclyde and leads projects on UV-B LED optimization, nanoscale defect behavior analysis, and semiconductor device design. His work bridges materials synthesis, characterization, and device performance, with applications in energy-efficient lighting and solar cell technology. Key research areas include: Defect engineering in III-nitride semiconductors Porous AlGaN templates for high-efficiency UV emitters Correlative microscopy techniques (CL, EBSD, APT) Composition-structure-property relationships in photovoltaic materials Notable contributions include developing CL-based methods for nanoscale defect analysis and demonstrating improved Cu(In,Ga)S₂ solar cell efficiencies through compositional engineering. His laboratory focuses on translating microscopic insights into macroscopic device improvements.
Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.
Björn Brandenburg is a researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on real-time systems, scheduling algorithms, and operating system design, with a particular emphasis on predictable resource allocation and performance guarantees in multiprocessor and cyber-physical environments. His research interests include real-time response-time analysis (e.g., PROSA ), locking protocols for multiprocessor systems, side-channel mitigation in cloud environments, and the verification of real-time scheduling policies. He has contributed to foundational studies on deadline failure probabilities, self-suspending tasks, and predictable real-time Linux implementations. Scientific awards include recognition for outstanding papers on TimerShield (2017) Offline Equivalence (2017) . His work intersects with practical systems like LITMUSRT and ROS 2, aiming to bridge theoretical guarantees with real-world applications in safety-critical and distributed real-time systems.
Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).
