جست‌وجوی استاد و پژوهشگر

• Cybersecurity
• Network Protocols
• Cloud Computing
• +۷ مورد دیگر

Damir Regvart is a lecturer at Algebra University of Applied Sciences , specializing in Cybersecurity , Network Protocols , and Cloud Computing . With a background in Electrical Engineering and a Master's in Electrical Engineering from the Faculty of Electrical Engineering and Computing in Zagreb (2002), he focuses on advanced network security protocols, Zero-touch technologies, and cloud infrastructure. Research Highlights : Zero Trust Architecture, Honeypot Deception Strategies, Machine Learning in Threat Detection, Microsoft Azure Security, and Infrastructure as Code hardening. Key Projects : Pan-European and national initiatives in network automation, cloud forensics, and IoT security. Recent Publications (2024-2025) explore cutting-edge topics like AI-driven security testing, forensic capabilities in cloud environments, and the intersection of SDN and cybersecurity. His work addresses vulnerabilities in cryptographic systems and the legal implications of digital signatures. Technology Focus : Zero-touch provisioning, Microsoft Sentinel automation, and RedFish/vSphere API integration.

Lars Grasedyckمشاهده پروفایل

استاد

• Numerical Analysis
• Hierarchical Matrices
• Tensor Approximation
• +۷ مورد دیگر

Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.

Carlo Angiuliمشاهده پروفایل

استادیار

• Type Theory
• Programming Language Foundations
• Homotopy Type Theory
• +۳ مورد دیگر

Carlo Angiuli is an Assistant Professor of Computer Science at the Luddy School of Indiana University . He is a leading researcher in type theory , with a focus on its applications to programming language design and logic . His work bridges theoretical foundations and practical implementations, particularly through dependent types , proof assistants , and homotopy type theory . His research interests include: Type Theory (foundational systems for programs and proofs) Programming Language Foundations (formal semantics and reasoning) Homotopy Type Theory (higher-dimensional structures) Dependent Types (expressive type systems) Proof Assistants (formal verification tools) Computational Logic (algorithmic reasoning) Angiuli actively contributes to the programming languages community as a POPL Program Committee member and co-author of a forthcoming textbook on dependent type theory. His students include Johnson He, Huang Xu, Kelton OBrien, and Ian Ray , who joined in Fall 2025. He has received significant recognition, including the Best Paper Award at FSCD 2019 and the School of Computer Science Distinguished Dissertation Award from Carnegie Mellon University. His current work explores advanced type-theoretic frameworks and their implementation in proof assistants like the red* family of tools. Scientific awards: Best Paper Award, FSCD 2019 (Junior Researchers category) School of Computer Science Distinguished Dissertation Award, Carnegie Mellon University He teaches courses such as Modern Dependent Types (CSCI-B619), Programming Language Foundations (CSCI-B522), and Introduction to Computer Science (CSCI-C211).

• Distributed Decision Making
• Cyber-Physical Systems
• Control Systems
• +۵ مورد دیگر

Vijay Gupta is the Elmore Professor of Electrical and Computer Engineering and Associate Head of Graduate and Professional Programs at Purdue University's College of Engineering. His research focuses on distributed decision-making systems, combining data-driven and model-driven approaches for infrastructure networks like power grids, transportation systems, and water distribution networks. Key areas include compositional control, cyber-physical security, and incentive design in distributed estimation and control. Education: B.Tech from Indian Institute of Technology Delhi, M.S. and Ph.D. from California Institute of Technology, all in Electrical Engineering. Prior roles include faculty positions at Notre Dame and research roles at United Technologies Research Center. Research emphasizes resilient control strategies for large-scale systems, with recent work addressing secure estimation under adversarial attacks, model reduction techniques, and reinforcement learning frameworks for decentralized control. His publications span control theory, cyber-physical systems, and optimization algorithms. Grants and collaborations are not explicitly detailed here, but his work reflects significant engagement with foundational and applied research challenges in networked systems. No specific awards are listed in the provided texts.

• Physics Education Research
• Astrophysics and Cosmology
• Quantum Mechanics
• +۳ مورد دیگر

Andrew F. Heckler is a Professor and Vice Chair for Administration in the Department of Physics at The Ohio State University, within the College of Arts and Sciences. He holds a B.S. in Physics from Ohio State (1986) and a Ph.D. in Physics from the University of Washington (1994). His research focuses on Physics Education Research and Astrophysics/Cosmology, emphasizing cognitive processes in STEM learning, assessment methodology, and educational equity. Key areas include student response time analysis, mastery-based learning, and curriculum design. He leads initiatives in digital education tools, such as online vector practice platforms and equitable assessment instruments. His work explores how self-efficacy and social comparison impact academic performance, particularly in physics courses. Heckler has developed frameworks to improve graduate-level physics instruction, addressing misconceptions in quantum mechanics and materials science. Publications highlight analysis of procrastination patterns, pandemic-era digital divides, and the role of concreteness in educational examples. His research bridges educational psychology with physics pedagogy, aiming to enhance student success through evidence-based interventions. He actively contributes to national projects like QuSTEAM, advancing quantum information science education.

• Cryptography
• Elliptic Curves
• Post-Quantum Cryptography
• +۱ مورد دیگر

Dr. Huseyin Hisil is a Lecturer in Cryptography at the University of Wollongong's School of Computing and Information Technology. His research focuses on asymmetric cryptosystems, including elliptic curve cryptography, hyperelliptic curves, and post-quantum cryptographic solutions. He develops optimized algorithms for multiprecision arithmetic and finite field operations. Recent publications address efficient elliptic curve arithmetic using Karatsuba-friendly primes and Jacobian coordinate systems for genus 2 curves. His work bridges computational number theory with practical cryptographic implementations, enhancing security and performance in next-generation systems. He teaches undergraduate courses including Introduction to Web Technology (CSIT128) and Web Development (CSIT884), while coordinating Professional Capstone Projects (CSIT998). His research group explores cryptographic pairings and algebraic structures relevant to quantum-resistant protocols.

Risi Kondorمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +۶ مورد دیگر

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Heather Macbethمشاهده پروفایل

مدرس ارشد

• Kähler Geometry
• Geometric Analysis
• Formal Verification
• +۳ مورد دیگر

Dr. Heather Macbeth is a Senior Lecturer in Pure Mathematics at Imperial College London, specializing in Kähler geometry, geometric analysis, and the formalization of mathematics. Her research develops geometric analysis techniques for complex manifolds while advancing proof verification through the Lean theorem prover. She leads the development of Lean's Mathlib library, creating formalizations for differential geometry, functional analysis, and representation theory. Her textbook The Mechanics of Proof introduces proof writing through Lean, integrating computer verification with mathematical pedagogy. Funded by a Microsoft Research Lean Award, Dr. Macbeth organizes workshops on formal mathematics and serves on the AMS Committee on Publications. Her geometric research examines Ricci solitons, Yamabe invariants, and Kähler-Einstein metrics, while her formalization work includes Sobolev inequalities and semilinear functional analysis. Research Contributions: Geometric analysis of Ricci solitons and Kähler metrics Formal verification of functional analysis theorems Proof assistant pedagogy and textbook development Contributions to Lean's mathematical library

Ping Zhongمشاهده پروفایل

دانشیار

• Free Probability
• Random Matrix Theory
• Operator Algebras
• +۲ مورد دیگر

Ping Zhong is an Associate Professor in the Department of Mathematics at the University of Houston. He holds a Ph.D. from Indiana University Bloomington and joined UH in 2024 after serving as Assistant Professor at the University of Wyoming (2018-2024) and completing postdoctoral work at the University of Waterloo. His research explores free probability theory, operator algebras, and applications of random matrix theory to high-dimensional statistics. Educational background includes: Ph.D. Mathematics, Indiana University (2014) M.S. Mathematics, Peking University (2008) B.S. Applied Mathematics, Huazhong University of Science and Technology (2005) Research focuses on fundamental mathematical structures in probability with emerging applications in quantum physics and statistical learning. Current investigations bridge theoretical frameworks with computational approaches for high-dimensional data analysis. Recent publications demonstrate consistent contributions to spectral analysis of random operators and convergence properties in non-commutative probability spaces. Works frequently appear in premier mathematics journals including Transactions of the AMS and Journal of the European Mathematical Society.

• Combinatorial group theory
• Geometric semigroup theory
• Algorithmic problems in algebra
• +۶ مورد دیگر

Robert D. Gray is a Professor of Mathematics at the School of Mathematics, University of East Anglia. His research focuses on combinatorial and geometric group and semigroup theory, algorithmic problems in algebra, decidability, homological finiteness properties, and group actions on graphs and topological spaces. EPSRC Research Fellow Editorial Board: International Journal of Algebra and Computation Available for PhD supervision in semigroup and inverse monoid theory His recent work explores: Topological finiteness properties of monoids Undecidability in one-relator inverse monoids Algorithmic properties of inverse monoids Maximal subgroups in special inverse monoids Key article trends include: Geometric and algorithmic aspects of inverse monoids Homological properties of semigroups Connections between group theory and semigroup theory Applications to graph theory and automata Scientific Awards: EPSRC Fellowship EP/V032003/1 EPSRC grant EP/N033353/1 EPSRC Postdoctoral Fellowship EP/E043194/1 Contact: Room S1.29, School of Mathematics, University of East Anglia, Norwich NR4 7TJ. Email: Robert.D.Gray@uea.ac.uk . Phone: +44 1603 591443.

Jonah Gasterمشاهده پروفایل

استادیار

• Topology
• Geometric Group Theory
• Combinatorial Topology
• +۱ مورد دیگر

Jonah Gaster is an Assistant Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee, where he also serves as the Program Coordinator for the Topology Research Group and Colloquium Chair. His research focuses on mathematical topology, geometric group theory, and combinatorial structures in low-dimensional topology. His research explores the interplay between combinatorial methods and geometric structures, with specific interests in curve systems on surfaces, harmonic maps between manifolds, and the computational aspects of topological invariants. Recent work investigates combinatorial representations of geometric objects and their applications in low-dimensional topology. Analysis of Dr. Gaster's recent publications reveals a consistent focus on topological combinatorics, surface embeddings, and geometric group theory. His work bridges discrete mathematics with continuous geometric structures, particularly in the contexts of hyperbolic geometry and curve complexes. He leads the Topology Research Group at UWM, fostering collaborative investigations into fundamental questions in geometric topology and discrete mathematics.

Vladimir Podolskiiمشاهده پروفایل

دانشیار

• Computational complexity
• logical foundations of computer science
• tropical geometry

Vladimir Podolskii serves as an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering and holds a concurrent appointment in the Department of Mathematics within the School of Arts and Sciences. His academic work bridges theoretical computer science and mathematical foundations, with a focus on computational boundaries and algebraic structures. He completed his doctoral studies at Lomonosov Moscow State University in Moscow, Russia, receiving his PhD in 2009. Podolskii's research program centers on computational complexity theory, examining the inherent difficulty of computational problems through decision trees and threshold functions. His investigations extend to the logical underpinnings of computation and the application of tropical geometry in optimization contexts. This interdisciplinary approach connects discrete mathematics with theoretical computer science frameworks. His 2022 publications reveal concentrated exploration of decision tree efficiency for threshold functions and classification systems for ontology-mediated queries, demonstrating methodological rigor in analyzing computational limits and knowledge representation structures. Podolskii actively mentors graduate researchers through Dissertation Research courses while teaching core curriculum including Algorithms and specialized seminars on computational complexity toolkits, shaping the next generation of theoretical computer scientists.

Ram Mohapatraمشاهده پروفایل

استاد

• Mathematical Analysis
• Operator Theory
• Variational Inequalities
• +۳ مورد دیگر

Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.

Marco Aldinucciمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing (HPC)
• Cloud Engineering
• +۷ مورد دیگر

Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.

Xian Suمشاهده پروفایل

استادیار مهمان

• Distributed Machine Learning
• Coding Techniques for Straggler Tolerance
• Distributed Systems Optimization
• +۳ مورد دیگر

Xian Su is a Visiting Assistant Professor at the Knight Foundation School of Computing and Information Sciences, Florida International University. He holds a Ph.D. (2024) and M.Phil (2022) in Computer Science from the City University of New York, and a B.Sc. (2016) from Ocean University of China. His research focuses on distributed machine learning, particularly leveraging coding techniques to address straggler challenges in distributed systems. Education: Ph.D. in Computer Science, City University of New York (2024) M.Phil in Computer Science, City University of New York (2022) B.Sc. in Information Science and Engineering, Ocean University of China (2016) Research Interests: Distributed machine learning systems, straggler mitigation via coding theory, theoretical foundations of coded computing, and practical implementations of fault-tolerant algorithms. His work bridges theoretical advancements with real-world applications in distributed computing environments. Publications: Recent work emphasizes accelerating distributed machine learning through coded techniques, with contributions in coded matrix multiplication, gradient coding, and dynamic resource allocation strategies. Themes include improving computational efficiency and resilience in large-scale distributed systems.