Dr. Sonakshi Garg received her Ph.D. degree in Computer Science from Umeå University, Sweden, in 2025. She has a strong academic and research background with expertise in machine learning, data privacy, generative models, and large language models. Her experience spans research, academia, and industry collaborations across Sweden, Germany, and the United States. She was recently invited to speak at UNECE, Barcelona, addressing a distinguished forum of experts from federal agencies working on statistical data confidentiality. Her research is substantiated by publications in leading international conferences and journals, as well as contributions to collaborative projects with industry partners. In addition to her research, Dr. Sonakshi has been actively involved in teaching and mentoring, delivering courses in machine learning, artificial intelligence, and data privacy. She is deeply committed to bridging academic research with industrial impact and advancing responsible AI development in line with data protection regulations such as GDPR. She is a member of professional research communities and continues to pursue impactful work in privacy-preserving machine learning, foundation models, and privacy-enhancing technologies.
1. Garg, Sonakshi, and Vicenç Torra. "Task-Specific Knowledge Distillation with Differential Privacy in LLMs." In European Symposium on Research in Computer Security, doi: 10.1007/978-3-031-70890-9_19
2. Garg, Sonakshi, and Vicenc Torra. "Privacy in manifolds: Combining k-anonymity with differential privacy on Frechet means." Computers & Security, doi: 10.1016/j.cose.2024.103983
3. Kumar, Sandeep, Sonakshi Garg, and Pranab K. Muhuri. "A stratified review of COVID-19 infection forecasting and an efficient methodology using multiple domain-based transfer learning." Expert systems with applications doi:10.1016/j.eswa.2024.125277
4. Garg, Sonakshi, and Vicenç Torra. "Exploring Distribution Learning of Synthetic Data Generators for Manifolds." In European Symposium on Research in Computer Security, pp. 65-76. Cham: Springer Nature Switzerland, 2024. doi: 10.1007/978-3-031-82349-7_5
5. Garg, Sonakshi, Sandeep Kumar, and Pranab K. Muhuri. "A novel approach for COVID-19 infection forecasting based on multi-source deep transfer learning." Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105915