Data Scientist
2018 - 2019The University of Colorado — Office of Data Analytics- Performed statistical analyses and modeling in support of student success.
- Created and presented findings and visualizations to high-level administrators with Jupyter and Zeppelin.
- Developed a Monte Carlo simulation-based model to predict semester-by-semester student retention.
- Built a Bayesian model of reoffense after student misconduct.
- Modeled the effects of different kinds of Financial Aid with XGBoost.
- Created a model to predict student GPAs with Scikit-learn and Keras.
Technologies: Python, Pandas, Scikit-learn, PySpark, Keras, Jupyter, Zeppelin, Oracle DatabaseData Engineer
2017 - 2018NOMI Beauty- Designed and supported ETL from Couchbase to MySQL using Python.
- Architected a big data pipeline with Spark, Kafka, and Cassandra.
- Built data dashboards in Tableau for the operations team.
- Designed an ETL for survey data from Typeform's API into MySQL.
- Created reports in Jupyter notebooks with data visualizations in Python with Altair and Seaborn.
- Designed and implemented a database schema in MySQL.
Technologies: Python, Pandas, MySQL, PySpark, Kafka, Cassandra, AWS, Altair, JupyterData Science and Blockchain Integration Consultant
2017 - 2017Tanktwo, Inc.- Architected a Blockchain-based solution for managing IoT devices and the data they generate.
- Create a demo of a potential network using Hyperledger.
- Simulated a private blockchain network in action using Python.
- Helped present a demo to the VCs.
- Conducted research on the optimal Blockchain implementation to suit business needs.
Technologies: Python, Pandas, Hyperledger, AWSData Science Consultant
2014 - 2017Hospital for Special Surgery- Analyzed biosignal data with a Python data suite (NumPy, Pandas, and SciPy).
- Reverse-engineered an undocumented file format containing biosignal data.
- Extracted data from an undocumented file format to CSVs.
- Visualized biosignal data with Plotly.
- Investigated using Higuchi Fractal Dimension of nerve conduction readings taken during surgery as a means of assessing potential damage.
- Attempted to classify nerve conduction readings as indicating injury or anesthesia response using Scikit-learn.
- Used Scikit-learn to classify nerve-stimulation trials. Did feature engineering, hyperparameter optimization using Grid Search and Random Search.
- Looked at feature distribution of different types of nerve readings taken during surgery to discriminate injuries from healthy responses to anesthesia.
Technologies: Python, NumPy, Pandas, SciPy, Plotly, Jupyter, PyEEG, Scikit-learnNatural Language Processing Consultant
2015 - 2015New York City Department of Administrative Services- Scraped PDFs with Python in order to help digitize the back catalog for a publication, The City Record.
- Helped design a schema for entries (such as extracting addresses).
- Created data cleaning regimens to standardize entries from over a hundred city agencies that all reported in different formats.
- Used Python and NLTK to perform exploratory Natural Language Processing on a century-long corpus of publications.
- Worked to integrate this pipeline into MS Access.
Technologies: NLTK, PythonIntegration and Development Consultant
2013 - 2014Broadband Technologies Group- Provided computer vision-based assistance for digitizing video archives.
- Used OpenCV and Python to tag damaged video areas.
- Implemented Python to automatically fix certain types of damaged videoes.
- Helped architect an Android application to deliver simultaneous subtitles for live performances.
- Prepared presentations with Jupyter.
Technologies: Python, OpenCVResearch Assistant
2008 - 2013Hunter College- Designed and validated a novel psychometric scale.
- Analyzed survey data in SPSS.
- Presented findings at research conferences.
- Maintained relationships with the lab after graduation, eventually moving from data analysis to Python.
- Worked on the publication of older data.
Technologies: SPSS, Python, Pandas, SciPySummer Research Assistant
2009 - 2010Yale School of Medicine- Designed and piloted a small study investigating psychopathic traits and behavior during an ultimatum game.
- Analyzed GSR data.
- Ran research participants through computer-based tasks in a presentation and DMDX.
- Analyzed data from surveys and computer-based tasks.
- Built and maintained a database of participants.
Technologies: SPSS, Presentation, DMDX
Matthew Alhonte
Data Scientist and Developer in New York, NY, United States
Member since June 20, 2018
Matt has officially worked as a Python-based data scientist for the past six years; however, he's spent the last ten at the intersection of stats and programming (before the term data scientist had caught on). He combines strong technical skills with a rigorous background in experiment design and statistical inference. More recently, he's been focusing on machine learning, including some natural language processing and computer vision.
Matthew is now available for hire
Portfolio
- The University of Colorado — Office of Data AnalyticsPython, Pandas, Scikit-learn, PySpark, Keras, Jupyter, Zeppelin, Oracle Database
- NOMI BeautyPython, Pandas, MySQL, PySpark, Kafka, Cassandra, AWS, Altair, Jupyter
- Tanktwo, Inc.Python, Pandas, Hyperledger, AWS
Experience
- Statistics 11 years
- Data Visualization 11 years
- Experimental Design 11 years
- Python 6 years
- Pandas 5 years
- SQL 5 years
- Machine Learning 5 years
- Functional Programming 4 years
Availability
Part-time
Preferred Environment
Jupyter, VS Code, Spacemacs, Git, PyCharm
The most amazing...
...thing I've done is to reverse-engineer an undocumented file format containing electrophysiology readings.
Employment
Experience
- Spring 2018 Complexity Challenge (Development)https://github.com/mattalhonte/sfi-challenge
My entry in the Spring 2018 Complexity Challenge by the Santa Fe Institute.
- Graph Theory Notes (Other amazing things)https://github.com/mattalhonte/SFI-Algorithmic-Information-Theory/blob/master/2.%20Graph%20Theory%20(n-Cubes).ipynb
This is some code that I wrote to help me to understand the graph theory section of an online course on algorithmic information theory.
- Binary Grid Search (Development)https://hackersandslackers.com/tuning-machine-learning-hyperparameters-with-binary-search/
Here I am experimenting with using a binary search to tune hyperparameters for machine learning models in Scikit-learn.
- Recasting Low-cardinality Columns as Categoricals (Other amazing things)https://hackersandslackers.com/recasting-low-cardinality-columns-as-categoricals-2
A short tutorial on saving memory in Pandas by using categorical variables. It includes a code snippet to take a data frame and recast the low-cardinality columns as categoricals.
- Removing Duplicate Columns in Pandas (Other amazing things)https://hackersandslackers.com/remove-duplicate-columns-in-pandas
A short tutorial on finding and removing duplicate columns in Pandas.
- Downcast Numerical Data Types with Pandas (Other amazing things)https://hackersandslackers.com/downcast-numerical-columns-python-pandas/
A short tutorial on saving memory by downcasting Pandas columns into the smallest possible numerical representation.
- Sentiment Analysis With AWS SageMaker (Development)https://github.com/mattalhonte/sagemaker-deployment/tree/master/Project
Classifying movie reviews as positive or negative, using SageMaker's version of XGBoost.
- Epilepsy Classifier (Development)https://github.com/mattalhonte/epilepsy-classifier
A capstone project for Udacity's machine learning engineer nanodegree.
- Python to Rust (Development)https://github.com/snipsco/tract/tree/main/examples/jupyter-keras-tract
A short walkthrough of training a machine learning model in Python, exporting a model artifact, and serving predictions in Rust. It was accepted as official documentation for a relevant Rust crate called "tract."
- Splitting Columns With Pandas (Other amazing things)https://hackersandslackers.com/splitting-columns-with-pandas/
I wrote a tutorial on splitting up Pandas columns with nested data.
Skills
Languages
Python, SQL, Clojure, RustLibraries/APIs
Pandas, Sklearn, NumPy, Scikit-learn, Dask, PySpark, Keras, TensorFlowParadigms
Data Science, Functional ProgrammingOther
Experimental Design, Time Series, Machine Learning, Predictive Modeling, Data Visualization, Data Analysis, Data Analytics, Statistics, Scikit-Learn, Pyspark, Deep Learning, Natural Language Processing (NLP), Mathematical Modeling, Data Engineering, Deep Neural Networks, Neural Networks, Data EngineerTools
Jupyter, Git, AWS CLI, Amazon SageMakerFrameworks
ClojureScriptPlatforms
Linux, AWS EC2, AWS Lambda, Zeppelin, Apache KafkaStorage
AWS S3, AWS RDS, Cassandra, PostgreSQL, MySQL
Education
- Bachelor of Arts degree in Psychology2006 - 2012Hunter College - New York City, NY, USA
Certifications
- Machine Learning Engineer NanodegreeJANUARY 2020 - PRESENTUdacity
