Aman Chauhan

AI Researcher

Noida, Uttar Pradesh, India2 yrs 7 mos experience

Key Highlights

Strong foundation in Mathematics and Computing.
Research experience in Machine Learning and Quantitative Trading.
Internship experience at Goldman Sachs.

Stackforce AI infers this person is a Machine Learning and Quantitative Research specialist with a strong academic background.

Contact

Skills

Core Skills

Machine LearningStatistics

Other Skills

AlgorithmsC (Programming Language)C++Computer VisionData StructuresJavaJavaScriptLaTeXMarkov Chain Monte CarloNatural Language Processing (NLP)PyTorchPython (Programming Language)React.jsSMLTensorFlow

About

Hi, I am Aman Chauhan, currently in the final year of my undergraduate studies at IIT Delhi, on course to graduate in 2024 with a degree in Mathematics and Computing. I did my summer internship with Goldman Sachs, Bengaluru, India. I am interested in and have done extensive research work in machine learning and have a good understanding of Computer Vision and NLP. I have also developed interest in the field of Quantitative Research and Trading with a strong background in Probability and Statistics.

Experience

2 yrs 7 mos

Total Experience

10 mos

Average Tenure

11 mos

Current Experience

Graviton research capital llp

Quantitative Researcher

Jul 2025 – Present · 11 mos

Goldman sachs

Analyst

Jul 2024 – Jul 2025 · 1 yr · Bengaluru, Karnataka, India · On-site

Indian institute of technology, delhi

3 roles

Teaching Assistant

Aug 2023 – Dec 2023 · 4 mos · On-site

Served as TA for the course MTL766 - Multivariate Statistical Methods

Teaching Assistant

Jan 2023 – May 2023 · 4 mos · Delhi, India · On-site

Served as teaching assistant for the course MTL106 - Probability and Stochastic Processes. Helped teach and manage a class of over 300 students.

Research Intern

Jun 2022 – Aug 2022 · 2 mos · Delhi, India · On-site

Research intern at Machine Intelligence, Signals, and Networks (MISN) lab, IIT Delhi -
Estimated the parameters of Heavy tailed AR model with missing values via Variance Reduced Stochastic Expectation Maximization (VRSAEM) coupled with Markov Chain Monte Carlo procedure