Theoretical High Energy Physics ◆ Machine Learning
Introduction
I'm a PhD candidate in High Energy Theory group at Northeastern University,
and a graduate member of the NSF IAIFI.
I am fortunate to be advised by Prof. Jim Halverson.
My research lies at the intersection of Effective Field Theories and Machine Learning.
Driven by the need to understand the ultraviolet completion of our universe and origin of space-time,
I use Machine Learning to explore instances of computationally complex problems in fundamental physics.
Conversely, I use principles of particle physics and string theory to understand AI and Deep Learning.
Curriculum Vitae
Outreach
Apart from Fundamental Physics + Intelligence, I am passionate about promoting equity for underrepresented minorities in STEM.
To that end, I joined the Early Career and Equity Committee at IAIFI,
and Graduate Students Council of Northeastern University College of Science.
Additionally, I started monthly meetings and networking activities for Graduate Women in Physics at Northeastern University.
I'm driven by the need of better education opportunities for women, starting from grass-roots to graduate level, especially in poorer economies.
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This is underlined and this is code: for (;;) { ... }. Finally, this is a link.
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Preformatted
i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
