2019UC Santa Cruz
MS Computer Science
Researched in the areas of machine learning for relational ranking, recommendation, video manipulation detection, and social forecasting. See Publications  and .
2014University 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 .
2014University of Delaware
BME Mechanical Engineering
Dual degree with Computer Science. Minor in Mathematics. Focus in engineering mathematics.
2015 - 2016Adobe
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.
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 - 2016UC Santa Cruz
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.
- Best Paper Award Winner
- 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.
- Matthew J. Howard and Rakshit Agrawal
Predicting Substance Misuse Admission Rates via Recurrent Neural Networks in IEEE Global Humanitarian Technology Conference. Oct 2019.
- 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.