Hi, I’m Muno —
I am a Senior Associate Data Scientist at the Bank of New York (BNY).
M.Sc. in Artificial Intelligence & Innovation, Carnegie Mellon University ’24.
B.Sc. in Electrical Engineering (Machine Learning & Controls), University of California San Diego ‘22.
I’m passionate about AI, data science, product management, and business development, with a focus on developing and evaluating technology innovations. My work bridges strategy, investment, and market research to drive data-driven decision-making and impactful outcomes.
EDAhub: Data Analysis for Investor Relations Communications
This project spans Oct 2023 - May 2024. The focus is to build a multi-modal and multi-media AI system with a voice/text UI to extract relevant data from financial statements, create summaries, perform Exploratory Data Analysis (EDA), create charts/graphs, and analyze differences across financial documents.
BurgerBot: GPT-4, Segmentation, and Manipulation
The goal of our study is to develop "BurgerBot," a framework combining GPT-4 and a segmentation model to guide a robotic arm in assembling a plastic burger toy. The objective is to understand real-time human-robot conversational interactions, focusing on the robot's adaptability to diverse instructions.
Scientific Named Entity Recognition
The goal is to build an end-to-end NLP system involving collecting our own data and training a model to identify specific entities such as method names, task names, dataset names, metric names and their values, hyperparameter names and their values within scientific publications from recent NLP conferences (ACL, EMNLP, and NAACL).
Building My Own BERT
Develop a minimalist version of BERT (Bidirectional Encoder Representations from Transformers), implementing some important components of the BERT model (self attention, layers, model, optimizer, and classifier) to perform sentence classification on sst dataset and cfimdb dataset.
Machine-Generated Text Detection
This project leverages the Multi-Scale Feature Fusion Network (MSFFN), a CNN-based model that extracts and fuses features at different scales for nuanced classification. It achieves 80.64% accuracy, outperforming transformer-based baselines with just 1.1 million parameters, making it lightweight and cost-effective. The ensemble variant, MSFFN-Ensemble, further enhances performance, achieving 86.82% accuracy, showcasing the power of domain-specific and generator-specific learning.