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Matthew Zeidenberg

About Me

I am a Clinical Associate Professor of Computer Science at the Courant Institute of New York University. At NYU in the fall of 2022, I am teaching Introduction to Computer Programming (in Python) and Data Management and Analysis. In the spring of 2023, I will be teaching those two courses again, as well as the graduate-level Database Systems course. I am also the faculty director for and an instructor in Courant’s Pathways to AI program, a summer program that introduces undergraduates to AI research.

My career has been focused on research and teaching in the areas of data science, artifical intelligence, machine learning, labor and education. In 2020, I was the lead instructor for a three cohorts of a 13-week data science intensive course designed to train students for careers as data scientists.

I am familiar with a wide range of statistical and machine learning algorithms, and I have extensive experience with large data sets, statistical analysis including regression analysis and propensity score matching, cluster analysis, database systems, and text processing. I am the author of a book on neural networks.

In my social science work, I have focussed on research and program evaluation in workforce development and training, labor markets, income security, and higher education.

At Abt Associates, I worked on a study for the U.S. Department of Labor that focused on analyzing the career trajectories of workers to see how these trajectories can inform the design of career pathway programs. I also worked on site impact studies for the Pathways for Advancing Careers and Education (PACE) randomized controlled trial (RCT) evaluation of career pathway programs for the U.S. Department of Health and Human Services (HHS). I was on the analysis team for the Green Jobs and Health Care evaluation, which also looked at career pathways programs. I am worked on the Career Pathways Intermediate Outcomes (CPIO) Study, which is HHS’s follow-on study for PACE and the related Health Profession Opportunity Grants (HPOG) study, as well as its follow-up study (HPOG 2), which are also sponsored by HHS. I applied techniques from machine learning to processing survey responses collected in these studies.

I was a principal investigator for Abt’s RCT for the Robin Hood College Success Prize, which measured the impacts of behavioral interventions delivered through smartphone apps on students at two community colleges. I have co-authored papers on the well-known I-BEST career preparation program in Washington State, on worker mobility over the course of the career (using social sequence analysis), on mobility within various industries in the service sector, and on progression in college. I have worked heavily with administrative data on educational and labor market outcomes, as well as with survey data. I have also published peer-reviewed articles that apply machine learning and data science to educational and labor market data.

On the technical side, I have experience with a wide range of specialized and general purpose programming languages, including Python (and Scikit-Learn), SQL, R, Stata, SAS, Perl, and Lisp. I have strong knowledge of and experience with the design, conduct, and analysis of experiments.

Interests

  • Machine Learning
  • Artificial Intelligence
  • Information Retrieval
  • Computational Linguistics
  • Data Science
  • Education
  • Workforce Development
  • Social Experiments

Education

  • PhD in Computer Science

    University of Wisconsin, Madison

  • PhD in Sociology

    University of Wisconsin, Madison

  • BA in Physics (with Honors)

    Harvard University

Skills

HTML

JavaScript

LaTeX

Lisp

SQL

Perl

Python

Project Management

R

SAS

Stata

Statistics

Experience

 
 
 
 
 

Clinical Associate Professor of Computer Science

New York Univerity

Sep 2021 – Present New York, New York
Teaches courses in computer science, with an emphasis on Python, SQL, data science, and artificial intelligence. Faculty director of NYU’s Pathway’s to AI program in the summers of 2022 and 2023.
 
 
 
 
 

Principal Data Scientist, Lead Data Science Instructor

Galvanize

Apr 2020 – May 2021 New York, New York
Led instruction for a 13-week full-time immersive program in data science covering the following topics: scientific Python, probability, statistics (Probability, A/B Testing, Bayesian methods, Regression methods, Time Series), SQL, Machine Learning (Decision Trees, Random Forests, Boosting, Support Vector Machines, Clustering, Natural Language Processing, Recommenders, Graphs), Data Engineering (Hadoop, Hive, and MapReduce), Data Visualization, and data at scale.
 
 
 
 
 

Senior Scientist

Abt Associates

Jan 2014 – Mar 2020 New York, New York
Worked on and/or led a variety of research and evaluation projects, mainly in the policy areas of education and the workforce. Applied advanced techniques from computer science and data science to these projects. Lead researcher on a project for the U.S. Department of Labor that is applying machine learning and visualization to data from resumes and job postings in order to study career advancement and the relationship between labor supply and demand. Applied machine learning to the classification of responses to surveys. Principal investigator for a randomized controlled trial of the effectiveness of smartphone-based interventions at two large urban community colleges. Analyzed data and wrote impact analyses for a two large multi-site evaluations of career pathway programs. Worked on a planning project for the Department of Labor on future directions for career pathways research. Served as the main designer and technical lead for a proposed Department of Labor experiment with the crowd-sourcing of occupational data using wiki technology. Co-author of reports for the U.S Departments of Labor, Health and Human Services, and Agriculture, and for the federal Corporation for National and Community Service.
 
 
 
 
 

Senior Research Asociate

Community College Research Center

Oct 2006 – May 2014 New York, New York
Worked on a variety of research projects involving college student data. Analyzed programs of study and transcripts of students using advanced techniques from statistics and computer science, including clustering and machine learning. Studied such issues as workforce development, science and technology education, remedial education, student persistence, and college degree attainment. Participated in program evaluations, including a impact evaluation of developmental summer bridge programs at colleges in Texas. Lead author of the Center’s most recent evaluation of Washington State’s well-known IBEST program that connects low-skill people with training and jobs. Worked with state community college officials, analyzed large administrative data sets on community college students. Set up and managed the research group’s scientific computing network.
 
 
 
 
 

Senior Research Asociate

Center on Wisconsin Strategy

Sep 1991 – Sep 2006 Madison, Wisconsin
Performed research, wrote, and worked on a variety of reports concerning regional economic development, workforce development, career mobility, income and poverty, labor markets, and other subjects. Worked with geographic information systems to produce maps for these reports. Lead analyst for the Center’s signature publication, The State of Working Wisconsin. Installed and managed the Center’s computer networks. Performed data management and statistical computing for a research project on the federal litigation activity of large American corporations.
 
 
 
 
 

Instructor

University of Wisconsin Computer Sciences Department

Sep 1985 – May 1991 Madison, Wisconsin
Taught computer science classes in computer programming, artificial intelligence, and natural language processing as a highly-rated instructor. Developed lecture notes on computer programming.