About

Aparna Joshi is a highly skilled software engineer and machine learning expert with a passion for developing robust, fair, and unbiased machine learning models. Currently working as a Machine Learning Engineer at Apple, Aparna has gained extensive experience in developing and implementing cutting-edge algorithms that optimize complex processes. Aparna’s expertise in machine learning is evident from her work at Carnegie Mellon University, where she served as a Graduate Research Assistant. Here, she designed and implemented an algorithm for Bayesian optimization using constrained optimization with pairwise comparisons to incorporate expert feedback in Gaussian process framework. She also performed experiments to show its superior performance over baseline algorithms like GP-UCB (labels-only), a multi-fidelity algorithm MF-GP-UCB, and a comparisons-only algorithm (KernelSelfSparring). Aparna’s research work at Carnegie Mellon University also involved AI-driven investigation of the physics for material synthesis. She designed models to guide the design of experiments, simulations, and expert queries, optimizing certain parameters (energy and forces) in a complex phenomenon (synthesis of ZrO2). She implemented a graph convolutional network to learn the physics involved in a completely data-driven way. She also implemented a neural network potential (BPNN) as a baseline for comparison. Aparna holds a Master's degree in Machine Learning from Carnegie Mellon University, where she gained in-depth knowledge of various machine learning techniques. She also holds a Bachelor of Technology degree in Computer Engineering from the National Institute of Technology Karnataka. Aparna has a strong technical background and is skilled in various technologies such as ML, software engineering, research science, AI, and neural networks. With 4.19 years of relevant experience, Aparna is a highly competent and driven professional who thrives in challenging environments. Her passion for developing unbiased machine learning models makes her an asset to any organization she works for.