Sungjin Ahn

Sungjin Ahn


Assistant Professor
Department of Computer Science
Center for Cognitive Science
Rutgers University




I’m an Assistant Professor of Computer Science and Cognitive Science at Rutgers University where I lead the Rutgers Machine Learning Group (RUML). My research goal is to solve core machine learning problems to develop general-purpose problem-solver agents. I have particular emphases on unsupervised and structured learning of world models and representations using deep learning, probabilistic learning, and reinforcement learning. I also enjoy being inspired from cognitive science and neuroscience in pursuit of finding novel problems and proper inductive biases. I received my Ph.D. at the University of California, Irvine on the study of scalable approximate Bayesian inference under the supervision of Prof. Max Welling. I did my postdoc working on deep learning at MILA under Prof. Yoshua Bengio. I joined Rutgers University in Fall 2018.

Email: sjn.[last_name] at gmail
Address: CBIM 9, 617 Bowser Rd, Piscataway, NJ 08854

Research Interest

  • Deep architectures and probabilistic models for agents which learn world models and represetations leading to behaviors achieving goals in complex environments.

News

Service

  • Area Chair - NeurIPS 2021, ICML 2021, AAAI 2020 (Senior Program Committee)
  • Reviewers - NeurIPS, ICML, ICLR, AISTATS, AAAI (2015 - 2020)

Publications / Google Scholar

2021

Structured World Belief Representations for Reinforcement Learning
Gautam Signh, Skand Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn
ICML 21

Generative Video Transformer: Can Objects be the Words?
Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn
ICML 21

Generative Scene Graph Networks
F. Deng, Z. Zhi, D. Lee, S. Ahn
ICLR 21 [pdf]

ROOTS: Object-Centric Representation and Rendering of 3D Scenes
{C. Chen, F. Deng}, S. Ahn
JMLR 21, Preprint [arxiv] [project]

2020

Generative Neurosymbolic Machines
J. Jiang and S. Ahn
NeurIPS 20, [arxiv] [code] Spotlight (top 4% = 395/9454 submissions)

Improving Generative Imagination in Object-Centric World Models
Z. Lin, Y. Wu, S. Peri, B. Fu, J. Jiang, S. Ahn
ICML 20 [pdf] [project] [code]

Robustifying Sequential Neural Processes
J. Yoon, G. Singh, and S. Ahn
ICML 20 [arxiv]

SCALOR: Generative World Models with Scalable Object Representations
{J. Jiang, S. Janghorbani}, G. Melo, and S. Ahn
ICLR-20 [arxiv] [project] [code]

SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
{Z. Lin, Y. Wu, S. Peri}, W. Sun, G. Singh, F. Deng, J. Jiang, S. Ahn
ICLR 20 [pdf] [project] [code]

Hierarchical Decomposition and Generation of Scenes with Compositional Objects
F. Deng, Z. Zhi, S. Ahn
ICML 20, Workshop on Object-Oriented Learning Spotlight [pdf]

Generating Stochastic Object Dynamics in Scenes
Z. Lin, Y. Wu, S. Peri, B. Fu, J. Jiang, and S. Ahn
ICML 20, Workshop on Object-Oriented Learning [pdf]

Space and the Brain - A Review of the Cognitive Map Mechanisms in the Entorhinal-Hippocampal Circuit
Y. Friedman and S. Ahn
Henry Rutgers Scholar Award

2019

Sequential Neural Processes
{G. Singh, J. Yoon}, Y. Sohn, and S. Ahn
NeurIPS 19, Spotlight (top 2.4% = 164/6743)
[pdf] [project] [code]

Variational Temporal Abstraction
T. Kim, {S. Ahn, Y. Bengio}
NeurIPS 19

Neural Multisensory Scene Inference
J. Lim, P. Pinheiro, N. Rostamzadeh, C. Pal, and S. Ahn
NeurIPS 19

Learning Single-View 3D Reconstruction with Adversarial Training
P. Pinheiro, N. Rostamzadeh, and S. Ahn
ICCV 19, Oral (top 4.3% of all the submitted)

Generative Hierarchical Models for Parts, Objects, and Scenes
F. Deng, Z. Zhi, and S. Ahn
arXiv

Reinforced Imitation in Heterogeneous Action Space
K. Zolna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. O. Pinheiro}
arXiv

2018

Bayesian Model-Agnostic Meta-Learning
{J Yoon, T Kim}, O. Dia, S. Kim, Y. Bengio, S. Ahn
NeurIPS 18, Spotlight (top 3.5% = 168/4856)

Reinforced Imitation Learning from Observations
K. Żołna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. Pinheiro}
NeurIPS 18 Workshop on Imitation Learning and Its Challenges in Robotics

2017

Hierarchical Multiscale Recurrent Neural Networks
J. Chung, S. Ahn, Y. Bengio
ICLR 17

Denoising Criterion for Variational Auto-Encoding Framework
D. Im, S. Ahn, R. Memisevic, Y. Bengio
AAAI 17

SENA: Preserving Social Structure for Network Embedding
S. Hong, T. Chakraborty, S. Ahn, G. Husari and N. Park
ACM Hypertext and Social Media 17

2016

Pointing the Unknown Words
C. Gulcehre, S. Ahn, R. Nallapati, B. Zhou, Y. Bengio
ACL 16 Oral Presentation

Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
{I. V. Serban, A. G. Duran}, C. Gulcehre, S. Ahn, S. Chandar, A. Courville, Y. Bengio
ACL 16

Scalable MCMC for Mixed Membership Stochastic Blockmodels
{W. Li, S. Ahn}, and M. Welling
AISTATS 16

Scalable Overlapping Community Detection
I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
ParLearning 16, Best Paper Award

Learning Latent Multiscale Structure using Recurrent Neural Networks
J. Chung, S. Ahn, Y. Bengio
NIPS 16 Workshop on Neural Abstract Machines & Program Induction (NAMPI)

A Neural Knowledge Language Model
S. Ahn, H. Choi, T. Parnamaa, Y. Bengio
arXiv

Hierarchical Memory Networks
S. Chandar, S. Ahn, H. Larochelle, P. Vincent, G. Tasauro, Y. Bengio
arXiv

~2015 (selected publications)

Stochastic Gradient MCMC: Algorithms and Applications
PhD Dissertation

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
S. Ahn, A. Korattikara, N. Liu, S. Rajan, and M. Welling
KDD 15

Distributed Stochastic Gradient MCMC
S. Ahn, B. Shahbaba, and M. Welling
ICML 14

Distributed and Adaptive Darting Monte Carlo through Regenerations
S. Ahn, Y. Chen, and M. Welling
AISTATS 13

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
S. Ahn, A. Korattikara, and M. Welling
ICML 12 Best Paper Award