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 develop machine learning algorithms for human-level intelligence. I believe that AI agents should be able to learn representations of the world and their structured relations in a way to support systematic generalization. I have particular emphases on unsupervised and structured representation learning based on deep learning, probabilistic learning, and reinforcement learning. Inspiration from cog/neuroscience is also important to come up with 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

News

• Invited Talk at DeepMind, Jan 2021
• Area Chair, ICML 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
• Senior Program Committee, AAAI 2021
• Co-organizing ICML 2020 Workshop on Object-Oriented Learning (https://oolworkshop.github.io/)
• Teaching in Fall 2020: CS 444: Deep Learning
• A postdoc position is available. For more information, send me an email with your CV.
  
• 2 papers accepted in ICLR 2020
• Teaching CS 536: Machine Learning in Spring 2020
• 3 papers accepted in NeurIPS 2019 including one spotlight paper

Publications / Google Scholar

2021

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

2020

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

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

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
Reciepient of the 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