Joshua Loftus

Assistant Professor

Department of Statistics, London School of Economics

| +1 (203) 927-6196 | joshualoftus.com | |

About

I am a statistician working to improve practices in data science to reduce the impact of bias, particularly biases associated with social harms and scientific reproducibility. I research algorithmic fairness, high-dimensional inference, and interpretable machine learning, and I teach ethical and responsible data science, machine learning, and causal inference.

Current Positions

Jan. 2021- London School of Economics Assistant Professor of Statistics (London, UK)

Jan. 2021- LSE Data Science Institute Affiliate Faculty (London, UK)

Education

2016 Stanford University PhD in Statistics (Stanford, CA)

  • Dissertation: Post-selection inference for models characterized by quadratic constraints
  • Committee: Jonathan Taylor (advisor), Emmanuel Candes, Joseph Romano, Robert Tibshirani

2011 Rutgers University M.S. in Mathematics (New Brunswick, NJ)

  • Thesis: Logarithmic norms and applications to differential equations
  • Supervisor: Eduardo Sontag

2009 Western Michigan University B.S. in Mathematics (Kalamazoo, MI)

  • Summa cum laude
  • Elected to Phi Beta Kappa

2007 Kalamazoo Valley Community College A.A. in General Studies (Kalamazoo, MI)

Experience

2017-2020 New York University Assistant Professor of Statistics (New York, NY)

2019-2020 NYU Center for Data Science Affiliate Faculty (New York, NY)

2016- Statistical Consultant Freelance (Various)

2016-2017 University of Cambridge Research Fellow (Cambridge, UK)

2016-2017 Alan Turing Institute Research Fellow (London, UK)

2014-2015 Stanford University Statistical Consulting Lab Manager (Stanford, CA)

Selected Publications

Google Scholar citation count: 2098

Journals

2018 Selective inference with unknown variance via the square‐root lasso. Tian, X., Loftus, J. R., Taylor, J. E
Biometrika. (arXiv, link)

2016 Inference in adaptive regression via the Kac–Rice formula. Taylor, J. E., Loftus, J. R., Tibshirani, R. J.
Annals of Statistics. (arXiv, link)

Conferences

2023 Counterfactuals for the future. Bynum, L. E., Loftus, J. R., Stoyanovich, J.
Proceedings of the AAAI Conference on Artificial Intelligence. (arXiv, link)

2021 Causal Intersectionality and Fair Ranking. Yang, K., Loftus, J. R., Stoyanovich, J.
2nd Symposium on Foundations of Responsible Computing. (arXiv, link)

2021 Disaggregated interventions to reduce inequality. Bynum, L., Loftus, J. R., Stoyanovich, J.
Equity and Access in Algorithms, Mechanisms, and Optimization. (arXiv, link)

2019 Making decisions that reduce discriminatory impacts. Kusner, M., Russell, C., Loftus, J. R., Silva, R.
International Conference on Machine Learning. (arXiv, link)

2017 When worlds collide: Integrating different counterfactual assumptions in fairness. Russell et al. 
Advances in Neural Information Processing Systems. (arXiv, link)

2017 Counterfactual fairness. Kusner, M. J., Loftus, J. R., Russell, C., Silva, R.
Advances in Neural Information Processing Systems. (arXiv, link)

Software

2022 unbiasedgoodness: Goodness-of-Fit Tests After Model Selection. Loftus, J. R.
R Package. (arXiv, link)

2015 selectiveInference: Tools for selective inference. Tibshirani et al. 
CRAN R Package. (arXiv, link)

Preprints, Misc.

2023 Causal Dependence Plots for Interpretable Machine Learning. Loftus, J. R. et al. 
Preprint. (arXiv, link)

2022 An Interactive Introduction to Causal Inference. Bynum et al. 
IEEE VISxAI: Workshop on Visualization for AI Explainability. (arXiv, link)

2020 The long road to fairer algorithms. Kusner, M. J., Loftus, J. R..
Nature Comment. (arXiv, link)

2018 Causal reasoning for algorithmic fairness. Loftus, J. R., Russell, C., Kusner, M. J., Silva, R.
Preprint. (arXiv, link)

2015 Selective inference after cross‐validation. Loftus, J. R.
Preprint. (arXiv, link)

Awards and Honors

2015 NIH Grant Trainee in Biostatistics for Personalized Medicine (Stanford University)

2015 Alan M. Abrams Memorial Fellowship (Stanford University)

2014 Statistics Department Teaching Award (Stanford University)

2013 Best Potential Prize (Stanford-Columbia DataFest)

2012 NSF VIGRE Fellowship (Stanford University)

2010 GAANN Fellowship (Rutgers University)

Selected Talks

2022 IMS International Conference on Statistics and Data Science (Florence, IT)

2022 Panelist at NeurIPS Workshop on Algorithmic Fairness (New Orleans, LA)

2022 Keynote at International Meeting of Psychometric Society (Bologna, IT)

2021 Bernoulli-IMS 10th World Congress in Probability and Statistics (Seoul (virtual))

2021 NYU Workshop on Race and Racism in Science (New York (virtual))

2021 Conference on Machine Learning and Economic Inequality (Oxford (virtual))

2019 International Indian Statistical Association Conference (Mumbai, India)

2019 DataEngConf (New York, NY)

2019 INFORMS Annual Meeting. Session on Fairness in Machine Learning (Seattle, WA)

2019 Joint Statistical Meetings (Denver, CO)

2019 Econometrics and Statistics Conference (Taichung, Taiwan)

2019 International Conference on Machine Learning (Long Beach, CA)

2019 International Chinese Statistical Association Conference (Raleigh, NC)

2018 Data for Good Seminar (Columbia University)

2018 Workshop on Higher-Order Asymptotics and Post-Selection Inference (WUSTL)

2018 Conference on Statistical Learning and Data Science (Columbia University)

2015 IMS session on Post-Selection Inference. Joint Statistical Meetings (Seattle, WA)

2014 Joint Statistical Meetings (Boston, MA)

Research Experience

2021- London School of Economics Assistant Professor of Statistics (London, UK)

  • Data science research group
  • Supervising PhD students and serving on dissertation committees

2017-2020 New York University Assistant Professor of Statistics (New York, NY)

  • Organizing journal club
  • Supervising PhD students and serving on dissertation committees

2016-2017 University of Cambridge / Alan Turing Institute Research Fellow (Cambridge / London, UK)

  • Started research on counterfactual fairness
  • Co-supervised summer internship project on privacy

2012-2016 Stanford University Research Assistant (Stanford, CA)

  • Started research on post-selection inference
  • Participated in Tibshirani, Hastie, Taylor research group
  • Organized statistical consulting lab

2013-2015 Stanford University Biostatistics Trainee (Stanford, CA)

  • Funded by NIH training grant
  • Collaborated with Howard Chang lab on ATAC-seq data

2014 Stanford University Data Lab Team Member (Stanford, CA)

  • Poverty alleviation project using survey data for small-area poverty estimation

2013 Stanford University Bi-coastal DataFest Team Leader (Stanford, CA)

  • Led prize winning team with project analyzing election campaign finance data

2013 Google, Inc Decision Support Analyst Intern (Mountain View, CA)

  • Conducted literature review and experiments on crowd-sourcing

Teaching Experience

2021- London School of Economics Instructor: Machine learning (undergraduate) (London, UK)

  • Designed course based on An Introduction to Statistical Learning
  • Taught course in Winter 2021, Fall 2021, Fall 2022

2021- London School of Economics Co-instructor: Foundations of Machine learning (graduate) (LSE)

  • Lectured on support vector machines, high-dimensional regression, neural networks
  • Co-taught in Winter terms 2021, 2022, 2023

2022- London School of Economics Instructor: Ethics for data science (undergraduate) (LSE)

  • Designed course using professional guidelines and research experience
  • Taught course in Fall 2022

2021- London School of Economics Supervisor: Capstone project (graduate) (LSE)

  • Supervising projects for MSc students in Data Science

2018-2020 New York University Instructor: Regression and forecasting (undergraduate) (New York, NY)

  • Redesigned course to use R instead of Minitab
  • Taught course twice per year

2020 New York University Instructor: Modern statistics and causal inference for data science (graduate) (NYU)

  • Designed course based on Computer Age Statistical Inference and Statistical Learning with Sparsity

2014-2015 Stanford University Workshop Instructor (graduate) (Stanford, CA)

  • Organized statistical consulting lab, taught applied statistics for consulting
  • Taught qualifying exams workshop

2012-2016 Stanford University Teaching Assistant (Stanford)

  • PhD: Modern Applied Statistical Learning, Theory of Statistics, Theory of Probability
  • Masters: Introduction to Statistical Inference, Data Mining and Analysis
  • Undergraduate: Theory of Probability

2010-2011 Rutgers University Teaching Assistant (New Brunswick, NJ)

  • Undergraduate: Multivariate Calculus, Single Variable Calculus

2008-2009 Western Michigan University Teaching Assistant (Kalamazoo, MI)

  • Undergraduate: Mathematics for Liberal Arts

2007-2008 Western Michigan University Mathematics Tutor (WMU)

  • General mathematics tutoring, mostly for calculus

Professional Service

2023 Area Chair (ACM Conference on Fairness, Accountability, and Transparency)

2022 Reviewer (Philosophy and Technology)

2021- Reviewer (Annals of Statistics)

2021- Reviewer (Journal of Machine Learning Research)

2020- Reviewer (Proceedings of the National Academy of Sciences)

2020- Reviewer (International Conference on Learning Representations)

2020- Reviewer (Journal of the Royal Statistical Society, Series B)

2019- Reviewer (Harvard Data Science Review)

2019- Reviewer (Biometrics)

2019- Reviewer (International Conference on Machine Learning)

2019- Reviewer (Annals of Applied Statistics)

2019- Program Committee (ACM Conference on Fairness, Accountability, and Transparency)

2018 Reviewer (Applied and Computational Harmonic Analysis)

2017- Reviewer (Neural Information Processing Systems)

2016 Reviewer (Bernoulli)

2015- Reviewer (Biometrika)

University Service

2021- Statistics Seminar organizer (London School of Economics)

2021- Faculty Search Committee member (London School of Economics)

2018-2020 Statistics Seminar co-organizer (New York University)

2018-2020 Data Science Reading Group organizer (New York University)

2017-2018 Faculty Search Committee member (New York University)

Students

PhD Co-advisor

2022- Sakina Hansen (London School of Economics)

2020- Lucius Bynum (New York University)

2018- Weichi Yao (New York University)

PhD Committee

2020- Vishwali Mhasawade (New York University)

2019- Margarita Boyarskaya (New York University)

MS Supervisor

2022-2023 J. Hoekstra, M. Solomon, C. Mosk, Z. Liu (London School of Economics)

2021-2022 E. Goel, A. Kansal, L. Wagner (London School of Economics)

Mentoring

2021- Statistics Programme Advisor (London School of Economics)

2021- General Course Mentor (London School of Economics)

2019-2020 First-Gen Students Mentor (New York University)