activity
20182026
most citedFunctional Generative Design: An Evolutionary Approach to 3D-Printing

10 citations · 40 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CL2026

LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding

Haocheng Xia, Mihir Pamnani, Hanxi Fang +2

Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes even greater in long-context…

cs.DB2025

Chipmink: Efficient Delta Identification for Massive Object Graph

Supawit Chockchowwat, Sumay Thakurdesai, Zhaoheng Li +2

Ranging from batch scripts to computational notebooks, modern data science tools rely on massive and evolving object graphs that represent structured data, models, plots, and more.…

cs.HC2025★ 3 cited

Large-scale Evaluation of Notebook Checkpointing with AI Agents

Hanxi Fang, Supawit Chockchowwat, Hari Sundaram +1

Saving, or checkpointing, intermediate results during interactive data exploration can potentially boost user productivity. However, existing studies on this topic are limited, as…

cs.HC2025★ 6 cited

Enhancing Computational Notebooks with Code+Data Space Versioning

Hanxi Fang, Supawit Chockchowwat, Hari Sundaram +1

There is a gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or…

cs.DB2024★ 6 cited

Kishu: Time-Traveling for Computational Notebooks

Zhaoheng Li, Supawit Chockchowwat, Ribhav Sahu +2

Computational notebooks (e.g., Jupyter, Google Colab) are widely used by data scientists. A key feature of notebooks is the interactive computing model of iteratively executing cel…

cs.DB2023★ 8 cited

AirIndex: Versatile Index Tuning Through Data and Storage

Supawit Chockchowwat, Wenjie Liu, Yongjoo Park

The end-to-end lookup latency of a hierarchical index -- such as a B-tree or a learned index -- is determined by its structure such as the number of layers, the kinds of branching…

cs.DB2023★ 4 cited

Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility

Supawit Chockchowwat, Zhaoheng Li, Yongjoo Park

In machine learning (ML), Python serves as a convenient abstraction for working with key libraries such as PyTorch, scikit-learn, and others. Unlike DBMS, however, Python applicati…

cs.DB2023★ 1 cited

A Step Toward Deep Online Aggregation (Extended Version)

Nikhil Sheoran, Supawit Chockchowwat, Arav Chheda +3

For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designi…

cs.DB2022★ 2 cited

Automatically Finding Optimal Index Structure

Supawit Chockchowwat, Wenjie Liu, Yongjoo Park

Existing learned indexes (e.g., RMI, ALEX, PGM) optimize the internal regressor of each node, not the overall structure such as index height, the size of each layer, etc. In this p…

cs.CV2022

Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose

Supawit Chockchowwat, Chandrajit L. Bajaj

A crucial step in single particle analysis (SPA) of cryogenic electron microscopy (Cryo-EM), 2D classification and alignment takes a collection of noisy particle images to infer or…

cs.DB2021

Airphant: Cloud-oriented Document Indexing

Supawit Chockchowwat, Chaitanya Sood, Yongjoo Park

Modern data warehouses can scale compute nodes independently of storage. These systems persist their data on cloud storage, which is always available and cost-efficient. Ad-hoc com…

cs.SI2021

Step Out of Your Comfort Zone: More Inclusive Content Recommendation for Networked Systems

Jiaxin Wu, Supawit Chockchowwat

Networked systems are widely applicable in real-world scenarios such as social networks, infrastructure networks, and biological networks. Among those applications, we are interest…

cs.LG2019

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning

Caleb Chuck, Supawit Chockchowwat, Scott Niekum

Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation.…

cs.NE2018★ 10 cited

Functional Generative Design: An Evolutionary Approach to 3D-Printing

Cem C. Tutum, Supawit Chockchowwat, Etienne Vouga +1

Consumer-grade printers are widely available, but their ability to print complex objects is limited. Therefore, new designs need to be discovered that serve the same function, but…