How to Set Yourself up for a Successful Career in AI

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How to Set Yourself up for a Successful Career in AI

By Brijraj Singh, Research Scientist at Sony Research India

22^nd November 2022

Brijraj Singh, Research Scientist at Sony Research India

Brijraj Singh, Research Scientist at Sony Research India pens down advice for aspiring researchers looking to pursue a successful career in Artificial Intelligence (AI). He also shares a list of books, journals and conferences that would be of great value.

As an awardee of the 40 Under 40 Data Scientists in 2022 by the Analytics India Magazine, Brijraj has garnered extensive experience presenting his research work at multiple conferences such as NeurIPS Montreal Canada, ICCV Seoul South Korea, and top universities like IPAM UCLA (Los Angeles USA), IIT Roorkee, IIT Indore to name a few.

What attracted you to pursue a career in Artificial Intelligence (AI)?

“I have been naturally attracted to subjects related to data (like Database Management Systems (DBMS)), and I used to read them through multiple books during under graduation. My first interaction with AI was during B. Tech through an introductory course in AI. My interest grew stronger when I was admitted to Indian Institute of Information Technology (IIIT) – Allahabad for a specialization in “Intelligent Systems”. There I learned about the architecture of intelligent systems, how AI is embedded in the circuits and about cognitive process modelling. When I read about the evolution of AI from its inception, I was keen on knowing about natural intelligence. I was also given the opportunity to work on natural intelligence from the perspective of brain science. I wrote my master’s thesis on brain signals (EEG signals) which marked the beginning of my research career in AI. I went on to develop a brain signal-based biometric system using minimal electrodes. “

What advice do you have for those who want to work in AI research? What are the most common mistakes that one can avoid?

“I have always advise picking up good research problems which can create an impact on society when the problem is solved.

My advice to newcomers is, when you go for a job after a PhD or research, no matter how many papers you have published, if your problem is not big enough, you won’t be able to impress the interviewer with your solution. In my opinion, even if you have limited publications, by solving good-quality problems, you can create an impression on the interviewers and expect a better career.

Continuous experiments and brainstorming sessions with peers eventually spark the idea, so do not hesitate to share the ideas with your colleagues. When you explain your thoughts or hypothesis to someone, you get clarity of your idea. It is alright if the listener is not from the same domain because even a fundamental doubt may drag you to a point you never explored.”

Can you share a list of noteworthy academic books / journals and conferences on artificial intelligence that have helped you?

Books	Journals	Conferences
The Elements of Statistical Learning – Trevor Hastie Deep Learning - Ian Goodfellow Artificial Intelligence - Peter Norvig Machine Learning: A Probabilistic Perspective – Kevin P. Murphy	IEEE Transaction on Pattern Analysis and Machine Intelligence Elsevier Neural Networks Elsevier Expert Systems with Application Elsevier Knowledge-Based System Elsevier Pattern Recognition IEEE Transaction on Neural Networks and Learning Systems Springer Applied Intelligence	Conference on Neural Information Processing Systems (NeurIPS) International Conference of Machine learning (ICML) Computer Vision and Pattern Recognition Conference (CVPR) International Conference on Computer Vision (ICCV) - Home \| ICCV 2021 (thecvf.com) International Conference on Learning Representations (ICLR) The Association for the Advancement of Artificial Intelligence (AAAI) International Joint Conference on Artificial Intelligence (IJCAI) ACM Conference on Recommender Systems (RecSys) - RecSys 2021 (Amsterdam) European Conference on Information Retrieval (ECIR) Special Interest Group on Information Retrieval (SIGIR)