Embedded Files
I am a Senior AI Engineer at Microsoft in Berkeley. I pursued advanced graduate studies at the University of Massachusetts, Amherst. My expertise centers around Machine Learning, refined through roles such as an ML Research Intern at Harvard University and a Graduate Researcher at Microsoft in 2020. Prior to my time at UMass, I held a full-time Research Associate position at the Indian Institute of Science (IISc), Bangalore, from 2017 to 2019.
My research interests include: Reinforcement Learning, Deep Learning, and Robotics
Contact: abhiksingla10@gmail.com
Updates:
Updates:
[Apr 2021 - Present] Working as a Senior AI Engineer at Microsoft, Berkeley.
[May 2020 - Sept 2020] Worked as a Machine Learning Research Intern at Harvard University.
[Jan 2020 - May 2020] Worked with Microsoft Research as a Graduate Student Researcher.
Abhik Singla*, Sindhu P., Shalabh Bhatnagar. "Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge". Accepted for publication in IEEE Transactions on Intelligent Transportation Systems 2019. (Impact factor: 9.55) [Link]
2 papers selected in IEEE International Conference on Robot & Human Interactive Communication (Ro-Man) 2019.
Shounak Bhattacharya, Abhik Singla*, Abhimanyu, Dhaivat Dholakiya, Shalabh Bhatnagar, Bharadwaj Amrutur, Ashitava Ghosal, Shishir Kolathaya. "Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots". Accepted for presentation at IEEE International Conference on Robot & Human Interactive Communication (Ro-Man) 2019. [Link]
Shishir Kolathaya, Ashish Joglekar, Suhan Shetty, Dhaivat Dholakiya, Abhimanyu, Aditya Sagi,Shounak Bhattacharya, Abhik Singla*, Shalabh Bhatnagar, Ashitava Ghosal, Bharadwaj Amrutur. "Trajectory based Deep Policy Search for Quadrupedal Walking". Accepted for presentation in IEEE International Conference on Robot & Human Interactive Communication (Ro-Man) 2019. [Link]
Invited speaker at "Learning legged locomotion" workshop organized by Google Brain at ICRA 2019, Montreal. More details [Link]
Abhik Singla*, Sindhu P., Shalabh Bhatnagar. "Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV". Accepted for poster presentation in Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019, McGill University, Canada.
Abhik Singla*, Shounak Bhattarchya, Dhaivat Dholakia, Shalabh Bhatnagar, Ashitava Ghosal, Bhardwaj Amrutur and Shishir Kolathaya. "Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives". Accepted for presentation in IEEE International Conference of Robotics and Automation (ICRA), 2019, Canada. [Link]
Dhaivat Dholakiya, Shounak Bhattacharya, Ajay G., Abhik Singla*, Shalabh Bhatnagar, Bharadwaj Amrutur, Ashitava Ghosal, Shishir Kolathaya. "Design, Development and Experimental Realization of a Quadrupedal Research Platform: Stoch". Accepted in IEEE International Conference on Control, Automation and Robotics (ICCAR), 2019, China. [Link] [Best Presentation Award!]
Abhik Singla*, Partha Pratim Roy and Debi Prosad Dogra. Visual Rendering of Shapes on 2D Display Devices Guided by Hand Gestures. Accepted in Elsevier Displays Journal 2019. [Link]
In November 2018, I was selected for the "Winter School of Quantitative Systems Biology: Learning and Artificial Intelligence" held at Trieste, Italy organized by International Centre for Theoretical Physics (ICTP). A travel grant was also awarded by ICTP.
In October 2017, I was selected for the "ACAI Summer School of Reinforcement Learning" held at Nieuwpoort, Belgium organized by Google Deepmind and European Association of Artificial Intelligence (EurAI). A travel grant was also awarded by Deepmind and EurAI.
I have worked with esteemed professors and scientists: Prof. Hima Lakkaraju (Harvard University), Yash Chandak (UMass), Prof. Shalabh Bhatnagar (IISc), Prof. Bharadwaj Amrutur (IISc), Prof. Chiranjib Bhattacharyya (IISc), Dr. Shishir N.Y.K. (IISc), Dr. Partha Pratim Roy (IIT Rookee) and Dr. Debi Prosad Dogra (IIT Bhubaneswar).
Google Sites
Report abuse