School of Information Science and Technology
Email: songfu(at)shanghaitech.edu.cn, songfu1983(at)gmail.com(permanent) Tel: +86-(0)21-20685397 Office: Room A-504.C, SIST Building 1, No.393 Huaxia Middle Road, Pudong Area Shanghai. My ha-index: 73 orcid.org/0000-0002-0581-2679
Fu Song is an Assistant Professor at School of Information Science and Technology in the ShanghaiTech University.
Fu received his Ph.D. from University Paris Diderot(Paris 7) in 2013. He was an Associate Research Professor and Lecturer at School of Computer Science and Software Engineering in the East China Normal University (2013-2016), and
a visiting researcher at Computer Security Lab of Nanyang Technological University in 2014. Fu's paper (TACAS 2012) on malware detection won the EASST best paper award at ETAPS 2012.
His research group develops theory and tools to aid the construction of provably dependable and secure systems. His interests are related to Formal Methods (see A Framework for Formal Analysis), Cybersecurity, Programming Languages and Multi-Agent systems. For model checking algorithms and tools, we are developing model checkers support automated reasoning about various systems (e.g., recursive, concurrent and programs, multi-agent systems and so on), and investigating foundation problems of related automata and logics (e.g., FSTTCS 2014, CONCUR 2015, SSCI 2017).
We have developed model checker PuMoC for pushdown systems, Boolean programs, Java/C/C++ programs (e.g., CONCUR 2011&2015, ASE 2012). For cybersecurity, we are working at malware modeling, detection, classification and generation with the focus on desktop and Android malwares.
We have proposed novel type-based and model-counting based approaches for verifying masked cryptographic implementations (cf. CAV 2018, TACAS 2019).
We have developed a model-checking based malware detector POMMADE (e.g., ESEC/FSE 2013, TACAS 2012&2013, FM 2012).
We are developing tools for vulnerability modeling and detection using (both static and dynamic) program analysis and machine/deep learning on source and binary code (e.g. ICSE 2016).
We are developing tools for phishing website detection using machine/deep learning (cf. YSECURE), and investigating defense and attack of machine/deep learning.
For multi-agent systems, we are working on the topics related to formal modeling, specificaiton and verification of various multi-agent systems (e.g., AAAI 2016, IJCAI 2016).
For programming languages, we are working on formal semantics, type rules and static analysis framework for Rust,
Current Ph.D Students
Current Master Students
Visiting Researcher and Students