Student Research Initiative (SRI)
Pathway for students to gain first-hand research experience working with faculty on interesting and novel challenges industry and society face.
Attend Information Session | March 28
The Student Research Initiative (pronounced SiRI) at Saunders allows eligible undergraduate and graduate students to participate in research projects that seek to solve interesting business trends and challenges, allowing them to develop basic research and data-analytics skills, methods or tools. SRI Student Scholars closely collaborate with faculty members in solving real business problems using research mindset, processes, methods, and analytical tools and techniques.
Students may be able to use summer research experience to replace one co-op; students are paid a stipend over the summer and hourly during the fall and spring semesters.
To apply, students must complete the application form. Below is a listing of SRI research projects and student requirements. Research projects are comparable to paid full-time work (40hrs.)
Charles S. Brown, Jr. and Renee A. Brown International Project Fund
Students working on a project with an international component may be eligible for additional support. Please check with faculty sponsors to see if the Charles S. Brown, Jr. and Renee A. Brown International Project Fund supports their project.
Important Dates for Student Research Initiative
Kick-off Event
April 28, 2024 | 1:00 pm - 2:00 pm | Free pizza
View event details
Application Deadline
TBD
Faculty Research Selections
TBD
Research Work Begins
TBD
Research Projects
An Explainable AI Framework to Identify Deceptive Reviewers on E-commerce platforms Evidence from Amazon.
Professor Ali Tosyali
Since the introduction of public rating systems, there has been manipulation by those aiming for personal advantage. An increasing trend of misleading reviews on e-commerce platforms has severely undermined consumer trust. This has spotlighted "fake reviewers," real individuals who are hard to distinguish from genuine ones, creating a significant challenge for existing detection methods. Our study evaluates various supervised machine learning models using a new dataset from both primary and secondary sources. We introduce a deep learning model and employ GNNExplainer to analyze and identify patterns among fraudulent reviewers, aiming to differentiate between fake and genuine reviewers more effectively.
Skills required
Python (Pytorch, Pandas, Sklearn), Deep Learning (RNNs, GNNs, and Transformers).
Duration of the project
The project is scheduled for the start of Fall 2024 and conclude by the end of the same semester, with a potential extension into Spring 2025.
The downward spiral competition in AI?
Professor Zhi Tang
The project examines how the two AI-leading countries, the US and China, define and apply artificial intelligence, machine learning, and other similar technologies differently. A longitudinal lens will be used to examine this issue. Through text mining and content analysis of news articles from leading newspapers in these two countries, we hope to contribute to the emerging literature on the evolutionary perspective on AI.
Skills required
- Patience to help collect data via downloading news articles as instructed.
- Conduct preliminary manual content analysis as instructed.
- Be able to read Mandarin.
- (preferred) Computer language skills for text mining.
Duration of the project
From the middle of June to the end of 2024, with a possible extension to the end of next spring semester.
International Component
The project compares the different evolutionary paths of AI in the US and China, the two largest markets adopting drastically different approaches. The study's findings have significant implications for the future development and modeling of AI.
Examining AI and Auditing Between the US and China
Professor Manlu Liu
This study consists of two phases. The first phase explores the integration of AI (Artificial Intelligence) in auditing processes and its impact on audit quality and efficiency. The study also analyzes how AI technologies are utilized in the auditing field to predict audit outcomes, enhance the quality of audit fieldwork, and manage financial fraud risks.
The student expands in the second phase to include a literature review covering the broad influence of AI technologies across accounting and auditing. This review could address how AI is currently implemented in accounting and auditing fields, its potential impact on the professions, the readiness of different countries to embrace AI in accounting and auditing, policy implications, and ethical concerns. This study's data collection mainly focuses on two countries (the US and China). This study identifies differences in multiple perspectives in both countries by comparing the utilization and impact of AI in auditing between the US and China. This comparison is expected to reveal unique challenges and opportunities in leveraging AI for auditing purposes within different regulatory and economic contexts.
Skills required
If students do not possess the preferred skills; they will be taught, provided the required skills are met.
- High motivation to learn about research.
- Great work attitude.
- (preferred) Foundational knowledge in AI and Machine Learning.
- (preferred) Proficiency in Big Data analysis and statistical skills.
- (preferred) Strong critical thinking and problem-solving abilities.
Duration of the project
One year (Summer, Fall, Spring) International components
International Component
The study of AI integration in auditing between the US and China involves several international components.
- Comparative Legal and Regulatory Frameworks.
- Technological Infrastructure and Access.
- Cultural and Ethical Considerations.
- Economic and Market Dynamics.
- International Data Sources and Collaboration.
- Global Auditing Firms and Multinational Corporations.
Strategies for Utilizing Artificial Intelligence Solutions for the Climate Crisis
Professor Kenan Guler
In recent years, there has been a growing focus on both artificial intelligence and societal challenges, especially climate change. Scholars and practitioners are increasingly exploring innovative approaches to address grand societal challenges. This paper centers on the intersection of artificial intelligence and climate change, aiming to investigate how AI can contribute to resolving the ongoing climate crisis. The study will utilize an analysis of over 18,000 academic articles and 30,000 news articles related to AI and climate change, bridging the fields of strategic management, innovation, and AI literature to provide valuable insights into orchestrating innovative solutions for the existing climate crisis. The intended outlet for this paper is submission to an upcoming special issue of the Strategic Management Journal. I am enthusiastic about collaborating with an interested student during the summer on this project.
Skills required
The student's tasks will involve assisting in the literature review, analyzing data, and performing specific tasks using Python, R, and Excel.
Duration of the project
Summer semester.
Investigating Residential Real Estate Market Conditions via the Deep Learning Large Language Models (LLMs)
Professor Soon Hyeok Choi
The US residential real estate market comprises both structured and unstructured texts. In particular, real estate agents use the multiple listing service (MLS) to describe key features of a property as part of a prospectus. Over time, the textual information contained in MLS can reveal important market conditions (e.g., cold market, bidding war). The project intends to use the Bidirectional Encoder Representations from Transformers (BERT), a deep learning AI transformer model, to deploy its natural language processing capacity in the context of the agent descriptions on the MLS. In the final outcome, we want to unambiguously measure the impact of the market conditions on the transaction price, listing price, and ex-ante estimates.
Skills required
- Familiarity with Excel, R or Python is a plus.
- Data collection and processing.
- Attention to detail.
- Ability to meet in-person or via Zoom on a weekly basis.
Duration of the project
Summer and Fall 2024.
Effects of Corporate Transparency and Risks on Capital Markets
Professor Hao Zhang
In an age of information overload, how much do a company’s transparency and risk exposure really matter to investors? This research project delves into this crucial question, investigating the impact of corporate transparency and risk factors on capital markets. The study will explore how different levels of transparency and various dimensions of risk exposure influence investor trading behaviors and capital market quality. By collecting and analyzing data from diverse sources, we aim to uncover insights into the effects of the corporate information environment and risk landscape on capital markets. The findings can inform corporate decision-makers and regulators seeking to promote a more transparent and efficient capital market.
Skills required
- Excellent Python programming skills.
- Experience with Python for data collection and analysis.
- Good written and verbal communication skills.
Duration of the Project
The duration of the project will be approximately one year.