About

Harsh Parikh is a highly accomplished Ph.D. candidate in the field of causal inference and machine learning at Duke University. He is currently a Research Engineer at meta, working with the Causality and Privacy Research Group on the project of inferring network interference from user randomized experiments. Harsh has a strong academic background with a B.Tech in Computer Science from Indian Institute of Technology, Delhi, and an M.S in Economics and Computation from Duke University. He is expected to complete his Ph.D. in Computer Science from Duke University in 2023. Harsh has a proven track record of success in research, having received the 2020 Amazon Fellowship for working on 'Evaluating Causal Methods'. He has also worked as an Applied Scientist at Amazon, where he designed a 'validation of causal estimation methods' framework. The framework learns the parameters of a simulator for generating data that imitates the dynamics of real-world data of interest. It generates a synthetic dataset with known ground truth causal effect using the learned simulator to validate the performance of causal estimation methods based on their ability to recover true treatment effects. Harsh is highly skilled in research and machine learning, with a strong tech stack that includes expertise in Research Scientist and ML. He has published several research papers in top-tier conferences and journals in the field of machine learning and causal inference. Harsh's research interests include causal inference, machine learning, and their applications in healthcare, social science, and economics. Harsh is a highly motivated individual with a passion for research and a strong desire to make a difference in the world. He is a quick learner and has excellent communication skills, making him an asset to any team. With his expertise in machine learning and causal inference, Harsh is well-positioned to make significant contributions to the field of data science and machine learning.