Neukom Prize for Outstanding Graduate Research
1St. Prize: Simulating and Learning the Physical systems: Clebsch Gauge Fluids
Shuqi Yang (Computer Science), Advisor: Bo Zhu
Capturing and evolving the visually appealing vorticities is challenging for computer graphics and computational physics. Our research shows that such vivid and intricate vorticity evolution can be generated and preserved robustly in a numerical setting by solving a gauge transformation of Navier-Stokes equations. We devise our computational tool based on Clebsch wave functions, a critical mathematical expression widely used in quantum mechanics. Our method combines the expressive power of Clebsch wave functions to represent coherent vortical structures and the generality of gauge methods to accommodate different types of fluid. We showcased the efficacy of our computational approach by simulating a broad array of fluid phenomena, including leapfrogging, turbulent smoke, and surface tension flow.
Project page: https://y-sq.github.io/proj/clebsch_gauge_fluid/
Shuqi Yang, Shiying Xiong, Yaorui Zhang, Fan Feng, Jinyuan Liu, and Bo Zhu. Clebsch Gauge Fluid. ACM Transactions on Graphics (SIGGRAPH 2021), 40(4):99.
2nd. Prize: Between- subject Prediction Reveals a Shared Representational Geometry in the Rodent Hippocampus
Hung-tu Chen (Psychological & Brain Sciences), Advisor: Matthijs van der Meer
The hippocampus is a brain structure known for storing a map of episodic nature for each environment. In particular, neurons in the hippocampus selectively fire in their preferred locations (place fields) when an animal runs through the environment. However, where hippocampal place cells have their fields is famously hard to predict: if you know how a given subject encodes location of environment A, that doesn't tell you much about how it encodes B.
We adapted a technique from human neuroimaging work (hyperalignment, inspired by Haxby Lab) enabling us to use how subject 1 encodes A and B (e.g. left and right arms of a maze), and how subject 2 encodes A, to predict how subject 2 encodes B.
Surprisingly, this cross prediction worked better than the within-subject controls we tried, and simulations suggest simple explanations such as correlated firing rates between A and B can be ruled out. Thus, we think between-subject prediction is a novel analysis approach that suggests an underlying regularity in how different places are mapped in the rodent hippocampus.
Chen, H. T., Manning, J. R., & van der Meer, M. A. (2020). Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. bioRxiv
2nd. Prize: Mitigating Political Bias in Language Models through Reinforced Calibration
Rubio Liu (Computer Science), Advisor: Soroush Vosoughi
As more potentially-biased language models are adopted in AI applications, it is a growing concern that the political bias will be amplified if fairness is not taken into considering. In this work, we investigate the popular language model, GPT-2, to demonstrate what political bias is and how to mitigate such bias in generative language models. We describe metrics for measuring the bias and propose a reinforcement learning (RL) framework for mitigating political bias in the generated text. Requiring neither collecting extra data nor retraining the model from scratch, our method is especially meaningful in realistic settings. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.
3rd Prize: Two-Step Training Deep Learning Framework for Computational Imaging without Physics Prior
Rubio Shang (Thayer School of Engineering), Advisor: Geoffrey P. Luke
Computational imaging is an emerging field where the hardware requirements of an imaging system are relaxed by incorporating algorithmic reconstruction of the image. A critical challenge in computational imaging is that imperfections in the idealized mathematical model of the imaging system degrade reconstruction performance. Furthermore, many imaging inverse problems are ill-posed, which leads to suboptimal image reconstruction. We developed a two-step training deep learning framework to solve these challenges. It directly learns the inverse model from the data in its first step of training and applies regularization constraints in its second. Overall, it is a flexible framework for diverse imaging systems.
Ruibo Shang, Kevin Hoffer-Hawlik, Fei Wang, Guohai Situ, and Geoffrey P. Luke, "Two-step training deep learning framework for computational imaging without physics priors," Opt. Express 29, 15239-15254 (2021)