Jianing "Jed" Yang

Jianing "Jed" Yang

M.S. in Machine Learning

Machine Learning Department

Carnegie Mellon University


Hello! This is Jianing “Jed” Yang (杨佳宁). I am a Master’s student in Machine Learning at Carnegie Mellon University. I received my Bachelor’s degree in Computer Science from Georgia Tech.

I am fortunate to be advised by Prof. Louis-Philippe Morency and Prof. Matt Gormley at CMU. My research interest lies in Multimodal Natural Language Understanding, Dataset Bias Analysis, and Machine Learning in general!

Before I delved fully into research, I had been a Software Development Engineer at  Amazon Web Services, intern and full-time.

Outside of work, you will find me skiing/cooking/baking/photographing.

I will be joining UMich CSE as a PhD student in Fall 2021!


  • Multimodal NLP
  • Machine Learning
  • Question Answering
  • Dataset Bias Analysis


  • Ph.D. in Computer Science and Engineering, 2025 (expected)

    University of Michigan

  • M.S. in Machine Learning, 2020

    Carnegie Mellon University

  • B.S. in Computer Science, 2018

    Georgia Institute of Technology


  • [Mar. 2021] 🎉 Our paper MTAG was accepted to NAACL 2021! The code is available here.
  • [Dec. 2020] 🎉 I finished my Master’s and graduated from CMU! 🎉 I will continue to stay as a Research Assistant at CMU’s MultiComp lab until my PhD starts in Fall 2021.
  • [Nov. 2020] I’m attending EMNLP 2020 virtually. Happy to chat there!
  • [July 2020] I’m attending ACL 2020 virtually. Check out our video presentation on QA bias analysis!

Academia Experience


Graduate Researcher

CMU MultiComp Lab

Aug 2019 – Present Pittsburgh, PA
  • Designed a graph neural network (GNN) algorithm for fusion of multimodal temporal data
  • Analyzed language artifacts in video QA datasets
  • Built pipeline for multimodal question answering about social situations
  • Coordinated annotation of dataset
  • Advisor: Prof. Louis-Philippe Morency

Graduate Researcher

CMU ML/NLP Research Group

Aug 2019 – Present Pittsburgh, PA
  • Designed new algorithms to improve scheduled sampling training for seq2seq models
  • Validated effectiveness of the method on NER, Machine Translation and Text Summarization tasks
  • Advisor: Prof. Matt Gormley

Undergraduate Research Assistant

Georgia Tech Machine Learning for Healthcare (SunLab)

Aug 2017 – Dec 2018 Atlanta, GA
  • Built cardiac arrest prediction pipeline using multimodal temporal data collected from ICU patients
  • Advisor: Prof. Jimeng Sun

Industry Experience


Software Development Engineer


Feb 2019 – Aug 2019 Seattle, WA
  • Led a load balancing project to decrease system latency from 20 seconds to milliseconds.

  • Received award for technical soundness and leadership at 2019 Q2 AWS Identity organization meeting.


Software Development Engineer Intern


May 2018 – Jul 2018 Seattle, WA
Extended AWS Microsoft Active Directory to support multiple Availability Zones to increase service availability

Software Development Engineer Intern


May 2017 – Jul 2017 Seattle, WA
Designed and implemented a backup mechanism for AWS Simple Active Directory to enhance system reliability