Software Engineer Machine Learning

Monika Getsova

ï monika-getsova # ********@******.*******.*** H 757-***-**** § mgetsova

I’m a software engineer with several years of back-end experience developing novel solutions to problems involving ML applications. I have several years of scientific research experience in materials science and physics. I am looking for exciting opportunities in Machine Learning and Data Science! (I am a US Citizen) Experience

Software Engineer, Hedgefog Research Inc. Nov 2022 - Aug 2023

• Designed a clustering and regression pipeline to generate customized contact lenses given patient eye scan data and feedback

• Optimized multicomponent 3D lens modeling and optics simulation software for efficient point cloud gener- ation, allowing production of first 120 batches of lenses for patients

• Created a protocol for projector image calibration and dewarping (using OpenCV) to ensure proper function between eye test hardware and software components and debugged issues with the UI of the hardware controls in a .NET application

• Researched & wrote proposal for an integrative, state of the art neural network verification python library for autonomous naval applications

Software Engineering Intern, Topdeck.ai Aug 2020 - Jan 2021

• Implemented novel method from existing literature to quantify image blur (an open problem) using local phase coherence of the Fourier transform

• Developed an ML algorithm for image similarity detection allowing scores of image blurriness to be con- textualized relative to frames at a given camera orientation laying the groundwork for real time software enabled live stream quality optimization of existing camera infrastructure Research Fellow, Caltech Materials Simulation Center Jan 2019 - Sep 2019

• Modeled kinematics of molecules and contributed to larger codebase (mainly for energy optimization for proper side chain positioning in protein folding)

• Defined a metric for the angular correlation between water molecules in a simulated box, analyzed the resulting time series data and compared to colleague’s experimental results Research Fellow, High Energy Physics Jun - Sep 2018 and 2017 CERN: Projection for WZZ Production Cross Section Measurements at the HL-LHC

• Proposed ML techniques to identify signatures of decay products indicative of WZZ triboson coupling from other possible couplings of W and Z bosons

Fermilab NOvA: Neutrino Physics

• Tested handles for determining wrong-sign component of anti-neutrino beam using Monte Carlo simulated and real data + statistical methods (eg. chi-squared analysis to determine model efficacy)

• Ensured a given purity of an antineutrino beam beyond a 5 σ significance threshold for each neutrino mass Skills

Languages: Python, C++, Java

Skills: TensorFlow, PyTorch, Data Structures & Algorithms, Git, Object Oriented Programming (SOLID), Machine Learning (Data Science, Deep Learning, LLMs) ML Certification from Stanford and DeepLearning.ai on Coursera Natural Language Processing with Classification and Vector Spaces by DeepLearning.ai on Coursera Education

California Institute of Technology BS Physics, minor in Computer Science Jan 2022 Projects

Deep Learning for Neutrino Physics Competition on Kaggle Implemented convolutional graph neural network and LSTM models to infer the origin of detected neutrinos utilizing PyTorch Geometric and GPU acceleration. Familiarized myself with the Graphnet for physics GNNs. Solved package dependency issues between remote notebook environments. Tentative GNN model can be found here.

Ph/CS120: Quantum Cryptography

Conducted a review of papers proposing theoretical quantum blockchain frameworks and P2P verification protocols for a distributed voting protocol (final for quantum cryptography class). My write up can be found here.

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