Ken Boling, PhD

■ Developed Data Science and Data Engineering capabilities across multiple networks within a secure environment while maintaining a TS-SCI with CI-poly security clearance. ■ Interfaced directly with client stakeholders, interpreted the needs of the client and translated those needs into actionable requirements while adapting to changing requirements and scope of various projects. ■ Deployed a pre-trained LLM (Falcon 7b) for basic text summarization tasks as part of a weekly report generation tool on a p3.8xlarge GPU cluster. ■ Created an analytic tool for alerting analysts when certain conditions exceeded a predicted baseline, based on a live timeseries dataset. Deployed in a distributed computing environment using pyspark, SQL, and the Facebook Prophet Python package. ■ Reworked a legacy Pyspark ETL process running in Databricks, resulting in a 93% reduction in processing time/DBU usage. ■ Maintained Version Control and CI/CD workflows with git and various VCS providers (primarily Gitlab), implemented dependency and vulnerability scanning etc. Also Performed code reviews through merge/pull requests to enforce coding standards among juniors. ■ Developed an ETL process that was completed during my first month with the client that received a great deal of attention from the immediate client and at least six other outside agencies, including all of the "big five" intelligence agencies. ■ Completed the Emergent Skills Program by passing the Databricks Certified Machine Learning Professional Certification exam and changing job families to "Advanced Data Scientist/Researcher". ■ Mentored junior Data Scientists/Engineers through the Tech Excellence and Technical Mentorship programs, as well as my immediate teams and through my direct reports as a Career Manager. ■ Performed technical interviews of potential external candidates. ■ Implemented the migration of a critical Tableau dashboard from a local environment to a scheduled job in Databricks.

■ Developed a tool written in Python for unsorted document topic modeling for use by the USMC to assist in the rapid analysis of Captured Enemy Material (CEM). ■ Implemented data cleaning and processing pipelines for Natural Language Processing (NLP) topic modeling using Python packages such as pandas, spacy, and nltk. ■ Followed coding standards and version control procedures for reviewing and checking out approved code using git and Bitbucket. ■ Wrote a proposal to lead a three person team on a project applying Reinforcement Learning to a decision-making model using a Partially Observable Markov Decision Process (POMDP). The project was selected to be funded through the Naval Innovation Science and Engineering (NISE) under a Strategic Investment Area (SIA 3).

■ Implemented a computer vision object detection system using Opencv and a pre-trained Single Shot Detector (SSD) model trained on the Common Objects in Context (COCO) dataset to support the DARPA Subterranean Robotics Challenge (SubT). ■ Implemented a Real-Time Appearance-Based Mapping (RTAB-Map) algorithm to assist in Self-Localization And Mapping (SLAM) to allow the Unmanned Ground Vehicle (UGV) to operate autonomously after total loss of GPS signal. ■ Deployed along with the computer vision object detection system on the Nvidia Jetson Nano (ARM-64/cuda) platform. ■ Designed and 3D printed a prototype mount using Solidworks which incorporated a series of stereo camera arrays (Intel RealSense D435i), that allowed them to be secured to the front of the UGV without requiring modification of the metal chassis. ■ Supported the data science team involved with healthcare data from the MHS Data Repository (MDR). Built an automated script in Python using pandas and graphviz for generating data visualizations of the connections and data pipelines in and out of the MDR. ■ Set up virtual environments within Oracle VM Virtual Box for testing configurations before loading them onto the UGV platform.

■ Developed an automated data engineering process in Python and ArcGIS for data collection, cleaning, validation, and integration of a dataset of 25,000 oil and gas wells and 900 geologic quadrangle maps across KY, TN, and AL into an SQL database of surface and subsurface data containing ~7,200,000 georeferenced data points. ■ Generated regional-scale, high-resolution maps of surface and subsurface horizons for use by state oil and gas boards and independent resource extraction operations. ■ Presented results at AAPG (2017, 2018), GSA (2017, 2018, 2019), and the Tennessee Oil and Gas Association meetings. ■ Performed various laboratory tasks such as preparing sediment samples for m-RNA and DNA extraction. ■ Analyzed the results of these analyses and reported them to the funding agency. ■ Performed other research tasks unrelated to PhD project.

Supported the data science team involved with healthcare data from the MHS Data Repository (MDR). Built a small workflow for the automated visualization of the data pipelines in and out of the MDR.

- Implemented a computer vision object detection system using Opencv and a Tensorflow trained convolutional neural network to support the DARPA Subterranean Robotics Challenge (SubT). - Implemented a Real-Time Appearance-Based Mapping algorithm to assist in Self-Localization And Mapping (SLAM) to allow the UGV to operate autonomously after total loss of GPS signal. - Designed and 3D printed a prototype mount using Solidworks which incorporated a series of stereo camera arrays, that allowed them to be secured to the front of the UGV without requiring modification of the metal chassis.

- Successfully reformed the inactive AAPG student chapter at UTK. - Facilitated the improvement of the relationship between the Geology department and industry professionals. -Invited and hosted speakers from a variety of industries to discuss their careers with students

-Organized the first IBA team from the University of Tennessee to compete. -Assembled a team of four graduate students, two industry advisors and one faculty advisor. -Directed the team's evaluation and research of the provided data set and delegated tasks to each team member based on skill set and interests. -Resulted in the delivery of a high quality final presentation by our team at the Eastern section competition in Pittsburgh.

■ Reduced client uncertainty and risk by developing individualized, data-driven solutions to improve stratigraphic correlations and aid in geosteering operations in the Permian, East Texas (Eagle Ford), and Anadarko Basins. ■ Responsible for the quality control and delivery of high precision XRF elemental analyses and reports to clients. ■ Developed well-log geochemical, and geophysical data based solutions for down-hole drill localization during horizontal drilling operations. ■ Identified chemostratigraphic packages and marker horizons based on XRF elemental data for use in correlation and logging-while-drilling analysis.

■ Spearheaded the initial conversion of the Baylor X-Ray Fluorescence laboratory by developing operational procedures capable of quantifying major and trace element compositions, permitting analyses to be run in-house at a significant cost savings. ■ Managed the conversion of a defunct XRF laboratory into the fully functional lab in use today. ■ Established standard operating procedures for sample preparation and elemental analysis on a Rigaku ZSX-Primus-2 wavelength dispersive X-ray fluorescence spectrometer (WD-XRF). ■ Designed and implemented a material specific calibration of the instrument for the analysis of the elemental concentrations for 28 major and trace elements using various strategies for each (peak-deconvolution to reduce interference, measuring alterative spectral lines if the K, L, or M-α lines interfered, etc.) using linear or non-linear regressions depending on the inter-element interactions for each sample type. ■ Instructed faculty and students in the preparation of samples and operation of the XRF spectrometer. ■ Identified problems with the analytical methods and implemented solutions to improve accuracy and increase performance.

■ Devised a field collection strategy for collecting minimally geochemically altered samples from the Late Cretaceous, Cenomanian-Turonian (~96 million years old) Eagle Ford shale. ■ Collected ~170 samples from outcrops of Eagle Ford shale, prepared and analyzed samples using X-Ray Fluorescence (XRF), powder X-Ray Diffraction (XRD), Stable Isotope Ratio Mass Spectrometry (IRMS), Nuclear Magnetic Resonance (NMR), Gas Chromatography Elemental Analysis (GC/EA), and Gas Chromatography Mass Spectrometry (GC/MS) analyses. ■ Constructed chemostratigraphic profiles of each outcrop, created geologic correlations between them, and identified productive zones based on geochemistry data using basic linear regression in excel and later rewrote this process in Python.

- Successfully lobbied for $12,000 in club funding through the student government organization after the previous administration had lost all financial support. - Organized club sponsored trips to professional and conferences such as the Geological Society of America (GSA) - Organized trip to the American Museum of Natural History in NYC and arranged a behind the scenes tour of the research facilities there - Led field trips to several of the state sponsored mineral collecting sites across Connecticut. - Organized a trip to a well known fossil-collecting site in Monmouth, New Jersey.

-Course work focused on unconventional resources (Eagle Ford Shale / Eagle Ford Formation), Sandstone Petrology, Biogeochemistry, Isotope Geochemistry, and 3D Seismic Interpretation. -Thesis project consisted of determining the origin of the organic matter accumulation in the Eagle Ford, as well as identifying the oceanic anoxic event using carbon isotopes. Thesis Title: "Controls on the Accumulation of Organic Matter in the Eagle Ford Formation in Central Texas"