2023 IEEE SPS SEASONAL SCHOOL ON QUANTUM COMPUTING AND MACHINE LEARNING
The 2023 IEEE SPS Seasonal School on Quantum Computing and Machine Learning is a unique initiative of the IEEE Signal Processing Society, Delhi Chapter, aimed at introducing students to the latest developments in the field of quantum computing and its applications in machine learning. The event will take place from 14 April to 15th April 2023 at the Indian Institute of Technology (IIT) Delhi (in both Physical and Virtual Mode) and will comprise a series of webinars, hands-on workshops, hackathons, and industrial seminars. It is an innovative project run in collaboration with IIIT Delhi with the express purpose of enlightening students about the most recent developments in the fields of quantum computing and machine learning.
The two-day event will break down boundaries between college academics and research-based industrial skills, giving young people access to fresh opportunities to launch their careers in the field of signal processing. It will reveal the revolutionary implications of quantum computing and machine learning in a massively interactive and structured manner in order to educate young minds.
The objectives of organizing this event are as follows:
- Motivate IEEE/Non-IEEE Members to take the experience of IEEE SPS Benefits.
- Bridging the gap between Academic-Based Learning with Research/Industrial Based Learning with Hands-on Experience Sessions.
- Introducing the skills required for Signal Processing based careers for IEEE YPs/PG/Doc/Post-Doc Members/Non-IEEE Members.
REGISTRATION
- Last Date of Registration April 5, 2023 11:59 PM.
- Acceptance Notification April 10, 2023
- Registration Details dispersal April 13, 2023
SPEAKERS
- Prof. Ram Krishna Verma-SRM
- Prof. Samaresh Das-IITD
- Prof. Subhashish Banaerjee-IITD
- Prof. Sayak Bhattacharya- IIITD
- Prof. C Patvardhan-DEI
- Prof. Abhishek Dixit-IITD
- Prof. Monika Aggarwal-IITD
- Dr. Shalaka Verma -Microsoft
- Dhiraj Madan -IBM-IRL
- Dr. Sangeeta Maini- IISER PUNE
- Amazon
- CDAC