Hi, I’m Muno —

I am a Master’s student in the Artificial Intelligence & Innovation (MSAII:SCS) program at Carnegie Mellon University (class of 2024). I completed my undergraduate degree in Electrical Engineering with a focus on Machine Learning & Controls at the University of California, San Diego (class of 2022).

My areas of interest span across different fields including artificial intelligence, product management, and business development. I am passionate about exploring AI's latest advancements and potential applications, as well as using my creativity to design and manage innovative products. I am also developing my skills in business development to identify and pursue strategic opportunities for growth and success.

Muno O 12/15/23 Muno O 12/15/23

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.

Muno O 12/15/23 Muno O 12/15/23

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.

Muno O 12/14/23 Muno O 12/14/23

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).

Muno O 12/14/23 Muno O 12/14/23

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.