Robert Weyant Email & Phone Number

Boston, Massachusetts, US

• Designed backend system to deliver personalized coaching content on how to reduce energy consumption
• Developed IoT data collection system for remote deployment in homes to gather additional training data for current and future projects
• Deployed novel, scalable unsupervised machine learning algorithms to disaggregate appliance energy consumption from whole home energy in real time
• Architected neural network to disaggregate appliance consumption from whole home energy
• Developed method to estimate personalized energy savings utilizing thermostat setbacks given environmental data and IoT sensor data
• Work closely with backend engineering team to design databases and cloud services in order to efficiently collect, store and utilize data for analytics

Livonia, MI, US

• Perform statistical analyses for clinical research projects
• Provide statistical and data warehouse consultation to researchers in preliminary phases of projects
• Source-to-target mapping from multiple Electronic Health Record systems to one unified data model
• Profile current data warehouses to identify structural and utilization gaps in data for downstream analytics
• Identified opportunities to improve and augment ETL process for downstream analytics

Ann Arbor, Michigan, US

• Analyst for the Renal and Lung Living Donor Evaluation Study (RELIVE), a collaboration to investigate long-term health outcomes for living kidney and lung donors
• Analyst for Nephrotic Syndrome Study Network (NEPTUNE), part of NIH Rare Diseases Clinical Research Network
• Developed and cross-validated statistical models to predict clinical trends in R
• Created a complex pipeline in SAS and SQL to manage and clean analytic datasets for analysis
• Automated data quality reports and visualizations for analysts and investigators using SAS, SQL and Excel
• Used machine learning algorithms to develop sampling methods for data validation and quality control

• Worked with the Genetic Investigation of Anthropometric Traits (GIANT) consortium, a collaboration to identify genetic loci associated with body shape and size
• Worked with Genetics of Type 2 Diabetes (GoT2D) consortium, identifying genetic factors of type 2 diabetes through multi-ethnic deep genome and exome sequencing
• Used simulations and data mining techniques to explore gene-level test performance as a function of gene features ○ Simulated large genetic datasets representing genic regions based upon both 1000 Genomes and HapMap.
• Performed meta-analysis of genetic data using Linux scripts, R and modern genetic analysis tools
• Compared weighting schemes for meta-analysis
• Compared accuracy of several genotype calling algorithms

Pittsburgh, PA, US

• Tested methods for detecting diseases and then replicating the result within the same sample
• Simulated large genetic datasets under various assumptions
• Tested effectiveness of genetic analysis programs
• Wrote reports for publication of results

Pittsburgh, PA, US

• Adapted and tested quantitative methods for detecting and replicating disease association
• Simulated large genetic datasets under multiple disease models
• Tested accuracy of genetic linkage analysis programs
• Wrote reports for publication of results

Master'S Degree, Biostatistics

Bachelor Of Science (B.S.), Statistics

Bachelor Of Arts (B.A.), Mathematics