Darby Losey Email & Phone Number

Pittsburgh, Pennsylvania, United States

I developed interpretable deep learning methods to enhance transparency of large artificial neural networks.• Created a novel method for interpretable deep learning using influence functions and matrix approximation methods.• Formulated and derived robust convergence bounds for the proposed method and its variants, providing theoretical accuracy guarantees underscored by detailed proofs.• Trained large convolutional and feedforward neural networks for method validation.• Streamlined model debugging and data cleansing workflows by implementing automation processes using explainable A.I., resulting in a substantial increase in time efficiency.• Led a series of seminars to train fellow research engineers on the use and application of explainable A.I.

Pittsburgh, Pennsylvania, United States

Leveraged advanced machine learning methodologies to analyze complex patterns within larger neuronal populations, uncovering rules that govern learning. Adapted these algorithms for the training of artificial neural networks.• Implemented continual deep learning models using PyTorch and JAX, with a focus on biological plausibility.• Modeled the evolution of noisy, high dimensional time series data using reinforcement learning algorithms. • Led and participated in deep reinforcement learning journal club and machine learning journal club.• Guest lecturer for graduate level classes, focusing on Kalman filters and dimensionality reduction methods. TA for two computational courses.• Awarded a five-year National Science Foundation Graduate Research Fellowship.• Research led to a multi-million dollar National Institute of Health grant.• Attained a master’s degree in machine learning research (GPA: 3.79).

Seattle, Washington, United States

Implemented deep learning models for the classification of noisy time series data.• Designed, trained, and evaluated various deep learning models such as autoencoders and convolutional neural networks for the prediction of cognitive states.• Attained over 90% accuracy in the real-time classification of a user’s mental state by monitoring their brain’s electrical signals. • Presented prototypes to venture capitalists, resulting in angel investment.

Seattle, Washington, United States

Implemented software for scientific experiments and conducted data analysis.• Developed and deployed real-time classification algorithms for high-dimensional, noisy timeseries data.• Designed and implemented robust, high-performance software for brain-computer interface experiments.• Executed comprehensive data analysis on large image-based datasets, utilizing statistical methods and machine learning algorithms to identify key patterns and trends. • Awards: Honors in Computer Science, Computer Science Outstanding Undergraduate Thesis Award, Mary Gates Foundation Research Scholarship, Washington Research Foundation Innovation Fellowship, North American Computing Research Association Outstanding Researcher (Honorable Mention).• Published three academic papers and filed one patent. More details available on personal website.

Doctor Of Philosophy - Phd, Machine Learning And Neural Computation

Master Of Science - Ms, Machine Learning Research

Bachelor Of Science - Bs, Computer Science

Honors in Computer Science Computer Science Outstanding Undergraduate Thesis Award Mary Gates Foundation Research Scholarship Washington.