Research ProfileMy research focuses on the security and privacy of machine learning systems, spanning differential privacy, membership inference attacks, adversarial robustness, and secure training methods. Much of this work centres on spiking neural networks, an emerging architecture where security and privacy challenges are still being actively investigated. My approach combines theoretical analysis, algorithm design, and empirical evaluation to develop trustworthy and privacy-preserving machine learning systems. PublicationsLatents at Risk: Membership Inference Attacks on Spiking Variational Autoencoders Under Latent and Central Differential PrivacySrishti Yadav, Balasubramanian Raman, Sanjeev Kumar Differentially Private Spiking Variational AutoencoderSrishti Yadav, Anshul Pundhir, Tanish Goyal, Balasubramanian Raman, Sanjeev Kumar Unveiling Robustness of Spiking Neural Networks Against Data Poisoning AttacksSrishti Yadav*, Anshul Pundhir*, Balasubramanian Raman, Sanjeev Kumar EducationPh.D. in Data Science and Artificial IntelligenceIndian Institute of Technology Roorkee, Uttarakhand, India M.Sc. in MathematicsBanaras Hindu University, Varanasi, Uttar Pradesh, India B.Sc. (Hons.) in MathematicsDayalbagh Educational Institute, Agra, Uttar Pradesh, India Awards & Scholarships
ReferencesProf. R. BalasubramanianProfessor, Department of Computer Science and Engineering Prof. Sanjeev KumarProfessor, Department of Mathematics Contact Template adapted from Dr. Jon Barron. |
