Data Scientist

Mason Biamonte

************@*****.*** 737-***-****

data scientist with record of developing interpretable machine learning solutions across sectors with execution on ambiguous 0-to-1 projects, currently developing pipeline for daily stock price prediction TECH STACK

• Programming Languages: Python, C/C++, SQL, Java, MATLAB, Ruby, Microsoft Excel

• Mathematics: Probability/Statistics, Time Series Forecasting, Linear Algebra, Graph Theory, Op- timization, Differential Equations, Numerical Analysis, Signal Processing, Nonlinear Systems

• Machine Learning: Deep learning, Explainable A.I. (XAI), Classification, Regression, Clustering, Unsupervised Learning, Computer Vision (CV), Natural Language Processing (NLP), TensorFlow, PyTorch, Scikit-learn, Knowledge Graphs, Large Language Models (LLMs), LSTM, RNN, CNN

• Cloud Computing: Google Cloud Platform (GCP), Amazon Web Services (AWS) RECENT EXPERIENCE

Neubus, Inc., Data Scientist Remote Nov 2022 — Oct 2023

• Independently developed interpretable machine learning framework for segmenting scanned com- posite case files for the Texas Department of Family Protective Services. Patent pending: PCT #: 63/582,360. “Non-sequential Multiclass Classification Algorithm for Document Extraction from Scanned Composite Files Without Deep Neural Networks”.

• Wrote handwriting/machine-print classifiers, improving accuracy with Fourier analysis and convolu- tional neural networks (CNNs) inspired by the AlexNet architecture. Transurban Group, Senior Econometrics Modeling Analyst Tysons Corner, VA Aug 2022 — Oct 2022

• Constructed company’s first machine learning prediction engine for I-495 toll revenue forecasting, incorporating Google Trends search volume data and macroeconomic indicators.

• Utilized Apache Arrow for processing ~10 GB traffic data files.

• Applied advanced statistical analysis techniques, including Fourier and wavelet decomposition to improve hourly traffic volume predictions on I-495.

InspiRD, Inc., Natural Language Processing Model Developer Remote Feb 2022 — Apr 2022

• Introduced novel semi-supervised document classification algorithms, isolating semantically-signifi- cant comments from noisy code files without deep learning in the context of hundreds of classes with high semantic overlap.

Moonshot Compost, Software Development Consultant Houston, TX Sep 2020 — Jan 2022

• Designed Cloud applications, in Python, automating reports and rewards programs for compost servicing company.

• Utilized TKinter for GUI construction and code freezing into Unix executables. Validere Technologies, Data Scientist Houston, TX Mar 2020 — Jul 2020

• Extended "virtual analyzers" estimating natural gas composition in pipelines without extra measure- ment devices via numerical root solving algorithm from physical chemistry.

• Tested stochastic and genetic optimization algorithms for hydrocarbon blending in Western Canada, enabling clients to identify beneficial blending opportunities. EDUCATION

M.S. Mathematics, Massachusetts Institute of Technology Sep 2015 — Feb 2020 B.S. Mathematics & Physics, University of Houston Aug 2010 — Jun 2013 HUMAN LANGUAGES

Spanish (C2), Italian (B2), Russian (A2)

ADDITIONAL EXPERIENCE

MIT, Quantum Computing Doctoral Researcher Cambridge, MA Dec 2017 — Jan 2020

• Explored quantum speedup of classical Markov chain based algorithms via quantum walks on graphs, focusing on quantum counting and search without the Quantum Fourier Transform (QFT). Supervisor: Peter Shor.

Skoltech, Quantum Computing Doctoral Researcher Moscow, Russia May 2018 — Aug 2018

• Found novel method for analyzing the sub-addivity of entropy in coupled quantum networks.

• Studied fermionic quantum walks for quantum speedup of search on 2D lattices and used super- symmetry to push the quantum advantage up to 3D lattices. Supervisor: Jacob Biamonte. MIT, Deep Learning Research Supervisor Cambridge, MA May 2017 — Aug 2017

• Led undergraduate students in developing a theorem linking restricted Boltzmann machines (RBMs) with random walks, highlighting connections with particle physics. Students: Kimberly Villalobos- Carballo and Max Vargas.

MIT, Computational Fluid Dynamics Research Supervisor Cambridge, MA May 2016 — Aug 2016

• Supervised undergraduate research on Faraday instability over variable topography, utilizing numer- ical simulations and demonstrating insights into surface wave mode coupling. Student: Nolan Reilly. Award: Siemens Semi-Finalist 2016. Supervisor: Ruben Rosales. Harvard University, Teaching Fellow Cambridge, MA September 2014` — May 2015

• Mentored and taught undergraduate STEM students through self-curated recitation sessions, grad- ed homework and held weekly office hours for PHYS 15A: Mechanics & Special Relativity (Fall 2014) and PHYS 15B: Electricity & Magnetism (Spring 2015). Supervisor: David Morin. CERN, Undergraduate Research Assistant Geneva, Switzerland May 2012 — June 2013

• Designed numerial simulations of charge carrier advection-diffusion in silicon particle detection wafers in C/C++ with direct application to radiation diagnostiics in both a medical context and for astronauts aboard the International Space Station. Supervisor: John Idarraga-Muñoz.

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