GRE: 335/340

• Provisioned servers for general development workload, GPU inference, and GPU training, cutting $300/month in server costs
• Set up development infrastructure to improve developer convenience, with declarative infrastructure as code through Terraform
• Created and maintained organizational infrastructure for team communication and consistency in tool usage

• Builds server infrastructure like virtual machines and software installation for distributed deep learning on H100 GPUs
• Provides support for students and faculty using the lab during courses, resolving any technical issues as they arise
• Assists in Hanlon Lab operations relating to hardware, software, and databases for professors in the finance departments

• Developed security control methods in JavaScript with the AWS SDK API for an auditing service on AWS cloud accounts
• Exposed OS vulnerabilities, misconfigurations, and malware that contained alert metadata from AWS documentation on them
• Managed an AWS cloud account for the purposes of debugging signatures and configured it to test security flaw detections

• Developed an app to automate the submission and approval workflow of a form for course management using Power Apps
• Oversaw meetings demonstrating technology to clients, responding to requests for changes, and explaining technology to lay users
• Used scripting languages such as Shell, Python, and PowerShell in order to automate SQL database accesses and program workflows

MATLAB, Research

• Dong et al. model the risk-neutral process by implying risk-neutral densities from equity options and transitioning between nodes
• In theory, any liquid options market could imply a willow tree for pricing path-dependent derivatives on a risk-neutral process
• My thesis work studies how this idea can be used to price exotic interest rate derivatives, with ideas from HJM and credit markets

Python, SHIFT

• Won second place by implementing a market making strategy, using VWAP to predict market trends and adjusting order size
• Led the team and delegated tasks to my teammates, such as backtesting and research, and took notes during meetings for reference
• Set up infrastructure on GitHub and acted as point of contact when IT issues arose related to a bug with SHIFT and Fix Protocol

Excel, VBA

• Participated in a trading contest with other students in Risk Engineering class for bonds, forwards, calls, and puts on USDMXN
• Wrote VBA code to calculate Greeks for each type of instrument traded: Delta, Gamma, Rho for both USD and MXN, and Vega
• Used macro information from current events on Bloomberg to correctly predict a falling dollar and adjust my delta exposure

Python, Pandas

• Cleaned gigabytes of raw data from an API to remove unnecessary columns and changed their types to fit better in memory
• Created backoff infrastructure to call from the API repeatedly and accumulate data without hitting rate limits while caching it
• Converted the analysis from Pandas dataframes to SQL tables, synced to an MSSQL Server on Azure SQL for use with Power BI

Rust, GitHub

• Rewrote the architecture, optimizing squeezing identical lines, removing bloated formatting machinery, and caching operations
• Improved performance by creating a flamegraph of application usage and profiling functions that bottlenecked common usage
• Maintained the repository over a long period of time by answering issues and fixing bugs that arose from my new architecture

• Secured $800 in additional funding in a meeting with the Rutgers Student Assembly by demonstrating how our number of members and our savings on our deal with Barnes & Noble made us a candidate for extra allocation after initial funding of $100