Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.
Lijie Chen is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he is part of the Berkeley Theory Group. Previously, he was a Miller Research Fellow at UC Berkeley, hosted by Avishay Tal and Umesh Vazirani, and earned his Ph.D. from MIT under Ryan Williams. His research focuses on theoretical computer science, particularly computational complexity theory, with applications to quantum physics and AI safety. Education: Ph.D. in Computer Science from MIT (2022), B.Sc. from Yao Class at Tsinghua University. Research Interests: Complexity theory, quantum complexity, derandomization, circuit lower bounds, and foundational aspects of AI safety. Chen has made significant contributions to understanding fundamental questions in complexity theory, including circuit lower bounds and the connections between randomness and computation efficiency. His work often bridges theoretical insights with practical implications in quantum computing and algorithm design. Awards and Honors: Machtey Award for Best Student Paper (2019). Danny Lewin Best Student Paper Award (2019). Invited to SICOMP Special Issues for FOCS and STOC papers. He has organized workshops on complexity theory and derandomization, and his research has been recognized in venues like STOC, FOCS, and the Journal of the ACM.
Jacob Fox is a Professor at Stanford University, specializing in Combinatorics and Probability. His research focuses on extremal combinatorics, Ramsey theory, graph theory, and additive combinatorics. He advises students like Maya Sankar. His work explores structural and enumerative aspects of graphs, hypergraphs, and combinatorial configurations. Recent studies include advancements in Ramsey numbers, sumset theory, and probabilistic methods in discrete mathematics. Key research areas include Ramsey numbers for sparse structures, hypergraph properties, and applications of combinatorial geometry. His publications often bridge theoretical insights with algorithmic applications. No scientific awards are listed in the provided text. His advising includes Maya Sankar, with research aligned to combinatorial problems. Collaborative projects involve extremal graph theory and probabilistic combinatorics. No labs or dedicated research groups are explicitly mentioned.
Kenneth McLaughlin is the Evelyn and John G. Phillips Distinguished Chair in Mathematics at Tulane University's School of Science & Engineering. He holds a Ph.D. and B.A. in Mathematics from New York University (1994 and 1989). Prior to Tulane, he served as faculty at the University of North Carolina, Chapel Hill, the University of Arizona, Universidade Federal de Brasília, and Colorado State University, where he also held leadership roles as Department Head and Chair. His research focuses on integrability, applying techniques across mathematics to study complex systems and phenomena. He has held visiting positions at institutions worldwide, including France, Italy, Brazil, Belgium, and the UK. McLaughlin’s research spans integrable systems, nonlinear dynamics, and asymptotic analysis. His work often involves the Riemann-Hilbert problem approach, orthogonal polynomials, and random matrix theory. Notable contributions include studies on soliton gases, the KdV equation, and universality in quantum operator dynamics. His recent articles explore topics such as asymptotic behavior of polynomials, soliton gas condensation, and hydrodynamic limits in integrable systems. McLaughlin’s academic career is marked by interdisciplinary collaboration and international research engagement.
Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Alexander Gorodnik is a Professor of Mathematics at the University of Zurich, focusing on the interplay between dynamical systems and number theory. His work bridges ergodic theory, homogeneous dynamics, and Diophantine approximation, with applications to arithmetic counting problems and geometric distribution of lattice orbits. Current lectures include MAT121: Analysis I and MAT221: Analysis III at the University of Zurich Co-author of the book The ergodic theory of lattice subgroups (Princeton University Press, 2010) Editor of the journal Ergodic Theory and Dynamical Systems His research explores Diophantine approximation through dynamical systems, investigating how orbits of group actions distribute in homogeneous spaces. Key topics include mixing properties , central limit theorems , and metric theorems for multiplicative approximation. Recent publications address automorphic density estimates , discrepancy in intrinsic Diophantine approximation , and effective equidistribution of translated measures. His work often employs tools from representation theory and spectral analysis . Current working group members include Zhiyuan Deng , Zouhair Ouaggag , and Yuval Yifrach . He has taught courses at institutions in Zurich, Bristol, Princeton, and Mumbai, with lecture materials covering topics from ergodic theorems to Každan's property (T) .
Dr. Mi Jung Park is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. She is also a Canada CIFAR AI Chair at the Amii. Her research focuses on privacy-preserving machine learning, particularly differential privacy, synthetic data generation, and their applications in healthcare. She holds a PhD in Electrical and Computer Engineering from the University of Texas at Austin, supervised by Dr. Jonathan Pillow, and has held postdoctoral positions at the University of Amsterdam and University College London. Education : PhD, Electrical and Computer Engineering, University of Texas at Austin (2016) Master's, Electrical and Computer Engineering, University of Texas at Austin (2012) Bachelor's, Electrical and Computer Engineering, Hanyang University, Seoul, South Korea (2009) Research Interests : Her lab develops methods to balance privacy and accuracy in data analysis, emphasizing differential privacy's role in healthcare. Key areas include: Generating synthetic data with privacy guarantees Integrating fairness, interpretability, and causality into privacy-preserving models Bayesian techniques for model compression and uncertainty estimation Recent Work Trends : Her publications explore differential privacy in generative models (e.g., diffusion models, kernel methods) and neural network pruning. Recent work highlights privacy-preserving techniques for image classification, latent diffusion, and perceptual feature integration. Awards : Canada CIFAR AI Chair (2021). Advising & Grants : Supervises postdocs (e.g., Mingyu Kim), master's students (e.g., Amman Yusuf), and PhD candidates (e.g., Margarita Vinaroz). Her research is supported by the CIFAR AI Chair program and collaborations with institutions like the Max Planck Institute for Intelligent Systems. Labs & Teams : Leads the Privacy-Preserving Machine Learning Lab at UBC, advancing technologies to protect sensitive healthcare data while enabling clinical and research use.
Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.
Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.
Benedikt Bünz is an Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He is also a co-founder and chief scientist of Espresso Systems, where he applies his research expertise to real-world blockchain solutions. His academic work bridges theoretical cryptography with practical blockchain implementations, focusing on enhancing privacy, security, and usability of decentralized systems. Dr. Bünz's research centers around applied cryptography, consensus mechanisms, and game theory as they relate to cryptocurrencies. His work spans zero-knowledge proofs, verifiable delay functions, secure multi-party computation, and privacy-preserving protocols. He has made significant contributions to Bulletproofs, a zero-knowledge proof system deployed on blockchains like Monero, and pioneered research in verifiable delay functions which are now part of Ethereum 2.0's design. His recent work focuses on recursive proof systems, accumulation schemes, and efficient verification techniques for blockchain scalability. His publication record shows a consistent progression from foundational cryptographic primitives to practical blockchain implementations. Recent work demonstrates increasing sophistication in recursive proof systems (ProtoStar, HyperPlonk), novel accumulation techniques (ARC, DewTwo), and foundational work on randomness generation (VDFs). His research consistently bridges theoretical cryptography with real-world blockchain applications, resulting in protocols that are both theoretically sound and practically implementable across multiple blockchain platforms. Dr. Bünz actively contributes to the academic community through teaching and mentorship. He teaches courses on cryptography of blockchains and computer security at NYU, providing students with hands-on experience in blockchain security and cryptographic protocols. His industry engagement through Espresso Systems demonstrates his commitment to translating academic research into practical solutions for the blockchain ecosystem.
Asaf Ferber is Associate Professor in Mathematics at University of California, Irvine, School of Physical Sciences. His research spans discrete mathematics including combinatorial games, random graphs, extremal hypergraph theory, and quantum computation. Research explores Hamiltonian cycles in random graphs, structural properties of pseudorandom graphs, and quantum algorithms for combinatorial problems. Recent work develops quantum approaches to graph learning and sparse recovery in random matrices. Awards: NSF CAREER Award Sloan Fellowship Distinguished Early Career Faculty Award for Research Air Force Research Grant NSF-BSF Grant Organizes conferences including SoCalDM Symposium and Desert Discrete Math Workshop, mentoring graduate students through UCI's Probability and Combinatorics Seminar.
Daniel A. Spielman is a Sterling Professor of Computer Science, Statistics & Data Science, and Mathematics at Yale University. He serves as the inaugural James A. Attwood Director of the Institute for Foundations of Data Science (FDS) and was previously co-Director of the Yale Institute for Network Science (YINS). He is also a member of TILOS, the NSF Institute for Learning-Enabled Optimization at Scale. His research interests span Spectral Graph Theory, Algebraic Graph Theory, Laplacian Matrices, Expander Graphs, and Random Walks on Graphs. Spielman has made fundamental contributions to understanding the mathematical foundations of data science, including work on smoothed analysis of algorithms, spectral sparsification, and solutions to the Kadison-Singer problem. Spielman's publications demonstrate a consistent focus on developing efficient algorithms with strong theoretical foundations. His work bridges pure mathematics, theoretical computer science, and practical applications in network analysis and machine learning. His research has evolved from foundational work on smoothed analysis to recent contributions in experimental design using discrepancy theory. His scientific honors include: Nevanlinna Prize for contributions to mathematical aspects of computer science Two Gödel Prizes (2008 and 2015) for outstanding papers in theoretical computer science MacArthur Fellowship ('Genius Grant') Simons Investigator award Membership in the National Academy of Sciences and American Academy of Arts and Sciences Spielman has advised numerous PhD students who have gone on to prominent positions in academia and industry. His teaching includes advanced courses in Spectral Graph Theory and Computation and Optimization. He has developed important software packages like Laplacians.jl for solving Laplacian linear equations and related problems.
