Operating since March 1999, Abbott Analytics leads organizations through the process of applying and integrating leading-edge data mining and machine learning methods to marketing, research and business endeavors. Abbott Analytics has been dedicated to improving efficiency, ROI, and regulatory compliance through machine learning.Since 2014, Abbott Analytics has primarily been the vehicle for delivering machine learning, predictive analytics, and data science content through media, books, course instruction, and interviews.Clients for Abbott Analytics have included (partial list):• Department of Treasury: IRS (LMSB), Revenue Canada, Texas Comptroller of Public Accounts• Department of Defense: US Navy, US Air Force Research Laboratory, US Marine Corps, Defense Finance Accounting Service, Sandia National Labs• Non-Profits: YMCA• Private Sector: MetLife, Nokia, Intel Corporation, Kaiser Permanente, Los Angeles Times, Alaska Airlines, Monsanto• Analytics Software Companies: IBM, SPSS, SAS, StatSoft, Neteeza, Insightful Corporation, Tibco, TeradataCourse topics (partial list)• Data Science Bootcamp for practitioners (3-days, including entire CRISP-DM process model)• Model Ensembles (Bagging, Boosting, Random Forests, Stochastic Gradient Boosting, Stacking)• Randomization methods to improve model accuracy, variable selection, model interpretation, model stability assessment.• Explaining predictive models to decision-makers

In 2022, Darden initiated the Bodily Bicentennial Professorship in Analytics to advance faculty research in the field. The position was created in honor of the contributions of Sam Bodily, the John Tyler Professor Emeritus of Business Administration to the field of decision analysis. Each year, this visiting professor will offer new insights and possibilities for collaboration with Darden faculty and students.

Actual start date was mid-2009, but I went from part-time involvement to full-time involvement on April 1, 2014.• Co-Founded SmarterHQ, bringing analytics and machine learning to retail email marketing in a SaaS context. Designed and built process to automate building of models for 100+ clients, rebuilding models as needed, either daily, weekly, or monthly. Analytics backend instrumental in obtaining the first $7M round of venture funding and future rounds. • Served as CTO of SmarterHQ for 3 years, hiring and managing the Software Development, DevOps, and Data Science teams (more than 20). Succeeded in migrating the entire backend infrastructure from SQL Server + MongoDB to Redshift + S3 + AWS microservices. • Customer brands included Bloomingdales, Sam’s Club, American Eagle, DSW, Dillards, Chicos, Omni Hotels, Barnes & Noble• Typical revenue for personalizing blast email sends using the SmarterHQ platform: 50% increase in open rate; 80% increase click-through rates. • Parametric models built automatically for each client with customized, automated, behavior-based tuning for each client. Campaigns based on these models typically generated $1-$2/email and an increase in average order value of 25%.• Supervised learning purchase propensity models built automatically for each client; Predictive model based campaigns typically generated $2-$4/email send.• Worked closely with executives in sales and marketing to assist in securing deals with larger brands (>$100k ARR). • As interim CTO (2016-2019), including accomplishments:o grew the team to over 20 developers, SREs and data scientists. o Led full back-end architectural transition from MongoDB+SQL Server to AWS (Kinesis, SQS, S3, Redshift). o Obtained first ever SOC 2, Type 2 certificationo Became GDPR compliant on timeo Hired VP Engineering to run day-to-day operations. Instrumental in hiring replacement CTO.

Data mining consulting; portfolio optimization

Data mining and pattern recognition in signal and image processing (ASW, flouresence imaging)Development of software for commercial data mining softwareOptical sensor simulation and testing

Building neural networks and polynomial neural networks for postal bar code reader, optical character recognition, image identification.Radar cross-section analysis and classification (tanks, trucks, aircraft) using neural networks, including X-band, K-band, etc.

Classification algorithms for RADAR and SONAR; Neural Networks applied to Optical Character Recognition; Classification algorithms for POSTNET barcode reader

Development of "Optimum Path-to-Go" guidance laws for missiles and glide bombs(combining polynomial neural networks with variational calculus optimum solutions)Development of 3DOF and 6DOF simulations for guidance and control.Development of classification models for passive ASW using polynomial neural networks