Utkarsh Patange, PhD

AI Researcher

New York, New York, United States9 yrs 3 mos experience

Highly StableAI Enabled

Key Highlights

PhD candidate specializing in Operations Research and Machine Learning.
Developed low-latency algorithms for high-frequency trading.
Published research in algorithmic game theory and network optimization.

Stackforce AI infers this person is a Fintech expert with strong quantitative research and algorithm development skills.

Contact

uspatange@gmail.com LinkedIn

Skills

Core Skills

Machine LearningData AnalysisPortfolio OptimizationQuantitative ResearchProgrammingComputer Science

Other Skills

AlgorithmsAlpha GenerationArtificial Intelligence (AI)CC (Programming Language)C++Data StructuresDecentralized FinanceDeep Reinforcement LearningEthereumFinancial ModelingLaTeXLendingMicrosoft ExcelMicrosoft Office

About

As a PhD candidate in Operations Research at Columbia Business School, I am passionate about applying advanced analytical and computational methods to solve complex and real-world problems. My research focuses on algorithmic game theory, network optimization, and machine learning, and I have published several papers in prestigious conferences and journals. I have also gained valuable industry experience as a Quantitative Research Intern at Optiver, a leading global market maker, where I worked on analyzing and optimizing internal trading systems. Prior to that, I worked as a Senior Analyst-Trading Systems at AlphaGrep Securities, a high-frequency trading firm, where I designed and implemented low-latency algorithms and systems for various asset classes. I am proficient in programming languages such as C/C++, Python, Matlab, and VBA, and I have experience working with large-scale integer programs using Gurobi in Python, and with deep RL libraries such as PyTorch and Ray/RLLib.

Experience

Verition fund management llc

2 roles

Quantitative Researcher

Oct 2024 – Present · 1 yr 6 mos · New York, United States · On-site

Quantitative Research Intern

Jun 2024 – Aug 2024 · 2 mos · New York, New York, United States · On-site

Python (Programming Language)Portfolio OptimizationAlpha GenerationQuantitative Research

Optiver

Quantitative Research Intern

Jun 2023 – Aug 2023 · 2 mos · Chicago, Illinois, United States · On-site

Analyzed an internal trigger on which the firm trades.
Used and improved the existing backtesting framework built in C to obtain the required data.
Used Python packages such as pandas, seaborn, Matplotlib and scikit-learn to analyze the trigger.
Suggested actionable insights into ways to improve the success rate of the trigger.

Data AnalysisStatisticsC (Programming Language)Python (Programming Language)ProgrammingQuantitative Research

Columbia business school

2 roles

PHD Graduate Student

Aug 2019 – Oct 2024 · 5 yrs 2 mos · Greater New York City Area · On-site

C++Data AnalysisStatisticsLaTeXPython (Programming Language)Machine Learning+2

Research Assistant

Sep 2018 – Jun 2019 · 9 mos · Greater New York City Area

C++LaTeXPython (Programming Language)ProgrammingData Analysis

Alphagrep securities

Sr Analyst - Trading Systems

Aug 2016 – Jun 2018 · 1 yr 10 mos

Built and maintained low-latency software used for high-frequency trading in different exchanges.
Coded in C++ ensuring a low latency, handling concurrency and using ideas such as core-pinning, pre-sending, direct copy, etc.
Improved existing hash functions based on trading behaviors of different trading teams. Backtesting found up to 70% reduction in collisions.
Improved internal logger latency by up to 50%.
Reverse-engineered libraries, then pre-loaded them (using LD PRELOAD) to use in-house code instead.

C++Computer ScienceProgramming