spokoyny.github.io

Daniel Spokoyny

About

I am sixth year PhD student at CMU working with Taylor Berg-Kirkpatrick (permanently remote at San Diego). My research focuses on modeling numerical quantities in unstructured texts, tables, long documents and spreadsheets. Further I aim to apply numerically pretrained transformers to downstream applications to extract information from unstructured climate reports. Previously, I studied computer science at the College of Creative Studies at UC Santa Barbara.

Papers

• NLP Tutorials for Climate Change AI Summer School 2023
  • Part 1: Evidence Synthesis
  • Part 2: Prompts for Sustainable Development Goals
• Numerical Correlation in Text
  • Daniel Spokoyny and Chien-Sheng Wu and Caiming Xiong
  • EMNLP Workshop on Mathematical Natural Language Processing 2022
• Towards Answering Climate Questionnaires from Unstructured Climate Reports
  • ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English
  • Daniel Spokoyny and Tanmay Laud and Tom Corringham and Taylor Berg-Kirkpatrick
  • EMNLP Workshop on NLP for Positive Impact 2022
  • Arxiv
• Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context
  • Daniel Spokoyny and Ivan Lee and Zhao Jin and Taylor Berg-Kirkpatrick
  • Findings at NAACL 2022
• BERT Classification of Paris Agreement Climate Action Plans
  • Tom Corringham, Daniel Spokoyny, Eric Xiao, Christopher Cha, Colin Lemarchand, Mandeep Syal, Ethan Olson, Alexander Gershunov
  • ICML 2021 Workshop on Tackling Climate Change with Machine Learning
  • Paper/Slides/Talk
• An Empirical Investigation of Contextualized Number Prediction.
  • Daniel Spokoyny and Taylor Berg-Kirkpatrick.
  • EMNLP 2020
• Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
  • Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick
  • ICLR 2019. arxiv code

Professional Experience

• Research Intern, Salesforce Research, Palo Alto Summer 2022
  • Defined a new numerical comprehension task of predicting the correlation relationship of quantities in text. Used crowd-workers to label and release a new dataset for this task.
• Research Project, Boeing, 2021
  • Worked on a task of email thread summarization. Along with engineers and technicians we collected a dataset, implemented several pretraining strategies and evaluated the performance of the models.
• Research Intern, Microsoft Research, Virtual Summer 2020
  • As a part of the MSR’s Deep-Excel team, worked on designing and training neural transformer architectures on self-supervised tasks using excel spreadsheet data and then finetuning the models for various downstream tasks.
• Research Intern, Google AI, Mountain View Summer 2019
  • As a part of Google Search’s Web Answers team, developed and experimented with numerical regression pretraining and transfer learning methods to improve accuracy for a downstream question answering task.

Talks

• Applying Artificial Intelligence for Climate Action for Climate in HiTech Course at Afeka Tel Aviv 2022
• Poster Numerical Correlation in Text and ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English at SoCal NLP 2022
• Modeling Numerical Quantities to Extract Measurements from Climate Text Sources at Doctoral Consortium on Computational Sustainability 2022
• NLP For Climate Documents for AI for Climate Change Bootcamp at Stanford 2022
• Machine Learning Model Compression for Computing for the Cloud and Internet of Things Class at UCSB 2022

Teaching

• Co-Lead, Seminar on Training Large Language Models, UCSD (Spring 2023)
• Teaching Assistant, Undergraduate and Graduate Natural Language Processing, CMU (Fall 2021)
• Teaching Assistant, Undergraduate Machine Learning, UCSD (Winter 2019)
• Co-Instructor, Topics in Machine Learning, UCSB (Spring 2017)
• Instructor, Graduate Seminar on Safety in AI, UCSB (Fall 2016)
• Co-Instructor, Introduction to NLP and Machine Learning, UCSB (Winter 2016)
• Co-Instructor, Graduate Statistical Learning Theory Colloquium, UCSB (Winter 2015)

Volunteer

• Core Member at Climate Change AI
  • Climate Change AI is a non-profit that organizes academic workshops, distributes grants, and partners with other organizations to promote the use of AI for climate change. I have helped organize a virtual two week long summer school this past year with 80+ participants and over 20+ hours of new content on climate change and AI.
  • New program fosters next generation of climate change, AI thought leaders
• Mentored at SDxAI 23 Hackathon

Participated

• CMU TartanX Fintech
• CMU Future Founders
• Fintech DevCon
• Madrona LLM Launchable

Contact

Email: dspokoyn@cs.cmu.edu Office: UCSD CSE 3242