
@article {168,
	title = {Truncating Multi-Dimensional Markov Chains with Accuracy Guarantee: Applications to Discriminatory Processor Sharing and Priority Queues},
	year = {2022},
	institution = {Stony Brook University},
	abstract = {The ability to obtain the steady-state probability distribution of a Markov chain is invaluable for modern service providers who aim to satisfy arbitrary tail performance requirements. However, it is often challenging, and even intractable, to obtain the steady-state distribution for several classes of Markov chains, such as multi-dimensional and infinite state-space Markov chains with state-dependent transitions; two popular examples include the M/M/1 with Discriminatory Processor Sharing (DPS) and the preemptive M/M/c with multiple priority classes and customer abandonment. In this paper, we propose a Lyapunov function based state-space truncation technique for such Markov chains. Our technique leverages the available moments, or bounds on moments, of the state variables of the Markov chain to obtain tight truncation bounds while satisfying arbitrary probability mass guarantees for the truncated chain. We demonstrate the efficacy of our technique for the multidimensional DPS and M/M/c priority queue with abandonment, and highlight the significant reduction in state space (as much as 72\%) afforded by our technique compared to the state-of-the-art.},
	author = {Gagan Somashekar and Mohammad Delasay and Anshul Gandhi}
}
@article {167,
	title = {Virtual Visit: A Web-based Assistive Interface for Touring Cultural Spaces Remotely},
	year = {2022},
	month = {May},
	institution = {Stony Brook University},
	abstract = {Virtual Visit successfully mimics the experience of visiting a gallery all from within the
browser. It blends multiple open source technologies together in a way that is novel and highly
functional: Marzipano for interactive panoramas, Webgazer for eye tracking, JSPsych for setup
and calibration, and the Web Speech API for speech recognition. It demonstrates that the
theoretical model of BayesGaze can work in a real world application and works better than the
Dwell Method.},
	author = {Conor Brandon},
	editor = {Anthony Scarlatos}
}
@mastersthesis {165,
	title = {Crop Yield Predictor for Small Farms in Monroe County, NY},
	volume = {BS},
	year = {2021},
	month = {May},
	pages = {21},
	school = {Stony Brook University},
	type = {Honors College},
	abstract = {There has been an economic shift recently towards larger farms, although small farms make up 91\% of farms in the United States and are {\textquotedblleft}more productive per unit of land than large farms{\textquotedblright} (Reynolds). One way we could help small farms compete and cope with climate change is through predictive analytics applications. However, these apps are catered more towards larger farms regarding prices and ease of use. If these tools were to focus more on the needs of small farms, they could help them make informed decisions based on predicted future yields. This is the goal of Yield Predictor, an application I developed that predicts the total yields for a specific crop users enter into the app. They will be able to see a scatter plot of predicted yields for the next 10 years based on two climate scenarios: RCP 4.5 (intermediate climate scenario) and RCP 8.5 (worst-case climate scenario).},
	keywords = {Agriculture, AI, TensorFlow},
	author = {Carolyn Bremer}
}
@mastersthesis {166,
	title = {The Long Island Museum Information and Wayfinding App},
	volume = {BS},
	year = {2021},
	month = {May},
	pages = {8},
	school = {Stony Brook University},
	type = {Honors College},
	abstract = {Since the massive closures due to the COVID-19 pandemic, many museums have been trying to find ways to encourage visitor engagement while they slowly reopen. New technologies, such as interactive kiosks, are becoming common in museums all around the world to attract visitors in a modern way. For this project, we developed an interactive display with a database backend for The Long Island Museum in Stony Brook, New York. The LIM has an extensive collection of art, historical pieces, structures, and exhibits spread across their large campus. They also host many events and visitors, such as tours and school field trips. This app would be a way for the museum staff to actively update their collection along with other pertinent information such as staff members and messages. Using iPads as kiosks, visitors can then query this database to see featured exhibits, upcoming events, as well as a map of the museum campus. Therefore, the museum staff would work with the administrative backend of this application, while visitors are interacting with the mobile and web frontend. As the user is interacting with the kiosk, the information will be projected on a large screen that would be set up in the main lobby of The Carriage Museum. In addition, when the kiosks aren{\textquoteright}t being used, they will enter an attract loop. In this state, the app would display a cycle of videos, animations, and general messages to greet visitors as they enter the museum. The main goal of this project is to use this interactive media display to create a more immersive and engaging atmosphere in the museum.
},
	author = {Michael Earvolino}
}
@article {164,
	title = {MERIT: Model-driven Rehoming for VNF Chains},
	year = {2020},
	month = {July},
	institution = {Stony Brook University},
	author = {Muhammad Wajahat and Bharath Balasubramanian and Anshul Gandhi and Gueyoung Jung and Shankaranarayanan Puzhavakath Narayanan}
}
@booklet {161,
	title = {Online Appendix for the Mascots 2019 publication titled Optimal Markovian Dynamic Control of Interference-Prone Server Farms},
	year = {2019},
	author = {Scott Votke and Jazeem Abdul Jaleel and Amoghavarsha Suresh and Mohammad Delasay and Sherwin Doroudi and Anshul Gandhi}
}
@article {159,
	title = {Significantly improving networked applications using congestion-aware transport modeling},
	year = {2018},
	month = {November},
	institution = {Stony Brook University},
	type = {Technical Report},
	author = {Yi Cao and Aruna Balasubramanian and Anshul Gandhi}
}
@article {157,
	title = {9-11 Oral History Project},
	year = {2017},
	institution = {Stony Brook University},
	author = {Goepfert, Ryan and Scarlatos, Tony}
}
@article {158,
	title = {The Query Complexity of Bayesian Auctions},
	year = {2017},
	institution = {Stony Brook University},
	abstract = {Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows {\textquotedblleft}each single bit{\textquotedblright} of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately, this assumption is very strong and may not hold in reality. For example, when the value distributions have exponentially large supports or do not have succinct representations, it is unclear how to find the optimal allocation and prices in many existing mechanisms.

In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players{\textquoteright} value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be a constant approximation to the optimal revenue. For single-item auctions, unit-demand auctions and additive auctions, we prove tight upper-bounds for the query complexity by constructing efficient DSIC Bayesian mechanisms. Finally, we show how to use our results to construct sampling mechanisms that use polynomially many samples from the distributions. Indeed, we provide the first constructive sampling mechanisms for unit-demand auctions and additive auctions.
},
	keywords = {mechanism design, quantile queries, query complexity, the complexity of Bayesian mechanisms, value queries},
	author = {Jing Chen and Bo Li and Yingkai Li and Pinyan Lu}
}
@mastersthesis {156,
	title = {A Study of Virtualization Overheads},
	volume = {M.S.},
	year = {2015},
	month = {08/2015},
	school = {Stony Brook University},
	type = {M.S.},
	abstract = {Containers or OS-based virtualization have seen a recent resurgence in deployment.
Containers have low memory footprint and start-up time but provide weaker security
isolation than Virtual Machines (VMs).  Incapability to load kernel modules and support
 multiple OS, and platform-dependence limits the functionality of containers.  On
the other hand,  VMs or hardware-based virtualization are platform-independent and
are  more  secure,  but  have  higher  overheads.   A  data  centre  operator  chooses  among
these two virtualization technologies---VMs and containers---when setting up guests
on cloud infrastructure.
Density
and
Latency
are two critical factors for a data centre
operator  because  they  determine  the  efficiency  of  cloud  computing.   Therefore,  this
thesis contributes updated density and latency measurements of KVM VMs and Linux
Containers with a recent kernel version and best practices.  This work also studies the
memory footprint of KVM VMs and Linux Containers.  In addition, it identifies three
ways to improve the density of KVM VMs by lowering the memory footprint:  improving existing memory 
deduplication techniques,  removing unused devices emulated by
QEMU, and removing unused pages from the guest address space.},
	author = {Kavita Agarwal}
}
