Associate Professor
School of Computing
KAIST

I’m an Associate Professor in the School of Computing at KAIST and also an Assistant Professor of Computer Science at Rutgers University, affilated with the Rutgers Center for Cognitive Science. My research interest is to develop machine learning models/algorithms for intelligent agents (e.g., real or virtual robots) in a way to make them more general-purposed like humans. In pursuing this, I’m particularly interested in learning representations, world models, and policies in an unsupervised and structured (e.g., causal, compositional, temporal, hierarchical) way. The main tools are deep learning, reinforcement learning, and probabilistic learning. As such, my research also has some intersection with Cognitive Science as I enjoy being inspired from cognitive science to find novel problems and inductive biases useful in making human-like intelligent machines. 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

• Machine Learning for General-Purpose Agents and Intelligent Robots
• Learning Representations, World Models, and Policies
• Unsupervised and Structured (causal, compositional, temporal, hierarchical) Learning

News

• I have no opening for Ph.D and M.S. students at Rutgers University.
• “ROOTS: Object-Centric Representation and Rendering for 3D Scenes” is accepted in the Journal of Machine Learning Research
• Two papers are accepted in ICML 2021
• Invited Talk at DeepMind, Jan 2021
• Our paper “Generative Neurosymbolic Machines” is accepted in NeurIPS 2020 as a spotlight!
• Invited Speaker for NeurIPS 2020 Workshop on Object Representations for Learning and Reasoning
• Co-organizing ICML 2020 Workshop on Object-Oriented Learning (https://oolworkshop.github.io/)
• Teaching in Fall 2020: CS 444: Deep Learning
• 2 papers accepted in ICLR 2020
• Teaching CS 536: Machine Learning in Spring 2020
• 3 papers accepted in NeurIPS 2019 including one spotlight paper

Service

• Area Chair - NeurIPS 2021, ICML 2021, AAAI (2021,2022)
• Reviewers - NeurIPS, ICML, ICLR, AISTATS, AAAI (2015 - 2020)

Publications / Google Scholar

2021

Illiterate DALLE Learns to Compose
Gautam Singh, Fei Deng, Sungjin Ahn
arXiv [pdf] [project] [code]

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
Fei Deng, Ingook Jang, Sungjin Ahn
[arXiv]

Structured World Belief for Reinforcement Learning in POMDP
Gautam Signh, Skand Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn
ICML 21 [pdf]

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

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, [pdf] [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