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.
My interests span artificial intelligence, product management, and business development. I’m particularly enthusiastic about the breakthroughs and lessons learned in AI development, as well as their potential to enhance operational efficiency and user experience.
Attention-based Speech-to-Text Deep Neural Network
In this Kaggle competition, I learned how to build an encoder to effectively extract features from a speech signal, how to construct a decoder to sequentially spell out the transcription of the audio, and how to implement an attention mechanism between the decoder and the encoder.
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.
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.