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.
- 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!
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
An efficient slot-based hierachical VAE for learning unordered, symmetric, and disentangled scene representations.
We propose a state space model and training objective that achieves better stochastic future prediction for object-centric world models.
Characterizing the machine learning problem of detecting disinformation-like coordinated activity on Twitter.
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.