Matt M. Email & Phone Number

New York, NY, US

Westport, Connecticut, US

Cambridge, MA, US

• 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.

Westport, Connecticut, US

Cambridge, MA, US

Cambridge, MA, US

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

Cambridge, MA, US

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

Cambridge, MA, US

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.

Miami Beach, Florida, US

• 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

Cambridge, MA, US

• 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

Chicago, IL, US

• 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

Greenwich, Connecticut, US

• 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

Cambridge, MA, US

• 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

Master Of Engineering - Meng, Computer Science

Bachelor'S Degree, Computer Science