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Published: Friday, September 04 2020 00:08
For physics Ph.D. student Amitava Banerjee, coming to the University of Maryland was a giant stepβliterally. Banerjee grew up more than 8,000 miles away in
Amitava BanerjeeKolkata, India, with a strong interest in science early on. Both of Banerjeeβs parents are physicists, and when he did his undergraduate and masterβs work at Presidency University in Kolkata, his own future in physics started coming into focus.
βAs I learned more and more, I saw that physics claims it can solve anything in the universe starting from small atoms up to the scale of the full universe and that kind of mission is very, very grand,β Banerjee said. βI feel that if I want to pursue any particular interest, physics will actually help me do that.β
By the time he was ready to begin his doctorate in fall of 2018, Banerjee had done his homework. Though heβd only left India once before, distance was no obstacle. He knew exactly where he wanted to go.
βI was already following the work of many UMD faculty members and I also knew many alumni personally,β Banerjee said. βI felt like I had a connection with them right from the beginning.β
That connection inspired Banerjee and his research, almost from the day he arrived.
βBefore coming to UMD I thought that I would be doing work in atomic, molecular and optical physics,β Banerjee recalled. βBut I got into working with the Chaos Group led by Professors Edward Ott and Rajarshi Roy and others, and they gave me some problems that I found were too interesting to ignore. So, I started working on them and Professors Ott and Roy became my advisors.β
Now, Banerjee is working to understand real physical systems via computer models made with artificial neural networks that learn to behave like those systems.
βAs of now, we have developed a theoretical framework using machine learning that can tell you how different components of a big system are influencing each other, just by looking at their behavior over time,β Banerjee explained. βExamples of such tasks can be very broad. They include understanding how neurons in the brain are wired together just by observing their firing patterns, or how different genes in some unknown biochemical circuitry turn each other on or off, or understanding how different elements of global climate affect each other only by looking at the weather of several days.β
Banerjeeβs hope as he continues to test this framework is that by looking inside neural networks or similar computer models, we may one day be able to better understand, or even predict, useful information about real systems in the physical world.
His research is ongoing, but in March 2020 when the COVID-19 pandemic hit, everything stopped. Labs were off limits and Banerjee had to improvise, recruiting his housematesβphysics classmates and an alumnus, Wrick Sengupta (Ph.D. β16, physics)βand taking his work in a different direction. In a letter to the online magazine Physics, he described the experience.
βWe have been able to uncover connections between concepts in vastly different areas of physics,β Banerjee wrote. βWhen we are not busy collaborating, we share in the housekeeping and eat free-delivery or buy-one-get-one-free pizzas. It also helps to have a Netflix subscription, a good stock of red wine, and someone who can bake cheesecakes.β
While Banerjee was working from home, his focus shifted to trying to create a simplified model for a certain class of plasmas, often difficult to deal with computationally because of their complex interactions. The idea was to map the plasmas to a very different set of systems.
βThese systems have traditionally been employed to describe synchronization in the natural world,β Banerjee said. βYou have synchronization all around in nature, like fireflies flashing in concert in the evening or crickets chirping together or frogs croaking. We have very nice analytical theory describing synchronizations and we have simple models predicting the emergence of synchronous behavior as seen in nature, so I tried to map plasmas onto those models in order to get a simplified model of plasmas. Weβve been able to take this stuff to a point where itβs too interesting to discontinue.β
Though science and physics drive Banerjeeβs research, he also has a strong creative side. When he missed his motherβs cooking after coming to the U.S., he taught himself how to prepare the traditional dishes he grew up with.
βWhen I came here, it was kind of a challenge,β he said. βI took it as a challenge, cooking simple foods like rice and curries, and now I can cook fairly good stuff.β
And although he wonβt call himself a photographer, Banerjeeβs Instagram account features a colorful patchwork of cellphone images, reflecting the simple beauty he sees around him.
βI like capturing simple things in nature, the little things,β he said.
Banerjeeβs creative side is also reflected in one of the studies heβs most proud of. Published in 2019 in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, this work used a neural network model of systems to infer their underlying interaction networkβin the form of a picture of meteorologist and mathematician Edward Lorenz.
It was Lorenzβs simple model for atmospheric convections, and computer simulations of that model in the β60s, which eventually led to a paradigmatic system of chaos theory. And they have been applied to model a variety of other systems since then, like lasers and electric circuits.
In a sense, Banerjeeβs research connected the dots.
βIn my work, I have a large number of interconnected Lorenz systems and I try to know how they are interacting using a neural network model of the system,β Baner
Edward Lorenzjee explained. βTo make things more interesting, we used a pixelated portrait of Edward Lorenz to construct the interaction pattern for the Lorenz systems. So now our task of recovering those interactions is equivalent to the reconstruction of the portrait of Lorenz. You can readily see how well our technique works for this case.β
Banerjee later learned about an untold part of the Lorenz storyβa woman named Ellen Fetter did many of the major calculations for Lorenzβs theories but wasnβt recognized for her work until decades later. To honor Fetter, and others like her, Banerjee repeated the process he tested with the Lorenz image, but used Fetterβs picture instead. 
βJust as we saw in the movie βHidden Figures,β many women were involved in the huge computations behind major scientific discoveries, but were never recognized,β Banerjee said. βI thought that with an increasing general interest to diversify physics and recognize the hidden faces behind many famous discoveries, now is a good time to tell the stories of underrepresented people through our ongoing research.β
For Banerjee, sharing these stories feels personal, because it is.
βMy mother had a Ph.D. in physics but she had to leave academia when I was born,β Banerjee explained. βAt those times it was harder, because in India I donβt think that we had really good childcare facilities. Knowing my mother had to quit academia makes me feel like itβs my obligation to make physics more diverse and more welcoming so more people can join us.β
Promoting gender and racial inclusion and equality in physics and all the sciences is as important to Banerjee as his research. Involved in groups like Women in Physics and inspired by the Black Lives Matter movement, he sees opportunities for change.
βRecently I became interested in working toward making physics more inclusive and also learning about what I should do or should not do to contribute more toward that,β he said.
Where will Banerjee be five years from now? Heβs not certain. But he believes with his love for teaching and mentoring, it will probably be somewhere in academia. One thing he knows for sureβhe wants to be part of a different kind of future, and not just for physics.
βIf you ask people whatβs the biggest open problem in physics, theyβll probably tell you itβs quantizing gravity or understanding the nature of dark matter or something like that,β Banerjee said. βBut I would say the biggest open question in physics and society at large is how to make us more diverseβbecause we canβt advance without answering that.β
Written by Leslie Miller
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