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Co-Founder @ EqualParts
Accomplished professional with 10+ years of amassed experience in leading research, experimentation, and implementation of Machine Learning models and approaches to maximize capabilities. Well versed in creating end-to-end ML pipelines, transforming companies to data advantaged organizations, and implementing hundreds of ML models in production. Solid history of success in identifying key opportunities and scaling AI capabilities and platforms. Graham has built ML and data systems that range from using neural networks in measurement and controls for semiconductor processes at nanometer scale to building IOT streaming data platforms that can deliver value and insight on huge data in real-time. Graham has led DS and software teams in transforming companies to data advantaged organizations and implemented hundreds of ML models in production. Currently, he is leading a team of MLOps professionals at System1.
Stanford University
Master of Science (MS), Management Science and Engineering
January 1, 2015 – January 1, 2018
Clemson University
Bachelor of Science (B.S.), Mechanical Engineering
January 1, 2010 – January 1, 2013
EqualParts
Co-Founder
February 1, 2025 – Present
System1
Senior Manager, Machine Learning Engineering
March 1, 2023 – February 1, 2025
Greater Pittsburgh Region
Fortive
Senior Software Engineering Manager - ML Engineering
January 1, 2021 – March 1, 2023
Fortive
Senior Data Scientist
October 1, 2019 – January 1, 2021
Lucas Systems
Data Scientist
April 1, 2018 – October 1, 2019
Wexford, Pennsylvania
Petuum, Inc.
Lead Machine Learning Engineer
October 1, 2017 – April 1, 2018
Pittsburgh, Pennsylvania
Applied Materials
Process Engineer
November 1, 2014 – October 1, 2017
Santa Clara, California
Eastman Chemical Company
Process Engineer
May 1, 2013 – October 1, 2014
Kingsport, TN
Michelin
Engineer
August 1, 2011 – August 1, 2012
Greenville, South Carolina Area
E1 Asset Management
Portfolio Manager
February 1, 2008 – May 1, 2009
New York City Metropolitan Area
Deep Learning approach to the Fake News Challenge
January 1, 2017 – April 1, 2017
Challenge driven by fakenewschallenge.org. NLP and Deep Learning methods were used to convert training data to vectorised form. Baseline was generated using a routine Bag of Vectors MLP (fully connected) model. RNN, CNN, LSTM, BiLSTM, LSTM +Attention, and featurized Bag of Vectors models were evaluated. Testing data shows a weighted accuracy near 90%, over a 10% improvement to the Fake News Challenge baseline (hand engineered NLP features using an xgBoost algorithm).
Wearables Product Design for Market Success
January 1, 2015 – June 1, 2015
A wearable device was presented with room to improve the material supply chain, BOM cost, technical and user interface features, and the road-map to the market. The current market conditions were analyzed and VOC data collected. Interacting with the potential customers and watching the customer interact with the MVP yielded a high-value customization to the product at a near zero increase to the development costs and BOM costs of the product. The Customer Value Chain was established and analyzed to produce a list of other desirable feature upgrades. Sensor designs were considered to add a number of feasible feature upgrades. Power requirements, VOC data, BOM cost, as well as Design for Variability and Manufacturability constraints were considered to select additional features. Value and Cost Basis models were developed for the entire workflow of the product from development to production. Distribution models were analyzed to select a viable path to market for the first product release. Design Tools: Solid Modeling CVCA Morphological Design Analysis Monte Carlo Simulations Business Model Analysis Engineering Design MATLAB & Excel Computational Models Cost Modeling Value Modeling VOC Data Collection and Interpretation Brainstorming Market Research
Cultural Fit Analysis
The candidate has a diverse background, starting from mechanical engineering and transitioning into data science and ML engineering. Their project experience includes both product design and deep learning challenges, showing a breadth of interests. The progression through various companies and leadership roles suggests adaptability and a drive for growth. However, the target role is ML Engineer, and while they have strong ML engineering experience, their recent roles have been more managerial. This could indicate a slight mismatch if the target role is purely individual contributor, but aligns well if it's a lead or principal ML Engineer role.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight strong leadership, communication, and decision-making skills. They have managed teams, budgets, and interfaced with cross-functional stakeholders, indicating good operational fit for senior roles. Agile methodologies and process optimization are also frequently mentioned.