Learn to code not by reading books but by coding


Sabin Hashimi is currently working on Building Machine Learning based Trigger System for LHCb, CERN.

He works on Machine Learning and Deep Learning applications in High Energy Particle Physics.

INDIAai interviewed Sabin to get his perspective on AI.

It’s admirable to see a physics student become an AI researcher. What inspired you to pursue a career in AI?

My passion for science and technology is my main motivation to become an AI researcher. It’s always interesting to be in a domain that is advancing at a fast pace. The AI ​​community includes people across different domains, where everyone is curious and passionate about advancements in technology and research. Physics helped me with critical thinking, and AI-based research helped in providing flexibility in research. Moving forward in the field of AI, it started getting more exciting, and I decided to pick AI as my primary research domain.

Instead of sliding away from Physics, my current research uses AI as the key-supporting tool in solving a problem that was initially done using traditional methods and transforming it into a more efficient solution using computational physics and AI.

Tell us about your PhD research in particle physics and machine learning. What are the main areas of concern?

In the Large Hadron Collider at CERN, two proton beams are accelerated to the speed of light (99.99%) and collide at the experiment sites designed to study subatomic physics. When particles collide at a very high velocity, there will be a shower of many subatomic particles within. My PhD research is primarily focused on particle reconstruction of rare decays resulting from the collision of protons at the experiment. It advances in developing a trigger system using Machine Learning that cherry-picks the particle tracks of rare decays in real-time.

The primary concern is the massive volume of data generated at each collision and designing a decision-making system that works in real time. In addition, due to the physics behind the rare decays, finding the decays of interest is challenging, and ML helps identify and characterize rare-decay tracks from other signals.

What are your current responsibilities and activities at CERN’s LHCb?

The Large Hadron Collider Beauty(LHCb) Experiment primarily studies the CP violations and rare decays of particles. My area of ​​research is mainly focused on developing a Machine Learning based pipeline for a software-based trigger system. As a developer, the responsibility includes being up-to-date on the changes happening in the detectors and software codebases, presenting progress on the research work with the collaboration of experts, and taking data-taking shifts at the experiment site.

What are the advantages and disadvantages of doing research in Poland as an Indian scholar?

It’s great to work in a dynamic environment with a peer group of researchers who are curious to see the developments in research. Doing research abroad can open more opportunities in front of you, including working closely with the pioneers and domain experts. This approach would give great global exposure that could lead the researcher to decide how to proceed further in their research. In addition, the research space that global research institutions provide will inspire you to develop yourself as a researcher.

There are fewer disadvantages, apart from leaving the home country and the people. But it is very promising to see the research and development in India in the past few years, leading to more opportunities within the country.

What are the three most pressing societal issues you hope to address with machine learning? Or if you want to make a unique appeal for any of them?

Machine Learning is still in its early phase. I understand the developments in AI are significant, but if we consider the potential of AI for a good cause, we are still yet to be there. There is a wide range of issues we can solve using AI. It is where the multi-domain expertise makes AI more prominent. For instance, AI-based advancements in healthcare are novel cases that pique our interest. We had developments in healthcare in the past, but AI gave a different approach to solving the problems we have had for decades. Some of the latest developments in AI-based drug delivery, target identification for cancer cure, etc., are on the top of the list. In addition, AI shows promises to predict weather and natural calamities with more accuracy than the traditional methods we had in the past.

In a nutshell, the potential of AI is vast. Rather than considering it a different domain altogether, I’d like to consider it a supporting tool that helps researchers look at the problem from a different angle. We are gradually transforming the existing technology into a novel system embedded with ML and AI.

What do you think about India’s AI education system? How does it measure up to the global situation?

The developments in India’s AI are on a global scale. There’re long-term and short-term courses that you can enroll in from prestigious colleges and other institutions in India. Depending on the career path, with some research, it is easy to decide on the course. In addition, there are many more opportunities to study online courses from experts.

What, in your opinion, should be upgraded at Indian universities to advance AI? What should their course of action be?

AI research is a vast domain, so the course can be as broad as the domain would be. Some courses revolve around the same concepts and how these are developed in the programming side of research. But, AI is based on solid fundamentals of Mathematics and Statistics. Universities focused on advanced AI should be a place for students interested in learning and developing their problems and finding solutions using advanced computational tools.

Apart from that, the Universities can develop associated research labs where the experts can design short-term courses and support the AI ​​researchers who are really into AI. Note that it would be better to get to the domain of computational skills and novel tools like AI in a different environment than that of traditional classes.

What advice do you give Indian students working in or aspiring to enter the ML field?

There are a lot of research opportunities out there. AI is in the early phase of development, and it is growing and developing quickly. It is one of the most exciting fields in which I have worked. The job prospects are open, and so is the competition. We are on the road where we have a long way to go, and we need more people interested in keeping the journey going. The question is, is it something you’d like to do? If yes, I’d suggest you start with getting a good grip on Mathematics, Probability, Calculus, and Statistics. Then, get a quick guide on what AI research is all about, try out small projects closely aligned with your current project, and how you can solve the problem using ML or even Simple Data Analysis using Programming.

Above all, Learn coding not by reading books but by coding!

Which books and resources would you recommend to ML aspirants?

There are a lot of resources available. The beauty of the community is that most of the research is open-source, where you can see the codebase and even contribute to the project.

To get started, pick up miniature introductory courses you can do for free, work on projects and slowly increase the challenges on the projects, learn git and build a git portfolio. In addition, participate in ML competitions and hackathons, where you can cross-check different approaches by other participants. Out of all this, try different methods and projects and keep curious.

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