Research Experience 

​​​​​​Learned to use state-of-the-art autonomous vehicle (AV) simulation environments, reinforcement learning, and path planning.

Deployed a Kubernetes pod with Docker to run CARLA on Nautilus with faster GPUs

Fine-tuned SAC, DQN, and DDPG algorithms from Stable Baselines on the CARLA simulator and visualized the AV’s training data using TensorBoard.

Presented results to UCSC students, auditors, and Professor Gilpin

Researching AV lane changes in heavy traffic conditions using RL in SUMO simulator.

• Worked on independent research projects. Applied for MIT THINK with a proposal using HMM-ANN to classify surgical maneuvers from kinematic data.

• Researched helmet rule violations for motorcyclists for the AICityChallenge 2024, using foundational vision-language models with open-vocabulary and zero-shot learning and AlexNet. Presented to the Vehicular Technology Conference 2024.

• Researched Hard Hat rule violations for construction workers using and analyzing cascaded methods for vision-language models with zero-shot learning. Presented to the MIT Undergraduate Research Technology Conference 2024.

Contributed to the assembly and sanitization of laparoscopic surgical tools for medical use.

Observed a live practice session on a pig and identified challenges in the surgical system's processes.

• Conducted research on blind docking ligands with proteins for protein structure prediction.

• Currently researching spinal cord disease classification using advanced Quantum, Convolutional, and Faster Region-based Convolutional Neural Networks.

Gained exposure to computational biology research methodologies.

Researched Schizophrenia and associated genes

Analyzed Protein-Protein interactions

Identified that abnormalities in the genes associated with the biological process of Axon Guidance are a probable cause of Schizophrenia

Researched and detailed six of the genes in our presentation to fellow researchers

Learned data collection, sorting, and visualization techniques using Python, HTML, and JavaScript to create a website displaying COVID-19 data through interactive graphs.

Developed an AI project on Human-Robot collaborative surgery 

Published a paper of a benchmarking dataset, titled “Human-Robot Task Handoff: A Probabilistic Modeling Approach Explored through Cooperative Parabola Drawing” to the Journal of Student Research

Participated in a molecular biology workshop focused on the biology of aging. Practiced lab skills with pipetting, western blotting, and centrifuges using live proteins related to aging. Researched and presented properties of Sirtuin 7, receiving a commendation.

Over a 2 week course, used python to learn the basics of machine learning. Learned about linear regression models, classifying different types of data such as covid stats and images, NLP, and Neural Networks.

Applied this knowledge to the project: detecting pneumonia in CT scan images with neural networks and presented in an expo at the end of the two weeks.