Machine Intelligence team, working on efficient AI.
Leading research on learning dynamics and optimization for foundation models.
Project Next-Gen Optimizer: Developing theoretical and practical frameworks to accelerate training while reducing resource usage.
Project Causica: Lead developing temporal causal models for time-series data across discrete and continuous-time settings.
Collaborate with Global Channel Sales (GCS) to apply causal ML for revenue growth.
Researcher
Microsoft Research
Project Causica: Lead causal time-series projects providing end-to-end causal inference pipelines for time-series data.
Led collaboration with Eedi on applying causality to education.
Lead organizer for NeurIPS 2022 CausalML competition; co-organized NeurIPS 2022 score-based method workshop.
Research Intern
Microsoft Research
Mentor: Dr. Cheng Zhang
Developed a deep generative model and an inference method for active learning and prediction with accurate uncertainty quantification in small data regime. Published in NeurIPS 2019 with two patents.
Education
PhD Engineering (Machine Learning)
University of Cambridge
Machine Learning Group. Supervisor: Dr. Jose Miguel Hernandez-Lobato. Thesis: Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy.
Research focus: approximate inference, generative model, MCMC sampling, Bayesian learning.
MPhil Machine Learning, Speech and Language Technology
University of Cambridge
Project: Wasserstein Generative Adversarial Network supervised by Prof. Richard E. Turner. Co-supervised by Dr. Yingzhen Li and Dr. Mark Rowland.
MEng Information and Computer Engineering
University of Cambridge
Honours pass with distinction in exams and project. Project: Sampling method for Indian buffet process. Supervisor: Prof. Zoubin Ghahramani. Co-supervised by Dr. Ge Hong and Dr. Nilesh Tripuraneni.
BA Electrical and Information Science
University of Cambridge
First class in all three years. Top 1 in Gonville and Caius College.