about.

Swee Kiat (SK) is currently co-founder and CEO of Pebblely, a company that applies generative AI to product and fashion photography. Pebblely was founded in January 2023 and has over 1 million users as of September 2023.

Prior to Pebblely, SK was working as a privacy engineer in the Google Payments team. His work in Google include kickstarting the Google Wallet project, reviewing privacy aspects of the Google Pay application, as well as helping with internal work related to differential privacy.

SK studied MSc. Computer Science at Stanford University, specializing in artificial intelligence. He is primarily interested in translating AI research to real-world solutions, having deployed AI applications in various early-stage startups. His research interests also include interpretability and AI governance, with previous experiences in algorithmic bias and differential privacy.

His previous projects include the Lucent library, backend engineering for Stanford's submission to the 2021 Alexa Prize, contributions to the Almond assistant at Stanford Open-source Virtual Assistant Lab (OVAL), improving the protein folding pipeline at the Protein Design Lab and an online primer to algorithmic bias - Machines Gone Wrong.

He also spent two years as an AI researcher in Singapore working on topics including privacy, meta-learning and adversarial attacks.

Contact him at sweekiat <at> cs <dot> stanford <dot> edu.


Latests Posts

Publications

Neural, Neural Everywhere: Controlled Generation Meets Scaffolded, Structured Dialogue
Ethan A Chi, Caleb Chiam, Trenton Chang, Swee Kiat Lim, Chetanya Rastogi, Alexander Iyabor, Yutong He, Hari Sowrirajan, Avanika Narayan, Jillian Tang, Haojun Li, Ashwin Paranjape, Christopher D Manning
Alexa Prize 2021 Proceedings

Visualizing Weights
Chelsea Voss, Nick Cammarata, Gabriel Goh, Michael Petrov, Ludwig Schubert, Ben Egan, Swee Kiat Lim, Chris Olah
Distill

Few-Shot Regression via Learned Basis Functions
Yi Loo, Swee Kiat Lim, Gemma Roig, Ngai-Man Cheung
International Conference on Learning Representations (ICLR) 2019
Learning from Limited Labeled Data Workshop

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, Yuval Elovici
IEEE International Conference on Data Mining (ICDM) 2018

MalwareTextDB: A Database for Annotated Malware Articles
Swee Kiat Lim, Aldrian Obaja Muis, Wei Lu, Chen Hui Ong
Annual Meeting of the Association for Computational Linguistics (ACL) 2017

Carbon‐Nanodot Solar Cells from Renewable Precursors
Adam Marinovic, Swee Kiat Lim, Steve Dunn, Maria‐Magdalena Titirici, Joe Briscoe
ChemSusChem 10.5 (2017): 1004-1013.