Current
I recently started as a research scientist in the supply chain replenishment data science team at Chewy.
Background
Bachelors: I worked with Dr. Gopal Patil on developing a cellular automata model for T-intersections in India (B. Tech. Project — Modeling Pedestrian Signal using grid-based cellular automata).
Masters: I worked with Dr. Steve Boyles on developing network models for battery electric vehicles, and the details of the research can be found in my masters thesis. I also helped develop a simulation-based dynamic traffic assignment model under connected vehicle and autonomous vehicle settings to evaluate the benefits of dynamic pricing.
Dell Medical School project: I worked with Dr. Karl Koenig (MD), Dr. Jonathan Bard, and Dr. Douglas Morrice, on developing discrete-event simulation models for flow of patients in Integrated Practice Units to optimize patient waiting times and resource utilization.
PhD: I worked with Dr. John Hasenbein on optimization problems related to detecting viruses in contact networks with unreliable detectors. My research examines the problem of detecting an anomaly that spreads on a network, possibly in a stochastic manner.
In the basic problem, the decision maker places a limited number of perfectly reliable detectors (called honeypots) on a subset of nodes in the network. The objective is to determine the placement of honeypots that minimizes the expected time until virus detection, or that maximizes the probability of detection before a given time.
My research extends the basic problem by considering the possibility of unreliable detectors and uncertainty in the network graph. Applications of such models include detection of a virus on cell phone networks, monitoring of water-quality, and detection of fake news on social networks. Previous work in this area assumed that the detectors are perfectly reliable. In this work, it is assumed that the detectors may produce false-negative results. In computational studies, the sample average approximation method is applied to solving the problem using a mixed-integer program and a greedy heuristic. The heuristic is shown to be highly efficient and to produce high-quality solutions. In addition, it is shown that the false-negative effect can sometimes be ignored, without significant loss of solution quality, in the original optimization formulation.
We also develop an agent-based disease spread model on a contact network, motivated by COVID-19, to proactively test staff to detect an outbreak of an epidemic in facilities of small to moderate size. In our computational experiments we compare the effect of network structure and testing protocols on the probability of detection. Additionally, we extend our agent-based disease spread model to immunize staff in moderate-size facilities. We develop an immunization protocol and through computational studies show that it performs better than some existing protocols in the literature for static networks.
Senior Operations Research Scientist, Optym: I helped develop an optimization model for creating truck schedules in mining operations and designed metrics to evaluate the schedules, for congestion in the network.
Research Documents
1. Agrawal, Sudesh K. Models for virus detection in contact networks. Diss. 2021.
2. Agrawal, Sudesh K., and John J. Hasenbein. "Detecting Viruses in Contact Networks with Unreliable Detectors." arXiv preprint arXiv:2106.07788 (2021).
3. Ding, Yanyue, et al. "Surveillance testing for rapid detection of outbreaks in facilities." arXiv preprint arXiv:2110.00170 (2021).
4. Agrawal, Sudesh K., et al. "Network route choice model for battery electric vehicle drivers with different risk attitudes." Transportation Research Record: Journal of the Transportation Research Board 2498 (2015): 75-83. [TRB]
5. Agrawal, Sudesh Kumar. Network models for battery electric vehicles. Diss. 2015.
6. Kockelman, Kara, et al. An assessment of autonomous vehicles: traffic impacts and infrastructure needs. No. FHWA/TX-17/0-6847-1. 2017.
Projects
[Will be added later rather than sooner]
Internships
1. Amazon (Summer 2020 and 2021)
2. Sabre (Summer 2019)
(Details will be added later!)