I am a systems builder and hacker with a focus on systems design and distributed systems.
My main project is on ML for caching. I spent my initial years at CMU building machine learning systems. In Summer ‘19, I interned at Google, working on scheduling for model parallelism in TensorFlow. Before my PhD, I worked on everything from graph clustering for bioinformatics to systems security.
Spring ‘21: Besides my active work in ML for Systems, I'm always interested in problems within distributed systems and Systems for ML. I remain interested in possible intersections with neuroscience and security. Reach out if you have problems, insights, or data to share!
Mar ‘21: It's PhD admit season again! Feel free to email me if you're an admit and want to chat about working with Greg (and more general CMU stuff too!)
Résumé (Feb ‘20) | Publications
Daniel Wong, Carson Molder, Daniel Berger, Nathan Beckmann, Greg Ganger
(and Facebook collaborators including Qing Zhao, Jimmy Lu, Jim Cipar, and Sathya Gunasekar)
I'm keen to explore interpretable machine learning methods that find correlations in time series and graphs, with an especial interest in visualizations, interpretability and causality. This applies for both systems and neuroscience datasets.
Thomas Kim, Daniel Lin-Kit Wong, Gregory R. Ganger, Michael Kaminsky, David G. Andersen. ACM SoCC 2020 [article].
Daniel Wong, Peter Ma^, Sudip Roy*, Yanqi Zhou*
^Google Platforms Performance, *Google Brain (ML for Systems)
Angela H. Jiang, Daniel L.-K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, Padmanabhan Pillai [preprint]
Angela Jiang, Daniel Lin-Kit Wong, Christopher Canel, Ishan Misra, Michael Kaminsky, Michael A. Kozuch, Padmanabhan Pillai, David G. Andersen, Gregory R. Ganger. USENIX ATC 2018.
Part of the Intel Science and Technology Center for Visual Cloud Systems (ISTC-VCS).
How can we balance initiating recovery quickly and overreacting to transient failures?
3-way cross-region replication is expensive and slow. It helps mitigate rare risks like a hurricane taking out a data center, but why pay that price for common events like equipment failures? Can we detect and predict correlated failures?Outcome: I performed simulations based on theoretical modelling and presented a poster on transient failures at PDL Retreat 2019. Although there was strong interest from industry who were also grappling with this problem, this project was put on hold because of a lack of real world data to model the failures. I would be keen to revisit this project. Hit me up if you are able to offer any datasets!
I'm a tinkerer at heart, and am always on the lookout for novel challenges to work on. In seeking opportunities, I aim to optimise for learning and to do meaningful, impactful work. I bask in the energy of synergistic collaborations, and the opportunity they give me to wade into new domains and learn from cool people.
I'm a software engineer and have a relentless urge to automate and optimize all parts of my work process.
I enjoy musicals, singing and karaoke (car karaoke setup), cooking, Singaporean food, skiing & snowboarding, gliding, long scenic drives (and walks), waterfalls, baking, rock climbing, ice skating, computer games, scuba diving, and last but not least, good nigiri. I did my undergraduate studies at the University of Cambridge and am a member of Churchill College. I grew up in Singapore, am a 华中子弟, and am a proud alumnus of my high school computer club EC3 (where I learnt to code and hack stuff together.)