I’m a current Stanford CS PhD student (and Symbolic Systems graduate) fascinated by how (and whether) algorithms can learn meaningful representations and reason, advised by Nick Haber and Noah Goodman. Perhaps the most
daunting exciting gap between biological and machine learning is the ease with which we learn to create and apply concepts about our world from experience: not only do we need much less experience to construct these concepts, but our understanding is flexible in novel situations, robust to slight changes, and generally disentangled.
Ultimately, I hope machine learning can teach us about non-machine learning and help us overcome the challenges facing humanity.
Also, I love baking bread, cooking, and exploring nature!
- Preprint 2022
- ICLR 2021International Conference on Learning Representations 2021
- ICML WorkshopICML Workshop on Uncertainty & Robustness in Deep Learning 2020
- NeurIPS WorkshopNeurIPS Workshop on Tackling Climate Change with Machine Learning 2020
- CVPR WorkshopShort Paper in CVPR Workshop on Adversarial Machine Learning in Computer Vision 2020
- Honors ThesisUndergraduate Honors Thesis 2020
|Deep Learning Engineer||Lazard||July 2020 – September 2021|
|GANs Curriculum Developer||DeepLearning.AI||June – October 2020|
|Machine Learning Intern||Argo AI||June – September 2019|
|Machine Learning Intern||Uncountable||June – September 2018
& April – June 2019
|Software Engineer||Philometrics||October 2016 – July 2017|
|Highlighted Reviewer (8%)||ICLR 2022||2022|
|Editor and 2019 Editor-in-Chief||Stanford Undergraduate Research Journal||2016 – 2020|
|Teacher||Stanford Splash||2016 - 2020|
|Section Leader||Stanford Code in Place||April - May 2020|
|TreeHacks, HopHacks, Cal Hacks, etc.||Hackathons||2016 - 2020|
|Cofounder - Machine Learning Lead||DeepDesigns.ai (defunct)||January - July 2020|