James Mcinerney Email & Phone Number

Los Gatos, Ca, Us

Methodological contributions to epistemic uncertainty quantification for large-scale models, variational inference for temporal point processes for inferring user satisfaction, machine learning for systems (ML4Sys). Generative modeling and simulation methods for personalization. Research advisory on bandits and causal machine learning for launching new content.Representative Publications:1. The Implicit Delta Method, NeurIPS 2022 https://neurips.cc/virtual/2022/poster/539152. Residual Overfit Method of Exploration https://arxiv.org/pdf/2110.02919.pdf3. Accordion: A Trainable Simulator for Long-Term Interactive Systems, RecSys 2021 http://www.cs.columbia.edu/~jebara/papers/recsys2021.pdf

Stockholm, Stockholm County, Se

Developed machine learning strategy for tech research (opportunities for machine learning in R&D) and engineering (homepage personalization).

Stockholm, Stockholm County, Se

Developed methodologies in counterfactual evaluation for offline evaluation and optimization.Publications:1. Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions, KDD 2020 https://arxiv.org/pdf/2007.12986.pdf2. Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms, WSDM 2019 https://dl.acm.org/doi/abs/10.1145/3289600.32910273. Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems, CIKM 2018 https://dl.acm.org/doi/abs/10.1145/3269206.3272027

Stockholm, Stockholm County, Se

Founding member of the homepage personalization team that established the first bandits approach in production at the company providing explainable recommendations with factorization machines.Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits, RecSys 2018 https://dl.acm.org/doi/abs/10.1145/3240323.3240354

New York, Ny, Us

Designed and taught the machine learning course at Masters level, leading a team of 7 TAs to teach 110 in-class students and 20 remote students.

New York, New York, Us

Research in methods for statistical machine learning, approximate Bayesian inference, and variational inference. Also explored applications to recommender systems, Raman spectroscopy, analyzing stroke patient mobility. Selected publications:1. The Population Posterior and Bayesian Modeling on Streams, NeurIPS 2015 https://proceedings.neurips.cc/paper/2015/file/5751ec3e9a4feab575962e78e006250d-Paper.pdf2. Variational Tempering, AISTATS 2016 http://proceedings.mlr.press/v51/mandt16.pdf3. Modeling Exposure in Recommendation, WWW 2016 https://dl.acm.org/doi/pdf/10.1145/2872427.28830904. An Empirical Bayes Approach to Optimizing Machine Learning Algorithms, NeurIPS 2017 https://proceedings.neurips.cc/paper/2017/file/b60c5ab647a27045b462934977ccad9a-Paper.pdf

Princeton, Nj, Us

Research in methods for statistical machine learning, approximate Bayesian inference, and variational inference.

Ba Computer Science

Msc Artificial Intelligence

Doctor Of Philosophy (Phd), Artificial Intelligence