
Rahul Sarkar
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
Bio
I am a Ph.D. student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford Univesity. I am advised by Biondo Biondi & András Vasy. I expect to graduate in 2022. I obtained a Masters from ICME in Computational Mathematics in 2017. Before coming to Stanford, I had the privilege to work for Schlumberger in USA and Mexico. I finished my undergraduate study in IIT Kharagpur, India.
Honors & Awards
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Shell Fellowship, Stanford University (2015-2016)
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DAAD WISE Scholarship, DAAD (Deutscher Akademischer Austauschdienst) (2010)
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Institute Silver Medal, Indian Institute of Technology, Kharagpur (2011)
Education & Certifications
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Ph.D., Stanford University, Computational and Mathematical Engineering
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MS, Stanford University, Computational and Mathematical Engineering (2017)
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Integrated BS and MS, Indian Institute of Technology, Kharagpur, Geophysics (Major), Physics (Minor) (2011)
Patents
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Rahul Sarkar, Marco Pistoia. "United StatesEfficient quadratic Ising Hamiltonian generation with qubit reduction", IBM Corporation., Nov 1, 2019
Personal Interests
Traveling, Physics, Finance.
Current Research and Scholarly Interests
Inverse problems, machine learning for seismic imaging, quantum computing
Projects
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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 Modified-Newton 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 Strong-Wolfe conditions, and conclude that the Strong-Wolfe conditions provide much better results.
Location
ICME, Stanford University
For More Information:
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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, School of Engineering
- Victor Storchan, Software Engineer, Adobe
For More Information:
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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/rsarkar-github/CS238-Automated-Aircraft-Landing-Reinforcement-Learning
Location
ICME, Stanford University
Collaborators
- Amy Shoemaker, School of Engineering
- Sagar Vare, School of Engineering
For More Information:
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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
Collaborators
- Andrea Zanette, School of Engineering
For More Information:
Work Experience
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Quantum Computing Graduate Intern, IBM Thomas J. Watson Research Center (6/17/2019 - 9/13/2019)
Location
Yorktown Heights, NY
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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
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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
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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
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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
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Density theorems with applications in quantum signal processing
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
2023; 430
View details for DOI 10.1016/j.cam.2023.115243
View details for Web of Science ID 000984552600001
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Joint inversion of the reflectivity and the velocity model
GEOPHYSICS
2022; 87 (1): R1-R12
View details for DOI 10.1190/GEO2020-0631.1
View details for Web of Science ID 000745267500001
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On sets of maximally commuting and anticommuting Pauli operators
RESEARCH IN THE MATHEMATICAL SCIENCES
2021; 8 (1)
View details for DOI 10.1007/s40687-020-00244-1
View details for Web of Science ID 000618360100001
- The index of invariance and its implications for a parameterized least squares problem arXiv 2020
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Texture Based Classification Of Seismic Image Patches Using Topological Data Analysis
81st EAGE Conference and Exhibition 2019
2019
View details for DOI 10.3997/2214-4609.201901608
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Illumination compensation of shadow zones in extended least squares migrated images by solving the linear inverse problem in tomographic full waveform inversion
89th SEG Annual International Meeting
2019: 4297–4301
View details for DOI 10.1190/segam2019-3215094.1
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Seismic velocity estimation: a deep recurrent neural-network approach
Geophysics
2019; 85 (6): 1--35
View details for DOI 10.1190/geo2018-0786.1
- On sets of commuting and anticommuting Paulis arXiv 2019
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Snell tomography for net-to-gross estimation using quantum annealing
SEG 88th Annual Meeting
2018: 5078–82
View details for DOI 10.1190/segam2018-2998409.1