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Hi there, I am Jiaxin 👋!

🔭 I am a Staff Research Scientist at Intuit AI Research where my focus is Generative AI (large language models (LLMs), and diffusion models), and AI Robustness & Safety (uncertainty, reliability, and trustworthiness) with extensive applications to complex real-world tasks. Previously, I was a Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory where my research aims at accelerating AI for Science on supercomputers, such as Summit and Frontier. I received my Ph.D. from the Johns Hopkins University with an emphasis on uncertainty quantification (UQ).

📫 You may find more information through my personal website and feel free to contact me via email at [email protected].

😄 Some recent publications in LLMs (full publication list in Google Scholar)

Jiaxin's GitHub stats

Jiaxin Zhang's Projects

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The code for our paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach (arXiv preprint 2209.06995).

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PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows (ECCV 2022)

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🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

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Probabilistic Gradient Boosting Machines

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Probabilistic Graphical Model Construction

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PITI: Pretraining is All You Need for Image-to-Image Translation

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Code for Latent Action Space for Offline Reinforcement Learning [CoRL 2020]

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Code for paper "PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics"

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PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

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PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows

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MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.

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Fast, flexible and easy to use probabilistic modelling in Python.

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A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

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DRL with population coded spiking neural network for optimal and energy-efficient continuous control.

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PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..

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POT : Python Optimal Transport

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A prize for finding tasks that cause large language models to show inverse scaling

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Probabilistic reasoning and statistical analysis in TensorFlow

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Must-read papers on prompt-based tuning for pre-trained language models.

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