Milin Kodnongbua

I am a Computer Science Ph.D. student at the University of Washington, advised by Adriana Schulz in the Computational Design Group and the Graphics and Imaging Laboratory (GRAIL). I expect to graduate in June 2027.

I am seeking full-time research and engineering opportunities for 2027. I am based in Providence, RI, and open to working anywhere in the United States. Please email me if my background is a fit.

Download my CV (PDF).

My research combines computational design, machine learning, and optimization to generate and edit structured 3D content. I work on CAD representations, shape programs, and neuro-symbolic methods that connect design intent to editable digital models and physical artifacts. Recent projects include text-driven physical image composition in Diceplay, gradient-based shape-program optimization in Design for Descent, and zero-shot CAD editing in ReparamCAD.

During my Adobe research internship (2026), I developed a neuro-symbolic method for resizing Adobe Express templates while preserving their content, reading order, and style. At Microsoft (2023), I formulated dense server layouts for immersion cooling as a mixed-integer linear program and paired it with a genetic algorithm for scalability. The published study reports server-volume reductions of 25–52% compared with traditional flat designs.

I have competed in programming contests since 2017. My honors include an IOI bronze medal (2018), 10th place at the ICPC North America Championship (2022), and an honorable mention at the ICPC World Finals (2022). I also coached UW’s ICPC teams from 2023 to 2025. More about competitive programming.

Outside research, I enjoy photography, music, language learning, badminton, graphic design, and content creation. I am learning Korean and share piano and guitar performances on YouTube.

Selected Publications

Diceplay: A Modular Canvas for Physical Image Composition

Milin Kodnongbua, Zihan Zhang, Shishi Xiao, Vivian Li, Heather Robertson, and 3 more authors

In SIGGRAPH 2026 Conference Papers 2026

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We present Diceplay, a modular physical display for abstract visual composition built from a grid of identical dice. Each die has six faces with distinct geometric primitives, and images emerge through the placement and orientation of the dice. While this medium enables reusable and reconfigurable physical imagery, it poses a challenging design problem: images must be expressed through discrete, extremely low-resolution abstractions, making manual authoring difficult. To address this challenge, we introduce a computational design system that automatically generates Diceplay configurations from text prompts. Our key technical contribution is a grammar-based formulation that relaxes this discrete design space into a smooth optimization landscape, enabling gradient-based optimization using score distillation sampling. We show that our approach consistently produces meaningful abstractions for this medium, whereas state-of-the-art smoothing techniques fail in this extremely challenging regime. We demonstrate our method across a range of prompts and fabricated examples, showing how computationally generated abstractions can be realized as physical visual artifacts.
Design for Descent: What Makes a Shape Grammar Easy to Optimize?

Milin Kodnongbua, Zihan Zhang, Nicholas Sharp, and Adriana Schulz

In SIGGRAPH Asia 2025 Conference Papers 2025

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Shape grammars offer a powerful framework for computational design, but synthesizing shape programs to achieve specific goals remains challenging. Inspired by the success of gradient-based optimization in high-dimensional, nonconvex spaces such as those in machine learning, we ask: what makes a shape grammar amenable to gradient-based optimization? To explore this, we introduce Stochastic Rewrite Descent (SRD), an algorithm that interleaves structural rewrites with continuous parameter updates, taking steps in both to optimize a given objective. We analyze the core challenges which have previously prevented optimizing shape programs via descent, and identify a set of desirable properties for grammars that support effective optimization, along with concrete grammar design recommendations to achieve them. We validate this approach across three shape grammars, demonstrating its effectiveness in diverse domains including image fitting, text-driven generation, and topology optimization. Through ablations and comparisons, we show that grammars satisfying our proposed properties lead to significantly better optimization performance. The goal of this work is to open the door to more general and flexible computational paradigms for inverse design with shape grammars.
Dense Server Design for Immersion Cooling

Milin Kodnongbua, Zachary Englhardt, Ricardo Bianchini, Rodrigo Fonseca, Alvin Lebeck, and 4 more authors

ACM Trans. Graph. Nov 2024

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The growing demands for computational power in cloud computing have led to a significant increase in the deployment of high-performance servers. The growing power consumption of servers and the heat they produce is on track to outpace the capacity of conventional air cooling systems, necessitating more efficient cooling solutions such as liquid immersion cooling. The superior heat exchange capabilities of immersion cooling both eliminates the need for bulky heat sinks, fans, and air flow channels while also unlocking the potential go beyond conventional 2D blade servers to three-dimensional designs. In this work, we present a computational framework to explore designs of servers in three-dimensional space, specifically targeting the maximization of server density within immersion cooling tanks. Our tool is designed to handle a variety of physical and electrical server design constraints. We demonstrate our optimized designs can reduce server volume by 25–52% compared to traditional flat server designs. This increased density reduces land usage as well as the amount of liquid used for immersion, with significant reduction in the carbon emissions embodied in datacenter buildings. We further create physical prototypes to simulate dense server designs and perform real-world experiments in an immersion cooling tank demonstrating they operate at safe temperatures. This approach marks a critical step forward in sustainable and efficient datacenter management.
ReparamCAD: Zero-shot CAD Re-Parameterization for Interactive Manipulation

Milin Kodnongbua, Benjamin Jones, Maaz Bin Safeer Ahmad, Vladimir Kim, and Adriana Schulz

In SIGGRAPH Asia 2023 Conference Papers Nov 2023

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Parametric CAD models encode entire families of shapes that should, in principle, be easy for designers to explore. However, in practice, parametric CAD models can be difficult to manipulate due to implicit semantic constraints among parameter values. Finding and enforcing these semantic constraints solely from geometry or programmatic shape representations is not possible because these constraints ultimately reflect design intent. They are informed by the designer’s experience and semantics in the real world. To address this challenge, we introduce ReparamCAD, a zero-shot pipeline that leverages pre-trained large language and image model to infer meaningful space of variations for a shape We then re-parameterize a new constrained parametric CAD program that captures these variations, enabling effortless exploration of the design space along meaningful design axes. We evaluated our approach through five examples and a user study. The result showed that the inferred spaces are meaningful and comparable to those defined by experts. Code and data are at: https://github.com/milmillin/ReparamCAD.
B-Rep Matching for Collaborating Across CAD Systems

Benjamin Jones, James Noeckel, Milin Kodnongbua, Ilya Baran, and Adriana Schulz

ACM Trans. Graph. (SIGGRAPH) Jul 2023

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Self-Supervised Representation Learning for CAD

Benjamin T. Jones, Michael Hu, Milin Kodnongbua, Vladimir G. Kim, and Adriana Schulz

IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) Jul 2023

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Computational Design of Passive Grippers

Milin Kodnongbua, Ian Good, Yu Lou, Jeffrey Lipton, and Adriana Schulz

ACM Trans. Graph. (SIGGRAPH). Jul 2022

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