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Vivek Boominathan

Vivek Boominathan

Assistant Research Professor Member, Ken Kennedy Institute

Electrical and Computer Engineering

CONTACT

vivekb@rice.edu

WEBSITE(S)| Profile | Personal Site

SURF Mentoring

Potential projects/topics: I am seeking undergraduate students interested in gaining hands-on experience in the fields of computer vision, robotics simulation, and medical imaging. Following are the projects offered: 1. Reconstructing 3D Scenes from Reflections (Computer Vision Project) 2. Simulating Robotic Surgery Using NVIDIA Isaac Lab (Robotics & Simulation Project) 3. Medical Image Segmentation for Ultrasound Images (AI for Healthcare Project)

1. Reconstructing 3D Scenes from Reflections (Computer Vision Project)
This project explores how reflections in everyday surfaces—such as glass windows or glossy objects—can be used to infer the 3D structure of the surrounding environment. Instead of directly viewing a scene, the camera observes distorted reflections, and the goal is to computationally reconstruct what the environment looks like. Undergraduate students will learn foundational concepts in computer vision, including image formation, geometry, and basic machine learning. Entry-level tasks may include analyzing images, writing Python code to process and visualize data, experimenting with simple reconstruction algorithms, or working with simulation data.

2. Simulating Robotic Surgery Using NVIDIA Isaac Lab (Robotics & Simulation Project)
This project focuses on using realistic robotics simulators to generate synthetic datasets for computer vision and AI applications in robotic surgery. Real surgical data is difficult and expensive to collect, so simulation and rendering play a critical role in creating labeled images and videos that can be used to train and test computer vision algorithms. Undergraduate students will learn how virtual surgical scenes—such as robotic arms, instruments, and anatomical models—can be simulated and rendered using modern tools like NVIDIA Isaac Lab. Entry-level tasks may include setting up simulated environments, adjusting camera viewpoints and lighting, rendering images or video sequences, and automatically generating ground-truth labels such as object masks, depth maps, or keypoints.

3. Medical Image Segmentation for Ultrasound Images (AI for Healthcare Project)
In this project, students will work with medical ultrasound images and help develop algorithms that identify and segment anatomical structures. Ultrasound is widely used in healthcare but presents unique challenges due to noise and low image contrast, making it an exciting and impactful area for research. Students will be introduced to medical imaging, basic machine learning, and data annotation workflows. Entry-level activities may include visualizing ultrasound data, implementing simple image processing techniques, training basic segmentation models, or evaluating algorithm performance.

Potential skills gained: Fellows will gain hands-on experience in Python programming, computer vision, machine learning, and simulation-based data generation, while working with real-world visual and medical datasets.

Required qualifications:

  • Required skills:The projects are programming heavy. Besides that, if students are open to learning, they'll be able to tackle any of the projects.

Direct mentor: Faculty/P.I., Graduate Student

Research Areas

I am an Assistant Research Professor in Electrical and Computer Engineering at Rice University. My research lies at the intersection of computer vision, machine learning, applied optics, and nanofabrication. My current research focuses on building end-to-end systems with novel optics, sensors, and machine learning algorithms to create devices with applications in robotics, autonomous driving, wearables, medical sensing, machine vision, the internet of things (IoT), and virtual/augmented reality. Furthermore, I use custom fabricated optics and chips to overcome the challenges of traditional lenses and image sensors, such as size, weight, and function. Broadly, my research is in computational imaging and draws from the foundations of computer vision, computer graphics, machine learning, wave optics, photonics, and materials science.