Robert Brown

Robert Brown

Data Scientist · Epidemiologist

http://elreb.me · San Francisco, CA · rebrown@berkeley.edu

Curriculum Vitae · Resume

Education

University of California - Berkeley

Berkeley, CA

Epidemiology and Biostatisitics, MPH

Aug. 2015 —June 2017

University of California, Davis

Davis, CA

Neurophysiology and Behavior BS, Psychology BA

Sept. 2006 —June 2011


Experience

Atropos Health

Atropos Health

Machine Learning Engineer

December 2022-Present

  • Built tools to transform how RWE is generated
  • Implemented new features and tools into production (including AI features with LLM's), including an autosummary feature that provides +20k per year savings, and optimized core production code (6x gains in speed through parallel processing and 100x memory usage efficiency)

Berlin Brands Group

BBG

Data Engineer

October 2021 — December 2022

  • Created and implemented computing, reporting, and ETL infrastructure using AWS (S3, Lambda, EC2) and Snowflake
  • Developed prospecting tool in Django for brand acquisition, monitoring product purchase behavior. Trained time series models (ARIMA, LSTM, FB Prophet) to forecast future revenue growth using data ingested from data mining, operational and third-party data API end points

Los Angeles County Public Health Department

LAPHD

Data Scientist

January — October 2021

  • Created multiple interactive internal and public facing dashboards displaying infectious disease data for LA County (R and Python)
  • Developed a whole genome sequencing (WGS) database and implemented ETL pipelines, implemented in Airflow, processing tens of inputs and 100s of GBs of epidemiologic, genomic sequence data and associated metadata

Insight Data Science

Insight Fellows

Health Data Science Fellow

August 2020 — November 2020

  • Consulted with an organization to provide a churn model using client records (Python)
  • Used advanced SQL querying to aggregate together 10+ tables, and used contextual knowledge to engineer features such as an engagement score to increase model performance
  • Used survivor analysis to generate a probability of of client churn over time, as well as used a binary classifier using logistic regression with lasso regularization to achieve 78% ROC AUC

Alameda County Public Health Department

ACPHD

Epidemiologist II

August 2019 — September 2020

  • Lead data analyses and surveillance reporting of tuberculosis and over 70 other notifiable acute communicable diseases (in R and SAS).
  • Conducted epidemiological investigations and evaluations of disease clusters and outbreaks, linkage-to-care projects, and quality improvement efforts.

HIV Data-to-Care Specialist

August 2018 — August 2019

  • Managed the HIV Data-to-Care program for the county, leveraging various case management and surveillance data with integrated inter-departmental work-flows to increase the number of HIV positive residents in care

UC Berkeley Extension

UC Berkeley Data Analytics Boot Camp

Data Analytics Substitute Instructor & TA

July 2019 - July 2020

  • Assisted teaching fundamentals of computer science, programming, Git, Bash, data munging, visualization, web development, project management, and machine learning to multiple different classes of students through the UC Berkeley Data Analytics Boot Camp

Method Data Science

Method Data Science

Resident Data Scientist

November 2018 - April 2019

  • Utilized a complex assortment of data sources to produce data models & prediction algorithms for various clients including Biotechnology start-up companies

California Rural Indian Health Board

CRIHB

Epidemiologist

October 2017 — August 2018

  • Developed state wide data collection and disease surveillance tools, IRB and data management, and wrote complex grants to secure future funding.
  • Developed databases, SQL Reports using advanced queries, technical reports, spatial data overlays and maps, performed data linkages, and produced tables and figures in R and Tableau

UCSF School of Medicine

UCSF

Statistician

June — August 2017

  • Performed database management, IRB management, data interpretation and conducted various analyses – including machine learning, and causal, time-variant, clustered and multilevel analyses– on two multi-million dollar NIH funded international studies on multi-drug resistant tuberculosis

UC Berkeley School of Public Health

UC Berkeley CHL

Center for Health Leadership Fellow

Aug. 2016 — May 2017

  • Selected to participate in a three-semester leadership development program
  • Learning activities include: consulting project with non-profit agency, training workshops, needs assessment, project management and meeting facilitation

UC Berkeley School of Public Health

UC Berkeley

Graduate Student Researcher

Aug. 2016 — May 2017

  • Conducted analysis on the accuracy of population estimates of those with a genetic disorder or genetic determinants (e.g. cancer, congenital malformation, etc.) with those of elusive populations (e.g. drug addiction) using both non-Bayesian and Bayesian methods

Google

Google

Public Health Data Specialist

May-December 2016

  • Responsible for data curation, data analysis of existing public health and medical research
  • Curated, verified, and provided expert feedback to improve the information in the health knowledge graph and related algorithm performance

San Francisco Department of Public Health

UC Berkeley

Research Data Analyst

May-December 2016

  • Conducted various statistical analyses, mathematical modeling, data management, and data interpretation on over four studies involving randomized control trials of pharmacologic interventions to treat substance use, HIV risk, and a prospective longitudinal study among transgender youth

Amity Foundation

Amity Foundation

Program Coordinator

February - August 2015

  • Managed reporting, program evaluation and program implementation for grant that provided mentors to formerly incarcerated individuals
  • Implemented program plan, maintained program management efforts for a financial literacy grant entitled, Financial Empowerment
  • Managed reporting, program evaluation and program implementation for grant that provided mentors to formerly incarcerated individuals

Native American Health Center

Native American Health Center

Data Coordinator

February 2014 - February 2015

  • Managed data collection, entry and analysis for health clinic that saw over 15 thousand patients at three sites in the San Francisco Bay Area. Supervised two Data Assistants
  • Responsible for creating clinical and behavioral health reports via SQL from our EHR service to track funding grants requirements, and provide general reporting for the board of directors, clinicians, staff, and the public
  • Provided evaluation and project management on a technology integration grant that helped fund the creation of the clinics EHR

Data Assistant

June 2012 - January 2014

  • Responsible for grant funded survey data collection, health behavior quantative analysis, and technical and statistical reporting of over 15 grants
  • Other roles included: consenting survey participants, determining insurance eligibility, and performing HIV rapid tests and HIV counseling

UC Davis Department of Physiology and Biology

Gomes Lab

Research Assistant

September 2008 - June 2011

  • Collected, maintained, and evaluated data on mutations of cardiac Troponin T and cardiac Troponin C and their relation to familial cardiomyopathies. Proteins were expressed in KO mice using in vitro techniques and structurally analyzed via homology modeling
  • Performed various molecular biology techniques such as gel electrophoresis, and western blots to determine relative levels of Ubiquitin levels and proteasome activity expressed KO mice

Skillset Overview

  • Languages/Technologies : Python (Jupyter, Pandas, Scikit-Learn, Numpy/SciPy, Matplotlib, Django, Flask/FastAPI, PySpark, Keras, Pytest), R, SAS, JavaScript (JQuery, D3.js), SQL, noSQL (MongoDB), HTML, CSS (Bootstrap, Grid), Bash, Latex, AWS (Redshift, S3, Lambda, DynamoDB, EC2, CodePipeline), Apache Airflow, Docker, CI/CD, Git, Tableau
  • Platforms : Linux (Ubuntu Desktop & Server), OS X, Windows

Projects

Awards & Certs

Publications

Jackson ML., Gombar S, Manickam R, Brown R, Tekumalla R, Low Y. Validating a clinical informatics consulting service using negative control reference sets. Poster presented at: 2023 OHDSI Symposium; October 20, 2023; East Bruswick, New Jersey.

Abstract

Context: Patients with culture-negative pulmonary TB (PTB) can face delays in diagnosis that worsen outcomes and lead to ongoing transmission. An understanding of current trends and characteristics of culture-negative PTB can support earlier detection and access to care.
Objective: Describe epidemiology of culture-negative PTB.
Design, setting, participants: We utilized Alameda County TB surveillance data from 2010 to 2019. Culture-negative PTB cases met clinical but not laboratory criteria for PTB per US National Tuberculosis Surveillance System definitions. We calculated trends in annual incidence and proportion of culture-negative PTB using Poisson and weighted linear regression, respectively. We further compared demographic and clinical characteristics among culture-negative versus culture-positive PTB cases.
Results: During 2010-2019, there were 870 cases of PTB, of which 152 (17%) were culture-negative. The incidence of culture-negative PTB declined by 76%, from 1.9/100 000 to 0.46/100 000 (P for trend <.01), while the incidence of culture-positive PTB reduced by 37% (6.5/100 000 to 4.1/100 000, P for trend =.1). Culture-negative PTB case-patients were more likely than culture-positive PTB case-patients to be younger (7.9% were children <15 years old vs 1.1%; P < .01), recent immigrants within 5 years of arrival (38.2% vs 25.5%; P < .01), and have a TB contact (11.2% vs 2.9%; P < .01). Culture-negative PTB case-patients were less likely than culture-positive PTB case-patients to be evaluated because of TB symptoms (57.2% vs 74.7%; P < .01) or have cavitation on chest imaging (13.1% vs 38.8%; P < .01). At the same time culture-negative PTB case-patients were less likely to die during TB treatment (2.0% vs 9.6%; P < .01).
Conclusions: The incidence of culture-negative PTB disproportionately declined compared with culture-positive TB and raises concern for gaps in detection. Expansion of screening programs for recent immigrants and TB contacts and greater recognition of risk factors may increase detection of culture-negative PTB.

Chen J, Marusinec R, Brown R, Shiau R, Jaganath D, Chitnis AS. Epidemiology of Culture-Negative Pulmonary Tuberculosis-Alameda County, 2010-2019. J Public Health Manag Pract. 2023 Mar 3. doi: 10.1097/PHH.0000000000001715. Epub ahead of print. PMID: 36867649.

Abstract

Context: Patients with culture-negative pulmonary TB (PTB) can face delays in diagnosis that worsen outcomes and lead to ongoing transmission. An understanding of current trends and characteristics of culture-negative PTB can support earlier detection and access to care.
Objective: Describe epidemiology of culture-negative PTB.
Design, setting, participants: We utilized Alameda County TB surveillance data from 2010 to 2019. Culture-negative PTB cases met clinical but not laboratory criteria for PTB per US National Tuberculosis Surveillance System definitions. We calculated trends in annual incidence and proportion of culture-negative PTB using Poisson and weighted linear regression, respectively. We further compared demographic and clinical characteristics among culture-negative versus culture-positive PTB cases.
Results: During 2010-2019, there were 870 cases of PTB, of which 152 (17%) were culture-negative. The incidence of culture-negative PTB declined by 76%, from 1.9/100 000 to 0.46/100 000 (P for trend <.01), while the incidence of culture-positive PTB reduced by 37% (6.5/100 000 to 4.1/100 000, P for trend =.1). Culture-negative PTB case-patients were more likely than culture-positive PTB case-patients to be younger (7.9% were children <15 years old vs 1.1%; P < .01), recent immigrants within 5 years of arrival (38.2% vs 25.5%; P < .01), and have a TB contact (11.2% vs 2.9%; P < .01). Culture-negative PTB case-patients were less likely than culture-positive PTB case-patients to be evaluated because of TB symptoms (57.2% vs 74.7%; P < .01) or have cavitation on chest imaging (13.1% vs 38.8%; P < .01). At the same time culture-negative PTB case-patients were less likely to die during TB treatment (2.0% vs 9.6%; P < .01).
Conclusions: The incidence of culture-negative PTB disproportionately declined compared with culture-positive TB and raises concern for gaps in detection. Expansion of screening programs for recent immigrants and TB contacts and greater recognition of risk factors may increase detection of culture-negative PTB.

Lloyd T, Bender M, Huang S, Brown R, Shiau R, Yette E, Shemsu M, Pandori M. Assessing the Use of PCR To Screen for Shedding of Salmonella enterica in Infected Humans. J Clin Microbiol. 2020 Jun 24;58(7):e00217-20. doi: 10.1128/JCM.00217-20. PMID: 32376667; PMCID: PMC7315023.

Abstract

Recovery from enteric bacterial illness often includes a phase of organismal shedding over a period of days to months. The monitoring of this process through laboratory testing forms the foundation of public health action to prevent further transmission. Regulations in most jurisdictions in the United States exclude individuals who continue to shed certain organisms from sensitive occupations and situations, such as food handling, providing direct patient care, or attending day care. The burden that this creates for recovering patients and their families/coworkers is great, so any effort to provide efficiency to the testing process would be of significant benefit. We sought to assess the ability of PCR for the detection of Salmonella enterica shedding and to compare that ability to culture-based testing. PCR would be faster than culture and would allow results to be generated more quickly. Herein, we show data that indicate that, while PCR and culture testing agree in the majority of cases, there are incidents of discordance between the two tests, whereupon PCR shows positive results when culture indicates lack of detectable viable organisms. Using culture-based testing as the standard, the negative predictive value of PCR was found to be 100%, while the positive predictive value was 79%. The nature of this discordance is briefly investigated. We found that it is possible that PCR may not only detect nonviable organisms in stool but also viable organisms that remain undetectable by standard culture methods.

Allgeier D, Gebreegziabher E, Ycasas J, Murgai N, Brown RE, Moss N. HIV in Alameda County, 2015-2017. 2018.

Brown RE, Turner C, Hern J, Santos GM. Partner-level substance use associated with increased sexual risk behaviors among men who have sex with men in San Francisco, CA. Drug Alcohol Depend. 2017;176:176–80.

Abstract

BACKGROUND
Substance use is highly prevalent among men who have sex with men (MSM) and is associated with individual-level sexual risk behaviors. However, few studies have explored the relationship between substance use and HIV risk behaviors within partnerships.
METHODS
We examined partner-level data between MSM participants (n = 23) and their sexual partners (n = 52). We used multivariable generalized estimating equations (GEE) logistic regression to assess the relationship between partner-level substance use during their last sexual encounter with each partner, and engaging in condomless anal intercourse (CAI) and serodiscordant CAI.
RESULTS
In multivariable analyses, participants had significantly higher adjusted odds ratio (AOR) of CAI when the participant (AOR = 22.2, 95%CI = 2.5-199.5) or their partners used any drugs (AOR = 21.8, 95%CI = 3.3-144.3); their partners (AOR = 5.7, 95%CI = 1.7-19.3) or both participant and partner had concordant use of methamphetamine (AOR = 10.5, 95%CI = 2.2-50.6); or when both used poppers (AOR = 11.4, 95%CI = 1.5-87). There were higher odds of SDCAI if the participant binge drank (AOR = 4, 95%CI = 1.01-15.8), used more than one substance (AOR = 15.8, 95%CI = 1.9-133), or used other drugs (AOR = 4.8, 95%CI = 1.3-18.4); if their partner used poppers (AOR = 7.6, 95%CI = 1.5-37.6), or used more than one substance (AOR = 7.9, 95%CI = 1.9-34.1); and when both participant and partner had concordant use of poppers (AOR = 4.4, 95%CI = 1.2-16.8).
CONCLUSIONS
This study observed significant relationship between substance use and HIV risk behaviors within partnerships. Specifically, when either the participant, the partner, or both used any drugs there was an increased odds of sexual risk behaviors. Findings suggest that partner-level substance use behaviors should be taken in account when developing sexual risk reduction interventions.