Joseph Miano

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I am a data scientist and machine learning engineer with 5+ years of experience building models for computer vision, natural language processing, and tabular datasets. With a B.S. in neuroscience and an M.S. in computer science, I am especially excited about the development of neural networks and the increasing complexity of problems they can solve. Over the past several years, I have had the opportunity to work on analytics for large-scale medication adherence outreach programs, multi-task neural networks for brain microscopy image segmentation, transformer-based NLP models to detect COVID-19 outbreaks from news articles, explainable machine learning for fraud detection, and more.

Currently, I am working at Radix as a team lead and senior machine learning engineer.

Joseph Miano Profile

Data Scientist & Machine Learning Engineer

Experienced in all aspects of the data science pipeline, including data exploration, feature engineering, model training, and model deployment.

  • University: Georgia Tech, UMiami
  • Employer: Radix
  • Location: Brussels, Belgium
  • Degree: B.S., M.S.
  • Email:
  • Languages: English, French, Spanish


Programming Languages

  • Python
  • SQL
  • Java
  • C

ML & Big Data

  • PyTorch
  • Transformers
  • Scikit-learn
  • PySpark
  • Dask


  • Matplotlib
  • Seaborn
  • Plotly
  • Streamlit
  • Tableau


  • Deep Learning
  • Feature Engineering
  • Ensemble Methods
  • Unsupervised Learning
  • Prompt Engineering

Data Domains

  • Computer Vision
  • Natural Language Processing
  • Audio & Speech
  • Tabular Datasets
  • Time Series

DevOps & Cloud

  • Docker
  • Git
  • Azure
  • GCP
  • AWS


Click here to view my resume as a PDF.


Joseph Miano

Data scientist and machine learning engineer with 5+ years of experience in computer vision, natural language processing, tabular datasets, and deep learning.

  • Brussels, Belgium


Master of Science in Computer Science

Aug 2020 - Dec 2021

Georgia Institute of Technology, Atlanta, GA

  • Machine Learning Specialization
  • Graduate Research Assistant at the Georgia Tech Research Institute
  • Coursework in deep learning, computer vision, natural language processing, and machine learning theory

Bachelor of Science in Computer Science

May 2018 - May 2020

Georgia Institute of Technology, Atlanta, GA

  • 2nd B.S.
  • Coursework in computer science and mathematics
  • Specializations in theory and artificial intelligence

Bachelor of Science in Neuroscience

Aug 2012 - May 2016

University of Miami, Coral Gables, FL

  • Minors in Finance and Chemistry
  • Research in cellular neuroscience
  • Pre-medical track with medical shadowing experience


AI & Machine Learning Summer Associate

Jun 2021 - Aug 2021

JPMorgan Chase & Co., Remote, USA

  • Developed object-oriented Python code to enable explainability and interpretability of credit risk assessment models
  • Presented results and conclusions to the broader intern group and organization (20+ colleagues)

Software Engineering Summer Intern

Jun 2019 - Aug 2019

American Express, Phoenix, AZ

  • Trained natural language processing machine learning models using Python to automate incident ticket routing
  • Explained summer project and results to VP-level organization (40+ colleagues) during end-of-internship presentation

Work Experience

Senior Machine Learning Engineer → Team Lead

Mar 2023 - Present

Radix, Brussels, Belgium

  • Lead a team of 7 machine learning engineers, which includes career growth mentorship, organizational planning, and project delivery support
  • Spearheaded a project to improve the efficiency of a medicine production pipeline via machine learning in collaboration with a pharmaceutical partner
  • Developed deep learning models (convolutional autoencoders) to denoise barcode images for a project with a retail partner, leading to a 15%+ improvement in barcode recognition accuracy
  • Modeled (predictive) patient outcomes after surgical interventions for a project with a medical partner, facilitating patient expectation management at the various stages of their journey

AI & Machine Learning Senior Associate

Feb 2022 - Mar 2023

JPMorgan Chase & Co., New York, NY

  • Engineered 100+ features for customer authentication risk assessment models, specifically to mitigate digital authentication risk
  • Trained machine learning models to predict fraudulent customer authentication events, balancing customer service experience (i.e., false positives) with fraud risk (i.e., false negatives)
  • Coordinated the explainable AI track for the inaugural 2022 JPMorgan Chase AI Summit, which brought together speakers from across the firm to present on how model explainability methods are being applied

Graduate Research Assistant

Sep 2020 - Dec 2021

Georgia Tech Research Institute, Remote, USA

  • Implemented neural natural language processing models (BERT and RoBERTa) to automate COVID-19 outbreak detection using web-scraped news article contents
  • Published a paper as first author in the Springer Lecture Notes in Artificial Intelligence as part of the 2021 Artificial Intelligence in Medicine Conference

Research Assistant

Aug 2018 - Jul 2020

Neural Data Science Lab @ Georgia Tech, Atlanta, GA

  • Developed multi-task convolutional neural network for segmentation and classification of mouse brain x-ray microtomography data
  • Presented joint poster at the Allen Institute BioImage Informatics 2019 Conference (funded with PURA Travel Award)
  • Collaborated to publish 3 papers (linked in the Papers section)

Consultant → Senior Consultant

Aug 2016 - Apr 2018

CVS Health, Woonsocket, RI

  • Identified patients at risk of medication non-adherence in outcomes-based contracts and executed adherence outreach programs
  • Quality-tested 50+ features for an enterprise-level predictive modeling project in collaboration with stakeholders from several departments
  • Coordinated onboarding for 8 new hires and guided curriculum development of the onboarding program, including the addition of a new SQL training


Hover or click on the images below to get a summary and link for each project.

Diabetes Readmission Dashboard Project Image
Diabetes Readmission Dashboard

In this project, I deployed a random forest model and dashboard on AWS visualizing data and predictions for diabetes hospital readmissions.

In addition to interactive visualizations, the dashboard enables the user to upload their own data and download model predictions.

Of the various models trained and tested, random forest performed the best, and the two most important features predicting hospital readmission were the number of lab procedures and the number of medications for the patient.

Neural Network Graceful Degradation Project Image
Neural Network Graceful Degradation

In this group project, we studied the impact of noisy samples and pruning neural networks on image and audio through the lens of the cognitive science model of graceful degradation.

My focus in the project was the audio data, for which I trained 1D convolutional neural networks to process raw audio and 2D ones to process spectrogram-transformed audio.

We found that our neural networks were quite resilient to pruning when retrained and could learn to adapt to noisy inputs.

Medication Review Modeling Project Image
Medication Review Modeling

In this group project, we studied the relationship between medication review text, metadata, and review usefulness.

My focus in the project was exploratory data analysis and training of text-only DistilBERT models to process the text and hybrid DistilBERT models to process the text and metadata jointly.

Overall, we were able to predict review usefulness successfully from both the text only and the metadata only, but that the hybrid model performed best.

Latency-aware Pruning for MTL Project Image
Latency-aware Pruning for MTL

In this group project, we developed a prototype machine learning inference system that leverages pruning of MTL (multi-task learning) neural networks.

My focus in this project was the multi-task neural network architecture design and implementation, as well as the experiments related to pruning and varying task-head length.

We found that pruned and fine-tuned MTL neural networks achieved higher accuracy-latency trade-offs than single-task models.

Interactive Story Generation Project Image
Interactive Story Generation

In this group project, we developed a framework for interactive story generation by leveraging GPT-2.

My focus in this project was to fine-tune GPT-2 to enable prompt-based story generation and to develop an interface for users to interact with.

By breaking up the story generation process into smaller chunks, we were able to create a compelling user experience for user-driven stories.