Roles

Software Engineer Intern at Amazon Web Services

May 2023 - Aug 2023 | New York, New York, United States

Project: Key Performance Indicator (KPI) Anomaly Observer

  • Developed high-visibility AWS application to fix product reliability problems.
  • Worked within a low-latency and high-throughput anomaly detection system with dozens of deep learning models that are retrained daily on customer data.
  • Implemented system which applies statistical methods to detect false positive anomaly findings caused by malformatted input data.
  • Reduced security engineer debugging phase by several hours and reduced number of incoming customer complaints about false positive scenarios.
  • Skills: Apache Spark, AWS Glue, DynamboDb, S3, Lambda, Java/Scala, Typescript

Lead Research Engineer at Miami University

May 2022 - May 2023 | Oxford, Ohio, United States

Project: CDC Adolescent Suicide Simulations Platform

  • Co-developed adolescent suicide simulation model alongside experts from the Centers for Disease Control and Prevention (CDC)
  • Implemented Python-based simulation model which coincided with the most recent research into adolescent suicide.
  • Applied machine learning principles to optimize the model parameters based on the most updated suicide/attempt/ideation statistics.
  • Designed a PHP-Laravel based web architecture to deliver the simulation model to the CDC experts, with key features such as a SQLite database, parallel simulation execution with Redis, and Docker containerization.
  • Delivered model and platform to CDC with their approval of all features.
  • Conducted additional supporting research tasks, such as categorizing and aligning dataset variables from disparate sources, writing official research reports, and creating comprehensive user documentation.
  • Skills: PHP (Laravel), Python (Numpy, Matplotlib), Docker, Nginx, Redis, SQLite
  • Publication in CDC Review:
    "An Artificial Intelligence Approach to Support Youth Suicide Prevention Initiatives in the U.S.A"
    L. Liang, R. Schuerkamp, K. L. Rice, M. M. Brown, N. Nataraj, J. Mendoza-Alonzo, C. R. Harper, C. Florence, B. D. Maldonado, B. Moran, P. J. Giabbanelli

Software Engineer Intern at Amazon Web Services

May 2022 - Aug 2022 | New York, New York, United States

Project: Deep Learning Model Serving and Load-Testing

  • Researched and tested machine learning model serving platforms.
  • Rigorously verified that these platforms met the team's domain requirements of low-latency, high-throughput, and system stability.
  • Designed parallel load testing system to test platforms under realistic load.
  • Collected comparative metrics on various systems and delivered resulting metrics in a report to the team.
  • Implemented the highest-performing platform, which has reduced load times between model retrainings by 7%.
  • Skills: Python, Typescript, AWS Sagemaker, S3, Lambda, Unit-testing, CI/CD

Machine Learning Intern at Miami University

Jan 2022 - Mar 2022 | Oxford, Ohio, United States

Project: Center for Analytics and Data Science (CADS) Field Hockey Statistics

  • Collected and cleaned data about player movements, actions, and decisions for the Miami Women's Field Hockey Team.
  • Delivered key insights from the data to the coaching staff of the team to help the players improve.
  • Skills: R, Data Collection, Data Cleaning, Data Engineering

Lead Research Assistant at Miami University

Oct 2021 - Mar 2022 | Oxford, Ohio, United States

Project: CDC Adolescent Suicide Model Analysis and Comparison

  • Headed a team of student researchers to deliver results to key stakeholders at the Centers for Disease Control and Prevention.
  • Facilitated communication as head researcher between research advisor, CDC experts, and student researchers.
  • Analyzed several previous network-based models of adolescent suicide.
  • Compared and contrasted key structural and contextual elements of these models with each other, such as node centrality, edge density, and node neighborhood size and similarity.
  • Applied existing CDC adolescent suicide prevention strategies to the models to demonstrate how the model can represent intervention.
  • Presented results of the research to experts from the CDC, and subsequently co-authored a report with them on the results.
  • Skills: Lead Research, Lead Research Communication/Presentation, Python (NetworkX), Graph Theory, Computer Networks, LaTeX
  • Publication in Peer Review at the Cambridge Journal for Network Science:
    "Guiding Prevention Initiatives by Applying Network Analysis to Causal Maps of Adverse Childhood Experiences and Adolescent Suicide"
    B. D. Maldonado, R. Schuerkamp, C. M. Martin, K. L. Rice, Nisha Nataraj, M. M. Brown, C. R. Harper, C. Florence, P. J. Giabbanelli

Software Engineer Intern at Amazon Web Services

May 2021 - Aug 2021 | Seattle, Washington, United States

Project: Centralized Alarm Management System

  • Designed generic alarm management system with the intent to decrease redundant developer effort in manual alarm configuration updates.
  • Worked in the context of a globally regionalized AWS product that optimized Amazon.com fulfillment warehouse througput to ensure promised delivery dates to customers.
  • Developed and tested system which used a central AWS DynamoDB database to maintain on-call alarm configurations and AWS Lambda to send alarm configuration updates to all fulfillment centers globally.
  • Lead design discussions with team leadership on the pros and cons of the prototype, using data from the tests to help the team decide whether the approach met the team's needs.
  • Skills: Typescript, Java, Java Unit Testing, Java Dependency Injection, AWS DynamoDB, Lambda

Research Assistant at Miami University

Jan 2020 - Dec 2021 | Oxford, Ohio, United States

Project: Cognitive Neuroscience and Vision Electroencephalography (EEG) Correlations

  • Conducted research with EEG brainwave data from five Air Force research subjects.
  • Trained machine learning models to predict which version of a Gabor patch a subject saw using only their brain activity with an 85.6% accuracy.
  • Analyzed data underlying model using factorial experiments to determine which data signals resulted in a model.
  • Performed confirmatory experiment to verify whether favored hand dominance affected any of the data, as other recent data suggested this could be a confounding factor.
  • Presented findings that the N200 peak amplitude was responsible for 96.3% of the results, regardless of handedness (consistent with prior research).
  • Skills: Machine Learning, Statistical Modeling, EEG, Cognitive Neuroscience, Research, Signal Analysis, Fourier Transforms, Jupyter Notebooks

Machine Learning Intern at Neurolex Labs

Feb 2020 - Sept 2020 | Virtual

Project: Machine Learning Models for ADHD Detection

  • Compiled, cleaned, and balanced patient vocal data from proprietary dataset.
  • Analyzed trends in data to find possible signals for ADHD diagnosis.
  • Trained several rounds of models to detect ADHD in sample data, applying industry standard practices such as parameter optimization where appropriate.
  • Presented research findings that there was no statistically significant signal in the data, proposing where the experimental error came from and how a future experiment might extract the signal better.
  • Skills: Machine Learning, Python (PyTorch, Pandas, Numpy, SciKit-Learn), Research, Research Presentation, Statistical Modeling

Artificial Intelligence Intern at Discovery Lab Global

Jan 2020 - Sept 2020 | Virtual

Project: Self-Driving Cars with Computer Vision

  • Researched existing body of information around reinforcement and deep learning.
  • Completed course in Computer Vision in Python to understand and apply concepts to a physical car
  • Constructed a small electronic car utilizing the NVIDIA Jetson Nano Developer Kit.
  • Applied concepts of deep learning and computer vision to teach the car to drive and avoid obstacles.
  • Demonstrated results and research findings in an end-of-internship presentation to the rest of my team.
  • Skills: Deep Learning, Reinforcmeent Learning, Computer Vision, Python (PyTorch, OpenCV), NVIDIA Jetson Nano

Research Assistant at Miami University

Mar 2020 - May 2020 | Oxford, Ohio, United States

Project: Computer Vision for Shortcutting HIV Simulations

  • Conducted research into shortcutting large and costly HIV simulations.
  • Improved and optimized existing cellular automata (CA)-based models of HIV to run more efficiently at a vastly increased simulation size.
  • Utilized pre-trained image machine learning models to extract visual features from the simulation models at various timesteps.
  • Trained machine learning models to intake simulation model start case and use extracted visual features to predict the simulation end case.
  • Ran this experiment with various simulations for HIV which included a number of effects on the HIV outcome such as drug introduction, drug interaction, and drug adhereance.
  • Communicated research findings in a final report.
  • Skills: Research, Machine Learning, Simulations, Python (Numpy, Cython, SciKit-Learn, SciKit-Image), High Perfomance Computing (HPC) Cluster
  • Unpublished Paper:
    "Predicting HIV Viral Body Load through Image Analysis"
    J. Li, B. D. Maldonado, G. Skidmore, Z. Xu, D. Calovini

Research Assistant at Miami University

Sept 2019 - Dec 2019 | Oxford, Ohio, United States

Project: Motion Sickness Prevention

  • Implemented research codes from pseudocode into Java.
  • Learned basics of lab research and operation.
  • Skills: Research, Pseudocode Translation, Java