Heather Zheng is the Neubauer Professor of Computer Science at the University of Chicago, co-directing the SAND Lab (Security, Algorithms, Networking and Data) with Prof. Ben Y. Zhao. She holds IEEE (2015) and ACM (2023) Fellowships, and was recognized as MIT TR35 (2005) for cognitive radio research. Her work bridges mobile/IoT security, adversarial ML, and generative AI ethics. Zheng earned her PhD in Electrical and Computer Engineering from University of Maryland in 1999, with prior roles at Bell-Labs, Microsoft Research Asia, and UCSB. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park (1999) Research Focus: Explores cutting-edge challenges in: Security implications of mobile/IoT sensors (e.g., bracelet-of-silence countermeasures) Adversarial machine learning defense mechanisms (e.g., Blacklight attack detection) Generative AI governance (Glaze, NightShade copyright tools) Awards: ACM Fellow (2023) IEEE Fellow (2015) World Technology Network Fellow Labs & Collaborations: Leads SAND Lab focusing on security, ML, and networked systems. Active in UChicago's Systems Group exploring cloud/edge computing architectures.
Furkan Alaca is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. His research focuses on user authentication systems, addressing security and usability challenges. He holds a Ph.D. (2018) in Computer Science from Carleton University, an M.A.Sc. (2012) in Electrical and Computer Engineering, and a B.Eng. (2010) in Communications Engineering, all from Carleton University. His academic career includes teaching roles at Queen's University and the University of Toronto Mississauga, where he taught courses such as Cryptography, Cybersecurity, and Discrete Mathematics. He is affiliated with Queen's Security Research Group and Computer Security Research Lab. Research interests include computer and internet security, usable security, authentication mechanisms, and systems security. He has contributed to advancements in web authentication frameworks, malware analysis, and privacy-preserving technologies. His work spans conferences like IEEE and ACM, with notable publications in cybersecurity, machine learning, and network efficiency. Current teaching includes CISC 447 (Introduction to Cybersecurity) and CISC 468 (Cryptography). He has advised on courses ranging from undergraduate programming to graduate-level security topics.
Rachel Cummings is an Associate Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University, with a courtesy appointment in the Department of Computer Science. She serves as Co-chair of the Cybersecurity Research Center at Columbia’s Data Science Institute. Previously, she was faculty at Georgia Tech’s School of Industrial and Systems Engineering (ISyE), holding a courtesy appointment in Computer Science. She holds a Ph.D. in Computing and Mathematical Sciences from Caltech, with research visits at UPenn, Hebrew University, Microsoft Research, and the Simons Institute. Her research focuses on differential privacy, integrating tools from machine learning, algorithm design, economics, optimization, statistics, HCI, usable security, and public policy. She emphasizes practical applications of theoretical privacy-preserving methods. Key roles include Managing Editor for the Journal of Privacy and Confidentiality , service on the ACM U.S. Technology Policy Council, IEEE Standards Association, and Future of Privacy Forum’s Advisory Board. She has advised on the U.S. Census Bureau’s Scientific Advisory Council and served as a Fellow at the Center for Democracy & Technology. Recent work includes papers on privacy elasticity, synthetic control methods, and differential privacy under class imbalance. Her awards include NSF CAREER, DARPA Young Faculty Award, and Best Paper recognitions at DISC, CCS, and SaTML. She actively chairs conferences (e.g., DEF CON Crypto) and mentors students like Tingting Ou (PhD 2025) and Peihan Liu (PhD 2024–present). Her lab explores privacy-preserving technologies, policy implications, and interdisciplinary collaborations.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Dr Jamie Woodcock is a Senior Lecturer in Digital Economy at King's College London, based in the Department of Digital Humanities within the Faculty of Arts & Humanities. He holds a PhD in sociology from Goldsmiths, University of London, and has held academic positions at institutions including the University of Leeds, University of Manchester, LSE, and the Open University. His research focuses on labour processes, platform work, gig economy dynamics, and worker organizing, with a particular emphasis on critical global capitalism and digital culture. Woodcock’s research interests include the games industry, unionization strategies, and resistance to algorithmic management. He is an editorial board member of Notes from Below and Historical Materialism , and contributes to interdisciplinary initiatives such as the Research Centre for Digital Culture and the Critical Global Capitalism Studies Collective at King’s. His work has been widely covered in media and he frequently speaks on digital labour issues. He has authored influential books like The Fight Against Platform Capitalism and The Gig Economy: A Critical Introduction , co-authored Guerrilla Democracy: Mobile Power and Revolution in the 21st Century , and published extensively on topics ranging from delivery platform strikes to gamification critiques. His recent recognition includes a 2025-26 Digital Futures Institute Fellowship.
Ahmed Eldawy is an Associate Professor in the Department of Computer Science at the University of California, Riverside. He leads groundbreaking research in databases, big data management, and spatial data processing, with a focus on scalable exploratory analytics through systems like Beast , UCR-Star , Raptor , and Spider . His work spans geospatial data infrastructure, distributed computing, and computational geometry. Research Interests : Databases, big data management, spatial data processing, geospatial analytics, distributed systems, computational geometry. Awards : NSF CAREER award (2021), 10-year Influential Paper Award (ICDE 2025), Best Demo award (SIGSPATIAL 2020). Grants : NSF (IIS-2046236, IIS-1954644, CNS-1924694), USDA (USDA NIFA 2020-69012-31914), UC Office of the President (M21PL3368). Labs & Centers : RAISE@UCR, Data Science Center, Center for Robotics and Intelligent Systems (CRIS), Affiliate of Center for Geospatial Sciences and Winston Chung Global Energy Center. His recent publications focus on LLM-driven geospatial visualization (LASEK), distributed raster analytics (RDPro), learned spatial query optimization, and scalable spatiotemporal systems. He advises numerous PhD and Master’s students, many of whom now work at top tech companies like Amazon, Microsoft, and Meta.
Bharat Lohani is a Professor at the Department of Civil Engineering in Indian Institute of Technology Kanpur . His work focuses on Geoinformatics , utilizing LiDAR and GIS for 3D modeling and spatial analysis. Education : PhD (University of Reading, UK), ME (IIT Roorkee), BE (MMM Engineering College Gorakhpur) Research Interests : LiDAR simulation, motion correction in scanning, flood propagation modeling, historical urban mapping, GPS signal analysis, agricultural land consolidation Article Trends : His publications emphasize LiDAR data processing , sensor integration , and optimization algorithms for geospatial applications. Scientific Awards : ISRS National Award (2012), Best Paper at INCA Congress (2012), Silver Award at ISPRS (2008) Advising : Mentored 8+ students in LiDAR and spatial data analysis Labs/Teams : Leads a research group at IITK including Aswani Kumar Munnangi and Salil Goel, affiliated with the Wadhwani School of AI & Intelligent Systems .
Florian Schäfer is an Assistant Professor at the School of Computational Science and Engineering at Georgia Tech. His research spans numerical computation, statistical inference, and competitive games, with applications in materials science, turbulence modeling, computer graphics, and computational geometry. He will join the Courant Institute at NYU in September 2025. PhD in Applied and Computational Mathematics, Caltech Bachelor’s and Master’s in Mathematics, University of Bonn His work focuses on information geometric mechanics to design structure-preserving numerical methods for continuum mechanics. This includes: State-of-the-art solvers for elliptic PDEs via Gaussian elimination and conditional independence Efficient multi-agent optimization algorithms Information geometric regularization for compressible fluid dynamics Enabling the first compressible fluid simulation exceeding 100 trillion grid cells His recent research trends integrate: Machine learning for materials science (e.g., active learning, Bayesian approaches) Stochastic modeling of microstructures and phase-field problems Neural operators for super-resolution fluid dynamics Generative models for polycrystalline material datasets Optimal transport and diffusion models for conditional density transformations High-performance computing at extreme scales Florian collaborates with researchers including Houman Owhadi, Jessie Liu, Spencer Bryngelson, Tamer Zaki, and Ali Mani. He actively presents at conferences like SIAM and UCLA seminars, and is recruiting PhD students for work at the Courant Institute starting 2025.
Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.
Benjamin Mako Hill is an Associate Professor in the University of Washington Department of Communication and an Adjunct Associate Professor in Human-Centered Design & Engineering , the Paul G. Allen School of Computer Science & Engineering , and the Information School at UW. He is a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and a Fellow at the Center for Information Technology Policy at Princeton University during the 2023–2024 academic year. Educational Background : PhD in an interdepartmental program at Massachusetts Institute of Technology (MIT) , involving the MIT Sloan School of Management and the MIT Media Lab , advised by Eric von Hippel, Yochai Benkler, Tom Malone, and Mitch Resnick. MS in Media Arts and Sciences from MIT. B.A. in Technological and Legal History from Hampshire College . Research Interests span peer production, online communities, collective action, cooperation, learning, and computer-mediated communication. His work explores how communication and information technologies shape social outcomes in collaborative environments like Wikipedia and Linux, focusing on governance, moderation, anonymity, and educational platforms such as Scratch. Article Trends : His publications analyze peer production dynamics, privacy in open collaboration, and computational thinking in youth. Topics include underproduction in open source software, algorithmic fairness, taboo knowledge production, and legitimate peripheral participation in online communities. Methodologies combine big data, quasi-experimental, and comparative analyses. Scientific Awards : Dordick Award for Best Dissertation (2013). CHI '22 Best Paper Honorable Mention (2022). CSCW '18 Best Paper Honorable Mention (2018). CHI '17 Best Paper Honorable Mention (2017). CSCW '13 Best Paper Award (2013). CHI '11 Best Paper Honorable Mention (2011). Advising and Grants : He co-founded the Community Data Science Collective and collaborates with researchers like Aaron Shaw. Grants include multiple National Science Foundation awards for studies on digital knowledge commons, anonymous participation, and collaborative success, as well as a Sloan Foundation grant for modeling underproduction in peer production. Labs and Teams : He leads the Community Data Science Collective , an interdisciplinary research group studying online communities, and contributes to open-source projects like Debian and Ubuntu . He actively edits Wikimedia projects and participates in the Cascadia Wikimedians User Group .
Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
Aaron Tohuvavohu is a Research Fellow in the Division of Physics, Mathematics, and Astronomy at the California Institute of Technology. His work focuses on high-energy astrophysics, particularly gamma-ray bursts (GRBs) and multi-messenger astronomy. He is deeply involved in the Neil Gehrels Swift Observatory mission, specializing in real-time localization of transient events using the BAT-GUANO pipeline and collaborating with gravitational-wave detectors like LIGO/Virgo/KAGRA. His research emphasizes rapid-response observations of GRBs and gravitational-wave events, leveraging the Interplanetary Network (IPN) for precise localization. He has contributed to studies of short-hard GRBs associated with compact object mergers and long-duration GRBs linked to hypernovae. Notable projects include the CASTOR mission concept for UV photometry and detector characterization for next-generation astronomical instruments. Aaron's recent work includes analyzing Swift/XRT and UVOT observations of GRB afterglows, setting upper limits for electromagnetic counterparts to gravitational-wave triggers, and improving IPN triangulation algorithms. His publications reflect a systematic approach to transient astronomy, integrating data from multiple observatories for comprehensive event characterization.
Prof. Martin Messner is a University Professor at the University of Innsbruck, leading the Controlling Institute within the Department of Organization and Learning. His work focuses on management accounting, performance measurement, and the intersection of accounting practices with organizational behavior. He holds editorial leadership roles, including Editor-in-Chief of Accounting, Organizations and Society (2021–present) and previously served on the Management Accounting Research editorial board (2015–2019). Prior to his current position, he was an Assistant Professor at HEC Paris (2006–2011). Research Interests: His research explores performance measurement systems, ethics in management control, and the evolving roles of accounting in organizational narratives and governance. He investigates topics like algorithmic decision-making, narrative reporting, and entrepreneurial governance structures. Professional Activities: Member of the European Accounting Association’s Publications Committee Co-organizer of the MASOP Workshops (2008–2023) His publications span academic journals and edited volumes, addressing themes such as zero-based budgeting, data scientist identities, and the ethical implications of control systems. He actively bridges theoretical and practical accounting issues through interdisciplinary collaboration.
Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).
