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.

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.

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Corporate Strategy and Product Management

In this course, I worked with Philips in a group of students to help them become more entrepreneurial. As part of the corporate strategy team, we evaluated new business ideas to boost their market share and revenue. This involved analyzing customer personas, market size, MAP planning, and value innovation, as well as developing skills for pitching to the C-suite.

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Face Classification and Verification using CNNs

In this Kaggle competition, the task was to build a face classifier that can extract feature vectors from face images and a face verification system that computes the similarity between feature vectors of images. I used a CNN architecture to build this model in order to achieve high accuracy on this classification and verification task.

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Frame Level Classification of Speech

In this Kaggle competition, the task was to predict the phoneme label for each frame in the test set of the speech recordings, which are raw mel spectrogram frames. I used a multi-layer perceptron model and explored various hyperparameters to improve the accuracy of the prediction of the phoneme state labels for each frame in the test set.

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Law of Computer Technology

This course is both a survey of computer law and an examination of how courts and administrative agencies make decisions on issues involving computer technology. The material is divided into these six primary subjects: legal process, evidence, eBusiness law, personal intrusions, intellectual property, government regulation. Here, I write key insights from these six topics and my personal reflection.

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Artificial Intelligence and Future Markets

This course aims to teach students about AI techniques across various applications, generate new product ideas in specific fields, and review AI company trajectories. In a team of 4-5, I researched and designed a presentation on the use of AI in randomly assigned fields such as Identity Verification, Data Privacy, Firefighting, Personalized Medicine, Perfume, and Advertising.

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