James Morris, PhD

Contact Info

email:
me@james-morris.com
www:
james-morris.com
linkedin:
linkedin.com/in/jamespmorris
github:
www.github.com/jpmorris
stackoverflow:
stackoverflow.com/users/795276

Exceptional Problem Solver
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Highly technical Practitioner
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Superb Hacker
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Strong Leader

Detail-oriented, broad-perspective researcher with more than a decade of experience with various methods of analysis and modern technologies. Expert at developing solutions, communicating results and delivering value. Broad experience with AI (with a strong emphasis on classical and deep machine learning), data mining, classical statistics, and physical and mathematical modeling. Strong programming skills in algorithms, data structures, software development, and high-performance computing. Hands-on experience with the full stack of data science technologies. Excellent communicator, skilled in project management, and experienced team lead. Seeking lead position as a practitioner solving very hard problems.

Degrees in physics, math, philosophy, computer science
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Data Science, Development, Architecture, Engineering,  Project Management
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Python, R, Spark, Java, C#, OOP
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Classical & Deep Learning, AI, Advanced Analytics

Recent Experience

INFOSYS

Silver Spring, MD

Senior Data Scientist – Practice Lead

03/2015—Present

For a multinational oil & gas supermajor:

Engineered machine learning model using high-frequency, real-time data with scikit-learn and other deep LSTM models using Keras/Tensorflow, enabling a savings of at least USD 10 million/year by preventing drilling failures
Helped lead team to architect and build real-time high-frequency low-latency data pipeline within Azure to enable USD 10 Million/year in value
Hands-on experience with big data infrastructure technologies: C#/.NET, NIFI, Kafka, Databricks, Spark, Kubernetes, Azure App Service, Cosmos, MongoDB among others
Created deployment pipeline using DevOps CI/CD to deploy to Azure Kubernetes Service
Hands-on experience with big data machine learning platforms including Azure ML, MLflow
Developed a GTK3 application to obtain consistent set of data labels from SME to apply to the dataset at scale
Detected anomalies in dirty hydraulic fracturing data by general engineering and mining the frequency space using Fourier decomposition, dimensionality reduction methods including PCA, and clustering (k-means, DBSCAN)
Developed code to optimize the chemical inputs of processing plants using sequential least squares programming

For a leading financial services company:

Developed model to predict smoking propensity as part of a rapid underwriting system
Created sales-lead lift model using R (Caret) for multi-model comparison with C5.0 as the final model
Built client prediction model using 3rd-party data broker to target individuals on Facebook
Created revamped sales-propensity model using SAS EM
Created turnover, retirement, and migration logistic regression model and library in R to mitigate up to USD 6 million lost annually in employee replacement
Developed NLP models for text mining for sentiment analysis

For a multinational auto manufacturer:

Created model in R to predict driver destinations from telemetric data

GEORGIA INSTITUTE OF TECHNOLOGY

Silver Spring, MD

Student

09/2015—08/2020

Developed AI algorithms to solve problems adversarial searches (minimax, alpha-beta pruning), graph searches (BFS, DFS, A*), Bayesian networks, Gaussian mixture models applied to image compression, among others
Programmed AI methods to solve Raven Intelligence Matrices
Created reinforcement learning solutions to Grid World via value and policy search methods
Mined Wikipedia data for a knowledge ontology using Spark on Databricks (GraphFrame) and deployed a CI/CD-supported web app on AWS (CodeDeploy, CodePipeline, ACS, and Fargate)

OHIO STATE UNIVERSITY, DEPARTMENT OF PHYSICS

Columbus, OH

Research/Teaching Assistant

09/2002—07/2009

Using C++ and ROOT, performed a statistically blind analysis using Monte Carlo based simulations developed from first principles. To test the Standard Model of particle physics, the resulting model processed data from the world's first petabyte database in search of rare decay modes of fundamental particles
Applied machine learning techniques (FDA, Fisher discriminates, and decision trees) in data mining to separate signal from noise (ROOT TMVA)
Research fellow at INFN (Padua, Italy) and KEK (Tsukuba, Japan), providing research assistance for infrastructure and simulation support
As sub-detector quality control expert and software release coordinator, used Perl to automate tasks
Extensive experience communicating complex ideas to experts and neophytes: teaching assistant winning multiple awards, presented at multiple conferences and regularly to experts

Education

PhD

Physics

Ohio State University

08/2002—07/2009

Computer Science

Georgia Institute of Technology

08/2015—08/2020

Physics/Mathematics

University of Arizona

09/1997—04/2002

Philosophy

University of Arizona

09/1997—04/2002

Medicine

Stony Brook University

08/2009—11/2013

Skills

Soft

Communication
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Problem Solving
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Leadership
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Neighbor

Engineering
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App Development
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Project Management
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Data Architecture
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Analytic

Classical Learning
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Advanced Analytics
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Mathematics
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Statistics
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Simulations
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Deep Learning
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Broader AI
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NLP
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Reinforcement Learning
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Languages

Python
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R
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SQL
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Spark
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C++/C#
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Java
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JavaScript
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Computer Science

Algorithms
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High-Perf Computing
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Big Data

Azure
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AWS
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Hacking

Solution Engineering
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Dirty Data
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Getting Data
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Domain

Energy
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Financial
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