Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is co-director of the Foundations of Data Science Institute (FODSI) and a member of MIT's Theory of Computation Group, Computer Science and Artificial Intelligence Lab (CSAIL), and multiple research initiatives like Wireless@MIT and Big Data@CSAIL. Education: Magister (MA) in Computer Science, University of Warsaw (1995) Ph.D. in Computer Science, Stanford University (2000), advised by Rajeev Motwani Research Interests: Focuses on high-dimensional computational geometry, data stream algorithms, sparse recovery, compressive sensing, and machine learning. His work includes foundational contributions like locality-sensitive hashing (LSH), the Sparse Fourier Transform, and efficient similarity search algorithms. Key Contributions: Known for developing FALCONN (Fast Approximate Nearest Neighbor Search library), and for pioneering work in sub-linear algorithms, streaming algorithms, and geometric computing. Awards: ACM Paris Kanellakis Award (2012) ACM Fellow (2015) Simons Investigator (2013) Member, National Academy of Sciences (2024) Member, American Academy of Arts and Sciences (2023) Teaching & Mentorship: Advised numerous PhD/MSc students and postdocs, and taught courses on geometric computation, streaming algorithms, and algorithmic aspects of embeddings. Labs & Teams: Leads research in areas like FODSI, geometric algorithms, and data science at MIT's CSAIL.
Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.
Serge Fehr is a Senior Researcher in the Cryptology Group at CWI (Centrum Wiskunde & Informatica) in Amsterdam and a part-time Professor at the Mathematical Institute of Leiden University. His research focuses on foundational aspects of cryptology, including post-quantum cryptography, information-theoretic security, zero-knowledge proofs, and secure multiparty computation. He participates in AMSec (Amsterdam Cyber Security Center) and leads work packages in the NWO-funded HAPKIDO consortium. Education: M.Sc. in Mathematics from ETH Zürich (1998) Ph.D. in Cryptography from ETH Zürich and University of Aarhus (2003) Postdoc at Macquarie University (2003-2004) Research Interests: Post-quantum cryptographic primitives (e.g., digital signatures, lattice-based schemes) Quantum-resistant protocols (e.g., non-resignable signatures, Fiat-Shamir transforms) Foundational security proofs in the quantum random oracle model (QROM) Secure multiparty computation and privacy-preserving healthcare systems Key Activities: Editorial Board Member: Journal of Cryptology , IEEE Transactions on Information Theory Program Committee Co-Chair: EUROCRYPT 2025 Steering Committee Member: Beyond IID Information Theory, QCrypt Co-organizer: Symposium Series on Post-Quantum Cryptography Grants/Awards: NWO Cybersecurity consortium grant (HAPKIDO - 2021) NWO Veni Grant (2005) NWO Open & Free Competition Grants (2008, 2013) Students/Advising: Supervised or served on committees for over 15 Ph.D. students, including work on post-quantum signatures, MPC applications, and lattice-based cryptography. Labs/Teams: Active in CWI’s Cryptology Group and Leiden’s Mathematical Institute, collaborating with industry partners (e.g., KPN, Microsoft) in cybersecurity initiatives.
Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.
Carlisle-Martin is an Associate Department Head and Professor of Practice in the Department of Computer Science & Engineering at Texas A&M University. They also serve as Director of the United States Air Force Academy Center for Cyberspace Research. Their research focuses on computer security, programming languages, and innovative computer science education techniques. Education: Ph.D., Computer Science, Princeton University (1996) B.S., Mathematics and Computer Science, University of Delaware (1991) Research Interests: Malware analysis and detection Cybersecurity frameworks for DNS and network protocols Visual programming tools like RAPTOR for education Ada language modernization and integration Cybersecurity education through CTF competitions Awards: 2016: Meritorious Civilian Service Award (USAF) 2014: SANS Institute Security Award 2009: ACM Distinguished Educator 2008: Colorado Professor of the Year 2007: Arthur S. Flemming Award Advising & Grants: Known for mentoring through cybersecurity initiatives and leading the USAF Academy's cyberspace research programs. No specific grant details listed, but their work aligns with defense and education funding priorities. Labs/Teams: Directs the USAF Academy's Center for Cyberspace Research, focusing on applied cybersecurity solutions and educational outreach.
Assoc Prof Wu Hongjun is an Associate Professor at the Division of Mathematical Sciences, School of Physical & Mathematical Sciences, Nanyang Technological University (NTU). His research focuses on cryptography and information security, with notable contributions to lightweight authenticated encryption algorithms like TinyJAMBU and ACORN, as well as cryptanalysis of stream ciphers (e.g., ZUC, HC-128) and hash functions (e.g., JH, SHA-3 candidates). His academic career includes over 15 years of contributions to cryptographic standards, IoT security frameworks, and secure cloud data management. Key areas of expertise encompass symmetric-key cryptography, algorithm design for resource-constrained devices, and vulnerability analysis of cryptographic primitives. Prof Wu has authored influential papers on authenticated encryption modes (AEGIS, MORUS), lightweight cipher optimizations (ACORN), and cryptanalysis techniques applied to Feistel networks and stream ciphers. His work bridges theoretical cryptography with practical implementations across telecommunications, IoT, and cloud computing domains.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Jelena Mirkovic serves as Principal Scientist at USC Information Sciences Institute (USC/ISI) and Research Associate Professor at the University of Southern California's Thomas Lord Department of Computer Science. She has held faculty positions at USC since 2010, progressing from Research Assistant Professor to her current role as Research Associate Professor since 2017, while also serving as Project Leader at USC/ISI. Her educational background includes: PhD in Computer Science from UCLA (2003) MS in Computer Science from UCLA (2000) B.Sc. in Computer Science from University of Belgrade, Serbia (1998) Mirkovic's research spans network security, human-centered attacks, and cybersecurity experimentation infrastructure. Her work focuses on critical security challenges including botnets, denial-of-service attacks, IP spoofing, vulnerability scanning, and user-centric privacy. She has pioneered methodologies for security experiments and led major infrastructure projects including the DETER testbed and SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation). Analysis of her recent publications reveals consistent innovation across multiple security domains. Her work demonstrates strong technical depth in DDoS defense systems (particularly DNS protection), binary vulnerability analysis, privacy-preserving systems, and security experimentation infrastructure. A notable trend is her focus on bridging theoretical security concepts with practical implementation through large-scale testbeds and real-world data analysis. Her significant scientific achievements include: IEEE Senior Member distinction Best paper award at IEEE COMSNETS 2023 for DNS DDoS defense research Mirkovic has secured substantial research funding as Principal Investigator or Co-PI on numerous grants from NSF, DHS, and other agencies. Current major projects include SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation), DISCERN (Datasets to Illuminate Suspicious Computations), and modernizing DeterLab education infrastructure. She has successfully led multiple REU sites focused on cybersecurity education and workforce development. She directs the STEEL (Security Research Lab) at USC/ISI, which develops innovative security solutions through interdisciplinary research in network security, human factors in security, and cybersecurity experimentation infrastructure. The lab emphasizes practical implementations that address real-world security challenges while advancing theoretical understanding of security systems.
Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.
Julia Len is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she conducts research in applied cryptography and computer security. She co-leads the Cryptography Group with Saba Eskandarian and is currently recruiting Ph.D. students for Fall 2026. She earned her Ph.D. in Computer Science from Cornell University in 2024 under the supervision of Tom Ristenpart, following a B.S. in Computer Science from UC San Diego in 2018, where she worked with Mihir Bellare. Before joining UNC, she was a METEOR postdoctoral fellow at MIT. Her research focuses on improving the security and privacy of deployed cryptographic protocols, particularly in end-to-end encrypted messaging. Key areas include interoperability, abuse prevention, key transparency, and authenticated encryption. She applies principled approaches ranging from identifying flaws in existing protocols to designing new cryptographic schemes and definitions. Her recent publications span top venues such as USENIX Security, CCS, Eurocrypt, and Crypto, demonstrating a strong and consistent research output in applied cryptography. Trends in her work show increasing focus on real-world protocol design, security analysis of widely used schemes, and privacy-preserving moderation mechanisms for secure communication platforms. METEOR Postdoctoral Fellow at MIT Julia Len has advised and collaborated with numerous researchers; current advisees are being recruited for Fall 2026. She has received research support through collaborations with industry partners including Zoom, Microsoft Research, and Meta. Her work on Partitioning Oracle Attacks has led to updates in Shadowsocks, age, OPAQUE, and HPKE, demonstrating significant real-world impact. She co-leads the Cryptography Group at UNC Chapel Hill, fostering research and education in cryptography. She also serves on the program committees of IEEE S&P 2026, USENIX Security 2025, and CATS 2023, contributing to the broader academic community.
Ron Steinfeld is an Associate Professor at the Cybersecurity Lab of the Faculty of Information Technology , Monash University . His research focuses on lattice-based cryptography , post-quantum cryptographic protocols , and privacy-preserving technologies . He has contributed to advancements in secure multiparty computation , digital signatures , and blockchain confidentiality . Key areas: Lattice-Based Cryptography, Zero-Knowledge Proofs, Blockchain Security Recent work trends: Quantum-safe protocols, Efficient sampling algorithms, Scalable blockchain solutions Scientific Awards : BEST PAPER AWARD (ASIACRYPT 2015) He has served on program committees for major conferences including CRYPTO , EUROCRYPT , and ASIACRYPT . Ron is a member of the Discrete Mathematics Research Group and has collaborated with institutions like Macquarie University in the past.
Eyal Z. Goren is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on arithmetic geometry, including studies of Shimura varieties, modular forms, complex multiplication, expander graphs, arithmetic dynamics, and mathematical cryptography. He is affiliated with the Centre Interuniversitaire en Calcul Mathématique Algébrique (CICMA), a Montreal-based group in number theory. Goren’s work bridges pure mathematics and applications in cryptography, with notable contributions to the theory of supersingular elliptic curves and cryptographic hash functions derived from expander graphs. Education : PhD in Mathematics from the Hebrew University of Jerusalem (1996), advised by Ehud De Shalit. Teaching : Teaches advanced courses such as Higher Algebra I/II, Algebra 1/2/3/4, Number Theory, and specialized topics like Unlikely Intersections. Affiliations : Active member of CICMA and the CRM (Centre de Recherches Mathématiques), collaborating on seminars and research initiatives. Research Interests : Goren’s work emphasizes the interplay between number theory and geometry, with recent focus on p-adic dynamics, canonical subgroups, and Faltings heights. His studies on Picard modular forms and Shimura varieties explore geometric structures in positive characteristic and their arithmetic implications. Publications : Over 40 articles in leading journals, including Inventiones Mathematicae , Compositio Mathematica , and Journal für die reine und angewandte Mathematik . His book Lectures on Hilbert Modular Varieties and Modular Forms is a key resource in the field. Grants and Collaboration : Engaged in collaborative projects on expander graphs, post-quantum cryptography, and the geometry of abelian varieties with complex multiplication. His research is supported by grants from the NSERC and other agencies.
Farinaz Koushanfar is a Professor in the Department of Electrical and Computer Engineering at the Jacobs School of Engineering, University of California San Diego (UCSD) . She holds the Siavouche Nemat-Nasser Endowed Chair and serves as Founding Co-Director of the Center for Machine-Intelligence, Computing and Security . Her affiliations include NSF Trust-Hub (Co-PI) and NSF TILOS AI Institute . She also serves on the Editorial Board of The Proceedings of the IEEE . Research Focus: Prof. Koushanfar leads research in secure and efficient computing , including robust/safe AI , hardware/system security , AI-based optimization , and cryptographically secure privacy-preserving computing . Her work pioneered logic obfuscation/locking for chip security, automated co-design of AI systems , watermarking/tracing of deep learning models , and physical proofs of provenance . She explores co-design with cryptographic constructs for privacy preservation and manages nonlinearities in ciphertext domains. Article Trends: Recent publications show expertise in neural watermarking (deepfakes, media authentication), zero-knowledge proof frameworks , Trojan attack defenses in ML models, secure federated learning , and hardware acceleration of cryptographic protocols . Her work combines machine learning , cryptography , and physical design security across 2022-2025 publications. Scientific Awards: Fellow of ACM Fellow of IEEE Fellow of National Academy of Inventors (NAI) Fellow of Kavli Foundation of NAS Inducted to NAI 2024 Fellows Advising & Leadership: She has advised multiple PhD students who became faculty at top universities (e.g., Stanford, Purdue). She chairs conferences like ACM WiSec 2024 and co-led the NSF SaTC decadal review. Her lab ( ACES Lab ) produces award-winning graduates like Bita Rouhani (DAC Under-40 Innovators) and Shehzeen Hussain (UCSD Best Dissertation Award).
Prabhanjan Ananth is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), holding the Glen and Susanne Culler Endowed Chair in Computer Science. He earned his Ph.D. from UCLA in 2017, followed by a postdoctoral fellowship at MIT's CSAIL. His research focuses on cryptography, particularly in classical and post-quantum cryptography, with emphasis on unclonable primitives, pseudorandomness, and cryptographic protocols. Education: Ph.D. in Computer Science from UCLA (2013–2017), postdoctoral research at MIT (2017–2019). Research Interests: His work spans theoretical computer science and cryptography, including post-quantum security, quantum-resistant systems, and unclonable cryptographic primitives. He explores topics like pseudorandomness in quantum models, revocable encryption, and cryptographic protocols for multi-user environments. Recent Publications: His 2025 work advances revocable encryption and pseudorandom unitaries in quantum models. He also contributes to unclonable secret sharing and modular cryptographic design. His research often bridges theoretical foundations with practical applications in quantum-safe systems. Awards: Glen and Susanne Culler Endowed Chair in Computer Science (UCSB). Advising: Current Ph.D. advisees include Aditya Gulati, Yao-Ting Lin, and Divyanshu Bhardwaj. Past students have secured roles at institutions like JPMorgan Chase and EPFL. Teaching: Teaches courses on automata theory (CS 138) and cryptography (CS 178), emphasizing rigorous mathematical proofs and foundational concepts.
Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.
