I'm Tatsuki Koga, a 5th-year Ph.D. candidate in Computer Science at University of California San Diego, advised by Prof. Kamalika Chaudhuri.

My research interests are machine learning and statistical analysis with privacy guarantees, in federated setting, for medical applications and their intersections.

I have obtained my B.S. in Information Science at the University of Tokyo in March 2020.

CV

Blog

Talk & Slides

Contact

Education

• 2020.9 - Present (Expected Graduation 2025): Ph.D. in Computer Science, University of California San Diego
• 2016.4 - 2020.3: B.S. in Information Science, The University of Tokyo
  • 2018.4 - 2020.3: School of Science, Dept. of Information Science
  • 2016.4 - 2018.3: College of Arts and Sciences
• 2013.4 - 2016.3: Tokyo Gakugei University Senior High School

Publications

• Koga, T., Chaudhuri, K. & Page, D. ”Differentially Private Multi-Site Treatment Effect Estimation”. https://arxiv.org/abs/2310.06237 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Toronto, Canada, April 2024.
  • [Paper]
• Koga, T., Song, C., Pelikan, M., Chitnis, M. ”Population Expansion for Training Language Models with Private Federated Learning”. Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities Workshop at 40th International Conference on Machine Learning (ICML), Hawaii, United States, July 2023.
  • [Paper]
• Koga, T., Meehan, C. & Chaudhuri, K. "Privacy Amplification by Subsampling in Time Domain". 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual, Mar. 2022.
  • [Paper]
  • A short version has been presented at TPDP 2021 - Theory and Practice of Differential Privacy at 38th International Conference on Machine Learning (ICML), Virtual, Jul. 2021.
• Koga, T., Rie, E., Hirose, K. & Seita, J. “Human and GAN collaboration to create haute couture dress”. NeurIPS Workshop on Machine Learning for Creativity and Design 3.0 at 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2019. Contributed Talk.
  • [Video]
• Koga, T., Nonaka, N., Sakuma, J. & Seita, J. “General-to-Detailed GAN for Infrequent Class Medical Images”. ML4H: Machine Learning for Health Workshop at 32nd Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2018.
  • [Paper]

Preprints

• Koga, T., Meehan, C. & Chaudhuri, K. ”Measuring Privacy Loss in Distributed Spatio-Temporal Data”.
  • [Paper]

Exhibitions

• TOKYO Fashion Week 2019 A/W Collection by EMarie, February 2019
  • [Link], [Video]

Experiences

• Apple Inc., ML Engineer Intern, Summer 2024
  • Worked at FedStats team on speeding up learning high-dimensional histograms in the unknown dictionary setting with private federated analytics.
  • Collaborated with MLR team members, Kunal Talwar, Vitaly Feldman and Audra McMillan.
• Microsoft Corporation, Data Scientist Intern, Summer 2023
  • Worked at Data Security and Privacy Research team and worked on applied research on compliance.
  • Developed a novel pipeline for Microsoft Purview Data Loss Prevention solution using the embedding learned from scratch with neural networks.
• Apple Inc., ML Engineer Intern, Summer 2022
  • Worked at Private Federated Learning (PFL) team and worked on building language models (LM) in PFL.
  • Conducted research on domain adaptation technique for LM in PFL to deal with the setting where we have smaller population size for specific use-cases.