Carrie Yuan

I am a graduate student researcher at University of Washington, applying for PhD in Computer Science in Fall 2024. I am fortunate to be supervised by Abhishek Gupta and Natasha Jaques. Before that I was a Software Development Engineer II at Amazon AGI Data Prep team, focusing on developing data preparation tools and infrastructure used for large language model (LLM) training. I graduated from Carnegie Mellon University with a B.S. in Neuroscience and Computer Science (Fun fact: I was the only person graduated with a second major in CS in the Neuroscience department).

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• [Jan 2025]

  InvestESG was accepted by ICLR 2025!

• [Dec 2024]

  I graduated as Master of Science in Computational Linguistics from University of Washington.

• [Dec 2024]

  I presented InvestESG at NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning.

• [Oct 2024]

  Preliminary result of InvestESG was accepted by NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning.

• [Oct 2024]

  Submitted my first first-authored paper, InvestESG, to ICLR 2025.

• [June 2024]

  My first paper, CASHER, was accepted by RSS 2024 Data Generation for Robotics (DGR) Workshop!

• [Sep 2022]

  Starting part-time master program at University of Washington.

• [July 2021]

  Relocating to Seattle and starting to work for Amazon.

• [May 2021]

  Graduated with a B.S in Neuroscience and Computer Science from Carnegie Mellon Univeristy (2 majors in 3 years). Go Tartan!

• [August 2020]

  Attending Computational Neuroscience summer course at NeuroMatch.

• [August 2019]

  Received Summer Undergraduate Research Fellowships (SURF) to conducting neuroscience research under the supervision of Dr. Alison Barth.

Research

I am broadly interested in reinforcement learning, multiagent learning, and robotics. .

We introduced, InvestESG, a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. The benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits from reducing climate risk, while ESG-conscious investors attempt to influence corporate behavior through their investment decisions.

Keywords: multi-agent reinforcement learning, climate change, ai for climate

We propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real (CASHER), a pipeline for scaling up data collection and learning generalist policies where human effort scales sublinearly with the number of environments where data is collected. The key idea is to crowdsource digital twins of real-world scenes using 3D reconstruction techniques and collect large-scale data in these simulation scenes, rather than in the real-world. Data collection in simulation is initially driven by reinforcement learning bootstrapped with human demonstrations.

Keywords: Deep Learning Methods; Reinforcement Learning; Deep Learning in Grasping and Manipulation

Course Projects

A system for emotion and personality detection in open-domain dialogue FETA-Friends dataset.

Keywords: Natural language processing, sentiment analysis, personality detection.

We use shallow and deep language processing techniques to preprocess the texts and apply some popular summarization methods such as Log-Likelihood Ratio (LLR), SumBasic, and LexRank in our multi-text summarization system. We evaluate our results with popular metrics such as ROUGE score, as well as human evaluation.

Keywords: Natural language processing, summarization.

A molecule visualiser allowing the user to generate a 3-D view of an organic molecule and to play a game to improve the command of knowledge in organic chemistry.

Keywords: 3D Modeling, Computational Chemistry

A MATLAB-implemented Texas Hold'em Player AI. Course project for CMU 85213 Human Information Processing and Artificial Intelligence. Beated all the player implementation in the class.

Keywords: Probability, Game theory

Awards

Amazon AGI DataPrep Team, Peer Recognition Awards, 2023

Amazon AGI DataPrep Team, Peer Recognition Awards, 2022

Carnegie Mellon University, University Honor (summa cum laude), 2021

Summer Undergraduate Research Fellowships (SURF), 2019

Services

AMLC Reviewer, 2024

WEIRDLab Tutorial Organizer, 2024

Research Philosophy

I like to model everything. Some might call a reductionist. But I cannot resist the appeal of describing the mundane everyday complex phenomenons, such as weather, grocery price, or interpersonal relations, in terms of elegant mathematical symbols and functions with certain input space and output space. Studying computer science and mathematics allows me to do that in a more rigorous fashion. It teaches me a language to describe a problem space so that my ideas can be communicated universally. Studying neuroscience allows me to see both the complexiticity and simplicity of the world I live in. We can describe our minds as simple as an ensemble of electrical pulses, but the complexity lies in that our coordination, our neuroplasticity and biological efficiency (compared to its power-hungry machine counterpart).

I believe that real science does not have boundary of subjects. There are techniques, or tools, (I like to imagine things in a visual sense) that might be potentially helpful to solve a problem, and these tools might be labeled with academic subjects such as mathematics, economics, cognitive science, psychology, biology and physics. We should be open to learn to any tools that would be useful to our goal.