Shane Storks profile image

Shane Storks, PhD

I’m a postdoctoral research fellow at the University of Michigan Weinberg Institute for Cognitive Science pursuing interdisciplinary research at the intersection of natural language processing and human cognitive psychology. From both perspectives, my research investigates natural language understanding, commonsense reasoning, and language grounding toward the broad goal of human-aligned, human-inspired assistive AI agents. I organize and volunteer for Queer in AI and Macomb Science Olympiad to promote diversity and opportunity in STEM.

Education
  • University seal.
    Doctor of Philosophy in Computer Science and Engineering

    2024, University of Michigan
    Dissertation: Coherent Physical Commonsense Reasoning in Foundational Language Models
    Advisor: Dr. Joyce Chai

  • University seal.
    Master of Science in Computer Science and Engineering

    2021, University of Michigan

  • University seal.
    Bachelor of Science in Mathematics and Computer Science

    2018, Lawrence Technological University

Research publications, preprints, and any associated presentation materials.

2024

    Publication preview image. Open preprint or publication for readable version.
    Eliciting In-Context Learning in Vision-Language Models for Videos Through Curated Data Distributional Properties
    Keunwoo Peter Yu, Zheyuan Zhang, Fengyuan Hu, Shane Storks, and Joyce Chai
    The 2023 Conference on Empirical Methods in Natural Language Processing (Miami, FL, USA)
    Preprint Code

2023

    Publication preview image. Open preprint or publication for readable version.
    From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
    Zheyuan Zhang, Shane Storks, Fengyuan Hu, Sungryull Sohn, Moontae Lee, Honglak Lee, and Joyce Chai
    The 2023 Conference on Empirical Methods in Natural Language Processing (Singapore)
    Publication Preprint Code Poster

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    Can Foundation Models Watch, Talk, and Guide You Step By Step to Make a Cake?
    Yuwei Bao, Keunwoo Peter Yu, Yichi Zhang, Shane Storks, Itamar Bar-Yossef, Alex de la Iglesia, Megan Su, Xiao Lin Zheng, and Joyce Chai
    Findings of the Association for Computational Linguistics: EMNLP 2023 (Singapore)
    Publication Preprint Code

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    NLP Reproducibility For All: Understanding Experiences of Beginners
    Shane Storks, Keunwoo Yu, Ziqiao Ma, and Joyce Chai
    The 61st Annual Meeting of the Association for Computational Linguistics (Toronto, ON, Canada)
    Publication Preprint Code Poster

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    In-Context Analogical Reasoning with Pre-Trained Language Models
    Xiaoyang Hu, Shane Storks, Richard L. Lewis, and Joyce Chai
    The 61st Annual Meeting of the Association for Computational Linguistics (Toronto, ON, Canada)
    Publication Preprint Code Poster Coverage

    Publication preview image. Open preprint or publication for readable version.
    SEAGULL: An Embodied Agent for Instruction Following Through Situated Dialog
    Yichi Zhang, Jianing Yang, Keunwoo Yu, Yinpei Dai, Shane Storks, Yuwei Bao, Jiayi Pan, Nikhil Devraj, Ziqiao Ma, and Joyce Chai
    Alexa Prize SimBot Challenge Proceedings
    Publication Coverage Coverage Coverage

2022

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    DANLI: Deliberative Agent for Following Natural Language Instructions
    Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Peter Yu, Yuwei Bao, and Joyce Chai
    The 2022 Conference on Empirical Methods in Natural Language Processing (Abu Dhabi, United Arab Emirates)
    Publication Preprint Code Video

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    Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts
    Shane Storks, Qiaozi Gao, Aishwarya Reganti, and Govind Thattai
    The First International Workshop on Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations at AAAI-22 (Vancouver, BC, Canada)
    Preprint Poster

2021

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    Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding
    Shane Storks, Qiaozi Gao, Yichi Zhang, and Joyce Chai
    Findings of the Association for Computational Linguistics: EMNLP 2021 (Punta Cana, Dominican Republic)
    Publication Preprint Code Poster Slides Video

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    Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers
    Shane Storks and Joyce Chai
    Findings of the Association for Computational Linguistics: EMNLP 2021 (Punta Cana, Dominican Republic)
    Publication Preprint Code Slides Video

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    Are We There Yet? Learning to Localize in Embodied Instruction Following
    Shane Storks, Qiaozi Gao, Govind Thattai, and Gokhan Tur
    AAAI-21 Workshop on Hybrid Artificial Intelligence (Online)
    Preprint Slides

2020

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    Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
    Shane Storks, Qiaozi Gao, and Joyce Y. Chai
    arXiv:1904.01172 [cs.CL]
    Preprint

Other talks and guest lectures.

2023

    Making Generative AI Better for You: Fine-Tuning & Experimentation for Custom Research Solutions
    Invited Talk, November 2023
    Michigan Institute for Data Science (MIDAS) Generative AI Tutorial Series (Ann Arbor, MI, USA)
    Slides Video Coverage

    Tutorial: Fine-Tuning LLMs
    Invited Tutorial, November 2023
    Michigan Institute for Data Science (MIDAS) Generative AI Tutorial Series (Ann Arbor, MI, USA)
    Code Video Coverage

    Commonsense Reasoning in Natural Language Understanding
    Guest Lecture, November 2023
    EECS 595: Natural Language Processing at University of Michigan (Ann Arbor, MI, USA)
    Slides

    Cognitive Motivations in Analogical and Physical Reasoning with Large Language Models
    Invited Talk, October 2023
    University of Michigan Weinberg Institute for Cognitive Science Seminar Series (Ann Arbor, MI, USA)
    Slides

    Prompt Engineering with Large Language Models: Basics and Research Applications
    Invited Talk, July 2023
    Generative AI for Research Faculty Workshop at University of Michigan (Ann Arbor, MI, USA)
    Slides

2022

    Language Model Prompting
    Guest Lecture, November 2022
    EECS 595: Natural Language Processing at University of Michigan (Ann Arbor, MI, USA)
    Slides

    Large Pre-Trained Language Models for Physical Action Understanding and Planning
    Invited Talk, October 2022
    2022 Microsoft Turing Academic Program (MS-TAP) Workshop (Online)
    Slides

    Learning Physical Action Schemas from Language and Experience
    Poster Session, September 2022
    DARPA PTG Site Visit at University of Michigan (Ann Arbor, MI, USA)

    Toward Coherent Commonsense Language Understanding in Machines
    Guest Lecture, January 2022
    EECS 692: Advanced Artificial Intelligence at University of Michigan (Ann Arbor, MI, USA)
    Slides

2021

    Language Model Prompting
    Guest Lecture, December 2021
    EECS 595: Natural Language Processing at University of Michigan (Ann Arbor, MI, USA)
    Slides

2019

    Natural Language Understanding and Inference: Benchmarks, Resources, and Approaches
    Invited Talk, October 2019
    Stanford University Human-Centered Artificial Intelligence (HAI) and AI Index Workshop on Measurement in AI Policy: Opportunities and Challenges (Stanford, CA, USA)
    Slides

2018

    Simulating Hot Topic Popularity with a Modified SIR Model
    Invited Talk, February 2018
    Lawrence Technological University Campus Open House (Southfield, MI, USA)
    Slides

2017

    Simulating Hot Topic Popularity with a Modified SIR Model
    Contributed Talk, July 2017
    Mathematical Association of America MathFest (Chicago, IL, USA)
    Slides

Courses for which I've served as a Graduate Student Instructor (GSI). Duties typically include assignment and project design, grading, supporting students through office hours and online interactions, and occasionally giving lectures.

University logo
EECS 595: Natural Language Processing
Fall 2022

University of Michigan

Graduate introductory NLP course led by Joyce Chai. Topics include syntax, semantics, discourse, deep learning for NLP, and their applications in information extraction, machine translation, and dialogue systems.

Electrical Engineering and Computer Science (EECS)
University logo
EECS 595: Natural Language Processing
Fall 2021

University of Michigan

Graduate introductory NLP course led by Joyce Chai. Topics include syntax, semantics, discourse, deep learning for NLP, and their applications in information extraction, machine translation, and dialogue systems.

Electrical Engineering and Computer Science (EECS)
University logo
EECS 595: Natural Language Processing
Fall 2020

University of Michigan

Graduate introductory NLP course led by Joyce Chai. Topics include syntax, semantics, discourse, deep learning for NLP, and their applications in information extraction, machine translation, and dialogue systems.

Electrical Engineering and Computer Science (EECS)

Professional appointments in industry.

Amazon Alexa AI
June 2021 - August 2021

Applied Scientist Intern

Natural Understanding, Teachable AI team (remote). Completed a self-contained research project on multi-hop reasoning advised by mentors Qiaozi Gao and Govind Thattai.

Sunnyvale, CA, USA
Amazon Alexa AI
June 2020 - August 2020

Applied Scientist Intern

Natural Understanding, Teachable AI team (remote). Completed a self-contained research project on embodied instruction following advised by mentor Qiaozi Gao.

Sunnyvale, CA, USA
Universal Logistics Holdings, Inc.
January 2017 - July 2018

Junior .NET Developer and Data Analyst

Used C#, VB.NET, and .SQL to create and maintain company databases, warehouse management applications, telemetric data stream processors, and big data visualizations.

Warren, MI, USA
Dominion Technologies Group, Inc.
June 2016 - December 2016

Junior Programmer

Used Visual C# to build and modify user interfaces for automotive assembly machines including fluid fill and alignment.

Roseville, MI, USA
Dominion Technologies Group, Inc.
September 2015 - June 2016

Technical Assistant

Authored and prepared technical manuals for automotive assembly machines. Synthesized schematic diagrams of fluid and electric circuits with input from subject matter experts.

Roseville, MI, USA
Selected awards and other honors I've received.
Students I've collaborated with and advised on research. If you're a University of Michigan student interested in my research and would like to work with me, please send me an email.

Unpublished course projects and side projects. Ask me about them if you're interested!

2021

    Publication preview image. Open preprint or publication for readable version.
    Invariant Extended Kalman Filter for Localization in Underwater Caves
    Samuel Ansaldo, AJ Bull, Xinyu Ma, Alyssa Scheske, and Shane Storks
    EECS 568 (Mobile Robotics), University of Michigan
    Code Video

2020

    Publication preview image. Open preprint or publication for readable version.
    Toward More Faithful Vision-and-Language Navigation Agents
    Shane Storks, Tianrong Zhang, and Wenyi Wu
    EECS 598 (Special Topics: Situated Language Processing for Embodied AI), University of Michigan

2019

    Publication preview image. Open preprint or publication for readable version.
    Using Twitter to Rank Musical Artist Popularity
    Shane Storks and Andrew Schmidt
    CSE 881 (Data Mining), Michigan State University
    Code

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