Deqian Kong
Deqian Kong
Contact: deqiankong AT ucla.edu
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University of California, Los Angeles, 2019 -
I'm a PhD candidate in the Department of Statistics and Data Science at UCLA, advised by Prof. Ying Nian Wu.
Currently, I am particularly interested in generative models, representation learning, computational neuroscience, and condensed matter physics. My research focuses on (1) designing efficient, generalizable latent space generative models (“world models”) with explicit value functions that readily support planning and thinking, and (2) developing brain-inspired representations and learning algorithms for human-like generalization and adaptation.
I have previously interned with the Lambda ML team, BioMap, Amazon AGI, and Amazon Alexa. My research has been generously supported by Lambda, Qualcomm and Amazon.
Selected Publications/Preprints
Reasoning Curriculum: Bootstrapping Broad LLM Reasoning from Math
B. Pang, D. Kong, S. Savarese, C. Xiong, Y. Zhou
arXiv:2510.26143 [cs.AI]
FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with O(N log N) Complexity
D. Kong, S. Feng. J. Xie, Y. N. Wu
arXiv:2510.13866 [cond-mat.str-el]
Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planning
M. Zhao*, D. Xu*, D. Kong*, W.-H. Zhang, Y. N. Wu
Neural Information Processing Systems (NeurIPS) 2025 [q-bio.NC]
Latent Adaptive Planner for Dynamic Manipulation
D. Noh*, D. Kong*, M. Zhao, A. Lizarraga, J. Xie, Y. N. Wu^, D. Hong^
Latent Thought Models with Variational Bayes Inference-Time Computation
D. Kong*, M. Zhao*, D. Xu*, B. Pang, S. Wang, E. Honig, Z. Si, C. Li, J. Xie^, S. Xie^, Y. N. Wu^
International Conference on Machine Learning (ICML 2025) | Blog [cs.CL]
Latent Space Energy-based Neural ODEs
S. Cheng*, D. Kong*, J. Xie, K. Lee, Y. N. Wu^, Y. Yang^
Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
D. Kong*, D. Xu*, M. Zhao*, B. Pang, J. Xie, A. Lizarraga, Y. Huang, S. Xie*, Y. N. Wu
Neural Information Processing Systems (NeurIPS) 2024 | Project Page
Molecule Design by Latent Prompt Transformer
D. Kong*, Y. Huang*, J. Xie*, E. Honig*, M. Xu, S. Xue, P. Lin, S. Zhou, S. Zhong, N. Zheng, Y. N. Wu
Neural Information Processing Systems (NeurIPS) 2024 (Spotlight) | Project Page | An extended verision to previous NeurIPS 2023 AI for Science Workshop Paper.
Long-term social interaction context: The key to egocentric addressee detection
D. Kong, F. Khan, X. Zhang, P. Singhal, Y. N. Wu
ICASSP 2024 (Work done during the internship at Amazon AGI)
Molecule Design by Latent Space Energy-based Modeling and Gradual Distribution Shifting
D. Kong*, B. Pang*, T. Han, Y. N. Wu
Diverse and Faithful Knowledge-grounded Dialogue Generation via Sequential Posterior Inference
Y. Xu*, D. Kong*, D. Xu, Z. Ji, B. Pang, P. Fung, Y. N. Wu
International Conference on Machine Learning (ICML 2023) | Code
A statistical approach to topological entanglement: Boltzmann machine representation of higher-order irreducible correlation
S. Feng, D. Kong, N. Trivedi
arXiv:2302.03212 (2023) [quant-ph]
Unsupervised Meta-learning via Latent Space Energy-based Model of Symbol Vector Coupling
D. Kong*, B. Pang* and Y. N. Wu
YouRefIt: Embodied Reference Understanding with Language and Gesture
Y. Chen, Q. Li, D. Kong, Y. L. Kei, T. Gao, Y. Zhu, S.-C. Zhu, S. Huang
International Conference on Computer Vision (ICCV) 2021 (Oral)
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