About Me

I am a machine learning researcher interested in representation learning, reinforcement learning, and AI for social good. I am a Ph.D. candidate at the University of Florida expecting to graduate in December 2021. My research has spanned topics including object-centric deep generative modeling, low-resource multi-object tracking, and traffic signal control. I’m also a Florida McKnight Fellow. During the summer of 2021, I worked as a research intern at the National Renewable Energy Lab. There, I built a tool that uses a scalable stochastic approximation method to calibrate traffic simulations and investigated a new deep reinforcement learning approach for NEMA-compliant traffic signal control.

Here’s my CV and resume.

Contact: pemami[at]ufl[dot][edu]


  • July 2021 I was recognized as a Top 10% reviewer at ICML’21!
  • May 2021 My paper “Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations” has been accepted as a short talk at ICML’21!
  • May 2021 I am interning this summer with the National Renewable Energy Lab’s (NREL) Complex Systems Simulation and Optimization group working on reinforcement learning for traffic signal control!


Long-range Multi-Object Tracking at Traffic Intersections on Low-Power Devices

IEEE Transactions on Intelligent Transportation Systems, Oct. 2021
A CNN architecture for joint object detection and Re-ID on low-power edge devices and a multi-sensor tracking algorithm for radar.

Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations

ICML 2021
An efficient slot-based hierachical VAE for learning unordered, symmetric, and disentangled scene representations.

A Symmetric and Object-Centric World Model for Stochastic Environments

NeurIPS 2020 Workshop on Object Reps. for Learning and Reasoning (Spotlight)
We propose a state space model and training objective that achieves better stochastic future prediction for object-centric world models.

On the Detection of Disinformation Campaign Activity with Network Analysis

CCSW 2020: The ACM Cloud Computing Security Workshop
Characterizing the machine learning problem of detecting disinformation-like coordinated activity on Twitter.

Machine Learning Methods for Data Association in Multi-Object Tracking

ACM Computing Surveys, 53, 4, Article 69 (August 2020)
A survey on algorithmic and recent deep-learning-based approaches to the data association problem in multi-object tracking.

See my CV for a complete list including older publications.

Leeuwenberg, E. L. J. "A Perceptual Coding Language for Visual and Auditory Patterns." The American Journal of Psychology, vol. 84, no. 3, 1971, pg. 338.