About

Zachary Fisher is an Assistant Professor of Human Development and Family Studies and a C-SoDA Faculty Affiliate at Pennsylvania State University. He holds an M.S. in Applied Statistics from Columbia University and a Ph.D. in Quantitative Psychology from the University of North Carolina at Chapel Hill. His research broadly focuses on methods development for complex time-dependent processes, with current interests at the intersection of time-varying measures of physiology and behavior, intensive longitudinal data, and the joint modeling of behavioral and biological data. He is also engaged in the synthesis of multi-way data, such as cross-sectional and time-series data, and in statistical programming.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Econometrics
• Statistics
• Applied mathematics
• Data Mining
• Natural Language Processing
• Machine Learning
• Physics
• Engineering
• Statistical physics
• Programming language
• Operations management
• Psychology
• Theoretical computer science
• Thermodynamics
• Mathematical analysis

Selected publications

• Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data

  Psychometrika · 2022 · 17 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Machine Learning

  Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this gap in the literature, we present the multi-VAR framework, a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals. Importantly, our approach estimates models for all individuals simultaneously and is capable of adaptively adjusting to the amount of heterogeneity present across individual dynamic processes. To accomplish this, we propose a novel proximal gradient descent algorithm for solving the multi-VAR problem and prove the consistency of the recovered transition matrices. We evaluate the forecasting performance of our method in comparison with a number of benchmark methods and provide an illustrative example involving the day-to-day emotional experiences of 16 individuals over an 11-week period.

  DOI

• Making Transformers Solve Compositional Tasks

  Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022 · 34 citations

  • Computer Science
  • Computer Science
  • Natural Language Processing

  Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the inductive biases given to the model by several design decisions significantly impact compositional generalization. We identified Transformer configurations that generalize compositionally significantly better than previously reported in the literature in many compositional tasks. We achieve state-of-the-art results in a semantic parsing compositional generalization benchmark (COGS), and a string edit operation composition benchmark (PCFG).

  DOI

• Fifty years of structural equation modeling: A history of generalization, unification, and diffusion

  Social Science Research · 2022 · 34 citations

  • Computer Science
  • Applied mathematics
  • Statistical physics
  DOI

• An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).

  Psychological Methods · 2021 · 37 citations

  • Computer Science
  • Statistics
  • Econometrics

  Structural equation models (SEMs) are widely used to handle multiequation systems that involve latent variables, multiple indicators, and measurement error. Maximum likelihood (ML) and diagonally weighted least squares (DWLS) dominate the estimation of SEMs with continuous or categorical endogenous variables, respectively. When a model is correctly specified, ML and DWLS function well. But, in the face of incorrect structures or nonconvergence, their performance can seriously deteriorate. Model implied instrumental variable, two stage least squares (MIIV-2SLS) estimates and tests individual equations, is more robust to misspecifications, and is noniterative, thus avoiding nonconvergence. This article is an overview and tutorial on MIIV-2SLS. It reviews the six major steps in using MIIV-2SLS: (a) model specification; (b) model identification; (c) latent to observed (L2O) variable transformation; (d) finding MIIVs; (e) using 2SLS; and (f) tests of overidentified equations. Each step is illustrated using a running empirical example from Reisenzein's (1986) randomized experiment on helping behavior. We also explain and illustrate the analytic conditions under which an equation estimated with MIIV-2SLS is robust to structural misspecifications. We include additional sections on MIIV approaches using a covariance matrix and mean vector as data input, conducting multilevel SEM, analyzing categorical endogenous variables, causal inference, and extensions and applications. Online supplemental material illustrates input code for all examples and simulations using the R package MIIVsem. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

  DOI

Frequent coauthors

• Kathleen M. Gates

  University of North Carolina at Chapel Hill

  21 shared

• Kenneth A. Bollen

  University of North Carolina at Chapel Hill

  19 shared

• Vladas Pipiras

  University of North Carolina at Chapel Hill

  12 shared

• Peter C. M. Molenaar

  11 shared

• Sy‐Miin Chow

  Pennsylvania State University

  10 shared

• Charles F. Geier

  University of Georgia

  6 shared

• Jonathan Park

  6 shared

• Barbara L. Fredrickson

  University of North Carolina at Chapel Hill

  5 shared

Labs

• Social Data AnalyticsPI

Similar researchers at Pennsylvania State University

• Long-Qing Chenh-150
• Qin Wangh-123
• Richard Alleyh-120
• William H. Bruneh-99
• Ayusman Senh-96