Find Academic Supervisors

• Programming Language Theory
• Semantics of Programming Languages
• Domain Theory
• +7 more

Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.

Lucia CarichinoView profile

Assistant Professor

• Mathematical Biology
• Fluid Dynamics
• Numerical Methods
• +5 more

Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.

Tulika MitraView profile

Professor

• Hardware-Software Co-design
• Real-Time Embedded Systems
• Energy-Efficient Computing
• +7 more

Professor Tulika Mitra is the Dean of the School of Computing and Vice Provost (Special Projects) at the National University of Singapore (NUS). She holds the Provost’s Chair Professor in the Department of Computer Science and has been instrumental in shaping academic policies and strategic initiatives at NUS since joining in 2001. PhD in Computer Science, Stony Brook University (2000) M.E. in Computer Science, Indian Institute of Science (1997) B.E. in Computer Science, Jadavpur University (1995) Her research focuses on hardware-software co-design for energy-efficient computing systems, particularly in real-time embedded systems, heterogeneous architectures, and AI accelerators. She leads major research programs such as the NRF Competitive Research Programme on Low-Power Edge Accelerators and the MOE Tier-3 Programme on Green AI , collaborating with industry leaders like ARM, AMD, and Meta. Her recent publications highlight innovations in CGRA optimization , sparse attention mechanisms , photonic-digital hybrid architectures , and low-power ML inference . These works often integrate compiler techniques, architectural design, and real-time constraints for edge computing applications. Scientific Awards : ESWEEK Test-of-Time Award (2022), ACM SIGDA Distinguished Service Award, IEEE CEDA Outstanding Service Recognition Award, Teaching Excellence Award (2006), and multiple best paper recognitions. Education Leadership : Spearheaded the Computer Engineering (CEG) Programme at NUS, a joint initiative between Engineering and Computing. As a mentor , she has supervised over 25 PhD students , many now in prominent academic or industrial roles. Her research group eCO Lab focuses on embedded computing challenges, while her grant collaborations include projects on 5G base stations, reconfigurable architectures, and IoT-optimized SoCs.

Anders RantzerView profile

Professor

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +7 more

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

Baijian YangView profile

Professor

• Cybersecurity
• Big Data
• Applied Machine Learning
• +4 more

Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.

Jonathan A. KelnerView profile

Professor

• Algorithms
• Spectral Graph Theory
• Combinatorial Optimization
• +7 more

Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.

Gil KalaiView profile

Professor

• Combinatorics
• Geometric Combinatorics
• Probabilistic Combinatorics
• +7 more

Gil Kalai is a Professor of Mathematics at the Hebrew University of Jerusalem since 1992, where he holds the Henry and Manya Noskwith Chair. He also serves as an Adjunct Professor of Mathematics and Computer Science at Yale University since 2004 in a long-term part-time visiting position. His academic career includes visiting positions at prestigious institutions including MIT, Cornell, IAS Princeton, Berkeley, Bell-labs, IBM, and Microsoft. Professor Kalai's research spans multiple areas within mathematics and theoretical computer science. His work in combinatorics encompasses geometric, probabilistic, and topological approaches. He has made significant contributions to the study of convex sets and polytopes, linear programming, and theoretical computer science. His influential 1988 paper with Kahn and Linial on Boolean functions pioneered applications of Fourier analysis in theoretical computer science. Kalai's research has evolved to include the application of Fourier analysis to thresholds, influences, symmetries, noise, percolation, and social choice. He has developed theories in algebraic shifting and studied face-numbers and other combinatorial invariants of polytopes. His work on the diameter of polytopes and randomized simplex algorithms has been influential in optimization theory. In 1993, his collaboration with Kahn produced a groundbreaking counterexample to Borsuk's Conjecture in 1325 dimensions. Professor Kalai's publications reveal a consistent focus on the intersection of combinatorics, geometry, and theoretical computer science. His work shows a progression from foundational combinatorial geometry to increasingly sophisticated applications of harmonic analysis in discrete mathematics. The recurring themes across his 30+ year career include Boolean functions, polytope theory, and probabilistic methods in combinatorics, demonstrating remarkable coherence in his research trajectory. 2016 European congress of Mathematics, plenary speaker 2013 ERC advanced grant 2012 Rothschild Prize 1994 International Congress of Mathematicians invited section talk, Zurich 1994 Fulkerson Prize 1993 Erdos Prize 1992 Polya Prize Though specific details of his advising are not provided in the source material, Kalai has written over 70 scientific papers and maintains an active research blog entitled "Combinatorics and More." His 2013 ERC advanced grant indicates significant research funding for his work. His extensive collaborations with researchers across multiple institutions suggest a robust research program with numerous PhD students and postdoctoral researchers, though specific names are not mentioned in the provided texts. Professor Kalai maintains active research connections across multiple institutions including Hebrew University, Yale, and various research centers worldwide. His work bridges pure mathematics and theoretical computer science, creating a unique interdisciplinary research environment that influences both fields.

John MacLaren WalshView profile

Professor

• Information Theory
• Network Coding
• Distributed Computing
• +4 more

John MacLaren Walsh is a Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Adaptive Signal Processing and Information Theory Research Group. He holds BS, MS, and PhD degrees from Cornell University, all completed under Dr. C. Richard Johnson, Jr. His research spans information theory, network coding, distributed computing, and machine learning applications in patent analysis. His work focuses on: Bounding entropic vectors and their impact on communication networks Rate region computation for network coding and distributed storage Information theory for distributed function computation Machine learning-enhanced patent processing systems Publications emphasize entropy geometry, network coding complexity, distributed algorithms, and patent analysis, with consistent themes of optimization and combinatorial methods. Recent work (2016-2019) shows increased focus on probabilistic supports and computational efficiency in network coding. Awards: 2011 NSF CAREER Award for 'Entropy Geometry in Variational Inference Signal Processing' He has advised PhD students on topics like entropy region mapping, network coding, and distributed control. Key grants include NSF CAREER and AFOSR funding for wireless network overhead control. He directs the Adaptive Signal Processing and Information Theory Research Group, which develops algorithms for network coding, distributed storage, and patent analysis systems.

• Information Theory
• Cryptography
• Lattice Theory
• +5 more

Cong Ling is a Professor of Information Theory and Cryptography at Imperial College London's Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His research focuses on lattice theory and its applications in coding, cryptography, quantum information, and number theory. Key affiliations include the Academic Centre of Excellence in Cyber Security Research and the Engineering Secure Software Systems group. Education details are not explicitly provided in the text, but his professional experience indicates advanced qualifications in electrical engineering and mathematics. Research interests span lattice-based cryptography, post-quantum security, algebraic coding theory, and quantum-resistant algorithms. His work bridges information theory and number theory, with contributions to MIMO systems, secure communication protocols, and cryptographic protocol design. Recent publications emphasize lattice reduction techniques, quantum algorithms for the shortest vector problem, and advancements in polar codes. Notable trends include exploration of non-commutative algebras for cryptography, Gaussian sampling optimizations, and hybrid quantum-classical approaches to hard integer problems. Over 50+ articles published since 2018 reflect his leadership in lattice-based research and quantum-safe technologies. Awards: None explicitly listed in the text. Grants/Advising: No specific grants or student advisees mentioned; focus remains on collaborative research outputs. Labs/Teams: Associated with Imperial's Cyber Security Research groups and quantum engineering initiatives.

Jelle HellingsView profile

Assistant Professor

• Resilient Systems
• High-Performance Data Management
• Database Theory
• +10 more

Jelle Hellings is an Assistant Professor in the Department of Computing and Software at McMaster University , Canada. His research focuses on high-performance large-scale data management systems with a strong theoretical and algorithmic component, including resilient systems (blockchains) , graph databases , and external-memory algorithms . He previously worked as a Postdoc Scholar at the University of California, Davis and earned his PhD from Hasselt University in Belgium. Education: Doctor of Sciences in Computer Science (2018), Hasselt University Master of Science in Computer Science and Engineering (2011), Eindhoven University of Technology His research interests include scalable resilient systems with Byzantine fault tolerance, database theory, graph query languages, constraints on graph data, and external-memory algorithms for large graph datasets. He has authored numerous high-impact publications on blockchain-based resilient systems, query optimization in graph databases, and theoretical advancements in relation algebra expressiveness. Hellings actively contributes to academic service through program committee memberships and tutorial organization, and he currently teaches courses on future resilient databases and foundational computer science topics.

Ivan DokmanicView profile

Assistant Professor

• Machine Learning
• Signal Processing
• Inverse Problems
• +11 more

Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .

Henry YuenView profile

Associate Professor

• Quantum Computing
• Complexity Theory
• Cryptography
• +2 more

Henry Yuen is the Srivani Family Associate Professor of Computer Science at Columbia University, with affiliations in the CS Theory group, Data Science Institute, and Columbia Quantum Initiative. His work bridges theoretical computer science and quantum information, focusing on the computational foundations of quantum systems. Yuen received his BA in Mathematics from the University of Southern California (2010) and PhD in Computer Science from MIT (2016). His research explores quantum complexity theory, quantum cryptography, and quantum information through both theoretical frameworks and practical applications. His recent publications reveal a strong focus on quantum pseudorandomness, quantum circuit complexity, and quantum cryptographic primitives. The research trajectory demonstrates increasing sophistication in connecting quantum computational models with fundamental questions in complexity theory, particularly through the lens of quantum interactive proofs and quantum state complexity. His work frequently intersects with operator algebras and quantum information theory, reflecting the interdisciplinary nature of modern quantum computing research. NSF CAREER award Sloan Fellowship Yuen actively mentors graduate students, with several PhD students having graduated to postdoctoral positions or faculty roles. His service includes co-organizing the Columbia CS Theory Seminar and serving on program committees for major conferences including QIP, STOC, and ITCS. He has organized multiple workshops on quantum computing challenges and cryptography. As a member of the Columbia Quantum Initiative, Yuen contributes to Columbia's growing quantum ecosystem, collaborating across departments to advance both theoretical foundations and potential applications of quantum information processing.

Alex BlumenthalView profile

Assistant Professor

• Dynamical Systems
• Smooth Ergodic Theory
• Chaos Theory
• +3 more

Alex Blumenthal is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology since Fall 2020. His academic background includes a Ph.D. from New York University (2016) with a dissertation titled 'Nonuniformly hyperbolic theory for Banach space mappings.' Prior to joining Georgia Tech, he held positions as an instructor at the University of Maryland, teaching courses in probability theory, linear algebra, and precalculus, and served as a recitation leader at New York University for courses in chaos theory, differential equations, and analysis. Blumenthal's research focuses on dynamical systems and ergodic theory, with specialization in: Chaotic behavior in deterministic and stochastic systems Smooth ergodic theory and SRB measures Lyapunov exponents in random dynamical systems Stochastic fluid mechanics and turbulence modeling Infinite-dimensional dynamical systems on Banach spaces Statistical properties of complex systems His work bridges abstract mathematical theory with physical applications like fluid dynamics and statistical mechanics. Analysis of his recent publications shows strong emphasis on stochastic dynamics, Lyapunov exponents, and fluid mechanical systems, with mathematical techniques drawn from ergodic theory, functional analysis, and probability theory. His publications frequently appear in top mathematical physics and dynamics journals. No scientific awards or honors are mentioned in the source materials. Similarly, no information is available regarding research grants, student advising, or laboratory affiliations.

Michael GoldsmithView profile

Associate Professor

• Cybersecurity
• Formal Methods
• Concurrency Theory
• +3 more

Michael Goldsmith is a Senior Research Fellow at the Department of Computer Science and Worcester College, University of Oxford. He holds multiple leadership positions including Director of the Oxford Martin Programme on AI Threat Detection, Associate Director of the Cyber Security Centre, and Co-Director of the Centre for Doctoral Training in Cybersecurity. His research bridges formal methods, concurrency theory, and practical cybersecurity applications. Goldsmith's research focuses on cybersecurity analytics including threat detection, risk management, and trust frameworks. He pioneered automated cryptoprotocol analysis and investigates multidisciplinary projects spanning mathematical models to socio-technical systems. His core interests include formal verification, AI threat landscapes, privacy architectures, and security protocol design. Analysis of his recent publications reveals strong emphasis on practical cybersecurity challenges: 63% focus on threat detection (especially insider threats), 22% on trust/risk frameworks, and 15% on formal methods applications. His work consistently integrates technical security mechanisms with human factors and organizational contexts. He currently advises Ahmed Salman and has supervised 10+ students including Mary Bispham, Rodrigo Carvalho, and Elizabeth Phillips. His research teams collaborate on projects funded by Technology Strategy Board, government agencies, and industry partners. Goldsmith leads the Oxford Martin Programme on AI Threat Detection and co-directs the Centre for Doctoral Training in Cybersecurity. His research group develops tools for security visualization (CyberVis), trust metrics, and identity management frameworks.

Paata IvanisviliView profile

Associate Professor

• Analysis
• Partial Differential Equations
• Probability
• +5 more

Paata Ivanisvili is an Associate Professor at the University of California, Irvine (UCI), Department of Mathematics, School of Physical Sciences. His research focuses on Analysis, Probability, Harmonic Analysis, and Functional Analysis, with a particular emphasis on isoperimetric inequalities, functional inequalities, and discrete structures such as the Hamming cube. He has held visiting positions at institutions including the Hausdorff Research Institute for Mathematics and Princeton University. Ivanisvili has organized conferences such as the Dual Trimester Program at the Hausdorff Institute on Boolean Analysis in Computer Science (2024) and annual Summer/Fall Schools since 2021. He earned his PhD in Mathematics from Michigan State University (2015) and a BS from Saint Petersburg State University (2011). His research interests include sharp inequalities in analysis (e.g., Poincaré, Beckner, Ehrhard), hypercontractivity, and applications to discrete mathematics and probability. He has collaborated with prominent mathematicians such as Fedor Nazarov, Alexander Volberg, and Roman Vershynin. Notable awards include the NSF CAREER Award (2021–2025) and Simons Fellowship in Mathematics (2025–2026). Ivanisvili’s recent work explores the interface between harmonic analysis and discrete mathematics, including studies on additive energies, convex hulls of space curves, and learning theory. His articles frequently address foundational questions in geometric functional analysis, often using tools like Bellman functions and optimal control theory. He actively advises PhD students and has mentored visiting researchers at UCI.