Mohamad Qadri

I am a PhD student at the Robotics Institute at Carnegie Mellon University, where I work with Michael Kaess in the the Robotics Perception Lab . I work on statistical methods for state estimation. I am interested in developing algorithms for efficient and robust inference at the intersection of linear algebra and probabilistic graphical models.

I completed my Masters in Robotics at CMU working with George Kantor on Simultaneous Localization and Mapping (SLAM) in agricultural environments. Prior to that, I worked with Simon Lucey and Laszlo Jeni on monocular 3D reconstruction. In my undergrad, I majored in Electrical Engineering at the University of Maryland - College Park.

Email / CV / Google Scholar / LinkedIn / Github

Updates

[Jun '23]	Our paper "Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation" was accepted to the Differentiable Almost Everything Workshop at ICML 2023.
[Jun '23]	Presented our work on neural surface reconstruction using imaging sonar at the Computational Cameras and Displays (CCD) Workshop at CVPR 2023
[Jan '23]	Our paper Neural Implicit Surface Reconstruction using Imaging Sonar was accepted to ICRA 2023.
[Jan '23]	Our paper Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping was accepted to ICRA 2023.
[Jan '23]	Our paper Runahead A: Speculative Parallelism for A with Slow Expansions was accepted to ICAPS 2023.

Research

	Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation Mohamad Qadri, Michael Kaess ICML, 2023 (Differentiable Almost Everything Workshop) arXiv We propose a gradient-based method for learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Our algorithm converges much quicker to model estimates that lead to solutions of higher quality compared to existing methods
	Neural Implicit Surface Reconstruction using Imaging Sonar Mohamad Qadri, Michael Kaess, Ioannis Gkioulekas ICRA, 2023 arXiv / video We present a technique for dense 3D reconstruction of objects using an imaging sonar. We represent the geometry as a neural implicit function.
	Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping Tianxiang Lin, Akshay Hinduja, Mohamad Qadri, Michael Kaess ICRA, 2023 arXiv / video This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images.
	Runahead A: Speculative Parallelism for A with Slow Expansions Mohammad Bakhshalipour, Mohamad Qadri, Seyed Borna Ehsani, Dominic Guri, Maxim Likhachev Phillip B. Gibbons ICAPS, 2023 We go beyond traditional parallelism of the A* algorithm and introduce RA* (Runahead A) which performs speculative parallelism of A.
	InCOpt: Incremental Constrained Optimization Using the Bayes Tree Mohamad Qadri, Paloma Sodhi, Joshua G. Mangelson, Frank Dellaert, Michael Kaess IROS, 2022 paper / video / code We present an Augmented Lagrangian-based incremental constrained optimizer that views matrix operations as message passing over the Bayes tree.
	RACOD: algorithm/hardware co-design for mobile robot path planning Mohammad Bakhshalipour, Seyed Borna Ehsani, Mohamad Qadri, Dominic Guri, Maxim Likhachev Phillip B. Gibbons ISCA, 2022 paper RACOD is an algorithm/hardware co-design for mobile robot path planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm extension for runahead path exploration.
	Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision Harry Freeman, Mohamad Qadri, Abhisesh Silwal, Paul O'Connor, Zachary Rubinstein, Daniel Cooley, George Kantor In Submission at IEEE Transactions on Robotics (T-RO) arXiv / video We develop a computer vision method to size and track the growth rates of apple fruitlets. Fruitlets are sized using a combination of deep learning algorithms and are associated across days using a novel Attentional Graph Neural Network approach.
	Toward Semantic Scene Understanding for Fine-Grained 3D Modeling of Plants Mohamad Qadri, Harry Freeman, , Eric Schneider , George Kantor AIAFS AAAI, (2021) paper / video In this paper, we use semantics and environmental priors to construct accurate 3D maps of agricultural environments.
	Robotic Vision for 3D Modeling and Sizing in Agriculture Mohamad Qadri, Masters thesis paper

Other Projects

	Semi-Supervised Learning via Offline Pseudolabel Generation and Consistency Regularization Mohamad Qadri, Maggie Collier pdf
	Exploring the link between Geodesically Convex Optimization and Contraction Analysis Mohamad Qadri, Chiheb Boussemma pdf
	A study of Joint-Space Control of Non-Linear Robotics manipulators Mohamad Qadri pdf

Source code