Summary
Karnik Ram is a PhD student in machine learning at TUM (ELLIS) with 11 years of research and engineering experience across robotics, computer vision, and physics-informed deep learning. He has worked on safety-focused active perception and novel depth sensors at CMU, robust visual-inertial odometry and trajectory prediction at IIIT Hyderabad, and now applies physics-based ML to green computational chemistry under advisors Daniel Cremers and Max Welling. His work blends hands-on systems building (embedded localization, sensor calibration GUIs, low-power camera-less solutions) with strong academic output and open-source releases. Comfortable moving between code, math, and experiments, he also teaches core geometry and SLAM topics and has a track record of turning research prototypes into reusable tools. For collaborations or code, he prefers email contact and maintains an updated CV and website.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Technical University of Munich
MS by Research, Computer Science and Engineering, 9.50/10, MS by Research, Computer Science and Engineering, 9.50/10 at IIIT Hyderabad
Senior Secondary Education, Central Board of Seconday Education, Science Stream with Computer Science, 94.4%, Senior Secondary Education, Central Board of Seconday Education, Science Stream with Computer Science, 94.4% at Vidya Mandir Senior Secondary School
B.Eng., Electronics and Communication Engineering, 7.2/10, B.Eng., Electronics and Communication Engineering, 7.2/10 at SSN College of Engineering
English, Tamil, Hindi