• Developed an innovative Scientific Machine Learning (SciML) methodology to reduce drift in Inertial Navigation Systems (INS), enhancing navigation accuracy and reliability in GPS-denied environments• Integrated neural network models with traditional INS components to effectively capture complex error patterns and adapt to dynamic conditions• Conducted extensive simulations using Julia and the High-Performance Inertial Navigation Development Repository (HIDR) to generate realistic datasets and evaluate the performance of the SciML approach• Demonstrated a 62.99% reduction in 3D RMSE and significant improvements in attitude estimation compared to traditional Kalman filtering techniques• Applied advanced concepts from rocket science and navigation to push the boundaries of inertial navigation technology• Contributed to the field of autonomous navigation, with potential applications in aviation, robotics, and aerospace systemsTechnologies: Julia, Neural Networks, Extended Kalman Filter, Inertial Navigation Systems, Scientific Machine Learning

• Direct a coding competition where participants learned machine learning concepts to create a poker bot

• Campaigned for over $80,000 in company sponsorships and managed club expenses

In this groundbreaking research project, we delved into the potential of Large Language Models (LLMs), specifically GPT-3, to enhance the decision-making process in cybersecurity scenarios. Our goal was to ascertain the viability of using LLMs for tasks such as anomaly detection, vulnerability identification, and attack forecasting within a cybersecurity context.The study introduced an innovative experimental setup, employing a graph-based cybersecurity network, where GPT-3 played a pivotal role in influencing the decisions of a defensive agent. The methodology we used involved complex simulation environments that mimicked real-world cyber threat landscapes to rigorously assess GPT-3's capabilities.The initial findings were promising, highlighting the LLM's proficiency in devising optimal paths and strategies in the simulated settings. The project not only yielded a more comprehensive understanding of LLMs' role in cybersecurity but also provided valuable insights with the potential to substantially advance cybersecurity methodologies and tools.As the leading researcher, I was responsible for conceptualizing the study design, executing the simulation-based experiments, analyzing the resulting data, and interpreting the outcomes, all of which led to a published research paper. This research represented a significant leap in utilizing AI's potential to bolster cybersecurity defenses and optimize decision-making processes.

• Created a risk framework for a new protocol being launched by Delphi Digital• Analyzed various statistical and machine learning techniques to help create a more quantitative approach to assessing risk• Researched ways to better assess different decentralized finance risk parameters

• Analyzing recent developments in how Transparency and Portfolio Trading is transforming the Corporate bond market• Performing traditional Quantitative methods and strategies on newly released data sources

• Developed Python application to parse thousands of financial lists from textual data sources• Automatically mapped application to Bloomberg’s API to identify new mortgage-backed securities• Aggregated and cleaned a dataset of over 12,000 mortgages to identify new trading opportunities

• Researched a trading strategy by analyzing the correlation between 10,000 intraday stock prices and closing cross• Engineered a script to monitor stock splits, earning reports, and daily fees for different U.S. equities• Built an automated report by querying fee data across the major U.S. Market Exchanges such as NYSE and NASDAQ

• Analyzed consumer preference data to create quantitative metrics that were used to understand consumer psychology• Compared consumer sentiment between traditional and automated wealth management solutions