“I worked with Ryan on a number of important projects for Margaret Gowen and Co. Ltd. Ryan did the plant remains from the medieval monastic island settlement at Skellig Michael, Co. Kerry (a World Heritage Site), while I did the insect remains. The combined results revealed fascinating details on the sources of material used in the Monks Garden on the island. We also worked together on two very important prehistoric riverine sites in Dublin - Ormond Quay and Clancy Barracks, which produced important plant and insect evidence for the nature of the Liffey river valley, prior to the famous Viking settlement there. Ryan is a very dedicated and thorough researcher and would be an assess to any company or project team.”
About
As a Distinguished Engineer in Artificial Intelligence at UnitedHealth Group, I help…
Experience
Education
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Activities and Societies: University of Sheffield Archaeological Society
Specialized in archaeobotany and quantitative statistical analysis. Spent time at the Neolithic Anatolian site Catal Huyuk working with the plant remains. Conducted focus groups among local villagers on rural Turkish life and agricultural practices.
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Activities and Societies: UW-L Archeology Club, Intramural Ultimate Frisbee
Senior thesis was on the plant remains from Sand Lake Oneota village in Onalaska, Wisconsin. Also worked as a teachers assistant for the philosophy department.
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Degree incomplete due to school transfer. Started college career in Platteville studying software engineering. Transferred to UW-La Crosse in order to pursue Archaeology.
Licenses & Certifications
Publications
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Plant Remains - Archaeological Excavations at the lost church of Drumkay, Glebe, Co. Wicklow in The Church in Early Medieval Ireland in the light of recent archaeological excavations
Wordwell Press, Dublin
Patents
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Interpretable Hierarchical Clustering
Filed US 17/165,444
There is a need for more effective and efficient hierarchical clustering. This need can be addressed by, for example, solutions for performing interpretable hierarchical clustering. In one example, a method includes performing a group of hierarchical clustering routines to generate a group of final hierarchical clusters; for each cluster-feature pair of a group of cluster-feature pairs, determining an intra-cluster variable importance measure, an incremental representation measure, and an…
There is a need for more effective and efficient hierarchical clustering. This need can be addressed by, for example, solutions for performing interpretable hierarchical clustering. In one example, a method includes performing a group of hierarchical clustering routines to generate a group of final hierarchical clusters; for each cluster-feature pair of a group of cluster-feature pairs, determining an intra-cluster variable importance measure, an incremental representation measure, and an overall variable importance based at least in part on the intra-cluster variable importance measure the incremental representation measure; generating predicted explanatory metadata for the group of hierarchical clustering routines based at least in part on each overall variable importance measure for each cluster-feature pair of the group of cluster-feature pairs; and performing one or more prediction-based actions based at least in part on the predicted explanatory metadata.
Other inventorsSee patent -
Techniques for Hierarchical Clustering with Tiered Specificity
Filed US 18/589,994
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Generating Input Processing Rules Engines Using Probabilistic Clustering Techniques
Filed US 17/557,946
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by generating input processing rules using at least one of general clusters generated using all of a set of prediction input data objects, high-confidence clusters generated using…
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by generating input processing rules using at least one of general clusters generated using all of a set of prediction input data objects, high-confidence clusters generated using prediction input data objects having threshold-satisfying clustering confidence scores, and low-confidence clusters generated using prediction input data objects having non-threshold-satisfying clustering confidence scores.
Other inventorsSee patent
Courses
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Artificial Intelligence
CS 6601
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Computer Vision
CS 6476
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Cyber-Physical Design and Analysis
CS 7639
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Graduate Algorithms
CS 6515
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Graduate Introduction to Information Security
CS 6035
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Machine Learning
CS 7641
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Mobile and Ubiquitous Computing
CS 7470
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Natural Language Processing
CS 7650
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Network Science
CS 7280
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Quantum Computing
CS 8803-O13
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Reinforcement Learning
CS 7642
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Robotics: Artificial Intelligence Techniques
CS 7638
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Software Development Process
CS 6300
Projects
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Deep Learning for Kidney Transplant Readmission Prediction based on Psycho-Social Clinical Notes
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Created a merged LSTM and feed forward neural network architecture that was able to accurately determine how likely someone was to be re-admitted post-kidney transplant based on their psycho-social clinical notes and various clinical metric. Used an attention mechanism to be able to determine which key phrases in the clinical notes contributed to the prediction.
Honors & Awards
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Bronze Mayo Quality Fellow
Mayo Clinic College of Medicine
I was awarded the Bronze Mayo Quality Fellow award for learning and implementing LEAN principles in my work.
Languages
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English
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Recommendations received
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