Key details
Dr Sakshyam Panda
Lecturer in Cybersecurity (Security and Privacy for AI)
Sakshyam Panda is a Lecturer in Cyber Security and a member of the Centre for Sustainable Cyber Security (CS2) at the School of Computing and Mathematical Sciences, Faculty of Engineering and Science, University of Greenwich. His core expertise is in cyber threat modelling and developing robust strategies against strategic adversaries with a special interest towards security and privacy for AI.
His research is published in top-tier journals including COSE, JMIR, IJIS, IEEE Access and at esteemed international conferences such as AAAI, IEEE DSC and GameSec.
Before his lectureship appointment, he was the lead researcher for the Horizon TANGO project at the University of Greenwich (2022-2023). He has also worked as a research associate for H2020 SPEAR, H2020 CUREX, H2020 SECONDO and NCSC/EPSRC MERIT project during his PhD (Optimal Strategies for Cyber Security Decision-Making) at University of Surrey, UK (2018-2022).
Responsibilities within the university
- R&D lead for Horizon Europe TANGO project
- Research on cybersecurity and privacy for AI
- Teaching Information Security (COMP1806) and Computer and Communication Systems (COMP1765)
- Final year and MSc project Supervision
- Personal tutoring
Recognition
Advisory Groups & Memberships
Technical Committee Member, Conference on Decision and Game Theory for Security (GameSec); Programme Committee Member International Workshop on Advances on Privacy Enhancing Technologies and Solutions (IWAPS - ARES); IEEE member; Member of the EPSRC NetworkPlus for Security, Privacy, Identity, Trust in the Digital Economy (SPRITE+).
Invited talks
University of Piraeus “Cyber Risk Management with Cyber Insurance” (2020).
Research / Scholarly interests
Research and scholarly interests
- Cyber Security and Privacy Risk Management
- Cyber Security Threat Modelling, Optimisation and Decision Support
- Security and Privacy of AI
Research supervision
Research Interns
- Aksshar Ramesh (University of Greenwich, 2023 - present). Works on Adversarial Machine Learning.