Location: Atlanta, GA
Email: [email protected]
Phone: 706-344-2878
Social Media: LinkedIn Page

1. Experience
2. Education
3. Licences and Certifications
4. Honors and Awards
5. Publications

Over two decades of leadership and practical experience in engineering and education. Seasoned Data Scientist and Senior Data/MLOps Engineer; AWS Certified. IBM Certified. OpenShift and Kubernetes Certified

High-level Synopsis:

1. Languages (Python, R, Julia, SQL, C, Rust)
   
2. OS: Linux (Debian, Mint, RHEL , Ubuntu)
3. CICD: GitLab

Data Science:

1. Python (Pandas, MatPlotLib, Sci-Kit Learn, Numpy), R, Julia, PySpark
2. IBM CP4D, Watson
3. AWS Athena, Quicksight, EMR, Elasticsearch, AWS Sagemaker
4. GCP AI Studio (Vertex AI)
5. Azure Databricks
6. Keras, Tensorflow, Tensorflow Federated, TinyML, PyTorch, FATE
7. DataRobot

Data Engineering, MLOps:

1. CI/CD: GitLab, CodeCommit, CodeDeploy
2. IaaS: TerraForm, Cloudformation, Ansible
3. Containers: Docker, Kubernetes, AWS EKS
4. Orchestration: MLflow, Kubeflow, Airflow
5. Data Ingestion: Kafka, NiFi, PubSub
6. Warehouses: Snowflake, Redshift, BigQuery

Machine Learning Model Deployment Stacks:

1. AWS EKS via Kubeflow, Sagemaker to EC2
2. GCP AI Studio to Kubernetes with Kubeflow
3. Flask on EC2 or GCP compute resource with REST
4. Served model as a microservice (usually Lambda or GCP Cloud Function)

Blockchain:

1. Hyperledger Fabric
2. Solidity
3. Smart Contracts

Artificial Intelligence Research Engineer
Lockheed Martin
Jan 2023 – Present

A member of the Aimlabs team, which is part of the Lockheed Martin Artificial Intelligence Center (LAIC), I help drive focus on building a strong and vibrant AI/ML community including tools, knowledge sharing, events, and training. I drive adoption of MLOps and DataOps solutions through interactions within Lockheed Martin’s developer community while providing strategic guidance and high-level support to AI/ML developers

Data Scientist / Engineer
IBM
Mar 2021 – Jan 2023

Member of IBM Client Engineering, National, Specialty Focus Areas. Data Science/Engineering role with focus on building data pipelines, machine learning models, and AI components for IBM client MVP applications.

Senior Data Science Mentor
The University of Texas at Austin
Apr 2014 – Jan 2023

Nightly teaching via Experiential Teaching Online, currently serving as the Senior Data Science mentor at University of Texas - Austin CPE where I mentor adult professional education students in Data Science and Machine Learning.

Chief Analytics Officer
Socratic Arts
Mar 2022 – Jan 2023

Managed all data and innovation product development initiatives for XTOL (sister company), including constructing and managing data science and data analytics teams, developing data related solutions for external partners and driving continual company growth through innovation.

Senior Data Scientist
Socratic Arts
Apr 2014 – Mar 2022

I assisted Fortune 100 companies and energy partners with their Data Science and Machine Learning needs such as migrations, project, educational and team development and management, governance and implementation.

Example projects types (mainly business, engineering and IoT):

1. Created and deployed production predictive models for Customer 360 Lifecycle using Supervised and Unsupervised Learning with R, Python, Jupyter and AWS/Sagemaker.
2. Created and Implemented Sentiment Analysis and Automated Keyword Extraction projects with Spark, AWS (EMR) and with Spark and NLTK and/or Spark/MLLib
3. Ops / Deployment - MLflow or Kubeflow or Argo and EKS, Seldon, Tensorflow Server, Flask/EC2

Senior Research Scientist
Georgia Institute of Technology Mar 2021 – Oct 2021

My research was at the intersection of Big Data Engineering, Machine Learning, and Cyber Operations. Work included Big Data Analytics and Big Data Engineering (ETL) with Spark, DevOps within Kubernetes, and streaming data ingestion with Kafka.

Lead Data Engineer
Hashmap, LLC
Sep 2018 – Aug 2019

Consulting role overseeing the creation and growth of Data Engineering vertical for this Big Data consulting company for energy clients.Created numerous analytics pipeline pipeline migrations for Oil and Gas customers using AWS Kenesis, Snowflake, AWS S3 and AWS Quicksight. Created Machine Learning solutions for drill process optimization using Python, GCP BigQuery, AIgym, GCP AI Studio or NiFi, AWS Kinesis and AWS Sagemaker. Assisted with prem-to-cloud migrations using Spark and Databricks on Azure.

Engineering Faculty
University of Georgia College of Engineering
Jul 2016 – May 2019

Engineering instructional faculty: Areas of focus Informatics, Computer Systems Engineering, Computational Engineering Methods, Senior Capstone.

Initiatives

1. College of Engineering Diversity Task Force Initiative Representative
2. Founder and Sponsor of Data Dawgs - Interdisciplinary Data Science Student Club
3. Founding member of Georgia Informatics Institutes

Program Manager and Big Data Architect
DeVry University
Nov 2007 – Apr 2015

Senior leadership position managing a large portfolio of curriculum development projects and programs for sixty-six campuses and online. Created data driven solutions for managing national persistence and completion rates; planning and implementation of innovation initiatives. Worked with Python, R, Hadoop, AWS, SQL, SAS

Neill Development
Software Engineer
Jun 2002 – Nov 2007

The University of Georgia
Doctor of Philosophy - PhD, Computer Engineering

Research areas: Renewable Energy / Deep Learning / Genetic Algorithms / Systems Design Dissertation: Improving Hybrid Global Horizontal Irradiance Prediction Using Deep learning and Sky Images

Colorado State University

Doctor of Philosophy - PhD (ABD), Systems Engineering

Research areas: Using machine learning to detect early on-set Alzheimer's Disease. Deep study of EEG data and using it for design and controlling Brain Controlled Interfaces.

The University of Southern Mississippi

MS, Engineering Technology

BS, Engineering Technology

Emphasis Area: Neural Networks and Machine Learning Thesis: Using Neural Networks to Decrease the Deficiencies in Raytracing Algorithms.

1. DoD Mandatory Controlled Unclassified Information (CUI) Training - Defense Counterintelligence and Security Agency (DCSA) Issued Mar 2021

2. Enterprise Design Thinking Practitioner - IBM Issued Jun 2021

3. IBM Global Sales School for Technology Garage - IBM Issued Jul 2021

4. Introduction to Containers, Kubernetes, and OpenShift - IBM Issued Aug 2021

5. Blockchain Essentials - IBM Issued Aug 2021 Credential ID 3557ecdf20144039b62eeadf3d1f7891

6. Maximo Visual Inspection Intermediate - IBM Issued Oct 2021

7. IBM Cloud Pak for Data V3.0.x Data Science - IBM Issued Jun 2021

8. IBM Machine Learning Specialist - Associate - IBM Issued Feb 2022

9. Amazon Certified Cloud Practitioner - Amazon Web Services Nov 2019

Google Internet of Things (IoT) Technology Research Award Pilot - Google

Over the past year, Google engineers have experimented and developed a set of building blocks for the Internet of Things - an ecosystem of connected devices, services and “things” that promises direct and efficient support of one’s daily life. While there has been significant progress in this field, there remain significant challenges in terms of (1) interoperability and a standardized modular systems architecture, (2) privacy, security and user safety, as well as (3) how users interact with, manage and control an ensemble of devices in this connected environment.

Postdoctoral Research in Deep Learning - University of Georgia School of Computer Science

1. Improving Global Horizontal Irradiance Prediction Using Deep Learning and Sky Images Improving Global Horizontal Irradiance Prediction Using Deep Learning and Sky Images. University of Georgia · Jul 10, 2018
2. A Machine Learning Based Application for Predicting Global Horizontal IrradianceA Machine Learning Based Application for Predicting Global Horizontal Irradiance. IEEE Southeastcon 2017 · Mar 2, 2017
3. Extreme Gradient Boosting and Behavioral BiometricsExtreme Gradient Boosting and Behavioral Biometrics. AAAI Conference on Artificial Intelligence (AAAI-17) · Nov 1, 2016
4. Effects of user physical fitness on performance in virtual realityEffects of user physical fitness on performance in virtual reality 2016 IEEE Symposium on 3D User Interfaces (3DUI) · Mar 1, 2016
5. Mobile Tracked Displays as Engaging and Effective Learning PlatformsMobile Tracked Displays as Engaging and Effective Learning Platforms IEEE Virtual Reality 2016 Workshop on K-12 Embodied Learning through Virtual and Augmented Reality (KELVAR) · Mar 1, 2016IEEE Virtual Reality 2016 Workshop on K-12 Embodied Learning through Virtual and Augmented Reality (KELVAR) · Mar 1, 2016
6. Usability and cognitive benefits of a mobile tracked display in virtual laboratories for engineering educationUsability and cognitive benefits of a mobile tracked display in virtual laboratories for engineering education. 2016 IEEE Symposium on 3D User Interfaces (3DUI) · Mar 1, 2016