Dennis Melamed is an R&D engineer working remotely on Kitware’s Computer Vision Team. His responsibilities include developing code for Kitware’s cyber-physical systems (CPS), which are trained using AI and deep learning to interact with the natural world. As such, he will also be focusing on the sensing capabilities for these CPS so they can adapt to ever-changing environments.
While earning his master’s degree, Dennis worked with Professor Kris Kitani on indoor pedestrian localization algorithms that used maps and low-cost IMU sensors. The focus of his thesis was finding better ways to fuse deep inertial odometry methods with commonly available map information to improve navigation in indoor environments.
Dennis received his master’s degree in robotics from Carnegie Mellon University in 2021. In 2019, he received his bachelor’s degree in computer engineering from the University of Minnesota.
M.S. in robotics from Carnegie Mellon University, 2021
B.S. in computer engineering from the University of Minnesota, 2019
Get to Know Dennis
What made you want to become a Kitwarean? Kitware’s prior work is what drew me in initially. I was excited about the mixture of developing systems for the real world alongside exploring exciting research topics. After interviewing, it was the people I talked to who made me decide to join Kitware – they are excited about the work they are doing, and the culture seemed like a great fit.
What do you love most about what you do? The ever-growing boundaries of what a computer can reason about the world. With the current pace of progress, it seems like the right combination of sensors and algorithms can achieve almost anything. I love being a part of that development, and seeing all the benefits it can bring to people.
Share something interesting about yourself that is not on your resume. I used to guide canoe trips in northern Minnesota, and love all things outdoors: canoeing, backpacking, climbing, and anything else that gets me outside.
Dennis’ publication list is below. To see all of Kitware’s computer vision publications, please visit the Computer Vision Publications page.
- S. Sun, D. Melamed, and K. Kitani, "IDOL: Inertial Deep Orientation-Estimation and Localization," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 7, pp. 6128–6137, May 2021. [URL]