I am a statistics PhD candidate at the University of California, Irvine advised by Dr. Padhraic Smyth and Dr. Stephan Mandt. My research largely focuses on (deep) probabilistic machine learning methods and probabilistically reasoning about sequential data. In particular, my primarly line of work is investigating efficient methods of evaluating various probablistic queries of black-box sequential models (e.g., large language models, temporal point processes, etc.) that extend beyond immediate next value prediction.

I have been fortunate to have the opportunity to research these topics and others due to receiving a NSF Graduate Research Fellowship in 2020. Prior to starting my PhD program, I received a Bachelor’s of Science degree in software engineering with a minor in data science at the California Polytechnic State University, San Luis Obispo under the advisement of Dr. Dennis Sun. I have also conducted machine learning research as an intern at Apple, Microsoft Research, and NVIDIA.


Inference for Mark-Censored Temporal Point Processes
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Conference on Uncertainty in Artificial Intelligence (UAI), 2023 [Spotlight Presentation]

Understanding Pathologies of Deep Heteroskedastic Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
Preprint (currently under review), 2023

Probabilistic Querying of Continuous-Time Event Sequences
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
International Conference on Artificial Intelligence and Statistics (AIStats), 2023

Predictive Querying for Autoregressive Neural Sequence Models
Alex Boyd*, Samuel Showalter*, Stephan Mandt, Padhraic Smyth
Neural Information Processing Systems (NeurIPS). 2022 [Oral Presentation]

Structured Stochastic Gradient MCMC
Antonios Alexos*, Alex Boyd*, Stephan Mandt
International Conference on Machine Learning (ICML), 2022

Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Neural Information Processing Systems (NeurIPS), 2021

Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions
Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, Padhraic Smyth
Machine Learning for Healthcare Conference (MLHC), 2021

Large Scale Multi-Actor Generative Dialog Modeling
Alex Boyd*, Raul Puri*, Mohammad Shoeybi, Mostofa Patwary, Bryan Catanzaro
Association for Computational Linguistics (ACL). 2020

User-Dependent Neural Sequence Models for Continuous-Time Event Data
Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
Neural Information Processing Systems (NeurIPS), 2020

(*) denotes shared first-authorship