Ajay Unagar

Ajay Unagar

MSc Student

ETH Zurich


Hi! I’m a final year MSc student studying Computational Science at ETH Zurich. My research interests lie in building adaptive intelligent systems which are aware of the 3D world around them and can use perception capabilities to make decisions. Towards this goal, I am interested in the topics of 3D Vision, Reinforcement Learning, and Continual Learning. Apart from gaining theoretical understanding of these topics, I also enjoy applying them to real-world robotics tasks.

Before starting my masters study, I finished my undergraduate at IIT Roorkee in India. Just after my undergraduate, I worked at ZS Associates as a Data Scientist for 2 years. I focused on many problems in healthcare domain including patient disease progression modeling, multi-channel marketing optimization, and real-time analytics.

  • 3D Vision
  • Reinforcement Learning
  • Continual Learning
  • Robotics
  • Theory of Machine Learning
  • MSc in Computational Science (Specialization in Robotics), 2021

    ETH Zurich

  • BTech in Civil Engineering (Minor in Computer Science), 2017

    IIT Roorkee, India


01/2021 Our paper "Back to the feature" has been accepted to CVPR 2021! It is on arxiv.
12/2020 Finished a semester project with IBM Research. My project report is online
11/2020 Our work on "Battery calibration using Deep RL" has been accepted at ML4Eng workshop at NeurIPS 2020. The talk is available online.
10/2020 Submitted my first main conference paper at CVPR 2021! Exciting times
07/2020 Started my semester project with the Digital Pathology team at IBM research, Zurich
06/2020 Started working as a Research Assistant with IMS Lab led by Prof. Olga Fink
06/2020 We finished 2nd in "Visual Localization for Autonomous Vehicle" competition organized by VisLocOdomMap workshop at CVPR 2020. Our talk is available online.
09/2019 Started my MSc at ETH Zurich!


Research Assistant
Jun 2020 – Present Zurich
Worked on battery model calibration using reinforcement learning project. Resulted in publication at NeurIPS 2020 workshop and full paper submitted to Energies journal.
Masters Project Intern
Jun 2020 – Dec 2020 Zurich
Worked on continual learning for image classification in digital pathology. Developed a method which remember the past knowledge via distillation. This method works in replay-free task-incremental setting.
Machine Learning Engineer
Oct 2017 – Oct 2020 Remote
Worked on building trading strategies using alpha signals from many sources. Applied techniques for filtering useful trading signals and combining them to make profitable strategies.
Data Scientist
Jul 2017 – Jul 2019 Pune, India
  • Patient desease progression modeling using patient history (ICD9 codes)
  • Multi channel marketing optimization for pharma companies
  • Real-time analytics for the sales representatives to make better decisions