● Hold a leadership role as U.S. Healthcare Technology Lead, overseeing strategic initiatives and projects in the healthcare technology domain and driving innovation within the healthcare sector● U.S. Lead for Generative AI in Healthcare, spearheading the development and implementation of innovative Generative AI solutions in healthcare and collaborating with researchers from Google and Microsoft● Provide strategic guidance and expertise in healthcare technology, ensuring the successful implementation of cutting-edge solutions● Developed a patient data streaming architecture for a major healthcare provider, capturing high-volumes of streaming data from patient medical devices across the care continuum● Leveraged a repository of 1000s of data assets from 100s of sources to develop data products enabling health equity analysis at the census tract level and facilitating resource allocation● Led a cross-disciplinary team in developing a patient journey tracking capability using claims data, providing valuable insights for life science partners about temporal and regional trends in genetic & molecular testing, advanced cancer therapies, and precision therapeutics● Initiated and grew a healthcare guild, recruiting & training over 100 members for healthcare technology pursuits● Coordinated global speakers from KPMG member firms to deliver presentations at healthcare guild meetings● Developed training material on clinical data science foundations and claims analytics, upskilling community members for healthcare engagements

● Technical lead for the data science team responsible for defining research objectives and supervising the research team● Clinical liaison to the clinical team, helping to bridge the understanding between medical and data science to guide research and ensure maximal impact of the VPICU resources● Manage the CHLA Data Science Internship program which pairs aspiring data scientists with CHLA physicians to work on important clinical problems using VPICU resources (data, algorithms, and mentors) to jump-start research● Created an internal CHLA data science training program. Successfully graduating researchers from research groups within CHLA (The Heart Institute, Saban Research Institute). Preparing to continue extending this expertise into additional research groups with future training planned (Neonatology, Epidemiology, Neurology)● Responsible for hiring and training new members of the data science● Provide team members with the necessary foundational understandings of machine learning● Immerse team members in the clinical culture of CHLA to prepare them for success as part of the clinical data science team at CHLA● Co-author numerous publications to help firmly assert CHLA as one of the national leaders of clinical data science, specifically with regards to critical care medicine. ● Evangelizes the work of the VPICU both nationally and internationally

● Technical lead for the data science team responsible for defining research objectives and supervising the research team● Clinical liaison to the critical care team helping to bridge the understanding between medical and data science to guide research and ensure maximal impact of the VPICU resources● Established the CHLA Data Science Internship program which teams up aspiring data scientists with CHLA physicians to work on important clinical problems using VPICU resources (data, algorithms, mentors) to jump-start research● Developed a deep learning course in conjunction with the NVIDIA Deep Learning Institute to teach time series modeling with Recurrent Neural Networks (RNNs) in Keras on real Pediatric Intensive Care Unit (PICU) data● Expanded lab and trained new data scientists

● Led team to develop a Residual Recurrent Neural Network to exploit Electronic Health Records (EHR) time-series data from the Pediatric Intensive Care Unit● Developed Convolutional Neural Network to infer future kidney glomerular filtration rate using high-resolution digital pathology scans of their kidney biopsies● Provided subject matter expertise to clinicians to determine potential data exploitations to improve patient care in the Pediatric Intensive Care Unit

● Provided subject matter expertise to the Virtual Pediatric Intensive Care Unit (vPICU) Data Science team● Developed framework to exploit 15 years of Electronic Health Record (EHR) time-series data, collected in the CHLA Pediatric ICU, utilizing Convolutional Neural Networks (CNNs) to create a Doctor Decision Aid to predict treatment options to optimize patient outcome

● Led effort to model performance of a critical national security program providing a new capability to accurately predict performance in new environments● Trained new data science team in core concepts including optimal detection theory, machine learning, deep learning, and advanced cross-validation techniques

● Developed modular machine learning framework in Python to extend domain expertise to entire company● Curated and instructed Python for Scientists training course to increase the efficacy of the technical staff and reduce reliance on expensive proprietary development licenses● Elucidated systemic risks in performance assessment methodologies and introduced more robust testing framework● Lead a team of scientists and software engineers to re-design the detection stream of a critical national security program to provide substantial performance improvement

● Contributed to the successful achievement of ambitious performance goals by analyzing algorithm performance against real data sets, finding failure modes, and refining algorithms● Implemented and utilized numerous discrimination techniques such as linear, quadratic, Bayesian, support vector machines, random forests, and gradient boosting machines● Designed, programmed, and analyzed algorithms in multi-threaded parallel MPI C codePresented both research and solutions to customers at quarterly briefings

● Extracted patterns and features from exploitable phenomenology by analyzing complex, high-dimensional data sets to improve algorithm performance● Prototyped algorithms for detection of aerosolized biological agents● Conducted field experiments to collect data and validate performance of biological agent detection algorithm

● Performed time-series analysis of millions of transactions to minimize excessive capital stock investment while ensuring commodity availability● Aided development of state of the art churn prediction model to significantly improve user retention capability of major telecommunications company with millions of customers● Generated geo-spatially augmented recommendation engine for millions of point-of-sale consumers to generate additional revenue