Undergraduate Researcher | UC Merced
Location: Merced, CA
Research Focus: TBA
Key Contributions: TBA
Principal Investigator: Dr. Katrina Hoyer
Graduate Student Mentor: Arianna Daniel
Location: Merced, CA
Research Focus: TBA
Key Contributions: TBA
Principal Investigator: Dr. Boaz Ilan
Location: Livermore, CA
Research Focus: Benchmarked U-Net vs. Neural Cellular Automata (NCA) for amodal segmentation on synthetic multi-view lab data, demonstrating that NCA achieved comparable IoU (0.46 vs. 0.25) while using 18% fewer parameters. Implemented a full model-to-amodal completion pipeline, converting 3D U-Net architectures into 3D convolutional video models and improving encoder-decoder RGB reconstruction across diverse comparisons.
Key Contributions: Led experimental design and analysis for model comparison, including model-to-amodal inference, RGB binarization, and multi-modal training. Analyzed model generalization by comparing RLS-based estimators with data-driven predictors, informing ongoing work on hybrid ML + control pipelines for autonomous driving systems.
Location: Merced, CA
Research Focus: Developed an online Recursive Least Squares (RLS) estimator for real-time parameter identification in Adaptive Cruise Control (ACC) systems, achieving < 0.05 steady-state error across highway, suburban, and stop-and-go simulations.
Key Contributions: Designed and analyzed a model-to-amodal completion pipeline to test control algorithms under varying driving conditions, enhancing system reliability and adaptability. Implemented a full model-to-amodal completion pipeline, converting 3D U-Net architectures into 3D convolutional video models and improving short-horizon velocity forecasting by 18%. Analyzed model generalization by comparing RLS-based estimators with data-driven predictors, informing ongoing work on hybrid ML + control pipelines for autonomous driving systems.
Principal Investigator: Dr. Ayush Pandey
Location: Merced, CA
Research Focus: Developed and analyzed computational simulations of the Susceptible-Infected-Recovered (SIR) model to study epidemiological trends for COVID-19.
Key Contributions: Implemented Kinetic Monte Carlo (KMC), a stochastic algorithm, to effectively model and study stochastic disease transmission patterns while implementing an agent-based framework within the simulations. Designed and implemented bash scripts that influenced parameter sensitivity and functionality of the KMC algorithm. Processed and analyzed data utilizing NumPy while ensuring visual comprehension utilizing Matplotlib.
Principal Investigator: Dr. Changho Kim