[AISWorld] Call for PhD Applicants - Stevens Institute of Technology

[Aron Lindberg]

Several open, fully funded Ph.D. positions in Finance, Information Systems and Business Analytics and Innovation and Entrepreneurship available for Fall 2019.

The school of Business Ph.D. program in Business Administration at Stevens Institute of Technology seeks highly motivated students who are passionate in their desire to seek answers to complex questions related to business and technology. The Ph.D. program at the School of Business is accredited by AACSB and offers a Ph.D. degree in Business Administration, with a specialization in Information Systems.

More information can be found here: https://www.stevens.edu/school-business/business-phd-programs/phd-business-administration

Research Topic: Deep Learning Models for FinTech
Faculty Member: Prof. Rong Liu (rong.liu at stevens.edu) and Prof. Feng Mai
(feng.mai at stevens.edu)
Description: FinTech, or financial technology, refers to new technology tools and platforms that are reshaping many areas of financial services. As the most exciting development in machine learning, deep learning combines layers of neural networks to learn new representations of data. Prof. Liu and Prof. Mai are interested in how financial institutions and regulators can apply deep learning to analyze large-scale, unstructured data such as texts and images. Our current research projects include using word embedding to quantify corporate culture from texts and using advanced deep learning techniques such as variational autoencoders and attention mechanisms to provide interpretable models for fraud detection.
Requirements: Master’s degree in Information Systems, Computer Science, Statistics, Business Analytics, or a related field is preferred. Strong coding skills are required. A bachelor's student interested in obtaining an M.S. along the way to the Ph.D. may also be considered.

Research Topic: Online Marketplace Design
Faculty Member: Prof. Apostolos Fillipas (apostolos.fillipas at stevens.edu)
Description: Over the last twenty years, digital technologies have catalyzed a transformation in how markets work, increasingly placing online platforms in the role of mediators of global economic activity. Today, technology firms operate large online platforms that have transcended retail, spanning a broad range of industries, aggregating demand and supply, and facilitating transactions. How are these platforms to be designed? How are consumers matched with sellers? What are the implications of the increasingly personal nature of the transactions in the Sharing Economy? What are the real-life spillovers of sharing economy platforms, and how should regulators account for them in their policy decisions? Interested students have a passion to explore how these effects arise, and how platform managers and policy makers can respond to these changes. To answer these questions, a variety of approaches can be employed, spanning the fields of economics, econometrics, and data science.
Requirements: Master’s degree in Information Systems, Management, Computer Science, or a related field is preferred. A Bachelor's student interested in obtaining an M.S. along the way to the Ph.D. may also be considered.

Research Topic: Collective Creativity
Faculty Member: Prof. Jeffrey V. Nickerson (jeffrey.nickerson at stevens.edu)
Description: I am interested in how crowds and communities can be organized to solve large scale societal design problems. Together with my students, I want to enable crowds and online communities to design products and services. My colleagues and I are currently performing research on 3D printing and energy sustainability. We have also studied Wikipedia editing. I am looking for students who want to both analyze and design systems. A background in information systems, computer science, or the natural sciences will be helpful, as well as an interest in computational social science and computational design. In particular, I am looking for students who would like to create organizations of humans and autonomous learning machines to tackle design problems.
Requirements: Master’s degree in Information Systems, Management, Computer Science, or a related field is preferred. A Bachelor's student interested in obtaining an M.S. along the way to the Ph.D. may also be considered.

Research Topic: Economic Optimization of Service Systems
Faculty Member: Prof. Chihoon Lee (Chihoon.Lee at stevens.edu)
Description: I am broadly interested in economic optimization of service systems, using queuing theory and stochastic control. For instance, my colleague and I are investigating: if a service provider, who is serving a market of price- and delay-sensitive customers, has the option of offering free trials to a new market of customers, how should he/she set optimal pricing decision? Other projects include modeling and analyzing in-hospital contact process data between environments and hosts, where environments are servers and hosts are customers, e.g., doctors and patients. Understanding the transmission dynamics of diseases is essential in controlling health care facilities. The key research question is how to find an optimal health care policy that maximizes an aggregated utility subject to certain resource constraints on budget and space. We intend to develop a versatile modeling framework by combining tools from queueing theory, optimization and statistics.
Requirements: Master’s degree in Statistics or Operations Research.

Research Topic: Digital Innovation
Faculty Member: Prof. Aron Lindberg (aron.lindberg at stevens.edu)
Description: This research program focuses on the relationship between routines and innovation in design contexts, primarily those with “open source-like” characteristics. This involves understanding variables and phenomena such as routine variation, sequential structuring, structural evolution, and temporal modes and their impacts on design outcomes such as effective coordination, digital artifact innovation, and requirements computation. To conduct this inquiry, we mainly use digital trace data and computational techniques such as text mining, sequence-, cluster-, and social network analysis, but also various forms of qualitative inquiry.
Requirements: Master’s degree in Information Systems, Management, or Software Engineering

Research Topic: Understanding the Role Competition plays in the Business Impact of Social Media
Faculty Member: Gaurav Sabnis (gsabnis at stevens.edu)
Description: Social media impacts many functions of businesses such as marketing, information systems, distribution, and public relations. The role that competition plays in this impact is not fully understood. For example, how does an uptick in social media posts about iPhones impact the sales of Android devices? This research project will build on my prior published work related to social media analytics and competition modeling to come up with conceptual frameworks and methodologies to better understand this role. The papers under it will involve data mining social media in various industries and using cutting edge statistical and econometric tools to rigorously test a variety hypotheses related to competition in these domains.
Requirements: Master’s degree in Information Systems, Computer Science, Management, Marketing, or any quantitative areas such as Statistics, Econometrics, or Mathematics.

Research Topic: Process Innovation and Decision Automation
Faculty Member: Prof. Michael zur Muehlen (michael.zurmuehlen at stevens.edu)
Description: I am interested in the design of organizational processes, both from a technological/methodological perspective (i.e., which tools and methods make transformation efforts effective), and from an organizational perspective (i.e., how should transformation efforts be organized). With my students I want to devise ways to help organizations and individuals to better serve their clients, become more efficient, and remain compliant. Currently, my colleagues and I study how organizations describe, acquire, and use process transformation skills. We have also looked at how technology standards emerge and are adopted (or not), and how the analysis and automation of decisions can help organizations rethink their processes. I am looking for students who have an interest in design methods, technology, and people. A background in information systems, computer science, or the social sciences will be helpful. In particular, I am looking for students who would like to study how individuals and organizations understand and improve their performance.
Requirements: Master’s degree in Information Systems, Management, Computer Science, or a related field is preferred. A bachelor's student interested in obtaining an M.S. along the way to the Ph.D. may also be considered.

Research Topic: Decision Analysis
Faculty Member: Prof. Tal Ben-Zvi (tbenzvi at stevens.edu)
Description: The research centers around the following research questions: Under what circumstances should a decision-maker take a certain course of action? How is human decision-making affected by interaction with other groups of individuals or organizations? How can decision-making improve performance? The main objective of the research is to improve the quality and effectiveness of decisions, and to increase understanding of human decision-making. We will study models, methods, tools and applications in which an individual decision-maker contemplates a choice of action in an uncertain environment. Our approach employs systematic analysis, which can help decision-makers clarify the course of action they should choose.
Requirements: Master’s degree in Operations Management or Operations Research.

Research Topic: Leveraging Crowdsourcing for Behavioral and Social-Science Experiments
Faculty Member: Jordan Suchow (jordan.suchow at stevens.edu)
Description: Behavioral and social scientists have begun to move from brick-and-mortar laboratories to the web, where participants are recruited through online crowdsourcing services and arranged into complex networks that guide their interactions. The transition from the lab to the web has enabled scientists both to scale traditional experiment designs and to imagine new kinds of experiments that make the fullest possible use of these technologies. Our research group therefore studies how we can leverage advances in crowdsourcing and human computation to better understand cognition and behavior, both by building new tools and running experiments with them.
Requirements: Master’s degree in Information Systems, Management, Computer Science, or a related field is preferred. A bachelor's student interested in obtaining an M.S. along the way to the Ph.D. may also be considered.