Summary
Sayantan Auddy is a Berlin-based postdoctoral researcher with 11 years of experience at the intersection of machine learning, robotics, and computer vision, currently leading research at Technische Universität Berlin. He holds a PhD in Computer Science from Leopold-Franzens Universität Innsbruck and an MS in Intelligent Adaptive Systems from the University of Hamburg, bringing strong academic rigor to applied research. His background spans deep learning software development for computer vision, scientific software packaging with CMake, and earlier data-warehouse engineering—illustrating fluency from low-level build systems to high-level ML models. Colleagues value his ability to translate research prototypes into reproducible tools and utilities that accelerate group workflows. Notably, his career trajectory blends industry-grade engineering practices from firms like Infosys and Capgemini with sustained academic contributions, making him adept at bridging production constraints and cutting-edge research.
11 years of coding experience
12 years of employment as a software developer
Master of Science - MS, Intelligent Adaptive Systems, 1.21 (on a scale of 1.0 -5.0 with 1.0 being the best), Master of Science - MS, Intelligent Adaptive Systems, 1.21 (on a scale of 1.0 -5.0 with 1.0 being the best) at University of Hamburg
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Leopold-Franzens Universität Innsbruck
B.Tech, Computer Science & Engineering, 8.8 GPA, B.Tech, Computer Science & Engineering, 8.8 GPA at Haldia Institute of Technology
English, German, Bengali, Hindi