Siddhant Haldar
Research Intern at NVIDIA
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSiddhant Haldar is a PhD candidate at NYU Courant and Co-Founder/CTO with nine years of experience building generalizable AI systems for robotics, multitask learning, and reinforcement learning. His profile blends deep research—internships at NVIDIA, Microsoft Research, Nuro, and visits with leaders like Dieter Fox—with hands-on engineering, including deployment of multi-modal goal-conditioned policies for on-road agents. He has contributed to the popular DoWhy causal inference library, improving statistical estimators and adding interpretable notebooks and an optimized ID algorithm, highlighting a rare mix of causal inference and robotic control expertise. Early work on adversarial robustness, long-form action quality assessment, and perception for autonomous vehicles shows a consistent focus on robustness and generalization across modalities and time scales. Based in New York, he leverages academic rigor to lead product-minded ML engineering as he scales a stealth startup.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at New York UniversityRyan International School , Vasant KunjDual Degree (B.Tech + M.Tech), Electrical Engineering, 9.08/10.00, Dual Degree (B.Tech + M.Tech), Electrical Engineering, 9.08/10.00 at Indian Institute of Technology, KharagpurGarden High School
English, Hindi, Bengali
Github Skills (13)
causal10
jupyter-notebook10
machine-learning10
causality10
python10
data-science10
causal-inference10
graphical-models9
pandas9
data-structure7
data-structures7
algorithm7
algorithms7
Programming languages (2)
Github contributions (5)
py-why/dowhy
May 2021 - Aug 2021
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
Data Scientist
Data ScientistContributions:13 reviews, 7 commits, 10 PRs in 2 months
Contributions summary:Siddhant primarily contributed to the DoWhy library by implementing and refining causal inference functionalities. They corrected standard errors and confidence intervals for custom treatments in the linear regression estimator, demonstrating a focus on improving the statistical accuracy of causal effect estimations. They also developed new example notebooks, including a notebook for visual interpreter plots for causal estimation and a causal discovery example notebook, indicating a focus on usability and illustrating the application of causal inference methods. Moreover, they added code for ID Algorithm and optimized its implementation.
fairness-mlcausal-modelspythoncausalbayesian-networks
siddhanthaldar/EEG_based_activity_recognition
Jul 2018 - Sep 2018
Contributions:6 commits, 2 pushes in 2 months
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