Curriculum Vitae

The detailed PDF verison of my CV can be found here - Update coming

Research Interests

I seek to solve theoretical machine learning problems using ideas from mathematical optimization, probability theory and statistics.

• Research Interests - Statistical Machine Learning, Large Scale Optimization, Interactive Learning, Algorithms.
• Other Learning Interests - Probability theory, Differential Geometry, Complexity Theory, Statistics.

Education

• M.Sc. in Computer Science, 2016 - 18
Chennai Mathematical Institute
• B.Sc. in Mathematics and Computer Science, 2013 - 16
Chennai Mathematical Institute

Work experience

• September 2019 - Present: Research Fellow
• Max Planck Institute for Software Systems, Saarbr$\ddot{u}$cken, Germany
• Project: Teaching and Learning complexity of hypothesis classes, e.g., regions induced by intersections of Halfspaces and non-linear ERM learners e.g. kernel perceptrons.
• Summer 2017 : Research Intern
• IBM India Research Lab (Bengaluru, India)
• Supervisor: Dr. Karthik Sankaranarayanan
• Project: Tabular Data Summarization
• Summer 2015 : Research Intern
• Indian Institute of Technology (Kanpur, India)
• Project: Circuit Complexity: MCSP (minimum circuit size problem)
• Summer 2014 : Research Intern
• Indian Institute of Technology (Delhi, India)
• Project: Graceful Labelling of Complete Graphs

Talks

• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach (BY CHO JUI HSIEH ET AL)
• SLIDES TALK (2018, Aalto University, Finland)
• Presentation given as part of the seminar course on Adversarial Deep Learning
• Manifold Learning and Tensor Decomposition
• SLIDES TALK (2018, CMI, India)
• Presentation given as part of the defense of the Master’s thesis at CMI.
• Tabular Data Summarization
• POSTER PRESENTATION (2017-18, IBM Research, India)
• Presentation given as part of the internship at IBM Research.
• The sum of d small-bias generators fools polynomials of degree d (BY E VIOLA)
• SLIDES TALK (2017, IIT Madras, India)
• Presentation given as part of the course Pseudorandomness at Indian Institute of Technology, Madras.
• Hierarchical Optimistic Optimization (HOO)
• BOARD TALK (2017, CMI, India)
• Presentation given as part of the seminar course Advanced Machine Learning. It covers the techniques from the paper $\chi$-armed bandits by Bubeck et al.
• Algebraic Independence and Blackbox Identity Testing (BY MALTE BEECKEN, JOHANNES MITTMANN, NITIN SAXENA)
• BOARD TALK (2016, CMI, India)
• Presentation given as part of the course Arithmetic Circuits.

Teaching

• Teaching Assistant: Machine Learning and Data mining, Fall 2017 (CMI)

• Teaching Assistant: Discrete Mathematics, Spring 2016 (CMI)

Professional Services

• Reviewer for International Conference on Artificial Intelligence and Statistics (AISTATS’21)