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.
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.