About
Ed Chien is an Assistant Professor in the Department of Computer Science at Boston University. He comes to BU from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), where he was a Postdoctoral Associate in the Geometric Data Processing group working with Professor Justin Solomon. His research applies tools and insights from differential geometry and topology to solve key problems in graphics, computational engineering, and machine learning. Before MIT, he was a postdoctoral researcher at Bar-Ilan University working with Ofir Weber. He earned his PhD in Mathematics from Rutgers University in 2015 and his A.B. in Mathematics & Physics from Dartmouth College in 2009. Dr. Chien has performed fundamental research characterizing the topology of hexahedral meshes used in Finite Element Method (FEM) modeling and has contributed to several novel machine learning applications of optimal transport. His work aims to further these important lines of research with mathematically rigorous results and computationally effective algorithms. He has served as an international program committee member for the Eurographics Symposium on Geometry Processing (SGP) in 2019, a Program Committee member for the AAAI conference in 2020, and as a reviewer for many top-tier publication venues. He has published in top venues including NeurIPS, Eurographics, SIGGRAPH, and SGP.
Research topics
- Computer Science
- Artificial Intelligence
- Mathematics
- Geometry
- Algorithm
- Computer graphics (images)
- Combinatorics
- Parallel computing
- Physics
- Theoretical computer science
- Acoustics
- Pure mathematics
- Mathematical analysis
- Geology
Selected publications
Helix-Free Stripes for Knit Graph Design
2023 · 11 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Combinatorics
The problem of placing evenly-spaced stripes on a triangular mesh mirrors that of having evenly-spaced course rows and wale columns in a knit graph for a given geometry. This work presents strategies for producing helix-free stripe patterns and traces them to produce helix-free knit graphs suitable for machine knitting. We optimize directly for the discrete differential (1-form) of the stripe texture function, i.e., the spinning form, and demonstrate the knitting-specific advantages of this framework. In particular, we note how simple linear constraints allow us to place stitch irregularities, align course rows and wale columns to boundary/feature curves, and eliminate helical stripes. Two mixed-integer optimization strategies using these constraints are presented and applied to several mesh models. The results are smooth, globally-informed, helix-free stripe patterns that we trace to produce machine-knittable graphs. We further provide an explicit characterization of helical stripes and a theoretical analysis of their elimination constraints.
Reducing the Carbon Impact of Generative AI Inference (today and in 2035)
2023 · 119 citations
- Computer Science
- Computer Science
- Artificial Intelligence
Generative AI, exemplified in ChatGPT, Dall-E 2, and Stable Diffusion, are exciting new applications consuming growing quantities of computing. We study the compute, energy, and carbon impacts of generative AI inference. Using ChatGPT as an exemplar, we create a workload model and compare request direction approaches (Local, Balance, CarbonMin), assessing their power use and carbon impacts.
Nature Cell Biology · 2022 · 97 citations
- Biology
- Genetics
- Cell biology
Keypoint-driven line drawing vectorization via PolyVector flow
ACM Transactions on Graphics · 2021 · 24 citations
- Computer Science
- Artificial Intelligence
- Computer Science
Line drawing vectorization is a daily task in graphic design, computer animation, and engineering, necessary to convert raster images to a set of curves for editing and geometry processing. Despite recent progress in the area, automatic vectorization tools often produce spurious branches or incorrect connectivity around curve junctions; or smooth out sharp corners. These issues detract from the use of such vectorization tools, both from an aesthetic viewpoint and for feasibility of downstream applications (e.g., automatic coloring or inbetweening). We address these problems by introducing a novel line drawing vectorization algorithm that splits the task into three components: (1) finding keypoints, i.e., curve endpoints, junctions, and sharp corners; (2) extracting drawing topology, i.e., finding connections between keypoints; and (3) computing the geometry of those connections. We compute the optimal geometry of the connecting curves via a novel geometric flow --- PolyVector Flow --- that aligns the curves to the drawing, disambiguating directions around Y-, X-, and T-junctions. We show that our system robustly infers both the geometry and topology of detailed complex drawings. We validate our system both quantitatively and qualitatively, demonstrating that our method visually outperforms previous work.
Angewandte Chemie International Edition · 2021 · 51 citations
- Chemistry
- Cell biology
- Biology
Guanosine tetra- and pentaphosphate, (p)ppGpp, are important alarmone nucleotides that regulate bacterial survival in stressful environment. A direct detection of (p)ppGpp in living cells is critical for our understanding of the mechanism of bacterial stringent response. However, it is still challenging to image cellular (p)ppGpp. Here, we report RNA-based fluorescent sensors for the live-cell imaging of (p)ppGpp. Our sensors are engineered by conjugating a recently identified (p)ppGpp-specific riboswitch with a fluorogenic RNA aptamer, Broccoli. These sensors can be genetically encoded and enable direct monitoring of cellular (p)ppGpp accumulation. Unprecedented information on cell-to-cell variation and cellular dynamics of (p)ppGpp levels is now obtained under different nutritional conditions. These RNA-based sensors can be broadly adapted to study bacterial stringent response.
The Lon Protease Links Nucleotide Metabolism with Proteotoxic Stress
Molecular Cell · 2020 · 31 citations
- Biology
- Biochemistry
- Cell biology
Octahedral Frames for Feature-Aligned Cross Fields
ACM Transactions on Graphics · 2020 · 22 citations
- Computer Science
- Artificial Intelligence
- Computer Science
We present a method for designing smooth cross fields on surfaces that automatically align to sharp features of an underlying geometry. Our approach introduces a novel class of energies based on a representation of cross fields in the spherical harmonic basis. We provide theoretical analysis of these energies in the smooth setting, showing that they penalize deviations from surface creases while otherwise promoting intrinsically smooth fields. We demonstrate the applicability of our method to quad meshing and include an extensive benchmark comparing our fields to other automatic approaches for generating feature-aligned cross fields on triangle meshes.
Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers
Joule · 2020 · 102 citations
- Computer Science
- Environmental science
- Engineering
Frequent coauthors
- 14 shared
Justin Solomon
- 9 shared
Sebastian Claici
- 6 shared
Mikhail Yurochkin
- 5 shared
Ofir Weber
Bar-Ilan University
- 4 shared
Farzaneh Mirzazadeh
- 3 shared
Kristjan Greenewald
- 3 shared
Paul Zhang
Walt Disney (United States)
- 3 shared
David Bommes
University of Bern
Labs
Education
- 2015
Ph.D.
Rutgers University
- 2009
B.A., Mathematics & Physics
Dartmouth College
Awards & honors
- Eurographics Symposium on Geometry Processing (2019)
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