جست‌وجوی استاد و پژوهشگر

Leslie G. Valiantمشاهده پروفایل

استاد

• Complexity Theory
• Machine Learning
• Parallel Computation
• +۴ مورد دیگر

Leslie G. Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences. He holds a Ph.D. from the University of Warwick (1974), with prior education at King's College, Cambridge, and Imperial College, London. His research spans complexity theory, learning algorithms, parallel computation, computational neuroscience, and evolutionary theory. Valiant's work bridges artificial and natural computational systems, addressing fundamental questions in both domains. His research interests include reconciling logical reasoning with statistical learning via 'robust logics,' exploring computational limits of cortical networks, and analyzing evolutionary processes quantitatively. He has authored seminal books like Circuits of the Mind and Probably Approximately Correct . His recent articles focus on holographic algorithms, neural computation, and evolutionary theory, reflecting his interdisciplinary approach. Notable awards include the Nevanlinna Prize (1986), the Turing Award (2010), and fellowship in the Royal Society. Valiant's contributions have redefined theoretical computer science and its intersection with biology and neuroscience.

• Internet of Things
• Machine Learning
• Distributed Systems
• +۲ مورد دیگر

Grzegorz Chmaj is Assistant Professor In Residence in Electrical and Computer Engineering at University of Nevada, Las Vegas. His research focuses on intelligent systems for IoT applications. Research emphases include: Machine learning approaches for resilient IoT systems and sensor networks Distributed computing architectures for embedded devices Blockchain applications in energy systems and smart contracts His extensive publication record addresses sensor data restoration, UAV power optimization, hardware teaching tools, and agricultural technology. Recent work explores fault tolerance in RTOS-based IoT systems and parking management solutions using predictive analytics. Chmaj teaches microcontroller programming and systems engineering.

Frederic Greenمشاهده پروفایل

پژوهشگر

• Theory of Computation
• Circuit Complexity
• Computational Complexity
• +۳ مورد دیگر

Dr. Frederic Green is a Senior Research Scientist in the Department of Computer Science at Clark University. With degrees in both physics and computer science, his research spans theoretical and computational aspects of computer science with interdisciplinary connections to physics. His scholarly work focuses on circuit complexity, computational complexity theory, quantum computation, and the application of computational methods to theoretical physics problems. Dr. Green has published extensively on topics including quantum circuit design, complexity bounds for algebraic operations, and the theoretical foundations of computation. He maintains an active scholarly review practice, contributing critical analyses of foundational texts in computation theory, discrete geometry, and mathematical physics. Recent publications include reviews of major works in complexity theory, computational geometry, and quantum gravity.

Pål Ellingsenمشاهده پروفایل

دانشیار

• Remote Sensing
• Cryptography
• Space Science
• +۶ مورد دیگر

Pål Ellingsen is an Associate Professor at the Department of Computing, Mathematics, and Physics at Western Norway University of Applied Sciences. His research spans remote sensing, cryptography, space science, and machine learning, with applications in structural monitoring, marine data management, and aerospace engineering. Contact: Pal.Ellingsen@hib.no | Office E407 Research Interests include: Remote sensing techniques like DInSAR for monitoring storage facilities Cryptographic properties and Boolean function analysis Space science via CubeSats and aurora observation Machine learning applications in port automation His recent publications highlight trends in interferometric SAR for volume tracking , free space optical communication in Arctic environments , and open data practices in marine science . Ellingsen contributes to projects like the Nansen Legacy Project and the Boreal Aurora Camera Constellation (BACC) . His work bridges technical and multidisciplinary fields, emphasizing data-driven solutions and advanced signal processing.

Razi Arshadمشاهده پروفایل

پژوهشگر ارشد

• Applied Cryptography
• Provable Security
• Privacy-Enhancing Technologies
• +۳ مورد دیگر

Dr. Razi Arshad is a Research Fellow in Security and Privacy at the University of Surrey, UK, affiliated with the Computer Science Research Centre and Surrey Centre for Cyber Security. Previously, he held an Assistant Professor position at the School of Electrical Engineering and Computer Sciences, National University of Sciences and Technology, Islamabad, Pakistan. He earned his PhD in Mathematical Cryptography from Otto-von-Guericke University (Germany, 2018) and MSc in Information Security from Sichuan University (China, 2007). His expertise includes applied cryptography, post-quantum security, privacy technologies, and authentication schemes. His research focuses on advancing cryptographic primitives, securing communication protocols, and exploring quantum-resistant solutions. Notable contributions include critiques of existing cryptographic schemes (e.g., identifying flaws in S-box constructions and SIP authentication protocols) and designing secure frameworks like elliptic curve-based mutual authentication systems. Recent work emphasizes AI-driven privacy solutions (e.g., emotion detection via wearable sensors) and cybersecurity challenges in fintech. Razi Arshad has published extensively in top-tier venues, addressing topics from S-box design and hyperchaotic encryption to regulatory compliance in privacy. He holds certifications as an ISO/IEC 17025 Lead Assessor and ISO/IEC 27001 ISMS Lead Auditor, reflecting his commitment to standards-based security practices. His consulting experience with tech firms like Xprime Inc. and Graxo Consulting Pvt. Ltd. underscores his industry relevance.

• Nanotechnology
• Computational Modeling
• Neural Networks
• +۳ مورد دیگر

Rudolf Martinus Josephus van Damme is a Lecturer in Applied Physics at the University of Twente. His research focuses on nanotechnology, unconventional computing, and medical imaging applications. He has contributed to advancements in nanoparticle networks, neural networks, and computational modeling. Key research areas include the development of reconfigurable nanomaterial systems, computational tools for evolving functionalities in disordered networks, and applying machine learning to medical surgery planning. His work bridges disciplines such as materials science, computer science, and biomedical engineering. Van Damme has delivered presentations on topics like wet-chemical etching methods, triangulation algorithms, and surface reconstruction. He has supervised 9 academic works and contributed to over 117 research outputs, including peer-reviewed articles and conference contributions. His most notable work includes the evolution of designless nanoparticle networks into Boolean logic and the application of neural networks for craniosynostosis surgery planning.

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Terry Windeatt is an Associate Tutor in the Department of Electrical and Electronic Engineering at the University of Surrey, where he lectures in Machine Intelligence. He is affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP), one of the world's leading research groups in these fields. His educational background includes a BSc in Applied Science from the University of Sussex, an M.Sc. in Electronic Engineering from the University of California, a B.A. (CNAA) in Theology, and a PhD from the University of Surrey. Prior to joining Surrey, he lectured in Control Engineering at Kingston University and spent eight years in the United States working on Intelligent Systems at General Motors and Xerox Corporation in Rochester, NY. His industrial R&D experience includes modeling and simulation for intelligent automotive and office-copying applications, as well as early versions of closed-loop control systems for car emissions and xerographic processes. Dr. Windeatt's research interests focus on Pattern Recognition, Neural Networks, Computer Vision, and Machine Learning , with particular expertise in Ensemble Classifiers, Error Correcting Output Coding (ECOC), and Feature Selection . His work in facial action unit recognition has made significant contributions to the field of computer vision and biometrics. He has applied these techniques to diverse areas including medical image analysis for breast cancer diagnosis and signal processing applications. An analysis of his recent publications reveals a strong focus on spectral analysis of ensemble classifiers, optimization of ECOC matrices, and innovative approaches to feature selection. His work consistently bridges theoretical developments in machine learning with practical applications in computer vision and pattern recognition. The evolution of his research shows a progression from fundamental classifier design to sophisticated ensemble methods that address real-world challenges in high-dimensional data analysis. His teaching responsibilities include courses in Control Engineering (eee3005) and AI and AI Programming (eeem005), reflecting his expertise in both traditional engineering principles and cutting-edge artificial intelligence techniques. At CVSSP, Dr. Windeatt contributes to the center's mission of advancing research in vision, speech, and signal processing through interdisciplinary collaboration. His work exemplifies the center's commitment to both theoretical innovation and practical application of advanced signal processing techniques.

Matthias Walterمشاهده پروفایل

استادیار

• Operations Research
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

Matthias Walter is an Assistant Professor in the Department of Mathematics of Operations Research. His research focuses on optimization, mathematical programming, and algorithm design, with applications in transportation, combinatorial optimization, and logistics. He contributes to the SCIP Optimization Suite and has developed the Combinatorial Matrix Recognition Library. His work spans topics such as linear programming relaxations, pseudo-Boolean solving, and multilinear optimization. Key research interests include operations research, integer programming, and algorithmic solutions for complex optimization problems. He has collaborated on projects like optimizing parcel transportation and analyzing congestion games. His recent publications address challenges in flow shop scheduling, graph realization, and pseudo-Boolean functions. Walter actively participates in academic activities, organizing events like the Mathematical Olympiad in Saxony-Anhalt and presenting invited talks on topics such as McCormick relaxations. His contributions to computational tools and theoretical advancements highlight his role in advancing optimization methodologies.

• Logic
• Philosophical Applications of Logic
• Modal Logic
• +۳ مورد دیگر

Lloyd Humberstone is an Adjunct Associate Professor (Research) in the Department of Philosophy at Monash University, Melbourne, Australia. He has been affiliated with Monash since 1975 and has held visiting positions at institutions including the Australian National University, University of Auckland, Princeton University, and the University of St Andrews. His primary research focuses on logic and its philosophical applications, with a specialization in modal logic and formal systems. Education: Humberstone holds degrees from the University of York and the University of Oxford, where he studied in the UK after moving there at age 11. His academic journey includes a formative period in the UK followed by a long-term career in Australia. Research Interests: Humberstone’s work spans modal logic, philosophical applications of logic, formal semantics, and non-classical systems. He explores topics such as negation operators, logical connectives, and the foundations of logical systems. His contributions include studies on axiomatic systems, translation methods in predicate logic, and the interplay between modal logics and philosophical questions. Projects: He has led or contributed to significant research projects funded by the Australian Research Council (ARC), including studies on rules in logic, two-dimensional semantics, and comparative analyses of sentential connectives. These projects highlight his expertise in foundational logical theory. Professional Activities: Humberstone has organized academic events, including a workshop in honor of Ross Brady (2018), demonstrating his role in fostering scholarly discourse in logic and philosophy.

• Mathematical Logic
• Model Theory
• O-Minimal Structures
• +۶ مورد دیگر

Marcus Tressl is a Lecturer in Pure Mathematics at the University of Manchester , affiliated with the Department of Mathematics. His academic journey includes a PhD and habilitation from the University of Regensburg, and he has been active at Manchester since 2007. Research Interests: Mathematical Logic, Model Theory, O-Minimal Structures, Lattice Ordered Abelian Groups, Boolean Algebras, Differential Algebra, Spectral Spaces, Stone Duality, Commutative Algebra Recent Publications: Focus on distributive lattices, matrix rings, differentially large fields, and applications of model theory to algebraic structures. Teaching: Delivers Mathematical Logic (MATH33021) and supervises MSc projects (MATH61000) , emphasizing LaTeX and mathematical typesetting. Contributions: Authored a book on Spectral Spaces, co-edited proceedings on O-Minimal Structures, and developed resources for Gödel's Incompleteness Theorems, Gröbner Bases, and Neural Network Learning applications.

Maximilien Gadouleauمشاهده پروفایل

دانشیار

• Coding theory
• Network coding
• Information theory
• +۱۳ مورد دیگر

Dr. Maximilien Gadouleau is an Associate Professor in the Department of Computer Science at Durham University, where he has been working since January 2012. He is a member of the Algorithms and Complexity research group, with secondary affiliations in Networks Engineering Science and Theory and Scientific Computing. Dr. Gadouleau received his MSc and PhD in Computer Engineering from Lehigh University in December 2005 and May 2009, respectively. After graduation, he held postdoctoral research positions at Université de Reims Champagne-Ardenne (until May 2010) and Queen Mary, University of London (until December 2011). Dr. Gadouleau's research spans multiple areas of theoretical computer science and discrete mathematics. His work focuses on the intersection of coding theory, network coding, and information theory with various branches of mathematics including combinatorics, graph theory, discrete optimization, matroid theory, logic, and group theory. He also investigates applications to network design, data storage, cryptography, data compression, and computation models. His recent work has particularly emphasized Boolean networks, finite dynamical systems, and semigroup theory. Dr. Gadouleau's extensive publication record demonstrates consistent contributions to theoretical computer science, with recent work showing a growing emphasis on the connections between discrete mathematics, dynamical systems, and coding theory. His research often bridges theoretical foundations with practical applications in network communication and data processing. Carnegie Trust for the Universities of Scotland: Research Assessor (2018) EPSRC Peer Review College member (2017) Dr. Gadouleau has successfully supervised PhD students including Izzy Friedlander (graduated July 2024) and Karl Southern (graduated July 2023). He was awarded an EPSRC first grant titled "Memoryless computation and network coding" which started on October 1, 2013. This grant supported research on computational models without memory and their applications to network coding problems. Dr. Gadouleau is an active member of the academic community, serving on editorial boards for journals including "Design, Codes, and Cryptography," "AIMS Mathematics," and "Mathematics and Systems Science." He regularly participates in international conferences and workshops, particularly in the areas of cellular automata, Boolean networks, and coding theory.

• Computational Complexity
• Cryptography
• Theoretical Computer Science
• +۳ مورد دیگر

Manuel Blum is the Bruce Nelson Professor of Computer Science at Carnegie Mellon University's School of Computer Science, Department of Computer Science. He is renowned for foundational contributions to computational complexity, cryptography, and theoretical computer science. His work includes pioneering research on CAPTCHAs, human computation, and self-testing programs. Blum has collaborated extensively with figures like Luis von Ahn, contributing to projects like Peekaboom and Verbosity. His research also extends to philosophical inquiries into consciousness, blending computer science with neuroscience and philosophy. Blum's teaching includes courses on theoretical cryptography (15-503) and undergraduate complexity theory (15-455). He has advised students such as Brendan Juba and Matt Humphrey, whose work explores computational complexity and related fields. His research spans grants on machine learning, game theory, and computational complexity, including an NSF proposal on understanding concept understanding. Blum's contributions to security, randomness, and algorithm design have had lasting impacts on computer science.

Ryan O'Donnellمشاهده پروفایل

استاد

• Algorithms and Complexity
• Security and Privacy
• Theoretical Computer Science
• +۶ مورد دیگر

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

David D. Gamarnikمشاهده پروفایل

استاد

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +۳ مورد دیگر

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Deeparnab Chakrabartyمشاهده پروفایل

دانشیار

• Algorithms
• Theoretical Computer Science
• Optimization
• +۳ مورد دیگر

Deeparnab Chakrabarty is an Associate Professor in the Department of Computer Science at Dartmouth College, specializing in theoretical computer science and algorithms. His research examines computational efficiency boundaries through the lens of mathematical optimization, addressing problems in machine learning, big-data analysis, and algorithmic economics. He teaches undergraduate courses in Discrete Mathematics and Algorithms, while his research group investigates submodular minimization, clustering algorithms, and property testing. Recent publications feature advances in monotonicity testing, graph connectivity algorithms, and fair clustering methods. As advisor to PhD candidates and undergraduate researchers, he mentors students in algorithmic theory and applied mathematics. His work has appeared in premier venues including FOCS, STOC, and SODA conferences.