Matthew Howard

I am a software engineer based in the San Francisco Bay Area with a background in computing research in the areas of machine learning and software engineering.

My interests revolve around building data-driven engineering solutions, designing analytics pipelines, and improving decision processes to enhance product functionality and social outcomes.


Check out my latest project SpotifyMap — explore and listen to the #1 daily most streamed songs on Spotify around the world.

SpotifyMap.com

SpotifyMap.com

What I Do

Software Engineering

Develop reliable, efficient software across the full stack. Collaboratively maintain sophisticated and well-documented code bases and repositories. Communicate and coordinate to rapidly develop software prototypes.

Computing Research

Publish high-quality research across the domains of machine learning and software engineering. Develop intelligent algorithms to solve open problems. Validate results with both human annotators and simulation environments.

Data Science

Wrangle unstructured data into manageable databases. Query and manipulate data to answer pertinent questions. Construct statistical and machine learning models to forecast trends and classify data.

Data Visualization

Design interactive dashboards and graphical representations to explore and identify trends within data. Present complex information in intuitive, understandable, and actionable ways.

Resume

1 Year of Experience

Education

2019
UC Santa Cruz

MS Computer Science

Researched in the areas of machine learning for relational ranking, recommendation, video manipulation detection, and social forecasting. See Publications [1] and [2].

2014
University of Delaware

BS Computer Science

Dual degree with Mechanical Engineering. Researched in the area of applied natural language analysis for improving software engineering tools. See Publication [3].

2014
University of Delaware

BME Mechanical Engineering

Dual degree with Computer Science. Minor in Mathematics. Focus in engineering mathematics. 

Experience

2015 - 2016
Adobe

Data Scientist Intern

  • Prototyped research models for resolving unique users across devices from anonymous and semi-anonymous web logs using the Java-based probabilistic soft logic (PSL) framework for probabilistic relational learning.

  • Aggregated web log data (e.g., geographic clustering, device similarities) to engineer features to capture transitive relationships of users across multiple modes of access

  • Trained and tested large-scale probabilistic models at scale with AWS.

2015
PARC

Research Scientist Intern

  • Developed novel probabilistic model to predict future mobile device usage activity based upon user context features (e.g., phone state, recent device actions) and historical application usage trends with Java-based probabilistic soft logic (PSL) framework.

  • Model successfully predicted future action in 75% of cases within 1 time step vs. 68% for baseline SVM-HMM model for 103 user sample

  • Wrote SQL/Python pipeline to digest terabyte-scale DeviceAnaylzer mobile device dataset into model-ready feature databases.

  • Automated device usage visualizations across timescales with Matplotlib to identify contextual dependence of features and user actions.

2014 - 2016
UC Santa Cruz

Graduate Researcher

  • Research conducted as Ph.D. student in the area of statistical relational learning and AI.

  • Developed probabilistic approaches to ranking/recommendation problems within the setting of relational networks (e.g., social nets).

  • Designed method to cast non-convex optimization of the ranking metric NDCG as a convex optimization for utilization in learning of rankings via graphical models known as Hinge-loss Markov random fields (HL-MRFs).

  • Created early-stage probabilistic implementation of the PC-algorithm for causal discovery within probabilistic soft logic (PSL) to identify causal structure within networked data.

Programming Languages

Python

95%

Java

80%

C/C++

75%

JavaScript

85%

Scala

75%

Data Science

SQL

80%

Pandas

85%

Matplotlib

75%

TensorFlow/Keras

70%

Scikit-learn

70%

Technical Skills

Communication

85%

Technical Writing

75%

Research Methods

80%

Front-End

ReactJS

85%

HTML/CSS/SCSS

80%

jQuery

80%

Publications

- Best Paper Award Winner

  1. Matthew J. Howard, Alexander S. Williamson, and Narges Norouzi
    Video Manipulation Detection via Recurrent Residual Feature Learning Networks in IEEE Global Conference on Signal and Information Processing. Nov 2019.
  2. Matthew J. Howard and Rakshit Agrawal
    Predicting Substance Misuse Admission Rates via Recurrent Neural Networks in IEEE Global Humanitarian Technology Conference. Oct 2019.
  3. Matthew J. Howard, Samir Gupta, K. Vijay-Shanker, and Lori Pollock
    Automatically Mining Software-Based, Semantically-Similar Words from Comment-Code Mappings in Mining Software Repositories. May 2013.

Portfolio

My Works
SpotifyMap.com

SpotifyMap.com

Data Visualization, Web Apps
GlobalSIP 2019

GlobalSIP 2019

Research Papers
GHTC 2019

GHTC 2019

Research Papers
MSR 2013

MSR 2013

Research Papers

Contact

Get in Touch

San Francisco Bay Area

matthoward.dev@gmail.com

Open to Work / Freelance

Send Me A Message