I am a Product Manager for Machine Learning Models on the Microsoft Azure AIOps Team. We develop models and tooling to ensure reliability and quality of cloud services.

We build apps and websites for idea development and prototyping, education, exploration, and entertainment.

Steered data-driven product development to satisfy customer (primarily Intel, secondarily Samsung, TSMC) needs and motivate product adoption. To do so, established roadmaps and deliverables, AI/ML models, data streams, statistical methodologies, data visualizations and dashboards, and software libraries to identify process shortcomings and orient solutions to overcome them. Critically, served as liaison between customer and internal teams, facilitating coordination among software, data infrastructure, engineering, and marketing stakeholders.• Drove customer adoption of apps team product suite valued at over $300M by successfully landing software applications critical to customer success and demonstrating benefits when used with multiple ASML products in combination.• Attracted over $30M in customer investments by designing software application for predicting misalignment between computer chip layers. Product achieved 50x improvement in resolution, exceeding established limits.• Attained 10% anomaly detection increase and 5% noise reduction in regressive models used by Intel SPC engine by incorporating process issue root-cause predictors.• Accomplished yearly benefit opportunity of $15M by developing dashboard and automated data retrieval/analysis software for modelling and visualization of machine data for failure prediction.• Improved accessibility and scalability of key pathfinding initiative by migrating metrology big data workflows from local systems to cloud platforms, including Azure and ASML-owned HPC.• Empowered team with valuable skills by establishing 10-course sequence on scripting and modelling, now integral part of engineer training library.• Led acquisition of metrology patent by creating predictive regression- and neural network-based models to predict SEM measurements based on optical data.NB: Title was "Applications Engineer", but edited title to reflect job functions, which were purely Product Management and Data Science.

Enabled angular momentum-based phase and magnetic imaging, as well as advanced electron microscope capabilities, by enhancing creation of x-ray and electron beams. Used wave mechanics and Fourier analysis to simulate behavior of optical components, laying foundation for noteworthy publication in Nature Photonics.• Optimized data analysis and research processes by coding Python repositories for image processing, physical modeling, and instrumentation control.• Engineered microwave antennae for generation of oscillating magnetic fields through application of computational tools, such as Mathematica and COMSOL, and theoretical knowledge in circuit and transmission line theory.• Pioneered method for acoustic wave production by engineering solid state diode and leveraging interaction of phonons with host material through light assistance.

Advanced light detection technology by driving development of quantum dot-based photodetector devices. Acquired photonics patent to facilitate imaging technology R&D by researching quantum dot applications in Si-based IR and x-ray light detectors.• IP generation: Successfully acquired patent by devising methodology to safeguard quantum dots from degradation.• Lab software modernization: Enhanced efficiency of company laboratory by implementing software-based coordination system for instrumentation and replacing previous ad-hoc approaches.

• Tutored students in physics, mathematics, and chemistry in individual and group settings.

• Studied liquid salts for use as an energy storage medium in supercapacitors.• Fellowships: 2009-2010 Undergraduate Research Fellowship, 2009 College of Natural Sciences Summer Undergraduate Research Fellowship.