About me.
AI and Machine Learning enthusiast broadly interested in topics of;
Artificial Intelligence,
Deep Learning,
Ethics in AI and Machine Learning,
Statistical Learning Theory,
Computational Learning Theory,
Topological Data Analysis (TDA),
Algorithms,
Mathematical Optimization and their applications in Computer Vision.
Short Bio: I completed my Ph.D. from Department of Computer Science at Rutgers University (2016), working under guidance of Prof. Ahmed Elgammal. Prior to that, I completed a Masters in Electrical Engineering and Computer Engineering from Rutgers University (2010), and a B. Tech. in Electrical Engineering from College of Engineering Pune (2008), India. My research interests lie in the areas of Deep Learning, Kernel Methods, Statistical Learning Theory, Topological Data Analysis (TDA) and Machine Learning with applications in Computer Vision. My doctoral dissertation focussed on Kernel Methods, specifically, learning kernels for kernel methods in Structured Prediction.
News.
[1/4/2016] I will be at New York Machine Learning Symposium.
Glad to win the AMEX Machine Learning Challenge held at the NY ML symposium! The goal was to predict fradulent credit card transactions from unsupervised transaction data.
[1/2/2016] Dissertation defense: Supervised Feature Learning via Dependency Maximization.
I have defended my dissertation titled 'Supervised Feature Learning via Dependency Maximization'. A big thank you to my committee members - Prof. Ahmed Elgammal, Prof. Pranjal Awasthi, Prof. Tina Eliassi-Rad and Prof. Lee Dicker. I would be joining Amazon (Seattle) as a Machine Learning Scientist in the Search and Discovery organization.
[1/7/2016] Arxiv Preprint arXiv:1601.01411 paper online in Learning (cs.LG)
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
[1/3/2016] Arxiv Preprint arXiv:1601.00236 paper online in Learning (cs.LG)
Supervised Dimensionality Reduction via Distance Correlation Maximization
[6/9/2015] Machine Learning Scientist Intern at Amazon Seattle.
I will be joining as a Machine Learning Scientist Intern at Amazon Seattle, in the Search and Discovery Group until the end of August.
[3/26/2015] Attending MLConf, NYC, 2015
I will be attending MLConf NYC, 2015, in New York City.
[11/19/2014] Symposium on Learning, Algorithms and Complexity
I have been accepted to participate in Symposium on Learning, Algorithms and Complexity, at IISC Bangalore, India.
[11/22/2014] Lens of Computation on the Sciences
I will be attending the Lens of Computation on the Sciences at Princeton, New Jersey.
[11/10/2014] Website update: Updated Resume.
Please find my update resume in the Links section or else click this!.
[11/07/2014] Amazon Fall Research Symposium, 2014
I will be presenting a poster at Amazon Research, Seatle at the Amazon Fall Research Symposium,2014.
[09/15/2014] Website update: New site!
Welcome to my new website, thanks to Twitter© Bootstrap!
[05/15/2014] IEEE CVPR, 2014
Attending CVPR 2014 from June-23rd to June 28th. Presenting a poster. See you there!
[05/13/2014] New England Machine Learning Day
Presenting our DISCOMAX project poster at MSR Cambridge at the New England Machine Learning Day (NEML) 2014.
[03/04/2014] Paper accepted CVPR 2014
Our paper on Twin Kernel Learning for Strucutred Prediction renamed "Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction" has been accepted to CVPR 2014!
[02/18/2014] Qualifying exam.
I passed the Qualifying Examination. Thanks to my commitee members - Prof. Ahmed Elgammal (advisor), Prof. Swastik Kopparty, Prof. Tina Eliassi-Rad, Prof. Amelie Marian.