Prof Zhong Chen (Peking University) and Prof Minghua Chen (City University of Hong Kong) visit NET Group

During 29.-31.07.2026, Prof. Zhong Chen (PKU) and Prof. Minghua Chen (CityU/CityU-Shenzhen) visit NET group and are going to share their research experiences and recent results with our institute.

Talk 1: Demystifying and Detecting Cryptographic Defects in Blockchain Smart Contracts

Date: 28.07.2026, 15:00-16:00, room IFI SR1.101.

Abstract: Cryptographic techniques have become an important way to expand the functionality and innovate applications of blockchain. Currently, blockchain platforms represented by Ethereum have introduced a series of system-level APIs, providing low-level cryptographic operations such as hashing and elliptic curve operations to support the implementation of various cryptographic applications in smart contracts. However, due to the complexity of cryptographic applications, developers often introduce potential security vulnerabilities when implementing upper-level cryptographic applications based on low-level cryptographic operations, which has led to multiple real-world attack incidents. This talk will introduce our research progress on the demystifying and detecting cryptographic defects in blockchain smart contracts. It includes (1) how to systematically mine, define, and classify cryptographic defects existing in real-world smart contracts based on threat intelligence such as real-world smart contract audit reports. （2）how to combine software engineering techniques such as fuzz testing and static analysis to accurately and efficiently detect new cryptographic defects in smart contracts.

Speaker: Prof. Zhong Chen, Ph.D., is Professor and Founding Director of the Metaverse Technology Institute in the School of Computer Sciences at Peking University. He was assumed as Chair Professor and Dean of School of AI and Liberal Arts, Beijing Normal-Hong Kong Baptist University in Zhuhai from Sept. 2025. Dr. Chen graduated and earned his Ph.D degree from Computer Science and Technology Department of Peking University in 1989, and then joined the faculty of computer science in Peking University. He became full professor in 1995. He was a visiting professor of UCLA from 2001 to 2002. He has been assumed the founding dean position of the School of Software and Microelectronics, Peking University from 2002 to 2010, Chairman of CS department from 2011-2015. Prof. Chen is the fellow and managing director of China Computer Federation (CCF), Vice Director of the Steering Committee of Higher Education on Computer Science and Technology of China, Vice-Chair of China Software Industry Association and Vice-Chair of China Open Source Software Promotion Union (COPU), Director of FinTech and Algorithms Expert Committee of China Society for Industrial and Applied Mathematics (CSIAM). His research interests include Domain-specific Software Engineering (especially in Banking and Financing, e-Commerce, Digital Government etc.) enterprise digital transformation, smart service applications, Blockchain and Web3 etc. He has published more than 200 research papers and Five academic books. He has supervised 35 Ph.D. and more than 100 Masters. He received the 1^st Prize National Higher Education Teaching Achievements Award in 2005 and Excellent Teacher Awards for Computer Science and Technology by China Teacher Development Foundation in 2019.

Invited talk 2: Machine Learning for Optimization under Hard Constraints

Date: 30.07.2026, 10:00-11:00, room IFI SR1.101.

Abstract: Optimization problems subject to hard constraints are common in time-critical applications such as autonomous driving, communications, networking, and power grid operation. However, existing iterative solvers often face difficulties in solving these problems in real-time. In this talk, we advocate a machine learning approach -- to employ NN's approximation capability to learn the input-solution mapping of a problem and then pass new input through the NN to obtain a quality solution, orders of magnitude faster than iterative solvers. To date, the approach has achieved exciting empirical performance and promising theoretical development. A fundamental issue, however, is to ensure NN solution feasibility with respect to the hard constraints, which is non-trivial due to inherent NN prediction errors. To this end, we present two approaches, predict-and-reconstruct and homeomorphic projection, to ensure NN solution strictly satisfies the equality and inequality constraints, respectively. In particular, homeomorphic projection is a low-complexity scheme to guarantee NN solution feasibility for optimization over any set homeomorphic to a unit ball, covering all compact convex sets and certain classes of nonconvex sets. The idea is to (i) learn a minimum distortion homeomorphic mapping between the constraint set and a unit ball using an invertible NN (INN), and then (ii) perform a simple bisection operation concerning the unit ball so that the INN-mapped final solution is feasible with respect to the constraint set with minor distortion-induced optimality loss. We prove the feasibility guarantee and bound the optimality loss under mild conditions. Simulation results, including those for computation-heavy SDP problems and non-convex AC-OPF problems for grid operations, show that homeomorphic projection outperforms existing methods in solution feasibility and run-time complexity, while achieving similar optimality loss. We will also discuss future directions. This is a joint work with Enming Liang (City University of Hong Kong) and Steven Low (Caltech).

Speaker's bio: Prof. Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He currently is a Presidential Chair Professor in School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, ACM e-Energy Best Paper Award in 2023, and Nature Communications Editors’ Highlights in 2025 (as one of the 17 highlight papers in Engineering and Infrastructure). Coding primitives co-invented by Minghua have been incorporated into Microsoft Windows and Azure Cloud Storage, serving hundreds of millions of users. Prof. Chen and his team’s carbon-minimizing schemes demonstrated promising fuel-saving performance in a heavy-duty truck field test spanning over tens of thousands of kilometers, offering compelling potential for decarbonizing the trucking industry. His recent research interests include online optimization and algorithms, machine learning in power system operation, intelligent transportation, distributed optimization, and delay-critical networking. He is a Fellow of the IEEE.