I'm Paolo, a student majoring in Artificial Intelligence at Purdue University. My current main interests are computer vision and adversarial machine learning, as well as the applications of AI in music. I'm currently leading a research team developing a real-time posture evaluation mobile app for cellists using computer vision and assisting with research on adversarial machine learning with a PHD candidate at Purdue. In my free time, I enjoy practicing the piano, reading, and occasionally hooping.
Leading team of 10 for AI4Musicians Evaluator project, working under Prof. Yung Hsiang Lu (ECE) and Prof. Yeon Ji Yun (Music) to build a real-time, on-device mobile app for cellist posture evaluation.. Trained, finetuned, and optimized YOLOv11 model through input size reduction, Pytorch to TFLite conversion, and FP16 quantization (overall 4x reduction in inference time). Led team in transition from cloud computing to on-device inference (2x reduction in frame processing latency)
Analyzed misinformation diffusion on social media with 3 other interns. Collected and classified ~200 posts from TikTok and Twitter, studied distribution patterns across platforms.
TA for AP Physics 1&2. Ran labs, answered questions, graded assignments.
Currently working with a PHD candidate at Purdue to research the adversarial machine learning.
Built variational autoencoders to generate images of hands and abstract paintings. Trained on 10k and 18k image datasets using Tensorflow.
Trained an LSTM to generate piano music from 1k+ MIDI files using Keras.
CNN to classify paintings by seven artists. Organized 2k labeled images.
BS in Artificial Intelligence / GPA: 3.96
Relevant: Linear Algebra, AI Basics, Probability
Skills: Python (PyTorch, TensorFlow), Java, C++, Kotlin
Certificate of Merit Level 9
High School Varsity Team
English (Native)
Mandarin (Native)