جست‌وجوی استاد و پژوهشگر

Nathan Linialمشاهده پروفایل

استاد

• Combinatorics
• Theoretical Computer Science
• Bioinformatics
• +۶ مورد دیگر

Nathan (Nati) Linial is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, where he has been a faculty member since completing his postdoctoral period at UCLA. He earned his undergraduate degree in mathematics from the Technion and his PhD in graph theory from the Hebrew University. His research spans multiple areas of theoretical computer science and mathematics, with primary focus on combinatorics, theoretical computer science, and bioinformatics. Linial's work has made significant contributions to high-dimensional combinatorics, expander graphs, metric embeddings, and computational molecular biology. His research often bridges geometry, analysis, and combinatorial structures, demonstrating deep connections between seemingly disparate mathematical fields. Linial's recent publications reveal a strong trend toward high-dimensional combinatorial structures, including simplicial complexes, hypertrees, and high-dimensional permutations. His work frequently employs probabilistic methods, linear programming techniques, and geometric approaches to solve fundamental combinatorial problems. The breadth of his research is evident in both pure mathematical contributions and applications to computational biology. Fellow of the American Mathematical Society ISI Highly Cited Researcher Conant Prize (2008) for the influential survey paper "Expander graphs and their applications" Linial has served on the editorial boards of several prestigious journals including the Israel Journal of Mathematics (as Chief Editor 2013-2017), Random Structures and Algorithms, and Combinatorica. His academic leadership extends to organizing conferences and workshops in combinatorics and theoretical computer science. He has mentored numerous students whose work spans theoretical computer science, combinatorics, and computational biology. Linial is associated with research projects including ProtoNet (for protein sequence classification) and EVEREST (for evolutionary conserved protein domains), demonstrating his commitment to interdisciplinary research that bridges computer science with molecular biology.

• Algebraic Combinatorics
• Polytope Theory
• Ehrhart Theory
• +۵ مورد دیگر

Yuhan Jiang is a Research Fellow in the Department of Mathematics at the University of California, Berkeley, where they work under the mentorship of Professor Sylvie Corteel. Previously, Jiang completed their PhD at Harvard University under the supervision of Professor Lauren K. Williams. Their academic address is 931 Evans Hall, Berkeley. Education: PhD in Mathematics from Harvard University, advised by Professor Lauren K. Williams Dr. Jiang specializes in algebraic combinatorics, with research spanning polytope theory, matroid theory, and algebraic statistics. Their work connects combinatorial structures with algebraic geometry and representation theory, particularly focusing on coinvariant rings, poset dynamics, and Ehrhart theory. Jiang's research demonstrates a strong interplay between discrete mathematics and geometric structures, often revealing deep connections between seemingly disparate areas of mathematics. Analysis of Jiang's recent publications shows a consistent focus on combinatorial structures with geometric interpretations. Their work on alternating diagonal coinvariants, echelonmotion, and positroid polytopes demonstrates expertise in connecting algebraic combinatorics with geometric reasoning. The publications reveal a progression from foundational combinatorial structures to applications in probability (through Markov chain analysis) and statistics (through algebraic statistical models). A notable trend is Jiang's ability to bridge theoretical combinatorics with concrete computational results, as seen in their work on Ehrhart series and k-ellipses. Dr. Jiang teaches Math 185: Complex Analysis for Fall 2025 at UC Berkeley, indicating active participation in the department's educational mission. While specific grant information isn't provided in the available text, their productive research output across multiple mathematical domains suggests successful funding support for their scholarly activities.

Lilya Budaghyanمشاهده پروفایل

استاد

• Boolean Functions
• Cryptography
• Information Security
• +۳ مورد دیگر

Lilya Budaghyan is a Professor at the Department of Informatics, University of Bergen, Norway, leading the Boolean functions research team at the Selmer Center. She holds a PhD from the University of Magdeburg (2005) and a habilitation from Paris 8 University (2013). Her research focuses on Boolean functions, particularly APN (Almost Perfect Nonlinear) functions and their cryptographic applications, with contributions to hardware implementations, equivalence relations, and cryptographic protocol design. She has authored the book *Construction and Analysis of Cryptographic Functions* and received awards including the TMS Starting Grant (2016) and Emil Artin Junior Prize (2011). Her work includes projects like the EU-funded BoolTI (2021–2025) and collaborations across institutions in Armenia, Germany, Italy, and France. Key research themes include constructing APN functions, analyzing their properties, and optimizing their hardware implementations for secure systems. Her recent publications emphasize hardware architectures for APN permutations, equivalence between cryptographic functions, and theoretical advancements in algebraic coding and finite fields. She serves as an editor for journals in cryptography and has organized international workshops such as WCC 2013.

Diptarka Chakrabortyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +۵ مورد دیگر

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Computational Complexity
• Boolean Functions
• Communication Complexity
• +۵ مورد دیگر

Shachar Lovett is a researcher at the University of California, San Diego (UCSD), specializing in computational complexity, combinatorics, and theoretical computer science. His work spans advanced topics in communication complexity, pseudorandomness, and coding theory, often intersecting with problems in additive combinatorics and Boolean function analysis. Education : Not explicitly detailed in the provided text. Research Interests : Lovett's research focuses on computational complexity, particularly in communication and circuit complexity, combinatorial structures like sunflowers and high-dimensional expanders, and the analysis of Boolean functions through Fourier and Gowers norms. His work explores the limits of deterministic vs. randomized computation, the structure of codes over finite fields, and the interplay between additive combinatorics and theoretical computer science. Article Trends : His recent publications address exact vs. approximate representations of Boolean functions, quasipolynomial bounds in combinatorics, hypercontractivity in high-dimensional expanders, and advancements in the log-rank conjecture. These works emphasize connections between computational complexity, discrete mathematics, and pseudorandomness, often yielding improved bounds or novel frameworks for understanding Boolean function behavior. Scientific Awards : No specific awards or honors were mentioned in the provided text. Advising and Collaborations : Lovett collaborates extensively with researchers like Hamed Hatami, Kaave Hosseini, and Jiapeng Zhang, contributing to fields such as non-malleable codes, matrix multiplication algorithms, and communication complexity. No formal student advising details were provided.

• Cryptography
• Cryptanalysis
• Stream Ciphers
• +۵ مورد دیگر

Prof. Dr. Willi Meier serves as a Lecturer for mathematics and cryptology at the Institute for Sensors and Electronics within the School of Engineering and Environment at FHNW (University of Applied Sciences and Arts Northwestern Switzerland) in Windisch, Switzerland. With an extensive publication record spanning over three decades (1988-2025), he maintains an active research profile in cryptographic analysis. Dr. Meier's research primarily focuses on cryptanalysis of symmetric cryptographic primitives, with particular expertise in stream ciphers, block ciphers, and hash functions. His work frequently employs algebraic techniques, differential cryptanalysis, and mathematical modeling approaches to analyze cryptographic security. Recent research has centered on analyzing modern ciphers like Grain, Keccak, RIPEMD-160, and various lightweight cryptographic designs, often developing novel attack methodologies such as coefficient grouping and algebraic meet-in-the-middle approaches. His publication trends over the last five years show consistent high productivity in top-tier venues including CRYPTO, EUROCRYPT, ASIACRYPT, and IACR Transactions on Symmetric Cryptology. The research spans both theoretical advancements in cryptanalytic techniques and practical applications to real-world cryptographic standards. A significant portion of his recent work involves collaborations with international researchers, particularly Fukang Liu, Takanori Isobe, and Santanu Sarkar, reflecting his integration within the global cryptographic research community. Dr. Meier's work has practical implications for cryptographic standardization and implementation security, with analyses of protocols used in telecommunications (TETRA), lightweight IoT applications, and post-quantum cryptographic candidates. His research continues to contribute to the fundamental understanding of symmetric cryptographic primitives and their security margins.

Elchanan Mosselمشاهده پروفایل

استاد

• Probability Theory
• Combinatorics
• Machine Learning
• +۴ مورد دیگر

Elchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), with a joint appointment at the Institute for Data, Systems, and Society (IDSS). His research focuses on probability, combinatorics, statistical inference, and their applications to computer science and social choice theory. He earned his B.Sc. from the Open University of Israel, and both his M.Sc. (1997) and Ph.D. (2000) in Mathematics from the Hebrew University of Jerusalem. Before joining MIT in 2016, he was a faculty member at UC Berkeley and held visiting positions at the Weizmann Institute and the Wharton School of the University of Pennsylvania. Mossel's work bridges theoretical foundations and applied problems, including computational complexity, randomized algorithms, Markov random fields, and evolutionary biology. He has made significant contributions to the theory of social choice, game theory, and the mathematical underpinnings of machine learning. Notable achievements include resolving the Majority Is Stablest conjecture and advancing phylogenetic reconstruction methods. His awards include the Sloan Fellowship and Miller Fellowship. He has mentored numerous graduate students, including Sebastien Roch, Allan Sly, and Miklos Racz, and has held editorial roles in journals like the Electronic Journal of Probability . Mossel's teaching spans topics from introductory probability to advanced courses on social networks and game theory.

Sihem Mesnagerمشاهده پروفایل

استاد

• Symmetric Cryptography
• Coding Theory
• Finite Fields
• +۱۰ مورد دیگر

Sihem Mesnager is a University Professor of Mathematics at the University of Paris VIII, affiliated with the Laboratory of Analysis, Geometry and Applications (LAGA) at Paris XIII (CNRS) and the AGC3 research group (Algebra, Geometry, Combinatorics) . She holds an adjunct professorship at Télécom Paris within the MIC2 Mathematics team of the Computer Science and Networks department (INFRES). Her work bridges pure mathematics and applied cryptography, focusing on Boolean functions, bent functions, and coding theory for secure communication and data protection. PhD in Mathematics, University of Pierre and Marie Curie (Paris VI), Sorbonne University (2002) Habilitation (HDR) in Mathematics, University of Paris VIII (2012) Research Interests Dr. Mesnager specializes in symmetric cryptography and coding theory , particularly their applications to secure digital communication, error correction, and post-quantum cryptographic protocols. Her algebraic approach employs finite fields, exponential sums, algebraic geometry, and finite geometry to analyze and construct cryptographic primitives like S-boxes, APN functions, and optimal linear codes. She also investigates algorithmic aspects of computer algebra in these domains. Scientific Awards George Boole International Prize (2020) PEDR Excellence Scientific Award (2019-2022) PEDR Excellence Scientific Award (2014-2017) Publications & Projects Her recent work includes constructing weightwise perfectly balanced Boolean functions for the FLIP cipher, optimizing Inner Product Masking schemes via coding theory, and developing post-quantum secure functional encryption using multivariate cryptography. She has contributed to Reed-Muller codes, BCH codes, and Gaussian sum-based linear codes with one-dimensional hulls, emphasizing their applications in side-channel attack resistance and quantum error correction.

Manuel Penschuckمشاهده پروفایل

پژوهشگر ارشد

• Algorithm Engineering
• Random Graphs
• Parallel and Distributed Computing
• +۳ مورد دیگر

Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.

Violetta Lonatiمشاهده پروفایل

استادیار

• Formal Languages and Automata
• Computer Science Education
• Theoretical Computer Science
• +۲ مورد دیگر

Violetta Lonati is an Assistant Professor at the University of Milan 's Department of Computer Science since 2005. Her research spans Formal Languages and Automata (operator precedence languages, Wang automata, tiling systems) and Computer Science Education . She co-authored over 15 publications in theoretical computer science and education, focusing on 2D language recognition, logic characterization of automata, and pattern statistics in stochastic models. Education : PhD in Computer Science (2005) and Laurea in Mathematics (2001) from University of Milan Research Groups : ALaDDIn Lab for Didactics and Dissemination of Informatics, Bebras International Initiative Her work on Wang automata established their equivalence to tiling systems while introducing deterministic variants. In education, she designed workshops for schools and contributed to Italy's national computing curriculum proposal (2019). She held leadership roles at ACM ITiCSE (WG5 leader 2022), served as Associate Program Chair (2019-2022), and reviewed for top venues like ICER and SIGCSE TS. She received Google CS[4]HS and Informatics Europe awards for her educational contributions. Key Publications (2017-2001): Input-driven locally parsable languages (TCS 2017) Operator precedence logic characterization (SICOMP 2015) Snake-deterministic tiling systems (MFCS 2009) Graph fibrations and PageRank (RAIRO 2006) Pattern statistics in rational models (STACS 2005) Scientific awards include Google CS[4]HS (2011, 2017, 2019) and the Informatics Europe Best Practices in Education (2016). As part of ALaDDIn, she developed teacher training programs and graduate courses on computing education. Her teaching experience covers Algorithms & Data Structures (2013-2023), Computer Science Teaching (2014-2023), and courses for Biotechnology and Geological Sciences programs (2005-2007).

Gaurav Rattanمشاهده پروفایل

استادیار

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +۵ مورد دیگر

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

• Finite Fields
• Cryptography
• Coding Theory
• +۴ مورد دیگر

Qiang (Steven) Wang is a Professor in the School of Mathematics and Statistics at Carleton University. His academic career spans several decades with a strong focus on finite fields and their applications in cryptography and coding theory. He maintains an active teaching schedule including courses like Abstract Algebra, Rings and Fields, and Fields and Coding Theory. Professor Wang's research interests center on Finite Fields and Applications in Cryptography and Coding Theory, Combinatorics, Algebra and Number Theory. His work particularly focuses on polynomials and functions over finite fields, sequences over finite fields, and applications in Coding Theory and Cryptography. He has made significant contributions to the study of irreducible polynomials, permutation polynomials, primitive polynomials, planar functions, Boolean functions, LFSR and NLFSR sequences, de Bruijn sequences, stream ciphers, pseudorandom generators, and linear complexity. His research bridges theoretical mathematics with practical cryptographic implementations. His extensive publication record shows a consistent and growing research program, with numerous publications in top-tier journals including Designs, Codes and Cryptography, Finite Fields and Their Applications, and Cryptography and Communications. His most recent work (2022-2024) has increasingly focused on compositional inverses of permutation polynomials, stable polynomials, additive codes with few weights, and generalized cyclotomic mappings, while maintaining strong connections to cryptographic applications. Editorial board member of Applicable Algebra in Engineering, Communication and Computing, Springer Editorial board member of Finite Fields and their Applications, Elsevier Professor Wang actively contributes to the academic community through his editorial work and seminar organization. He coordinates the Carleton Finite Fields eSeminar and participates in the Ottawa-Carleton Combinatorics and Optimization Seminar, fostering research collaboration in discrete mathematics and related fields. His teaching portfolio is diverse, covering both undergraduate and graduate courses that reflect his broad expertise across algebraic structures, coding theory, and discrete mathematics.

Kalikinkar Mandalمشاهده پروفایل

دانشیار

• Cryptography
• Cybersecurity
• Privacy
• +۷ مورد دیگر

Dr. Kalikinkar Mandal is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick (UNB), Fredericton, Canada. He holds the prestigious NB Power Cybersecurity Research Chair for smart grid security and privacy, a position supported by $500,000 in funding from NB Power for a five-year term. Dr. Mandal is also a member of the Canadian Institute for Cybersecurity (CIC), an ACM member, and a member of the International Association for Cryptologic Research (IACR). Education: PhD in Electrical and Computer Engineering from the University of Waterloo (2013) MTech in Computer Science from the Indian Statistical Institute, Kolkata (2009) Additional Master's degree in Mathematics Dr. Mandal's research broadly focuses on cryptography, cybersecurity, and privacy, with specific expertise in lightweight cryptography, privacy-preserving computation, security and privacy in smart grids and Internet of Things (IoT), trusted computing, and high-speed cryptography. His work addresses critical challenges in securing emerging technologies, particularly in energy infrastructure where cybersecurity threats can have severe consequences for essential services. His research bridges theoretical cryptography with practical applications in real-world systems. Analysis of Dr. Mandal's recent publications reveals a consistent focus on cryptographic techniques for resource-constrained environments, particularly for smart grid and IoT applications. His work spans theoretical foundations of cryptographic primitives, practical implementations of lightweight ciphers, and innovative privacy-preserving protocols for emerging technologies. A notable trend in his research is the development of efficient cryptographic solutions that balance security requirements with performance constraints in critical infrastructure systems. Scientific Awards: NB Power Cybersecurity Research Chair ($500,000 funding) Contributor to multiple cryptographic algorithms (ACE, SPIX, SpoC, WAGE) that reached Round 2 of NIST Lightweight Cryptography standardization Dr. Mandal actively mentors graduate students in cybersecurity research, currently supervising three students working on cryptographic protocols for cyber-physical systems, cybersecurity in advanced metering infrastructure, and privacy for electric vehicles. His NB Power Cybersecurity Research Chair supports research that provides training opportunities for both graduate and undergraduate students, preparing them as future cybersecurity leaders. Through direct applied research, knowledge dissemination, and student training, his work addresses critical challenges in power and security infrastructure. Dr. Mandal is actively involved in several research initiatives related to lightweight cryptography. He is part of the development teams for ACE, SPIX, SpoC, and WAGE - all of which were Round 2 candidates in the NIST Lightweight Cryptography standardization project. His GitHub repository (comsec-lwc) contains reference and optimized implementations of these cryptographic algorithms. He also contributes to the Canadian Institute for Cybersecurity at UNB, focusing on practical applications of cryptographic techniques in critical infrastructure security.

Benjamin Rossmanمشاهده پروفایل

دانشیار

• Computational Complexity
• Finite Model Theory
• Circuit Complexity
• +۳ مورد دیگر

Benjamin Rossman is an Associate Professor in the Computer Science and Mathematics Departments at Duke University , affiliated with the Theory Group. His research focuses on computational complexity, particularly circuit complexity and finite model theory, with notable contributions to lower bounds for subgraph isomorphism problems and logical definability. Rossman earned his Ph.D. from MIT under Madhu Sudan's supervision. He has held faculty positions at the University of Toronto and postdoctoral roles at the Tokyo Institute of Technology and National Institute of Informatics in Japan. He has received prestigious awards including the Alfred P. Sloan Fellowship and NSERC grants. His teaching includes courses on computational complexity, mathematical logic, and discrete mathematics. Recent publications explore homomorphism preservation theorems, formula complexity, and treedepth in subgraph isomorphism. Rossman advises graduate students in theoretical computer science and has led research on circuit lower bounds, algorithmic logic, and combinatorial complexity. His work bridges foundational theory with practical algorithmic challenges, contributing to the understanding of computational limits in combinatorial problems.

Tianren Liuمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Information Theoretic Cryptography
• +۴ مورد دیگر

Tianren Liu is an Assistant Professor at the Center on Frontiers of Computing Studies (CFCS), Peking University, where he joined in 2022. His research focuses on cryptography and theoretical computer science with particular emphasis on information-theoretic approaches to cryptographic problems. Dr. Liu received his Bachelor's degree from IIIS Tsinghua University, advised by Prof. John Steinberger, and his Master's and Ph.D. degrees from MIT, where he was advised by Prof. Vinod Vaikuntanathan. Prior to joining Peking University, he was a postdoctoral researcher at the University of Washington, mentored by Prof. Huijia (Rachel) Lin. His primary research interests include information-theoretic cryptography (covering problems such as Private Information Retrieval, Conditional Disclosure of Secrets, Private Simultaneous Messages, and secret sharing), secure multi-party computation with information-theoretic security properties, garbled circuits, analysis of practical ciphers from an information-theoretic perspective, and separation results regarding the possibility of basing cryptography on NP-hardness assumptions. His work often bridges theoretical foundations with practical cryptographic applications, favoring protocols with information-theoretic security guarantees. Dr. Liu's publication record demonstrates consistent contributions to top-tier cryptography venues including CRYPTO, EUROCRYPT, STOC, and TCC. His recent work explores garbled circuits for mixed computation types, statistical properties of cryptographic primitives like SPN block ciphers, and efficient protocols for secure computation. His research shows a trajectory of addressing fundamental questions in cryptographic theory while developing practical techniques that overcome previous limitations in circuit size barriers and security guarantees. Best Student Paper award at TCC 2018 Dr. Liu actively mentors graduate students, currently supervising two Ph.D. candidates: Luojian Wei and Liqiang Liu, who began their doctoral studies in 2023. He welcomes motivated students with background in cryptography and theoretical computer science for potential Ph.D. opportunities starting in 2026. He also teaches courses in Cryptography and Discrete Mathematics at Peking University. His office is located in Room 103-2, Jingyuan Courtyard No.5, Peking University.