I am a Big Data practitioner with experience in computer vision and machine learning

I live in New York City and am currently working as a Computer Vision Engineer at Arcules. I graduated from Duke University with degrees in Economics (B.S.) and International Comparative Studies (B.A.) and previously worked at J.P. Morgan as a data scientist & credit trading analyst as well as at Slang as a data scientist consultant.

I like working on computer vision and machine learning-related projects in my free time and am a web3 enthusiast.

Computer Vision Projects

Frameworks used: Python, Tensorflow, Keras, Git Read more
Frameworks used: Python, PyTorch, YOLO, Faster R-CNN, Folium, Git Read more
Frameworks used: C++, Python, OpenCV, Tensorflow, Git Read more
Frameworks used: Python, OpenCV, Pytesseract, sklearn, API's, Git Read more

Supervised ML Projects

Frameworks used: Python, Flask, Dash, pandas, numpy, sklearn, matplotlib, seaborn, API's, AWS, Git Read more
Frameworks used: Python, pandas, numpy, sklearn, matplotlib, seaborn, Git Read more
Frameworks used: Python, Natural Language Processing, statsmodels, R, pandas, numpy, sklearn, Selenium, scrapy, matplotlib, seaborn, Shiny, Git Read more
Frameworks used: R, caret, dplyr, ggplot2, Shiny, Git Read more

Unsupervised ML Projects

Frameworks used: Python, NLTK, Gensim, TextBlob, sklearn, bokeh, Git Read more
Frameworks used: Python, sklearn, plotly, matplotlib, Git Read more

Papers Published

January 2022

A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management

Proposed a supervised object detection approach leveraging You Only Look Once (“YOLO”) and the Faster R-CNN frameworks to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score experimental results ranking in the top 5 of 121 teams that entered the IEEE Global Road Detection Challenge as of December 2021.

Skills

Computer Vision: Pytorch | Tensorflow | Keras | You Only Look Once (YOLO) | Faster R-CNN


Python: pandas | numpy | scikit-learn | scipy | nltk | statsmodels | scrapy | Selenium | Dash | Flask | matplotlib | seaborn


Supervised ML: Linear & Logistic Regression | Random Forests | Gradient Boosting | XGBoost | Support Vector Machines | Neural Nets


Unsupervised ML: K-Means | K-Medians | HDBSCAN | Principal Component Analysis


R: dplyr | ggplot2 | Shiny | caret


SQL: MySQL | Teradata SQL | HiveQL


Dashboarding: Tableau | Alteryx | Adobe Analytics


Other Frameworks: Apache Spark | Git | Docker | AWS | Jira | Agile

Experience

March 2022 – Present

Arcules

Computer Vision Engineer

Responsible for development and deployment of computer vision models enabling object detection and instance segmentation applications using Pytorch and Tensorflow

March 2021 – March 2022

J.P. Morgan Chase

Data Scientist

Led machine learning classification project predicting customer deposit outflow transactions to competitors in following 30 days with responsibilities encompassing model training, data engineering and deployment tasks

March 2020 – December 2020

NYC Data Science Academy

Data Scientist Fellow

Graduated immersive data science program focused on machine learning, big data, Python and R development, SQL, Tableau, Docker and Git/GitHub with hands-on project work.

September 2019 – March 2021

J.P. Morgan Chase

Credit Trading Analyst

Structured and traded illiquid credit products as part of the Distressed Credit Trading desk with responsibilities including developing and presenting financial valuation models in Excel and Python

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