Rahul Sarkar
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
Bio
I was born and brought up in India, where I completed my undergraduate education. Upon graduation, I joined Schlumberger and worked on highend imaging applications in USA and Mexico. After spending 4 years in the industry, I decided to return to graduate school and completed a Masters in Computational and Mathematical Engineering at Stanford university in 2017. I'm currently a Ph.D graduate student in the same department, with my primary interests in convex and nonconvex optimization.
Honors & Awards

Shell Fellowship, Stanford University (20152016)

DAAD WISE Scholarship, DAAD (Deutscher Akademischer Austauschdienst) (2010)

Institute Silver Medal, Indian Institute of Technology, Kharagpur (2011)
Education & Certifications

Ph.D., Stanford University, Computational and Mathematical Engineering

MS, Stanford University, Computational and Mathematical Engineering (2017)

Integrated BS and MS, Indian Institute of Technology, Kharagpur, Geophysics (Major), Physics (Minor) (2011)
Stanford Advisors

Andras Vasy, Doctoral Dissertation CoAdvisor (AC)

Biondo Biondi, Doctoral Dissertation Advisor (AC)
Personal Interests
Traveling, Physics, Finance.
Current Research and Scholarly Interests
My current research interests are related to a category of large scale, convex and nonconvex optimization problems that arise in the context of waveform inversion.The nonconvexity of the problem presents interesting mathematical challenges, while the huge amounts of data that need to be processed present significant computational challenges.
I'm currently looking at frequency domain methods using sparse matrix factorization algorithms to solve the Helmholtz equation, in order to computationally speed up the solution to these large scale optimization problems, which often involve a million to a billion variables.
I'm also looking at several Artificial Intelligence and Machine Learning techniques which could help solve some interesting problems in Geophysics, where such techniques are yet to create any significant impact. In particular, I'm looking at using techniques from topological data analysis as a tool for detecting qualitative features in large scale seismic data sets.
Projects

Finding a cover for an ellipse with N rectangles, Stanford University (1/1/2016  3/31/2016)
This is an interesting class project that I did as part of my "Numerical Optimization" class at Stanford. The problem statement is to find a cover for an ellipse with N rectangles, such that the area outside the ellipse is minimized. In this project, I first formulate an equivalent problem that reduces to finding a cover for a circle with the same number of rectangles. The problem is then solved using a ModifiedNewton Hessian based approach, and the performance is compared against Steepest Descent. It is found that Modified Newton based approach is much more efficient, although each iteration is significantly more expensive compared to Steepest Descent. I also compare the performance of using different line search algorithms like Goldstein vs StrongWolfe conditions, and conclude that the StrongWolfe conditions provide much better results.
Location
ICME, Stanford University
For More Information:

Dynamic Asset Allocation using Reinforcement Learning, Stanford University (9/20/2016  12/16/2016)
This was a project done as part of the CS221 (Artificial Intelligence) class at Stanford University. In this project, we used reinforcement learning to perform asset allocation between two asset classes  a bond and a stock. Model based and model free techniques were used to perform this task, and the results were compared against one another and also against historical returns of the stock.
Location
ICME, Stanford University
Collaborators
 Enguerrand Horel, Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016, School of Engineering
 Victor Storchan, Software Engineer, Adobe
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Automated Aircraft Touchdown, Stanford University (10/1/2016  12/31/2016)
This was a class project done as part of the "Decision Making Under Uncertainty" class at Stanford University. In this project we experiment with a few Reinforcement Learning algorithms with the goal to safely land an aircraft, in the presence of stochastic winds. The project was implemented in python 2.7. Code on github: https://github.com/rsarkargithub/CS238AutomatedAircraftLandingReinforcementLearning
Location
ICME, Stanford University
Collaborators
 Amy Shoemaker, School of Engineering
 Sagar Vare, School of Engineering
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Information Directed Reinforcement Learning, Stanford University (1/1/2017  3/30/2017)
This is a project done as part of CS334 (Advanced Reinforcement Learning) class at Stanford University. In this project, we explored an efficient exploration strategy based on information directed reinforcement learning. Details are provided in the attached paper.
Location
ICME, Stanford University
For More Information:
Work Experience

Quantum Algorithms Researcher, QC Ware Corp. (7/1/2018  9/23/2018)
Research into quantum algorithms on finance, and topological data analysis.
Location
Palo Alto, USA

Application Developer, QC Ware (7/1/2017  9/25/2017)
Worked on binary optimization problems that can be solved using a quantum computer. Current projects include : Financial Applications, Topological Data Analysis.
Location
Palo Alto

Geophysicist, Schlumberger (10/1/2013  8/31/2015)
My role in this position is to foster growth of the new and advanced technology businesses in Mexico, which include Full Waveform Inversion, Seismic Guided Drilling, Advanced Depth Imaging among others. Other responsibilities include training the staff in using these technologies, assist the center in winning new businesses and help in the execution of these projects.
Location
Mexico

Incubator Program, Schlumberger (7/24/2011  9/30/2013)
I was part of a cohort of ~10 people recruited globally for a special incubator training program. Worked with advanced seismic imaging algorithms and model parameter estimation using full waveform inversion and ray based tomography.
Location
Houston, USA
All Publications

Snell tomography for nettogross estimation using quantum annealing
SEG 88th Annual Meeting
: 5078–82
View details for DOI 10.1190/segam20182998409.1