Anisa Ahmed

About

I am a recent graduate of Columbia University’s School of Engineering and Applied Sciences. Through Columbia’s dual bachelor’s degree program, I earned a BS in Operations Research and Financial Engineering from Columbia University (May 2024) and a BA in Pure Mathematics and Economics from Bard College (May 2022). My professional experience includes engineering trading simulation platforms at MathWorks and developing capital management solutions as a Summer Analyst at Citi. As a recent Kleiner Perkins Fellow at Codeium, I optimized AI-driven code tools to enhance developer efficiency across platforms. My passion for quantitative analysis and technical problem-solving drives my contributions in finance and technology.

Experience

• KP Engineering Fellow, Codeium Software Engineering Intern · Kleiner Perkins, Codeium06-01-2024 – 08-01-2024

  ● Engineered an autocomplete and chat product that efficiently supports daily active users, helping developers write code faster with fewer errors to ensure seamless developer experiences across multiple platforms and IDEs (Integrated Development Environments). ● Collaborated with senior leadership to develop “Codeium Live”, a real-time AI code assistant that delivers responsive and contextually relevant suggestions by integrating chat functionality directly within users' browsers for popular repositories. ● Designed and implemented an internal Kubernetes-native data processing framework, optimizing the handling of petabytes of data across thousands of spot CPUs, enhancing performance and scalability, and ensuring data processing efficiency. ● Developed a code attribution service that tracked the origin of code and verified it did not violate open-source or proprietary licenses to ensure compliance with licensing requirements, providing customers with assured compliance and robust intellectual property management. ● Developed and improved models for “Codeium Command”, focusing on instruction and edit capabilities, enhancing AI-driven code generation and editing. ● Optimized model inference performance using advanced techniques such as Nvidia CUTLASS, CUDA C++, and PTX assembly language, significantly improving computational efficiency. ● Developed features to enable remote parsing, embedding, and indexing of users' codebases, enabling AI to understand and generate code more accurately when dealing with large, complex codebases stored in different locations, facilitating more efficient AI-powered code generation and analysis.

• Algorithmic Trading Intern · Mathworks08-01-2023 – 05-01-2024

  ● Collaborated on the development of an advanced trading simulation platform by working closely with MathWorks engineers to integrate a backtester—part of MathWorks’ Financial Toolbox—into a streamlined process that transitions seamlessly from backtesting to live algorithmic trading, ensuring error-free execution. enhancing trading strategy validation. ● Engineered a command reconfiguration feature that enabled real-time translation of backtesting rules into live trading commands, directly interfacing with Bloomberg’s Execution Management System (EMSX) for automated order routing and execution. ● Developed solutions to eliminate the need for rewriting simulation code for production trading, optimizing the workflow, improving the efficiency of the trading process and reducing the risk of discrepancies between backtested and live trading strategies. ● Spearheaded the implementation of an object-oriented programming approach, encapsulating data and functions to mirror the Bloomberg trading interface, while maintaining the flexibility to work without any modifications to the existing MATLAB backtester. ● Enhanced the MATLAB Financial Toolbox by integrating the trading simulation platform, providing MathWorks with a product that outperforms existing market solutions. ● Contributed to a billion-dollar product line by expanding the functionality of MathWorks' Financial Toolbox, making it more attractive to hedge funds and banks looking for an advanced algorithmic trading solution.

• Trade Balance Sheet and Capital Management Summer Analyst · Citi06-01-2023 – 08-01-2023

  ● Conducted in-depth analysis of CECL & CCAR using SQL queries and Excel, reducing LGD miscalculations by 20% and improving the teams’ regulatory compliance. ● Analyzed and corrected 200+ LGD metrics for CECL/CCAR using Python scripts and data visualization tools, enhancing risk assessment accuracy. ● Developed a new Client Cost Factor Methodology using Monte Carlo simulations and optimization algorithms, leading to a 12% increase in pricing efficiency across new transactions involving both ICG and TTS Finance teams. ● Led project which reviewed client capital calculations related to Citi’s G-SIB Score component of TCE and created capital reallocation algorithm using Python to improve accuracy in Client Interface Warehouse. ● Integrated machine learning algorithms with existing credit risk models using Python and R to enhance prediction accuracy by 18% and aid in the development of more robust optimization strategies.

Education

• Columbia UniversityBachelor of Science, Operations Research and Financial Engineering
• Bard CollegeBachelor of Arts, Pure Mathematics and Economics

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