Kevin Jeffay is the Gillian T. Cell Distinguished Professor and Chair of the Department of Computer Science at the University of North Carolina at Chapel Hill. He earned his Ph.D. in 1989 from the University of Washington. His research and teaching focus on computer networks , real-time systems , operating systems , and multimedia systems , with an emphasis on improving internet communication performance. Current projects include protocols for airborne mesh networks and virtualized application resource management. Scientific Awards : Gillian Cell Distinguished Professor Contact: jeffay@cs.unc.edu | Brooks Building, Room 116 | Chapel Hill, NC 27599-3175
Alex Lombardi is an Assistant Professor of Computer Science at Princeton University, specializing in cryptography and theoretical computer science. His work explores cryptographic proof systems, post-quantum security, and quantum cryptography. Princeton University (Current) Simons-Berkeley Postdoctoral Fellow (Former) MIT (Graduate Training) Visiting Scientist, Cryptography 10 Years Later Program (2025) Education: PhD in Computer Science from MIT (advised by Vinod Vaikuntanathan) Master's Thesis on Provable Instantiations of Correlation Intractability and the Fiat-Shamir Heuristic Dr. Lombardi's research spans foundational cryptography, with a focus on indistinguishability obfuscation, worst-case assumptions, and quantum cryptographic protocols. His work on SNARGs and PPAD hardness has advanced cryptographic proof systems, while his recent projects address quantum verification and post-quantum security. He encourages prospective cryptography students to apply to Princeton's PhD program. His publications highlight advancements in LWE-based cryptography, quantum protocols, and complexity-theoretic foundations. Key themes include secure computation, hash function design, and cryptographic reductions under quantum assumptions. Scientific Awards: Simons-Berkeley Postdoctoral Fellowship Dr. Lombardi serves on program committees for STOC 2025, EUROCRYPT 2025, and other conferences. He has taught courses like COS 433/533 (Cryptography) and COS 533 (Advanced Cryptography) at Princeton.
Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.
Michael J. Freedman is the Robert E. Kahn Professor of Computer Science at Princeton University and co-founder/CTO of Timescale. He received his Ph.D. from NYU’s Courant Institute and degrees from MIT. Current roles: Professor, Co-founder & CTO Affiliations: Princeton University, SNS Group, CITP Associate Education: Ph.D. (NYU), S.B./M.Eng. (MIT) His research spans distributed systems, networking, and security, with innovations like CoralCDN, DONAR, and Ethane. His work impacts decentralized content delivery, software-defined networking, and privacy-enhancing technologies. His recent publications address scalable fusion algorithms, GPU acceleration for data systems, and distributed GPU resource management. These works intersect with cloud infrastructure, network optimization, and security. Scientific honors include: Presidential Early Career Award for Scientists and Engineers (PECASE) Sloan Fellowship NSF CAREER Award Office of Naval Research Young Investigator Award Test of Time Award (Theory of Crypto Conference) ACM SIGOPS Mark Weiser Award He advises graduate students like Sam Ginzburg and Ashwini Raina, who joined Meta AI and Timescale post-PhD. His projects have secured substantial grants, including $110M Series C funding for Timescale. Key labs/teams: Princeton SNS Group Co-founder, Timescale (enterprise data platform) Co-founder, iobeam (IoT analytics, acquired by Timescale) Collaboration with FCC on Consumer Broadband Test Contributions to OpenFlow/SDN standardization
Thomas S. Lontzek is Professor of Economics at RWTH Aachen University, holding the Chair of Computational Economics within the School of Business and Economics since October 2016. His research develops numerical methods for economic decision-making with focus on climate change and environmental risks. His educational background includes economics studies at Maastricht University and UC San Diego (1999-2003), followed by PhD from University of Kiel (2009). Prior positions include Assistant/Senior Assistant at University of Zurich (2010-2016) and visiting scholar at Stanford's Hoover Institution (2012). Lontzek's research spans Economic Growth, Quantitative Macroeconomics, Resource and Energy Economics, Computational Economics, Climate Risk Management, and Decision Making under Uncertainty. His work emphasizes unconventional methods for analyzing economic processes through multidimensional, nonlinear stochastic optimization while incorporating ethical principles into economic analysis for sustainability challenges. His publication record reveals a consistent focus on climate economics, particularly the social cost of carbon, climate tipping points, and integrated assessment modeling. These works demonstrate how accounting for economic and climate risks, especially potential tipping points, necessitates more aggressive climate policies than traditional models suggest, often requiring sophisticated computational techniques to handle high-dimensional uncertainty. 2021 Erik Kempe Award for work on calculating an optimal CO2 tax Lontzek actively mentors students through research seminars and teaching, emphasizing methodological diversity and interdisciplinary approaches. He leads the Global Challenges Research Seminar through the Global Challenges Lab, providing students with opportunities to conduct innovative research using quantitative decision-making techniques for sustainable development challenges. His teaching portfolio for Summer Semester 2025 includes Quantitative Macroeconomics, Sustainable Finance, and specialized research seminars. He heads the Chair of Computational Economics, which includes scientific staff members Dr. Marco Thalhammer, Dr. Yifan Zhao, and Philipp Olivier, M.Sc., working collaboratively on climate economics and computational methods. The chair's research bridges theoretical economic modeling with practical policy applications, developing dynamic stochastic integrated assessment models capable of handling complex climate-economy interactions under uncertainty.
Francesco Locatello is a tenure-track Assistant Professor at the Institute of Science and Technology Austria (ISTA), leading the Causal Learning and Artificial Intelligence lab. He is also an AI Resident at the Chan Zuckerberg Initiative. He holds a PhD from ETH Zürich, co-advised by Gunnar Rätsch and Bernhard Schölkopf. His research focuses on causal representation learning, score matching, and object-centric learning, with applications in machine learning and AI. His work has been recognized with prestigious awards, including the ICML 2019 Best Paper Award and the Hector Foundation Award (2023). Education: PhD in Machine Learning, ETH Zürich (advisors: Gunnar Rätsch, Bernhard Schölkopf) Research Interests: Causal Learning, Causal Representation Discovery, Score Matching Algorithms, Object-Centric Learning, Robust Generalization in AI, and Applications in Vision and Reinforcement Learning. Recent Work Trends: His publications emphasize causal mechanisms in neural representations, scalable causal discovery methods, and improving model generalization through latent space analysis. Recent studies explore geometric representations, mechanistic neural networks, and OOD detection using relative angles. Awards: ICML 2019 Best Paper Award Hector Foundation Award for Outstanding Achievements in Machine Learning (2023) Google Research Scholar Award (2024) Advising & Teams: Supervises a dynamic lab with students and postdocs across ISTA, ELLIS, and partner institutions. Notable advisees include Dingling Yao (ISTA), Riccardo Cadei (co-advised with Cordelia Schmid), and Marco Fumero (now a postdoc at ISTA). Collaborates with leading researchers like Arthur Gretton, Max Welling, and Volkan Cevher. Labs & Initiatives: Leads the Causal Learning and AI lab at ISTA, contributing to ELLIS programs and fostering interdisciplinary collaborations in causal AI.
Amy E. Fraley is an Assistant Professor at ETH Zürich, Department of Chemistry and Applied Biosciences, and leads the Medicinal Chemistry Research Group. Her cross-disciplinary work bridges natural products biosynthesis, pharmaceutical sciences, and environmental sustainability through enzymatic chemistry and biotechnology. BSc in Chemistry, Millersville University of Pennsylvania (2014) PhD in Medicinal Chemistry, University of Michigan College of Pharmacy (2019) Postdoctoral work at ETH Zürich Institute of Microbiology under Prof. Jörn Piel Her research focuses on harnessing biosynthetic enzymes (e.g., halogenases, monooxygenases, Diels-Alderases) to create sustainable bioactive metabolites. She explores natural product biosynthesis in fungi and marine bacteria, targeting disease mechanisms and green chemistry applications. Recent work includes metagenomic studies of Lake Chilika microbial mats and polyketide synthase engineering. Notable scientific awards include the 2024 JSP Fellow at Bürgenstock Conference, 2018 Rackham Predoctoral Fellowship, and multiple University of Michigan honors. Her group secured funding from the ETH4D Doctoral Mentorship Grant and Messerli Foundation . Collaborations span structural biology, synthetic chemistry, and microbiology, with key contributions to flavoenzyme characterization and bioactive compound libraries . The lab emphasizes interdisciplinary training and innovation in biocatalytic tools for organic synthesis.
Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)
Dr Luke Hespanhol is a Senior Lecturer in Design and Computation at the University of Sydney's School of Architecture, Design and Planning. He is affiliated with the Sydney Southeast Asia Centre and Sydney Nano Institute, and previously served as a guest researcher at Aarhus University's Department of Aesthetics and Communication. His work focuses on interactive media installations that foster reflection on human-environment relationships, blending urban design, technology, and social engagement. He holds a PhD in interactive media architecture from the University of Sydney's Design Lab, with research exploring user-centred hybrid urban environments. His doctoral work laid the foundation for his current interests in digital placemaking, urban informatics, and smart cities. Research Interests: - Designing technology-infused public spaces - Media architecture as a tool for inclusive urban development - Ethical implications of AI and blockchain in cultural contexts - Temporal and spatial dimensions of urban interaction design - HCI methodologies for multiperspectival cultural heritage interpretation - Antifragile city frameworks integrating citizen participation His recent publications highlight trends in reimagining urban futures through speculative design, ethical AI systems, and autonomous vehicle interaction paradigms. While no specific scientific awards are listed, his work often addresses critical societal issues like decolonization in technology and climate-resilient infrastructure. Grants and Collaborations: - 2025: 'Waste to Resilience: Sanitation Against Stunting and Climate Vulnerability in Informal Coastal Areas' (Sydney Southeast Asia Centre) - 2022: 'The Metaverse as Vehicle for Aboriginal Digital Inclusion, Wellness and Self-Determination' (Facebook Research) Dr Hespanhol is actively involved in the Media Architecture Institute and contributes to cross-disciplinary initiatives uniting technology, art, and urban planning. His research emphasizes co-creation processes with communities to ensure equitable technological integration in urban systems.
Julian Jara-Ettinger is an Associate Professor of Psychology and Computer Science at Yale University. He holds a Ph.D. from MIT (2016). His research focuses on understanding the cognitive and computational mechanisms underlying human social behavior, including fairness, linguistic communication, gesture, moral reasoning, and pedagogy. He employs interdisciplinary methods such as computational modeling, eye-tracking, cross-cultural studies, and developmental research to bridge psychology and artificial intelligence. Key research areas include the development of social cognition in children, the integration of theory of mind with communication, and the application of cognitive science principles to build socially intelligent machines. His work emphasizes how humans infer others' knowledge, intentions, and desires, with implications for AI safety and ethical systems design. Publications span topics like epistemic inference, moral judgments, and the computational foundations of social interaction. His lab's research often intersects with evolutionary simulations, neural modeling, and cultural psychology. No scientific awards are explicitly mentioned in the provided text. Collaborations involve cross-disciplinary teams addressing challenges in developmental science, AI ethics, and cognitive robotics. His work has practical applications in educational strategies, social policy, and human-AI collaboration frameworks.
Tosiron Adegbija is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he serves as Director of Graduate Studies and Thomas R. Brown Endowed Fellow. He is a member of the Graduate Faculty and actively contributes to research and teaching in computer architecture and embedded systems. Education: PhD in Electrical and Computer Engineering, University of Florida, 2015 MS in Electrical and Computer Engineering, University of Florida, 2011 BS in Electrical Engineering, University of Ilorin, Nigeria, 2005 His research centers on energy-efficient computing with a focus on bio-inspired computer architecture , including spiking neural network (SNN) accelerators and in-memory computing. He also explores domain-specific architectures , adaptable memory systems , and microprocessor optimizations for IoT . His work leverages novel memory technologies like STT-RAM to enhance performance and reduce energy consumption in embedded and resource-constrained systems. Recent publications highlight trends in hybrid SNN acceleration, domain-specific accelerator generation, and system-level design space exploration. His research is increasingly focused on neuromorphic computing, automated hardware design, and ultra-efficient architectures using emerging materials like antiferromagnetic tunnel junctions. Scientific Awards: National Science Foundation (NSF) CAREER Award (2019) Elected IEEE Senior Member (2020) Best Paper Award at IEEE ISVLSI (2014) Teaching Award, University of Arizona (2018) ACM GLSVLSI Travel Award (2015) He advises numerous graduate and undergraduate students, many of whom have pursued careers at institutions like Pacific Northwest National Labs, Micron Technology, and Amazon. He has secured significant funding, including a $1.9M NSF FuSE2 grant for energy-efficient computing. His lab collaborates with UA Physics, CMU, and UNL. He has also developed educational tools for Chipyard and RISC-V, supporting hands-on learning in computer architecture. Labs and Research Teams: Leads a research group focused on bio-inspired and domain-specific computing, fostering innovation in energy-efficient hardware. The lab emphasizes hardware/software co-design, neuromorphic engineering, and real-world deployment in IoT and biomedical applications.
Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.
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
Mirjana Stojilovic is a Researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC), specifically within the Institute of Computer Engineering (IINFCOM) and the Parallel Systems Architecture Laboratory (PARSA). She also serves as a Lecturer in the SSC - Teaching department at EPFL. Her office is located at INJ 235, Station 14, 1015 Lausanne, Switzerland. Mirjana Stojilović received her Dipl. Ing. and Ph.D. degrees from the School of Electrical Engineering, University of Belgrade, in 2006 and 2013, respectively. Her academic journey includes collaborating with the Processor Architecture Laboratory at EPFL as a Guest Researcher from 2010 to 2013, working at the University of Applied Sciences Western Switzerland as a senior researcher from 2013 to 2016, and joining the Parallel Systems Architecture Lab at EPFL in October 2016. Dr. Stojilovic's research spans field-programmable technology, electronic design automation (EDA), and electrical-level attacks and countermeasures for reconfigurable hardware. Her work bridges the gap between hardware design and security, with a particular focus on vulnerability analysis and protection mechanisms for FPGA-based systems in cloud environments. She has made significant contributions to understanding side-channel attacks, fault injection techniques, and secure multi-tenancy solutions for shared hardware resources. Her extensive publication record reveals a clear research trajectory from traditional FPGA design and EDA topics toward increasingly security-focused investigations. Recent work demonstrates deep expertise in power analysis attacks, hardware trojans, and countermeasures for cloud-based FPGA systems, reflecting the growing importance of hardware security in distributed computing environments. Scientific Awards and Recognitions: Best Paper Award at 2016 International Symposium on Electromagnetic Compatibility (EMC Europe 2016) Young Scientist Award at 33rd International Conference on Lightning Protection (ICLP2016) Young Author Best Paper Award at the 20th Telecommunication Forum in Belgrade (TELFOR 2012) EPFL School of Computer and Communication Sciences (IC) Teaching Award (2015) Nominated for Best Paper Award at the International Conference on Field-Programmable Technology (FPT) (2020) Dr. Stojilovic has advised numerous PhD students including Coulon Louis, Pirayadi Rouzbeh, and Shrivastava Shashwat, along with past EPFL PhD students Glamocanin Ognjen and Mahmoud Dina. She has supervised dozens of semester and diploma projects focusing on hardware security, FPGAs, design automation, side-channel attacks, and cloud computing. Her service to the academic community includes serving on program committees for FPGA, FCCM, FPL, and DATE conferences, reviewing for multiple IEEE and ACM journals, and acting as associate editor for IEEE ESL and ACM TRETS. As a member of the Parallel Systems Architecture Laboratory at EPFL, Dr. Stojilovic leads research projects investigating security aspects of reconfigurable hardware systems. Her team works at the intersection of computer architecture, electronic design automation, and hardware security, with particular emphasis on vulnerabilities and protections for shared FPGA resources in cloud environments.
Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.
