Biography

Hello! This is Jianing “Jed” Yang (杨佳宁). I am a Ph.D. student in Computer Science and Engineering at University of Michigan. I am a member of SLED lab, advised by Prof. Joyce Chai. I do research on Embodied AI, NLP and Robotics. I am interested in understanding natural language by grounding to robots and physical world, and using natural language as feedback to teach and improve embodied agent. My dream is to build and deploy household robots to homes around the world to help humans with daily tasks and needs.

Before joining UMich, I obtained my Master’s in Machine Learning from Carnegie Mellon University where I worked with Prof. Louis-Philippe Morency and Prof. Matt Gormley on Multimodal Natural Language Understanding, Dataset Bias Analysis, and Machine Learning. I got my Bachelor’s in Computer Science from Georgia Tech.

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

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

Click here for my full bio and here for my CV.

Interests
  • Embodied AI
  • Multimodal Machine Learning
  • Natural Language Processing
  • Robobtics
  • 3D Vision
  • Continual Learning
Education
  • 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

News

Click here for news archive

Academia Experience

 
 
 
 
 
CMU MultiComp Lab
Graduate Researcher
Aug 2019 – Aug 2021 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
 
 
 
 
 
CMU ML/NLP Research Group
Graduate Researcher
Aug 2019 – Aug 2021 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
 
 
 
 
 
Georgia Tech Machine Learning for Healthcare (SunLab)
Undergraduate Research Assistant
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

 
 
 
 
 
Amazon
Software Development Engineer
Amazon
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.

 
 
 
 
 
Amazon
Software Development Engineer Intern
Amazon
May 2018 – Jul 2018 Seattle, WA
Extended AWS Microsoft Active Directory to support multiple Availability Zones to increase service availability
 
 
 
 
 
Amazon
Software Development Engineer Intern
Amazon
May 2017 – Jul 2017 Seattle, WA
Designed and implemented a backup mechanism for AWS Simple Active Directory to enhance system reliability

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