Jul 2009 to May 2013
Bachelor of Technology (Honors)
Department of Civil Engineering,
Indian Institute of Technology, Kharagpur.
Thesis: Extremes of non-stationary stochastic load sequences
Aug 2015 - Oct 2018
PhD., Department of Civil Engineering,
Johns Hopkins University, Baltimore, MD.
Jan 2019 - Till date
Department of Mathematics and Industrial Engineering,
Polytechnique Montreal, Montreal, QC.
May 2012 - Jul 2012
Intern, Equity Quant,
Jun 2013 - Feb 2015
Analyst, Structured Credit Trading,
Feb 2015 - May 2015
Senior Analyst, Structured Credit Trading,
Jan 2016 - May 2016
EN.560.442 Equilibrium Modeling in Systems Engineering
Responsibilities: Setting assignment questions, grading assignments and exams
EN.500.111.13 Decomposing the World: Engineering and Modeling Complex Systems
Hopkins Engineering Applications and Research Tutorials
Jan 2017 - May 2017
EN.560.442 Equilibrium Modeling in Systems Engineering
Responsibilities: Setting assignment questions, grading assignments and exams, Lecturing
 (Working Paper) A reinforcement-learning paper to obtain quick primal solutions for two-stage stochastic facility location problem. (With Yoshua Bengio, Andrea Lodi, Rahul Patel)
 (Working Paper) A bargaining model for the Chilean waitlist management in public hospitals. (With Jorge Acuna Melo, Felipe Feijoo, Diego Martinez, Jose Zayas-Castro)
 (Working Paper) A bilevel model to locate electric vehicle charging stations with power grid integration. (With Vignesh Subramanian, Felipe Feijoo, Kevin Melendez)
 (Working Paper) When Nash meets Stackelberg. (WIth Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo, Andrea Lodi). Code available - Pre-preprint by popular request: arXiv.
 (Under review) A multi-scale food model to evaluate regionalized impacts from policy changes: application to an export ban policy in Ethiopia. (With Ying Zhang, Benjamin Zaitchik, Yalemzewd Nigussie, Jess Carnie and Sauleh Siddiqui)
 Mixed-integer bilevel representability. In Mathematical Programming - Series A (With Amitabh Basu and Christopher Ryan). [pdf]
Abstract: We study the representability of sets that admit extended formulations using mixed-integer bilevel programs. We show that feasible regions modeled by continuous bilevel constraints (with no integer variables), complementarity constraints, and polyhedral reverse convex constraints are all finite unions of polyhedra. Conversely, any finite union of polyhedra can be represented using any one of these three paradigms. We then prove that the feasible region of bilevel problems with integer constraints exclusively in the upper level is a finite union of sets representable by mixed-integer programs and vice versa. Further, we prove that, up to topological closures, we do not get additional modeling power by allowing integer variables in the lower level as well. To establish the last statement, we prove that the family of sets that are finite unions of mixed-integer representable sets forms an algebra of sets (up to topological closures).
 Can cut generating functions be good and efficient? In SIAM Journal on Optimization. (With Amitabh Basu). [pdf]
Abstract: Making cut generating functions (CGFs) computationally viable is a central question in modern integer programming research. One would like to find CGFs that are simultaneously good, i.e., there are good guarantees for the cutting planes they generate, and efficient, meaning that the values of the CGFs can be computed cheaply (with procedures that have some hope of being implemented in current solvers). We investigate in this paper to what extent this balance can be struck. We propose a family of CGFs which, in a sense, achieves this harmony between good and efficient. In particular, we provide a parameterized family of $b+\Z^n$ free sets to derive CGFs from and show that our proposed CGFs give a good approximation of the closure given by CGFs obtained from all maximal $b+\Z^n$ free sets and their so-called trivial liftings, and simultaneously, show that these CGFs can be computed with explicit, efficient procedures. We provide a constructive framework to identify these sets as well as computing their trivial lifting. We follow it up with computational experiments to demonstrate this and to evaluate their practical use. Our proposed family of cuts seem to give some tangible improvement on randomly generated instances compared to GMI cuts; however, in MIPLIB 3.0 instances, and vertex cover and stable problems on random graph instances, their performance is poor.
 The future of natural fas infrastructure development in the United States. In Applied Energy. (With Felipe Feijoo, Charalampos Avraam, Gokul Iyer, Marshall Wise, Leon Clarke, Matthew Binstead, Pralit Patel, Natalia Prates, Evelyn Torres-Alfaro and Sauleh Siddiqui). [pdf]
Abstract: Changes in the natural gas market have spawned the need for pipeline infrastructure planning. Previous studies have analyzed natural gas infrastructure development largely independent of the interactions of the natural gas sector with the broader economy. However, natural gas infrastructure development is strongly influenced by broader domestic and international socioeconomic conditions. We couple a global Human-Earth system model with state-level detail in the United States (GCAM-USA) that provides the broader socioeconomic context for natural gas supply and demand with a natural gas infrastructure investment model (NANGAM) to examine interstate natural gas pipeline infrastructure development in the U.S. under a range of socioeconomic scenarios. Here we show that existing pipeline infrastructure in the U.S. is insufficient to satisfy the increasing demand for natural gas and investments in pipeline capacity will be required. However, the geographic distribution of investments within the U.S. is heterogeneous and depends on the capacity of existing infrastructure as well as the magnitude of increase in demand. Our results also illustrate the risks of under-utilization of pipeline capacity, in particular, under a scenario characterized by long-term systemic transitions toward a low-carbon economy. More broadly, our study highlights the value of integrated approaches to facilitate informed decision-making.
 Sensitivity and covariance in a large scale stochastic complementarity problem for Natural Gas Markets, In European Journal of Operational Research. (With Felipe Feijoo and Sauleh Siddiqui). [pdf]
Abstract: We provide an efficient method to approximate the covariance between decision variables and uncertain parameters in solutions to a general class of stochastic nonlinear complementarity problems. We also develop a sensitivity metric to quantify uncertainty propagation by determining the change in the variance of the output due to a change in the variance of an input parameter. The covariance matrix of the solution variables quantifies the uncertainty in the output and pairs correlated variables and parameters. The sensitivity metric helps in identifying the parameters that cause maximum fluctuations in the output. The method developed in this paper optimizes the use of gradients and matrix multiplications which makes it particularly useful for large-scale problems. Having developed this method, we extend the deterministic version of the North American Natural Gas Model (NANGAM), to incorporate effects due to uncertainty in the parameters of the demand function, supply function, infrastructure costs, and investment costs. We then use the sensitivity metrics to identify the parameters that impact the equilibrium the most.
Conferences and Talks
STEM Achievement in Baltimore Elementary Schools (SABES) is an NSF-funded collaboration between Baltimore City Public Schools and Johns Hopkins University to improve educational outcomes in STEM disciplines throughout Baltimore City’s elementary schools with a targeted focus on community engagement in three neighborhoods.