About Me

⭐ If you have opportunities or collaborations in mind, please feel free to reach out. ⭐

I am currently an Applied Research Scientist at AWS, Amazon, working on Generative AI.

I got my Ph.D. from University of Illinois at Urbana-Champaign, guided by Prof. Naira Hovakimyan. I’ve also been a research scientist in machine learning at Intelinair, mentored by Jennifer Hobbs. Additionally, I interned as an applied research scientist at Amazon.

🔍 Research Interests

My research is primarily in the fields of LLM, computer vision and multi-modality learning. In particular, I’m interested in exploring improved representation learning techniques to aid machines in comprehending the structure of massive amounts of unlabeled data.

In industry applications, my efforts are devoted to remote sensing, robotics and sustainable agriculture. My philosophy towards research is centered around bringing people’s lives and AI technology together at scale.

📰 News & Updates

May 2024: I am pleased to announce the successful defense of my Ph.D. thesis.
April 2024:
- Our paper, "The new agronomists: Language models are experts in crop management" has been accepted at CVPR in AgVision.
- Our paper, "Residual-based Language Models are Free Boosters for Biomedical Imaging" has been accepted at CVPR in AI-MIA as oral presentation.
Jan 2024: Our paper, "SwitchTab: Switched Autoencoders Are Effective Tabular Learners" has been accepted at AAAI.
Oct 2023: Our work on "ReConTab: Regularized Contrastive Representation Learning for Tabular Data" has been accepted at NeurIPS workshop.
September 2023: Our paper titled "Balanced Training for Sparse GANs" has been accepted at NeurIPS.
July 2023:
- Our paper, "Hallucination Improves the Performance of Contrastive Learning," got accepted at ICCV. Read the paper here.
- Our work "GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing" received the spotlight at ECAI. Read the paper here.
May 2023: I'm joining Amazon as an Intern Applied Research Scientist.
April 2023:
- Our research on "Optimizing Crop Management with Reinforcement Learning and Imitation Learning" has been accepted at IJCAI. Read the paper here.
March 2023:
- New paper on Arxiv titled "Dynamic Sparse Training for GANs". Read the paper here.
- Our work "Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis" got accepted at TMLR. Read the paper here.
June 2022: Presented our research at CVPR in New Orleans.
May 2022:
- Our paper, "Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations", was accepted for an oral presentation at CVPR in AgVision. Read the paper here.

Jing Wu

About Me

🔍 Research Interests

📰 News & Updates

📑 Selected Publications