From Curiosity to Capability: A Student’s Journey into Machine Learning

 “Sir, do you have any project related to machine learning?”

A second-year undergraduate student asked me this question, eyes gleaming with curiosity and ambition.

I felt genuinely pleased—it’s always encouraging to see students eager to explore new areas. But alongside that, I was intrigued. Why the specific interest in machine learning?

“Is there a particular reason you’re looking for a machine learning project?” I asked.

“If I learn machine learning, I’ll get better career opportunities,” came the candid reply.

I paused to reflect. It’s a valid reason. After all, college education is often seen as a path to employability. In today’s job market, having a competitive edge is crucial—and machine learning has rapidly emerged as one of the most sought-after skills by companies offering attractive roles.

But this exchange made me think deeper—not just about skills, but about the bigger picture.

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In the 19th century, when automobiles were first invented, every component was handcrafted and assembled manually. The process was slow but labor-intensive, offering jobs to skilled workers who knew how to build and fit parts together.

Then came Henry Ford and his revolutionary moving assembly line—a system that changed manufacturing forever. While some jobs were lost, new ones were created to design, operate, and optimize the new process. The focus shifted from craftsmanship to process efficiency.

Later, the arrival of computers triggered another transformation. Computer-Aided Manufacturing (CAM) streamlined production planning and execution. Jobs based on outdated manual skills gave way to roles that required understanding systems and automation. At every step, companies aimed to reduce costs and boost efficiency—constantly evolving to stay ahead.

Now, Artificial Intelligence—and especially Machine Learning—is doing the same.

It’s no surprise that companies are replacing employees with obsolete skills in favor of those proficient in modern tools and techniques. Disruption, though unsettling, is part of progress.

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“Sir, I’ve taken an online course on Machine Learning,” the student added, bringing me back from my thoughts.

That’s good. Machine learning is undoubtedly a powerful skill. But here’s the question I often find myself asking:

Is learning the tool enough, or should we also understand the process it’s meant to improve?

Think back to those major technological leaps. Ford didn’t just introduce a tool—he reimagined the assembly process. John Parsons and Patrick Hanratty didn’t just use computers—they pioneered numerical control and CAD/CAM systems that reshaped manufacturing. Arthur Samuel, in 1959, created a checkers-playing program that laid the foundation for machine learning. And even earlier, in the 1950s, Alan Turing asked the bold question: Can machines think?

Machine learning, as a concept, has been around for over seven decades. So why did it take so long for industries to embrace it?

Because tools are only as useful as the understanding of where—and how—to apply them. It’s not just about knowing machine learning. It’s about knowing what problem to solve with it.

I smiled at the student and said, “Yes, I do have some problems you could work on. They may not look like typical machine learning projects at first glance, but they will help you deeply understand the underlying process. And once you do, you’ll see how machine learning can be used meaningfully to improve it.”

Why No One Asks Questions in Class — And Why They Should

It’s been twenty minutes since class began. I had just introduced a new topic and walked everyone through its applications and importance. Now came that crucial moment when I turned to the students and asked, “Any questions?”

Silence.

Not the peaceful kind. The kind that echoes. Some students buried their heads in their notebooks as if decoding a secret formula. A few tried their best to look asleep. Others just looked confused, wearing expressions that screamed, “Wait, what did he just teach us?”

I paused, giving them time to process, hoping a hand might go up. But nothing. The longer I waited, the more certain it became—they were waiting for me to just move on.

So I did what every teacher secretly dreads but knows is necessary: I pointed to students at random and asked them to explain what we’d just covered. They stammered through a few keywords, clearly unsure. When I asked a follow-up, the room returned to its natural state: silence.

This wasn’t about shyness alone. It was clear that many didn’t fully understand—but didn’t want to ask. That got me thinking.

Why do teachers ask, “Any questions?” in the first place? Is it just to check if the class is following? Or is it a clever excuse for a short break before jumping into the next topic?

And what about the students? Why don’t they ask, even when they clearly need help? Is it fear of embarrassment? Worry about sounding “dumb”? Or maybe they’ve already tuned out?

But here’s the thing: asking questions isn’t just about clearing doubts. It’s about opening a door for both the student and the teacher.

When a student asks a question, they’re inviting the teacher into their thought process. That’s a powerful thing. No one, not even the best teacher, can read minds. But a question gives us a glimpse inside. It shows where the student is struggling or what sparked their curiosity. It lets the teacher respond better, explain differently, and connect more deeply.

Over time, this kind of interaction also helps teachers understand how a student thinks. That’s incredibly valuable, especially when it comes to writing recommendation letters or helping students grow. Even if a student struggles academically, good questions show potential. And for those struggling to ask good questions? Teachers can guide them on how to get better.

All it takes is one question to start a conversation that could lead to understanding, confidence, and even opportunity.

So next time you're in class—whether you're completely lost or just a little unsure—ask the question. It might just change the way you learn.

Why It Rains Cats and Dogs: A Thermodynamic Tale

 “It’s Raining Cats and Dogs!” – What Does That Even Mean?

You’ve probably heard someone—most likely from an older generation—exclaim, “It rained cats and dogs today!” Now, if you pictured furry creatures tumbling from the sky, you're not alone. But of course, no actual pets were harmed in this expression.

So, what does it mean? Simply put, it's a quirky way to describe a heavy downpour. But have you ever wondered: why cats and dogs? Why not frogs and elephants? Or books and boots?

To uncover the strange beauty of this idiom, let’s take a surprising detour into thermodynamics and philosophy.

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Part I: Thermodynamics and the Art of Rain

In the world of thermodynamics, we often analyze systems using two approaches:

1. Control Volume (Open System): Here, we fix our gaze on a specific region in space—say, the inside of a jet engine—and track the energy and mass flowing through it. It’s like watching what enters and leaves a room without following the guests around.
   
   

2. Control Mass (Closed System): This time, we focus on a specific chunk of matter—like a mix of air and fuel in a car engine—and observe how it transforms, wherever it goes. We follow the guests through the party, watching how they change costumes.
   
   

These two perspectives—space-focused vs. object-focused—are key to understanding both thermodynamics... and pets.

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Part II: Cats, Dogs, and Human Nature

Now for the fun part: philosophy. If you've ever had a cat or a dog, you’ll know they behave quite differently.

• Cats are homebodies. They get emotionally tied to places. Move their favorite cushion, and you’ll hear about it.
  
  

• Dogs, on the other hand, are all about people. You could shift homes, cities, even planets—and your dog will wag its tail as long as you’re there.
  
  

So, if cats are like open systems—rooted in a particular space—then dogs are like closed systems—attached to a particular mass (you!).

Humans, too, reflect this dichotomy. Cat people tend to be inward-focused, loving their cozy corners and personal space. Dog people are more outward facing, thriving in social circles and human connections.

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Part III: And Now... the Rain

So, where does rainfall fit in?

When someone says, “It’s raining cats and dogs,” they’re unknowingly referencing both space and mass, just like our two thermodynamic systems. It means the rain is coming down in torrents (mass) and drenching everything across the area (volume). The phrase playfully captures both the intensity and scale of the event.

So next time you hear it's raining cats and dogs, remember: it’s not just an idiom—it’s an open and closed system coming together in poetic chaos.

Book Review: The Maverick Effect

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Reading biographies is the best way to draw inspiration and learn lessons from other's experiences. The biography of an individual can give us an idea of the incidents that shaped the person into what they have become. Mahatma Gandhi's book "My Experiments with Truth" is one such example. For leadership and entrepreneurial enthusiasts, reading the biography of an institution or company helps. The story of "Made in Japan" by Akio Morita of Sony is the best example of this. How often have we come across the biography of an association such as NASSCOM?

I recently read "The Maverick Effect" written by Harish Mehta. The book narrates the growth of NASSCOM as a multilateral body whose sole aim is to tap the huge economic potential the IT industry offered, convincing and negotiating with other companies/organizations to represent their challenge in an unified voice, and lobby with the government to keep the economy rolling. I strongly recommend this book to all young and fresh bright minds of India.

Disclaimer: The link to buy the book contains personal affiliate id.

Nocturnal cooling of earth and atmosphere

Sun is the primary source of energy for all living things on earth. Sunlight is needed for photosynthesis by the leaves of the plants, which in turn produces food in the presence of few other ingredients. The heat from the sun evaporates water, forming clouds and rain, creating a water cycle. The land surface gets heated up due to sunlight, causing the atmosphere to become warmer and more liveable for most creatures. A fraction of the energy gained by various parts of the earth’s surface is transferred to the atmosphere.

Air in the atmosphere is a mixture of gases, predominantly oxygen and nitrogen, with other trace gases. Air is often loaded with a dynamic distribution of dust or solid suspended particles and water in liquid, vapour, and solid phases. Air which does not contain a significant amount of solid and liquid particles is called dry air. Certain gases in dry air can potentially absorb the energy transferred back by the earth’s surface, making the atmosphere we live in habitable. If the earth and its atmosphere retain the heat from the sun continuously, the temperature will rise till it becomes unbearable. Certain gases in the atmosphere, especially carbon dioxide, have this property. We call this greenhouse gas.

But what happens in the absence of sunlight? The darker parts of the earth no longer get heated up. The heat from the earth’s interior (a few meters below the earth’s surface) will reach the earth’s surface. The surface, in turn, will try to transfer the heat accumulated during the daytime to colder surroundings, which are the surrounding atmosphere and the deep dark space. The heat reaching from the interior to the surface is called conduction. The surface, in turn, transfers heat to the surrounding air in the form of convection, and a part of the energy will be radiated by the earth’s surface.

The radiated component is quite complex. Part of the energy released by the surface gets absorbed by greenhouse gases in the atmosphere. These are carbon dioxide, water vapour, clouds, methane, etc. The remaining part will escape the atmosphere to reach the colder space. Under clear dry sky conditions, heat convection to the surroundings becomes negligible. The energy stored from the deeper soil layers will conduct to the surface, which in turn gets radiated to the cold space. The balance between this conduction and radiation helps us to understand how quickly the earth’s surface gets colder at night, also known as nocturnal cooling.

Under suitable environmental conditions, people worldwide have used nocturnal cooling for the mass production of ice, even when the surrounding atmosphere is warmer! Tetsu Tamura, a Japanese meteorologist, in his published work [1], describes how soil conduction and clear sky radiation contribute to the faster rate of surface cooling. This work was published more than a century ago and is considered a classic example of a conduction–radiation problem in heat transfer.

[1] S. Tetsu Tamura, (1905), Mathematical Theory of the Nocturnal Cooling of the Atmosphere, Monthly Weather Review, Vol. 33, pp. 138-147.

Thermal Protection System

Rub your palms together, and you will feel the heat generated between them. This frictional heat often keeps us warm during cold winter nights. So what happens if you rub your palms faster? You will feel more heat. Imagine that you could rub your palm at 7500 m/s! Your palm can catch fire at this speed. Rockets which are used to put satellites in orbit or other heavenly objects like Moon, Jupiter, etc., are typically launched or re-entered through our atmosphere travelling at incredible speeds. The outer surface of the rocket and the surrounding air from the atmosphere act as two palms.

The extreme heat generated by friction is called aerodynamic heating. The generated heat is so high that aluminium (often used for building the space vehicle) melts and gets removed from the main body. A thermal protection system (TPS) is needed to protect the space vehicle, which can withstand extreme heat and yet does not allow the heat to penetrate the interior of the vehicle.

Heat penetrates through a solid material through conduction. If the conductivity of solid material is very high (true for aluminium and other metals), they can quickly transfer the heat to the other side. A good TPS should have low thermal conductivity to prevent the heat from penetrating inside. At a high heat rate, the vehicle’s temperature can also be very high (exceeding 1500 deg C), at which most materials melt. A good TPS should also be made from a material having a very high melting point.

However, in space, mass is a premium. Every mass added to the vehicle’s design will reduce the mass of scientific payloads such as sensors and instruments. As a trade-off, during the re-entry, a small part (layer) of the TPS material is allowed to melt. This serves two purposes: Firstly, melting involves latent heat (the amount of heat absorbed by the material to change its phase from solid to liquid). As latent heat is usually very high for most substances, part of TPS gets removed upon melting, carrying significant heat away from the vehicle. This ensures that less heat remains for conduction. The removed TPS material now allows a fresh layer of TPS to absorb the heat, and the process continues till the entire TPS material is removed by this process. The other purpose is that since melted TPS material gets detached from the remaining solid part, the melt layer gets removed from the vehicle, thereby reducing its overall weight.

To summarize, more TPS material results in an increased non-scientific payload of the vehicle. While lesser TPS material could result in the entire TPS getting melted off, exposing the base material (an alloy of aluminium) to the harsh environment. So the thickness of TPS should be designed considering the above constraints.

In a recent paper co-authored with Prof. Katte [1], we presented a one-dimensional transient heat conduction model involving phase change at the boundary to simulate the performance of TPS as a function of time. The model considers the outer surface of TPS exposed to extreme heat, while the other surface shielding the base material is insulated. The simulations are done until the TPS base reaches a predefined temperature in non-dimensional form.

Reference:

S. R. Kannan and S. S. Katte, (2018), Numerical Investigation and Correlations for Heat Diffusion through Planar Ablative Thermal Protection Systems, Thermal Science & Engineering Progress, 7, 279-287.

Intercomparison between IMD ground radar and TRMM PR observations using alignment methodology and artificial neural network

Echolocation refers to locating the size and distance of objects in the surroundings using echo. Thousands of species use echolocation to navigate the world. Drawing inspiration from this nature-driven technology, humans have designed and built radar systems to detect and track objects remotely.

During the Second World War (when applied sciences flourished), radar technology was used to detect and target enemy's warplanes. A series of false-alarm whenever it rained offered an opportunity for developing radar to observe weather systems.

There has been no turning back since then. Radar is considered an irreplaceable modern technology that can provide accurate rainfall measurement over a large area. Meteorological organizations from around the world soon integrated weather radar into their observation systems. The polar plot (aka radar chart) helped us detect and track organized precipitation systems and provided more insight into the microphysics of rainfall.

Today, more than 4000 radar stations are built worldwide that continuously monitor the weather on a large scale. When ground stations are not sufficient due to their restricted mobility, radars are also carried on specially designed aircraft for conducting field observations such as the Indian summer monsoon.

In 1997, NASA of the U.S and JAXA of Japan jointly launched the Tropical Rainfall Measuring Mission (TRMM) satellite. The satellite was the first to carry a radar onboard to monitor precipitation systems on a global scale within the tropics. This allowed researchers worldwide to tune in to their surface observations whenever the satellite made an overpass.

However, cross-comparison of radar observations from satellite and ground-based poses several challenges. Even though technically, both the instruments work on the same principle, there could be a mismatch between them due to the difference in their viewing geometry, radar frequency, and other issues such as clutter.

Concept diagram to illustrate intercomparison of ground and space radar

In a recent paper co-authored by my research scholar [1], we had compared the ground radar observations maintained by the Indian Meteorological Department (IMD) with TRMM's Precipitation Radar using alignment methodology. The comparison study showed that the ground radar overestimates rainfall during the Indian summer monsoon period of 2013. We demonstrated that the positive bias of the ground radar measurement could be "corrected" to match with TRMM PR observations using an artificial neural network.

[1] Alok Sharma and Srinivasa Ramanujam Kannan, 2021, Intercomparison between IMD ground radar and TRMM PR observations using alignment methodology and artificial neural network, Journal of Earth System Science, Vol. 130, Article ID 0020.