Damon Maldonato
9450 Gilman Dr. #30272
La Jolla, CA 92092-0100
Dmaldonato@ucsd.edu
415-559-4651

Professional Summary

Electrical Engineering sophomore at UC San Diego with a focus on Machine Learning and algorithmic modeling. Developed Hype Predict, a multimodal Bi-LSTM engine that achieved 55% directional accuracy by fusing technical indicators with NLP sentiment analysis. Some experience at using PyTorch and Pandas to solve data stationarity issues and build strategies that consistently outperform market benchmarks.

Education

University of California, San Diego | La Jolla, CA

B.S Electrical Engineering with a focus on Machine Learning | Expected June 2028

Skills

  • Programming: C, C++, Python , HTML/CSS, JavaScript.

  • Machine Learning & Math: Linear Algebra for Data Science, Stochastic Gradient Descent, Optimization Theory, Time-Series Analysis.

  • Hardware & Digital Systems: Verilog/SystemVerilog, Combinational & Sequential Logic, Computer Architecture, Feature Engineering.

  • Software Tools: PyTorch, Pandas, NumPy, Scikit-Learn, Git/GitHub, VS Code, MatplotLib, Librosa.

    Professional Experience

Coding Instructor | Esporterz | Larkspur, CA December 2022 – June 2023

  • Developed and delivered engaging educational programs, translating complex technical information into accessible content for a diverse, non-expert audience of young learners.

  • Created structured lesson plans and interactive materials to enhance participant engagement and achieve learning objectives, mirroring skills needed for event and program planning.

  • Provided patient one-on-one support to students, fostering a positive learning environment and successfully troubleshooting individual challenges to ensure program success.

Project Experience

Hype Predict | Python, PyTorch

  • Applied linear optimization and stochastic gradient descent (ECE 174) to train a Bi-LSTM model on non-stationary financial data.

  • Developed efficient data structures to preprocess high-frequency market data, reducing latency in feature engineering.

  • Implemented a Binary Classification objective function to predict market direction, achieving a 55% accuracy and 30% strategy return.

Acoustic Classifier | Python, PyTorch, Librosa

  • Designed an end-to-end Signal Processing and classification framework to transform 1D acoustic waveforms into 2D Mel-Spectrograms for deep learning inference.

  • Engineered a Fixed-Window Normalization layer using zero-padding and windowing to maintain consistent tensor dimensionality across variable-length audio inputs.

  • Developed a Convolutional Neural Network (CNN) in PyTorch utilizing max-pooling layers for temporal translation invariance, achieving robust pattern recognition in environmental signals.

    Volunteer Experience

    Academic Tutor | Archie Williams High School 2023 – 2024

  • Provided confidential, one-on-one academic support to peers in mathematics, demonstrating discretion and the ability to build trust while explaining complex material.

  • Adapted teaching methods to individual learning styles, reinforcing strong interpersonal and communication skills.

References available upon request.