End-User Probabilistic Programming

Probabilistic programming aims to help users make decisions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics. We consider probabilistic programming for end-users, in particular spreadsheet users, estimated to number in tens to hundreds of millions. We examine the sources of uncertainty actually encountered by spreadsheet users, and their coping mechanisms, via an interview study. We examine spreadsheet-based interfaces and technology to help reason under uncertainty, via probabilistic and other means. We show how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclusions about the promise and limitations of probabilistic programming for end-users

Cost analysis of nondeterministic probabilistic programs

We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach using experimental results on a variety of programs for which we efficiently synthesize tight resource-usage bounds

Cost Analysis of Nondeterministic Probabilistic Programs

We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach by presenting experimental results on a variety of programs, motivated by real-world applications, for which we efficiently synthesize tight resource-usage bounds.Comment: A conference version will appear in the 40th ACM Conference on Programming Language Design and Implementation (PLDI 2019

Passive Acoustic Detection System

This Device is a passive acoustic detection system (P.A.D.S). This system is able to detect large objects (i.e. airplanes and tanks) at various distances within its hearing radius despite the visual conditions. These detection signals are transmitted wirelessly to a display unit, which can potentially be located out of sight of the sensor

Harmony and conflict prediction of space IQP teams using MBTI personality descriptors

This study looks at the relationship between cognitive diversity among team members, as measured by the MBTI and group performance. There were 44 WPI students formed into 14 IQP teams working concurrently on space-related topics. Group dynamics (leadership, conflict, division of labor and performance) were covered by a student questionnaire administered in the first third of the project and again toward the end. The results indicated that diversity was related to the student but not the advisor rating of how things went. The advisor ratings were most affected by whether there were certain "anchor" types of students on the team