Search Talks

In the two sessions we will explore what it means to approach physics through the lens of computation. I’ll introduce some of the ways in which computation has shaped physics in terms of modelling, measurement and also how we learn the subject. No prior programming knowledge is expected. We’ll begin from first principles, using just our hands, some paper, and a few simple rules to build up a system. 
 

In the second session, we’ll move to a visual programming environment (Tinkercad and Seelab). You’ll use a laptop (preferable) or smartphone to try out basic computational constructs like loops, conditionals, and sensor-based responses. The goal is to begin thinking computationally in a  physics context, and to see how we can even start doing simple experiments by interfacing with real hardware.

 Why can’t we predict the behavior of a gas molecule the same way we track a planet? In this talk, we’ll explore why classical and quantum mechanics alone are not enough to explain the messy, complex world around us—and why we need statistical mechanics to bridge the gap between microscopic particles and macroscopic behavior. Along the way, we’ll look at stochastic processes as powerful tools for dealing with randomness and uncertainty in nature. We’ll also touch on big ideas like chaos, self-organization, networks, biological complexity, and even how diseases spread—all from a statistical perspective. This talk aims to give you a fresh way of looking at the world: not as a set of exact rules, but as a dynamic system full of patterns, surprises, and probabilities

We will talk about large scale flows in the atmosphere, containing droplet or particulate matter, such as snow avalanches and pollutant dispersal. Being a late evening lecture, we'll not go into too much of the mathematics, but we'll try to appreciate why these problems are beautiful, hard and important.

AI is an increasingly integral part of doing research. This hands-on, very practical session will provide a brief overview of the capabilities, pitfalls, and ethical concerns with its use in Science.  We will cover four main domains: 1. data gathering and research summaries,
2. scientific writing, 3. graphs and statistics in science, and 4. coding. I will end with a sketch of major AI tools like Alpha Fold and agents, and give a glimpse of where the field may be going.

This lecture journeys through my multidisciplinary art practice grounded in the language of mathematics, mythology and metaphysics. From sacred geometries inspired by temple architecture to recursive drawings based on chess algorithms, my work seeks to bridge the material and the infinite. Influenced by constructs such as the golden ratio, Fibonacci sequences, Penrose tilings, and knight’s tours, my sculptures and drawings embody systems of order that reveal poetic possibilities. This talk will also explore how myth, memory, and cultural frameworks from South India—such as Kolams and cosmological diagrams—become vehicles for visualizing the vast, often hidden structures that underpin reality.
 

Counting the number of divisors of an integer is an interesting question that can be approached in multiple ways. We will explore this question and the multiple streams of thoughts and techniques that originate from it.

In this talk, we will explore the transformative role of artificial intelligence (AI) in modern banking. Since the release of ChatGPT in November 2022, AI technology has seen a rapid evolution. This rapid development has led to a surge in interest and adoption across various sectors. The presentation will explore the mathematical foundations and AI techniques that drive innovative solutions in the banking sector. We will look at how AI-powered solutions have reduced scams and frauds, the application of generative AI in enhancing customer messaging and accelerated decision making.

Optimal foraging theory provides a rich set of predictions for where, what, and how animals should forage in the natural world. These predictions are anchored on maximizing calorie intake while minimizing time and energy costs. However, food is more than calories, and animals should, in fact, be seeking specific protein and carbohydrate intakes to meet (darwinian) fitness goals. In this talk, I will go from classic optimal foraging theory to the nutritional geometry approach to illustrate how wild animals meet nutritional targets. Using our work with insectivorous and herbivorous lizards, I will share how foraging in the wild can be an impressive mathematical task.

‘Luck’ has always been seen as a vague and difficult-to-quantify aspect of sport. Commentators and players often talk about it, but in imprecise terms, putting an approximate ‘gut-feel’ value to its cost to the opposing team. (‘The dropped catch of Virat Kohli cost England at least 30 runs’)

In a collaboration with IIT-Madras, a small team at ESPNcricinfo worked on this aspect as part of a suite of new, in-depth metrics which would help understand better the nuances of T20 cricket. Luck Index attempted to define these specific ‘lucky events’ along with the team that benefited, and then work out the value of that event based on putting together an alternative narrative of what would have transpired in that team innings without that ‘lucky event’.

Luck Index was part of a set of three metrics developed by ESPNcricinfo along with IIT-Madras, the other two metrics being the Forecaster and Smart Stats. The Forecaster is a predictive tool which forecasters team score and win percentages for each team based on the current match situation, while Smart Stats gives impact values to each batting and bowling performance based on detailed match context inputs. These metrics have helped in a better and more nuanced understanding of T20 cricket.

Every time Netflix suggests what to watch or your phone predicts your next word, it is "predicting" based on a classification or regression model built using huge amounts of data. But here is the catch -- using all that data can be slow, messy, and even unnecessary. What if we could make smart predictions by using just the right amount of data? In this talk, we will explore how picking a small, well-chosen part of a dataset -- instead of the entire big dataset -- can still lead to accurate results. Through simple ideas and visual examples, we’ll see how this approach, called "subdata selection", can help us learn faster and smarter from the data around us.