Jinhyung Park is an Associate Professor in the Department of Mathematical Sciences at KAIST, Daejeon, Korea. He obtained his Ph.D. from KAIST in 2014 under Prof. Sijong Kwak and was mentored as a CMC Fellow at KIAS (2014–2019). His research focuses on Algebraic Geometry , particularly syzygies of algebraic varieties , secant varieties , and positivity of line bundles . Education: Ph.D., KAIST (2014); MS/BS, KAIST and University of Chicago (2002/2001). Research Interests: His work explores geometric properties of projective manifolds, singularities in algebraic geometry, and applications of Okounkov bodies to divisors and syzygies. Recent trends include Castelnuovo-Mumford regularity bounds (e.g., for threefolds with rational singularities) and subadditivity of Okounkov bodies in fiber spaces. Teaching: Courses include Matrix Groups, Algebraic Geometry, and Calculus. He previously taught at Sogang University and Purdue University. Professional Affiliations: Member of the Korean Mathematical Society, American Mathematical Society, and other international societies. Organized the 8th East Asia Number Theory Conference at KAIST (2019).
Professor Asmita Mukherjee is a faculty member in the Department of Physics at the Indian Institute of Technology Bombay (IIT Bombay), where she holds the position of Professor. She is actively engaged in theoretical particle physics research with a focus on Quantum Chromodynamics (QCD) and related phenomena. Professor Mukherjee's research interests primarily include Theoretical Particle Physics, Quantum Chromodynamics, QCD spin physics, Light-cone wave functions, and collider phenomenology. She teaches several advanced courses including Elementary Particle Physics (PH540), Mathematical Physics-I (PH407), Mathematical Physics-II (PH408), Quantum Physics and Applications (PH107), and various Quantum Mechanics courses (PH422, PH423, PH105). Her publication record shows a strong focus on gravitational form factors, transverse momentum dependent distributions, angular momentum structure of hadrons, and electron-ion collider physics. Recent work has explored gluon contributions to proton structure, dressed quark states, and azimuthal asymmetries in various production processes. Her research demonstrates expertise in light-front quantization techniques and their application to fundamental questions in hadron physics. Professor Mukherjee has received recognition for her work in High Energy Theory, with numerous publications in prestigious journals addressing fundamental questions in hadron structure and QCD dynamics. Her work provides crucial theoretical foundations for upcoming experiments at the Electron Ion Collider. Current PhD students: Sudeep Saha, Amol Pawar Graduated PhD students: Ravi Manohar, Sreeraj Nair, Vikash K. Ojha, Sangem Rajesh, Raj Kishore Postdoctoral researchers: Shaik Khatiza Banu (CFNS), Jai More, Tanmay Maji, Mariyah Siddiqah Research projects: BRNS project with Ravi Singh as Junior Research Fellow Her research group at IIT Bombay continues to explore fundamental questions in hadron physics, with ongoing projects examining the mechanical properties of protons, the decomposition of angular momentum in QCD, and the development of theoretical tools for interpreting data from next-generation colliders.
Professor Hongdong Li is a Tenured Professor at the School of Computing, Australian National University (ANU), within the College of Engineering and Computer Science. His research focuses on 3D Computer Vision, Machine Learning, and their applications in dynamic environments. He has held visiting roles at Carnegie Mellon University and has contributed to significant projects like the Australia Bionic Eyes initiative. Education: PhD (Electrical Engineering). Research Interests : 3D Computer Vision fundamentals and applied AI systems Learning-based 3D perception for plant sciences Robot navigation in unfamiliar environments Awards : Marr Prize Honourable Mention CVPR Best Paper Award Advising & Grants : Supervised 40+ PhD students, with funding from ARC, CSIRO, Microsoft, and firms like OPPO/Tencent. Active in projects such as bushfire detection via video analytics and sign language translation systems. Labs/Teams : Co-founder of the Australian Centre for Robotic Vision (ACRV). Collaborates globally on cross-view localization and autonomous systems.
Kaki Ryan is a Teaching Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. She holds a Ph.D. (2025), M.S. (2021), and B.S. (2020) from UNC, with a minor in AAAD. Her research focuses on hardware security, symbolic execution, and computer science education. Education Ph.D., 2025, UNC-Chapel Hill M.S., 2021, UNC-Chapel Hill B.S., 2020, UNC-Chapel Hill (Computer Science and Mathematics) Ryan's research centers on hardware security, particularly using symbolic execution to identify bugs and vulnerabilities in hardware designs. She also emphasizes broadening participation in computing and improving pedagogical strategies in computer science education. Her recent publications (2023-2025) explore symbolic execution techniques for hardware security verification, including query caching, information flow analysis, and path explosion mitigation. These works span conferences like ASPLOS, VTS, FMCAD, and workshops such as HASP. Scientific Awards John M. Glotzer Graduate Teaching Assistant Award (2020-2021) Tanner Award for Excellence in Undergraduate Teaching (2022) As an educator, Ryan has served as Instructor of Record for COMP 435 (Computer Security Concepts) and COMP 311 (Computer Organization) in Fall 2025. She has extensive teaching experience as a Graduate Teaching Assistant, Head Teaching Assistant, and Undergraduate Teaching Assistant for courses like COMP 210, COMP 110, and COMP 435, with a focus on data structures, security concepts, and programming fundamentals.
Harvey B. Meyer is a Professor of Theoretical Physics at Johannes Gutenberg University Mainz since 2014. Previously, he held positions including Junior Professor at Mainz (2010), Fellow at CERN's Theoretical Physics Division (2009), Research Scientist at MIT (2008), and postdoctoral roles at MIT (2006-2008) and DESY (2004-2006). He earned his D.Phil. in Theoretical Physics from the University of Oxford (2001-2004) and a Diplome de Physique from the University of Lausanne (1996-2001). His research focuses on lattice field theory, QCD phase diagrams, thermal field theory, and hadron structure. He leads the NEPhEuQCD collaboration and has received the ERC Consolidator Grant (2018) for the SIMDAMA project. Meyer teaches courses in theoretical physics and mathematical methods at Mainz, including 'Theoretische Physik 4' and 'Mathematische Rechenmethoden'. His work integrates advanced computational techniques to address fundamental questions in particle and nuclear physics. Key achievements include pioneering studies on the muon's anomalous magnetic moment, hadronic light-by-light scattering, and quark-gluon plasma dynamics. Collaborations include MIT, CERN, and institutions globally through lattice QCD projects. His lab and team contributions are central to the PRISMA+ Cluster of Excellence at Mainz.
Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.
Andreas Bode is a Research Fellow at the University of Wuppertal, where he is part of the group led by Sascha Orlik in the Department of Mathematics. His research focuses on D-modules on rigid analytic spaces, integrating geometric representation theory and nonarchimedean geometry. His research interests span several areas of algebraic geometry and number theory, including the study of p-adic differential operators, nonarchimedean geometry, and representation theory. He explores the interplay between D-modules and rigid analytic spaces, contributing to the understanding of holonomicity, Auslander regularity, and the application of almost mathematics in p-adic contexts. His work bridges geometric and analytic approaches to problems in nonarchimedean settings. Bode's recent publications include advancements in the theory of Auslander regularity for p-adic structures, contributions to the holonomicity of D-cap-modules on rigid analytic spaces, and explorations into locally analytic representations of p-adic groups. His work often intersects with homological algebra and functional analysis, reflecting a deep engagement with both theoretical and methodological challenges in nonarchimedean geometry. He has not been mentioned as having received any scientific awards in the provided texts. Bode's current role as a postdoctoral researcher does not involve formal academic advising of students. No grants are specifically mentioned in the provided information. He is affiliated with the Algebra and Number Theory group at the University of Wuppertal, collaborating with colleagues such as Sascha Orlik and contributing to the broader research community in nonarchimedean geometry.
Professor Anders C. Hansen is a mathematician at the University of Cambridge and University of Oslo, leading the Applied Functional and Harmonic Analysis group. His work bridges functional analysis, artificial intelligence, and computational mathematics, focusing on the Solvability Complexity Index (SCI) hierarchy and stability issues in deep learning. He has held prestigious fellowships, including a Royal Society University Research Fellowship and Peterhouse Bye-Fellowship. Educated at the University of Cambridge, UC Berkeley, and the Norwegian University of Science and Technology Developed groundbreaking theories in compressed sensing and deep learning, revealing algorithmic instability paradoxes Organized workshops on computational mathematics and AI interpretability His research explores the SCI hierarchy , exposing computational barriers in AI, quantum mechanics, and inverse problems. Key projects include Smale’s 18th problem and analyzing neural network stability. His work has transformed understanding of compressed sensing, particularly in medical imaging. Recent scientific awards include the Whitehead Prize (2019), IMA Prize (2018), and Leverhulme Prize (2017). Collaborations span institutions like Caltech, MIT, and the University of Vienna. As an educator, he teaches NST Part IA Mathematical Methods , Part II Numerical Analysis , and a Part III course on Compressed Sensing . His group has mentored 17 PhD and postdoctoral researchers since 2012.
Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .
Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions
Harvey Reall is a Professor of Theoretical Physics at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and a Fellow of Trinity College . His research focuses on General Relativity and Effective Field Theory , particularly in the context of black hole mechanics , higher-dimensional gravity , and cosmic censorship . He has held prestigious positions including a Royal Society University Research Fellowship from 2005 to 2013. Education: PhD from DAMTP, University of Cambridge. Previous Appointments: Lecturer at the University of Nottingham (2005-2007); Postdoctoral positions at the Kavli Institute (2003-2005), Queen Mary University of London (2000-2003), and University of California, Santa Barbara (2003-2005). Reall's work explores the uniqueness and stability of black holes , causality in gravitational theories , and effective field theory approaches to gravity . His recent publications address nonperturbative second law formulations , event horizon dynamics , and axisymmetry theorems in extended theories of gravity. He has supervised numerous researchers including Aidan McSharry (2025-) , Maxime Gadioux (2022-) , and Iain Davies (2020-24) , contributing to the training of the next generation of physicists. Scientific Awards: Royal Society University Research Fellow (2005-2013)
Evrim Acar Ataman is a Research Professor and Chief Research Scientist at Simula Metropolitan, where she serves as Head of the Department of Data Science and Knowledge Discovery. Her research focuses on advanced data mining techniques for complex, multi-modal datasets across biomedical and network domains. Her primary research interests include Data Mining , Matrix and Tensor Factorizations , and Data Fusion for multi-modal data analysis. She develops constrained and coupled factorization methods to extract interpretable patterns in applications spanning neuroimaging, metabolomics, and mobile network analysis, with emphasis on dynamic and longitudinal data structures. Her work integrates mechanistic models with data-driven approaches to enhance biological and system understanding. Analysis of her recent publications (2024-2025) reveals a dominant trend applying tensor and coupled matrix-tensor factorizations to biomedical data for biomarker discovery, particularly in metabolomics and neuroimaging. Key innovations include tracking evolving patterns in temporal data (tPARAFAC2), constrained fusion methods (dCMF), and integration of mechanistic models with tensor decompositions for longitudinal analysis. As Head of the Department of Data Science and Knowledge Discovery, she leads research in developing novel data mining methodologies and their real-world applications at Simula Metropolitan, with significant contributions to interpretable AI for complex systems.
Andrew Radin is a Senior Political Scientist at the RAND Corporation and Associate Director of the Master of National Security Policy Program at the RAND School of Public Policy. His work focuses on European security, military activities in space, and state-building, with expertise in NATO, Russia, Ukraine, and Afghanistan. Ph.D. in Political Science, MIT B.A. in Political Science and Mathematics, University of Chicago Postdoctoral Fellow, Harvard Kennedy School Postdoctoral Fellow, University of Southern California His research centers on European and Eurasian security , military strategy , and state-building in weak states . He emphasizes the role of local politics in institution-building and has conducted case studies in Bosnia, Kosovo, Iraq, Timor-Leste, and Ukraine. His work explores hybrid warfare, deterrence, security sector reform, and U.S. policy toward Russia and China. His recent publications reveal a strong trend in analyzing space as a domain of military competition , NATO deterrence strategies , and Russian gray-zone aggression . Topics frequently include escalation dynamics, allied cooperation, and the future of the liberal international order. His research combines policy relevance with academic rigor. Scientific Contributions and Recognition: Author of Institution Building in Weak States: The Primacy of Local Politics (Georgetown University Press) Published in Security Studies , Journal of Conflict Resolution , Washington Quarterly , and War on the Rocks Contributor to high-impact RAND reports on Ukraine, NATO, Russia, and international order Andrew Radin has advised on national security policy, including serving as Country Director for Afghanistan in the Office of the Under Secretary of Defense for Policy. He previously taught as an adjunct professor at Georgetown University. He has not received mention of specific grants or student advisees, but his leadership in the Master of National Security Policy program suggests mentorship and program development responsibilities. He is active in shaping U.S. defense and foreign policy through research and commentary. Laboratories, Teams, and Research Groups: He is affiliated with the RAND Corporation’s international security research division and contributes to projects on global competition, military strategy, and political warfare. His work is often collaborative, involving multidisciplinary teams focused on defense policy and strategic stability.
Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under the Berkeley Artificial Intelligence Research (BAIR) umbrella and serves on the advisory board of Wonder Dynamics. Her research focuses on the intersection of computer vision, computer graphics, and machine learning, with a particular emphasis on 4D reconstruction of dynamic scenes, neural radiance fields (NeRF), and systems that model human-environment interactions from 2D visual data. Education : Ph.D., Computer Science (2017), University of Maryland, College Park BA, Mathematics and Computer Science (2012), New York University (NYU) Her work aims to build systems that can capture, perceive, and understand complex 3D/4D worlds from photographs and videos, enabling applications in scene reconstruction, motion analysis, and generative modeling. She has pioneered techniques for scaling NeRFs across GPUs (NeRF-XL), developing open-source tools like nerfstudio and gsplat. Her recent publications focus on topics like self-occluded avatar recovery (SOAR), decentralized diffusion models, and 4D reconstruction of articulated objects for robotics. Kanazawa's research has been recognized with prestigious awards including the IEEE CS TCPAMI Young Researcher Award (2024) , Sloan Research Fellowship (2023) , and Google Faculty Research Award (2021) . Her lab has trained numerous students who now hold positions at leading institutions and companies like Anthropic, Meta Reality Labs, and Luma AI. Key Scientific Awards : IEEE CS TCPAMI Young Researcher Award (2024) Sloan Research Fellow (2023) Hellman Fellow (2022) Bakar Fellows Spark Award (2022) Google Faculty Research Award (2021) Her KAIR lab collaborates extensively with industry partners and academic institutions, including the Max Planck Institute and Google Research. She has served as an advisor for PhD students and postdocs who now lead teams at UC Berkeley, MIT, Stanford, and Luma AI, while her teaching includes graduate courses like CS 280A (Computer Vision) and CS 294-173 (Learning for 3D Vision).
Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.
