I finished my undergrad in ECE with a minor in Management Engineering in April 2015. Currently, I am a master's student at the Center for Theoretical Neuroscience, University of Waterloo. I also have two years of experience working in industry in the field of engineering which I got during my undergrad.
My master's research is focused on exploring the biological plausibility of bayesian computations, specifically can we implement bayesian inference using spiking neural networks? I presented a model at CogSci 2017 which is capable of doing this and am now working on learning priors for the inference problem at hand in a generalizable way. The goal is to suggest a neurally plausible mechanism for how humans might be learning these priors.
In general, I am highly interested in exploring the fundamental questions pertaining to the world which we do not have the answers to. Some examples are: how do we infer things? What enables us to understand or learn? How do we recognize things we have encountered before? In short, the mystery of how our brain works! Another set of questions which fascinate me are: how did our universe originate? how big is the universe? Is there more life out there (I think there probably is)? In short, my love for the universe!
Interests:
Computational Neuroscience
Artificial Intelligence
Hobbies:
- Enjoy Watching TED Talks, Reading research papers, watching videos on Artificial intelligence, Quantum Physics, Brain and Universe!
- Working of Brain - I believe that the answers to the fundamental questions can trigger significant advancements in technology (potentially enabling us to build intelligent machines) as well as the field of health and medicine.
- I also enjoy singing and dancing, love running, biking and hiking and practice yoga & meditation.
- I have been involved in volunteering, mentoring and tutoring within as well as outside of the university and have always loved doing it.
linkedin: ca.linkedin.com/in/sugandhasharma17/
My master's research is focused on exploring the biological plausibility of bayesian computations, specifically can we implement bayesian inference using spiking neural networks? I presented a model at CogSci 2017 which is capable of doing this and am now working on learning priors for the inference problem at hand in a generalizable way. The goal is to suggest a neurally plausible mechanism for how humans might be learning these priors.
In general, I am highly interested in exploring the fundamental questions pertaining to the world which we do not have the answers to. Some examples are: how do we infer things? What enables us to understand or learn? How do we recognize things we have encountered before? In short, the mystery of how our brain works! Another set of questions which fascinate me are: how did our universe originate? how big is the universe? Is there more life out there (I think there probably is)? In short, my love for the universe!
Interests:
Computational Neuroscience
Artificial Intelligence
Hobbies:
- Enjoy Watching TED Talks, Reading research papers, watching videos on Artificial intelligence, Quantum Physics, Brain and Universe!
- Working of Brain - I believe that the answers to the fundamental questions can trigger significant advancements in technology (potentially enabling us to build intelligent machines) as well as the field of health and medicine.
- I also enjoy singing and dancing, love running, biking and hiking and practice yoga & meditation.
- I have been involved in volunteering, mentoring and tutoring within as well as outside of the university and have always loved doing it.
linkedin: ca.linkedin.com/in/sugandhasharma17/
| CV.pdf |
