There are still very few women- let alone Asian women- in Machine Learning, but Vidhi Lalchand is breaking the mould by pursuing a PhD at Cambridge University in the Department of Physics, Laboratory for Scientific Computing. What is Machine Learning? And why did Vidhi switch from a promising career with Credit Suisse to do this?
Vidhi Lalchand describes an interdisciplinary field that combines aspects of computer science and statistics .
“It is popularly called Machine Learning. The word is a bit of a misnomer as it is not as plain as about machines that can learn. It is about using mathematical algorithms to capture hidden relationships between variables (the variables are expressed in the form of data).”
She is also passionate about “the potential of existing machine learning methodologies to data driven problems in particle physics, astronomy, quantitative finance and medicine.”
Vidhi has also already completed an MPhil in Scientific Computing from Cambridge.
Before joining Cambridge in 2015 she worked as a quantitative analyst at Credit Suisse and as a high frequency trader at the Chicago based hedge fund, Citadel Securities (Europe).
Born in Chennai, Vidhi is not Tamil but feels South Indian. “My ancestors moved to Madras from Sindh in current day Pakistan just after Partition. I could be biased but Chennai serves as a microcosm of the Indian science effort,” she notes.
Early Learning and Influences
Vidhi Lalchand remembers doing “fairly well at school, this can be partly attributed to my mother. Academic achievement was important to her and as a result I developed a certain comfort level with long periods of concentrated study. When I look back from where I am now I can connect the dots. I spent hours reading up stuff, meandering, led by curiosity. I had a great academic record, with high grades from a private school.
I was accepted for a Masters at the LSE where I was supervised by Prof. Norman Biggs, a leading British mathematician, and there has been no looking back. At Cambridge, I have benefitted from the tenor of life here.”
Switching from the City
Why did Vidhi choose to change career paths? “While I was working with high calibre individuals and doing very interesting work (not always, but most of the time) I switched for two reasons - first, I had both the inclination and appetite for academia, and second, I missed the undercurrents of academic life and the freedom of living on my own terms.
In the current era where everything is connected and fast, the possibility that you can create something on a computer or a lab that can take the world by storm is a tantalising one.
But for that you need ultimate freedom, which is not afforded in a day job which doesn’t leave you with the energy for unrelated side pursuits.”
What is the reason comparatively few Indians, Indian women in particular, focus on scientific research?
Vidhi has a theory for this. “India doesn’t invest in building great science institutions, it is just not a priority at this time of its evolution. Furthermore there is the undeniable universal male bias in science subjects, and in particular physics and computer science. I think firstly the emphasis on the advantages (or joys) of a science education are weak especially for girls. There is an urgent need to widen people’s perception about who scientists can be. Further, excellence in research and the ability for abstract thought are a function of sustained effort and focus. Maybe this is something which is compromised by the ‘choice’ to have a family, and this choice does not affect men and women equally.”
Vidhi has plenty of expectations for herself. “I would define a successful Ph.D as one which is an assimilation of publishable results. I want to oscillate between working on interesting projects in industry and quiet periods of academic retreat.
The broadness of the research field allows me to work on problems in very distinct worlds. I would like to test machine learning to data from football games to detect predominant movement patterns in players which lead to goals. At the lab, I am focussing on applying learning algorithms to data from the high energy physics world.”
Vidhi wants to produce research that has a wide impact. “Something that influences the discourse after I am gone. The scale is unimportant, it can be something that is understood and used by a very small group of people. It must be effective and stand the test of time. Much of what G.H Hardy writes in the book ‘A Mathematician’s Apology’, about his expectations for his legacy, resonates with me.”