جست‌وجوی استاد و پژوهشگر

Andrea Lincolnمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Fine-Grained Complexity
• Average-Case Complexity
• +۳ مورد دیگر

Andrea Lincoln is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. Her research focuses on theoretical computer science, particularly average-case and fine-grained complexity. She holds a PhD from MIT (2020) and completed a postdoctoral fellowship at UC Berkeley with Barna Saha (2020-2021). Her work explores algorithmic complexity through reduction networks and dynamic systems analysis. Research interests include computational problem complexity, algorithm design for dynamic graphs, and average-case scenario analysis. She has contributed to understanding hardness proofs for problems like k-SUM and Orthogonal Vectors, as well as developing techniques for evaluating Boolean formulas and subgraph counting. Her recent work (2022-2025) emphasizes algorithmic efficiency under predictions, compression impacts on string distance measures, and cache-adaptive performance analysis. Notable publications address hypercycle database problems and delegation protocols for search problems. While no specific awards are listed, her prolific publication record reflects sustained contributions to theoretical computer science. Dr. Lincoln advises no listed students and has no documented grants in the provided text. Her work is associated with foundational algorithmic research rather than applied lab settings.

• Cryptography
• Cryptanalysis
• Algebra
• +۴ مورد دیگر

Igor Aleksandrovich Semaev is a Professor at the Department of Informatics, University of Bergen. His research focuses on cryptography, cryptanalysis, algebra, and computational problems in finite fields. He has contributed to areas such as sparse equation systems, Gröbner bases, and algorithmic security. Position: Professor Affiliation: Department of Informatics, University of Bergen Research interests include cryptanalysis of symmetric-key systems (e.g., DES), combinatorial problems in cryptography, and polynomial system solving techniques. His work often bridges algebraic methods with cryptographic applications, addressing challenges like sparse equation solutions and algorithmic complexity. Key contributions include the analysis of LFSR-based stream ciphers, probabilistic methods for Macaulay matrices, and multidimensional linear cryptanalysis. His publications span from 2005 to 2024, reflecting sustained engagement with both theoretical and applied cryptography. Notable projects include funding from the Norwegian Research Council (Project 247742) for work on Gröbner basis complexity. While no awards are explicitly mentioned, his prolific publication record highlights academic recognition. He has advised collaborative projects but no named students are listed. His work intersects with labs or teams through institutional affiliations, though specific lab details are not provided.

Sunil Khatriمشاهده پروفایل

استاد

• VLSI Design
• Secure Computing Systems
• Machine Learning Hardware
• +۷ مورد دیگر

Sunil Khatri is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Engineering and Systems Group. He holds a Ph.D. from the University of California, Berkeley (1999), an M.S. from the University of Texas at Austin (1989), and a B.S. from the Indian Institute of Technology Kanpur (1987). His academic progression includes roles as Assistant Professor (2000–2004) at the University of Colorado Boulder and Associate Professor (2010–2015) at Texas A&M. Educational Background: Ph.D., University of California, Berkeley, 1999 M.S., University of Texas at Austin, 1989 B.S., Indian Institute of Technology Kanpur, 1987 Research Interests: Intelligent and Secure Computing Systems: Focused on hardware architectures for machine learning (e.g., neuromorphic circuits, NoCs, sub-threshold circuits) and secure hardware design (blockchain, physically unclonable functions). Logic Synthesis and Applications: Includes VLSI CAD, gene regulatory network modeling, noise-based logic, and Boolean satisfiability (SAT) solvers. Interdisciplinary Extensions: Explores routing table compression, radar signal processors, optical networking (DWDM), and LDPC decoders. Research Trends in Publications: Recent work emphasizes neuromorphic hardware acceleration (e.g., CapsPDNet for insulator discharge prediction, FFIR filters for IoT), secure protocols (multi-factor authentication frameworks), and energy-efficient circuits (flash-based designs, low-voltage regulators). His contributions span VLSI, machine learning, and cybersecurity, often leveraging FPGA/GPU acceleration. Grants & Labs: Leads projects in the Computer Engineering and Systems Group, focusing on secure computing, machine learning hardware, and interdisciplinary VLSI applications. Active in grant-driven research with a focus on practical implementations in industry.

David Kebo Houngninouمشاهده پروفایل

دانشیار

• Logic synthesis
• Hardware formal verification and validation
• Boolean/switching theory
• +۵ مورد دیگر

David Kebo Houngninou is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. (2017), M.S. (2010), and B.S. (2008) in Computer Engineering from Southern Methodist University, Washington University in St. Louis, and the University of Evansville, respectively. His research focuses on logic synthesis , hardware formal verification , and Boolean/switching theory , with notable contributions to RISC-V architecture education through the FLIP simulator. He has received awards including the Research Days Outstanding Poster Award (2017) and multiple Outstanding Graduate Student Awards (2015-2016). Teaching interests include micro-architecture, formal verification, and computer systems courses. Professional experience spans roles at Texas Instruments (design engineer), IBM Research (researcher), and adjunct faculty positions. His work includes developing tutorials on microcontroller interfacing (STM32F107VC), CUDD BDD package usage, and Verilog parsing tools. Key projects involve creating educational simulators, formal verification frameworks, and hardware-software co-design methodologies. He actively contributes to diversity initiatives through roles like Doctoral Consortium Chair at the CMD-IT/ACM Tapia Conference.

Aukosh Jagannathمشاهده پروفایل

دانشیار

• Probability theory
• Mathematical foundations of data science
• Mathematical physics
• +۳ مورد دیگر

Aukosh Jagannath is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, with cross-appointments in the Department of Applied Mathematics and the Cheriton School of Computer Science. He is also an affiliate of the Perimeter Institute and Waterloo.AI. His research focuses on probability theory, mathematical foundations of data science, and mathematical physics, particularly in areas like high-dimensional learning, statistical-computational tradeoffs, and spin glasses. Before joining Waterloo, he held postdoctoral positions at Harvard University and the University of Toronto, and completed his PhD in mathematics at NYU’s Courant Institute under Gerard Ben Arous, alongside an undergraduate degree in math and physics from NYU. His work has been supported by grants from NSERC, the Canada Research Chairs program, the Canadian Foundation for Innovation, and the Ontario Research Fund. He has contributed to groundbreaking research on topics such as stochastic gradient descent dynamics, spectral alignment in neural networks, and the theoretical underpinnings of deep learning. Jagannath has advised numerous graduate and postdoctoral researchers, including current PhD students Taj Jones-McCormick and Varnan Sarangian, and former advisees like Aseem Baranwal (now at XTY Labs) and Yiming Xu (now at the University of Kentucky). His awards include an Outstanding Paper Award at NeurIPS 2022 for work on high-dimensional SGD dynamics.

Gillat Kolمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Communication Complexity
• Interactive Coding
• +۴ مورد دیگر

Gillat Kol is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. She joined the faculty in 2016 after postdoctoral positions at the Institute for Advanced Study and the Technion - Israel Institute of Technology. Her research focuses on theoretical computer science, with emphasis on interactive coding, communication complexity, distributed computing, and algorithmic theory. She has been recognized with prestigious awards including an NSF CAREER Award and a Sloan Research Fellowship, as well as multiple teaching accolades from Princeton's School of Engineering and Applied Science. Education: Ph.D. in Computer Science from the Weizmann Institute (2013) Her recent work explores fundamental tradeoffs in communication efficiency for noisy channels, streaming algorithms for graph problems, and cryptographic verification protocols in distributed systems. The majority of her publications address interactive error resilience, multi-pass streaming complexity, and theoretical challenges in networked environments. NSF CAREER Award (2018) Sloan Research Fellowship Kol's research has been honored with multiple teaching awards, reflecting her commitment to academic mentorship. She actively contributes to theoretical advancements in communication models and has published extensively at top conferences in theoretical computer science.

Natalia Kushikمشاهده پروفایل

مدرس

• Model-Based Testing
• Finite State Machines
• Software-Defined Networks (SDN)
• +۷ مورد دیگر

Natalia Kushik is a Lecturer at Telecom SudParis, part of the Institut Polytechnique de Paris. Her primary affiliation is with the ACMES (Algorithmics and Computer Models for Engineering and Systems) research group. Her research focuses on model-based testing methodologies, formal verification of systems, and applications in software-defined networking (SDN), cloud computing, and distributed systems. Her work extensively employs finite state machines (FSMs) and automata theory to design test strategies for complex systems. Notable areas include deriving homing/synchronizing sequences for automata, race condition detection in distributed systems, and optimizing network configurations using timed models. She has contributed to improving the reliability and security of SDN controllers and cloud infrastructures through formal methods. Recent trends in her publications emphasize probabilistic approaches for test suite minimization, emulation-based validation of dynamic networks, and formal analysis of reactive systems. She has collaborated on projects involving QoE (Quality of Experience) evaluation for multimedia services and fault models for digital circuits. Her work often bridges theoretical computer science with practical system implementation challenges. Her articles frequently explore intersections between formal methods and real-world applications, such as applying cellular automata for network parameter modeling and using SMT solvers for configuration validation. She has also published on optimizing interpreted programming languages using state models and proactive trust assessment mechanisms for service systems. Kushik’s research is characterized by interdisciplinary collaborations, with contributions to both academic conferences (e.g., ICTSS, ENASE) and industry-oriented venues (e.g., IEEE NCA). Her work addresses challenges in system reliability, security, and scalability across diverse domains like telecommunications, cloud computing, and embedded systems.

• Digital Circuits
• VLSI Design
• Signal Processing
• +۴ مورد دیگر

Paulo Flores holds the position of Associate Professor at the Department of Electrical and Computer Engineering within the Instituto Superior Técnico of the University of Lisbon. His academic career is deeply rooted in Electronics and Digital Systems, with a focus on VLSI design, FPGA optimization, and hardware acceleration for bioinformatics applications. He has contributed extensively to research in multiplierless constant multiplication algorithms, low-power circuit design, and quaternary logic implementations. Notable achievements include the SAT Competition 2014 and 2013 Bronze Medals for innovative contributions in formal methods and algorithmic efficiency. His teaching responsibilities include advanced courses in digital systems design, electronic engineering projects, and laboratory guidance in topics like operational amplifiers and FIR filters. Flores actively collaborates with research units like INESC-ID and has supervised multiple academic projects in electronic engineering and digital signal processing. Research interests span across circuit testing, embedded systems optimization, and multi-valued logic architectures. His work frequently explores trade-offs between computational efficiency and energy consumption in hardware implementations.

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

Prof. Pawel M. Idziak is a distinguished academic at the Faculty of Mathematics and Computer Science, Jagiellonian University, where he holds a Professorship in the Department of Algorithmics. His primary research focuses on algebra, computational complexity, and theoretical computer science, with notable contributions to the study of algebraic structures, satisfiability problems, and modular circuits. Idziak has led and contributed to numerous grants, including managing the 'Numerical and Structural Invariants in Algebra, Logic and Constraint Satisfaction Problems' (2015–2022) and co-ordinating initiatives like 'Programmers of the Future 4.0'. His work bridges abstract algebra with computational challenges, addressing topics such as group theory, circuit satisfiability, and algorithmic complexity. His research interests span a broad spectrum of theoretical computer science and algebra, including the interplay between algebraic structures and computational problems. Idziak has advised over 20 PhD students, mentoring them in areas such as algorithm design, logic in computer science, and discrete mathematics. He has also contributed to foundational works like Generative Complexity in Algebra (2005) and co-authored influential papers in journals like SIAM Journal on Computing and Transactions of the American Mathematical Society . Idziak’s academic leadership is evident through his roles in institutional committees and his active participation in international conferences. His recent work (2024) on equation satisfiability in solvable groups highlights his ongoing contributions to advancing computational algebra and complexity theory.

• Computational complexity
• Communication complexity
• Coding theory
• +۴ مورد دیگر

Anna Gal is a Professor at the Department of Computer Science, University of Texas at Austin, specializing in computational complexity, communication complexity, coding theory, and circuit complexity. She has made significant contributions to understanding lower bound methods and complexity measures of Boolean functions. Ph.D. in Computer Science from University of Chicago (1995) Her research focuses on theoretical aspects of computer science, including Boolean function analysis, sensitivity vs. block sensitivity, and combinatorial structures in coding theory. Recent publications indicate a strong emphasis on complexity measures, certificate games, and interactions between coding theory and circuit complexity. Key trends include applications of communication complexity to non-monotone circuits and sensitivity analysis of transitive functions. Anna Gal has received prestigious awards, including the Machtey Award for best student paper at FOCS 1991 and the EATCS best paper award at ICALP Track A in 2003. She has advised multiple Ph.D. students, including Jeff Ford, Vladimir Trifonov, Andrew Mills, Keith Jing-Tang Jang, and Siddhesh Chaubal. Her teaching includes advanced courses like Analysis of Boolean Functions and Communication Complexity.

• Algorithms
• Counting and Sampling
• Random Structures
• +۷ مورد دیگر

Mary Cryan is a Senior Lecturer in the School of Informatics at the University of Edinburgh, where she has been a faculty member since July 2003. She is affiliated with the Laboratory for Foundations of Computer Science (LFCS) and conducts research in theoretical computer science, with a focus on algorithms, counting and sampling, random structures, learning theory, and pseudorandom generators. Her educational background includes a joint undergraduate degree in Computer Science and Mathematics from University College Dublin (1993), an MSc by research at UCD (1995), and a PhD from the University of Warwick (awarded 2000) under the supervision of Leslie Ann Goldberg. She held postdoctoral positions at BRICS, University of Aarhus (1999–2001), and the University of Leeds (2001–2003) working with Martin Dyer. PhD, University of Warwick (2000) MSc, University College Dublin (1995) BSc (Joint Hons), Computer Science and Mathematics, University College Dublin (1993) Her research interests lie in the design and analysis of algorithms, particularly randomized algorithms for counting and sampling combinatorial structures. She has made significant contributions to the understanding of Markov chains for sampling matchings, contingency tables, Euler tours, and phylogenetic trees. Her work often bridges theoretical computer science with probability and discrete mathematics. The trend in her publications shows a sustained focus on fundamental algorithmic problems involving random structures and probabilistic methods. Her work spans exact and approximate counting, mixing times of Markov chains, and learning models in phylogenetics. Recent work includes analyzing log-Sobolev inequalities for log-concave distributions and exact counting on bounded-treewidth graphs. She has supervised several PhD students, including Veselin Blagoev ("Counting and Sampling Acyclic Orientations", 2018), Páidí Creed ("Counting and Sampling problems on Eulerian graphs", 2010), and James Matthews (co-supervised, "Markov chains for sampling matchings", 2008). She has also been active in academic service, serving as Deputy Director of the Informatics Graduate School since August 2020. Her research has been published in top-tier venues such as FOCS, STOC, SIAM Journal on Computing, and Journal of the ACM. She regularly lectures courses such as "Informatics 2 - Introduction to Algorithms and Data Structures".

Andreas Venerisمشاهده پروفایل

استاد

• Computer Engineering
• Blockchain Technology
• Formal Methods
• +۴ مورد دیگر

Professor Andreas Veneris, currently at the University of Toronto , holds cross-appointments in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering , Department of Computer Science , and the Munk School of Global Affairs & Public Policy . He earned his Diploma in Computer Engineering from the University of Patras (1991), M.S. in Computer Science from USC (1992), and Ph.D. in Computer Science from UIUC (1998). AAAS ACM Fellow IEEE Fellow Professional Engineers of Ontario Technical Chamber of Greece Planetary Society NSERC COHESA Network Director His research spans two decades of CAD/VLSI design automation followed by blockchain technology focusing on CBDCs , smart contract verification , DeFi mechanisms , and techno-legal Web3.0 policy . Recent work includes ASTRAEA decentralized oracle , DeFi insurance protocols , and privacy-preserving CBDC architectures . Award highlights: ACM SIGSOFT Distinguished Paper (ICSE 2024) IEEE Best Paper Awards (2024, 2022, 2020) ACM SIGARCH Maurice Wilkes Award MICRO Hall of Fame He advises Ph.D. candidates in blockchain and machine learning while leading research sponsored by Ripple (UBRI) , Huawei , and IBM . His group develops value-based accelerators for deep learning and formal verification frameworks for smart contracts. Selected projects include: HEMVM (Interoperable Blockchain VMs) BAKUP (DeFi Insurance Protocol) SigVM (Event-Driven Smart Contracts) CnvluTin (Ineffectual Neuron-Free CNNs) Stripes (Precision-Variable DL Accelerators)

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +۱۳ مورد دیگر

Eshan Chattopadhyay is a prominent researcher in theoretical computer science, focusing on computational complexity, pseudorandomness, and cryptography. His work centers on the explicit construction of randomness extractors, condensers, pseudorandom generators, and non-malleable codes, often improving entropy requirements and error bounds. He has made significant contributions to derandomization, space-bounded computation, and tamper-resilient cryptography. His research is published extensively in the Electronic Colloquium on Computational Complexity (ECCC), indicating deep engagement with foundational aspects of computer science. Research Interests: Eshan's research spans randomness extraction from weak sources, including sumset sources, polynomial sources, and adversarial models. He investigates pseudorandomness for branching programs, linear threshold functions, and Fourier-based constructions. His work in cryptography includes non-malleable codes, leakage resilience, and secret sharing under bounded collusion. He also contributes to combinatorics through extremal hypergraphs and designs, and to complexity theory via lower bounds and derandomization techniques. The recent articles (2021–2025) show a continued focus on improving extractor and condenser constructions under challenging models such as number-on-forehead protocols, online adversaries, and interleaved or adversarial sources. There is a strong trend toward handling sources with very low entropy, achieving near-optimal parameters, and extending results to two-sided and unbalanced settings in expander graphs. His collaborations with leading researchers like Xin Li, David Zuckerman, and Jesse Goodman reflect his central role in the community. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, the volume and depth of publications suggest active mentorship and likely grant funding in theoretical computer science. His work often involves junior collaborators, indicating a role in guiding emerging researchers. Labs and Teams: No specific lab or team affiliations are mentioned in the scraped content. However, his frequent co-authorship with researchers from institutions like UT Austin, CMU, and others implies participation in collaborative research networks focused on complexity and cryptography.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +۷ مورد دیگر

David Zuckerman is a prominent theoretical computer scientist known for his foundational contributions to computational complexity, pseudorandomness, and randomness extraction. His research spans over two decades, with consistent publication in top-tier venues under the Electronic Colloquium on Computational Complexity (ECCC). Although his current institutional affiliation is not specified in the text, his work is widely recognized and frequently collaborative with leading figures in the field. His research interests include: Randomness extraction and derandomization Computational complexity and circuit lower bounds Pseudorandom generators and hitting set constructions Error-correcting codes and combinatorial constructions Communication complexity and Boolean function analysis Applications in cryptography and distributed protocols Analysis of his recent publications reveals a sustained focus on improving the efficiency and scope of randomness extractors, especially for weak or structured sources such as Chor-Goldreich sources, interleaved sources, and algebraic sources. His work often bridges hardness and randomness, leveraging lower bounds to construct pseudorandom objects. A recurring theme is the derandomization of space-bounded and distributed computation, with implications for cryptography and coding theory. David Zuckerman has received significant recognition in the theoretical computer science community, though specific awards are not listed in the provided text. His collaborations with researchers like Salil Vadhan, Raghu Meka, and Eshan Chattopadhyay suggest leadership in major research initiatives. He has also contributed to foundational results in derandomization, such as optimal pseudorandomness from hardness assumptions and improved bounds for coin-flipping protocols. There is no mention of advising students or managing labs in the provided data, but his long-standing research output and mentorship through collaboration imply an academic leadership role. His continued publication into 2025 indicates active engagement in research.

• software verification
• automated reasoning
• code synthesis
• +۳ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University's School of Engineering & Applied Science, where she leads the Rigorous Software Engineering (ROSE) group. She joined Yale's Department of Computer Science in 2013 after serving as an Independent Research Group Leader at the Max Planck Institute for Software Systems in Germany. Her educational background includes a PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Her advisor was Viktor Kuncak, and her thesis focused on Decision Procedures for Program Synthesis and Verification . Piskac's research centers on improving software reliability and trustworthiness through formal techniques. Her primary interests span software verification, security and applied cryptography, automated reasoning, and code synthesis. She has developed innovative approaches in symbolic execution for Haskell, privacy-preserving formal methods, functional reactive synthesis, and verification for configuration files. Her work bridges theoretical foundations with practical applications in security, programming languages, and AI systems. Her recent publications (2022-2024) demonstrate a strong focus on applying formal methods to emerging challenges in AI, legal reasoning, quantum computing security, and zero-knowledge proofs. There's a clear trend toward interdisciplinary work connecting formal verification with legal accountability, large language models, and security applications. Multiple Amazon Research Awards (2021-2023) for work on formalizing legal reasoning and automated reasoning Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation Donna L. Dubinsky Associate Professor of Computer Science (2019) Patrick Denantes Prize for her PhD dissertation Piskac has graduated five PhD students, four of whom now hold assistant professor positions in computer science. Her research has been supported by multiple Amazon Research Awards and other funding sources. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and on the Steering Committee of the Formal Methods in Computer-Aided Design conference. Her ROSE group at Yale conducts cutting-edge research in formal methods with applications across software engineering, security, and AI systems.