Research
Working Papers
Economics Research
Price Discrimination with Costly Information Acquisition
This study investigates third-degree price discrimination under the cost of market segmentation. I explore the trade-offs between segmentation benefits and information acquisition costs, revealing a non-monotonic relationship between consumer surplus and the cost of information acquisition for monopolist. I showed that consumers can be adversely affected in some markets when the cost for monopolists to acquire information about consumers increases. Additionally, I specify conditions characterizing markets where this phenomenon occurs.
Advisor: Yuhta Ishii
Empirical Analysis of Optimality of Auction Mechanisms: A Structural Approach
This study employs a structural econometric model to investigate whether real-world auctions align with Myerson's optimal auction design theory in the Independent Private Value (IPV) setting. By analyzing data from repeated auctions, particularly first-price or second-price formats, I aim to estimate the underlying distribution of bidder valuations F(v) to determine if it exhibits increasing hazard rates, a key component of the IPV framework.
Advisor: Karl Schurter
Computer Science Research
Classification of Dialogue Segment Breaks in GUIDE Dataset
Collaborators:Ming Zhu,Sai Chandana Priya,Lakshmi Chandrika,Yichao Chen
Dept. of CSE, Pennsylvania State University
Supervisor: PROF. REBECCA J. PASSONNEAU
Abstract
In this paper, we explore the task of classifying conversation segment breaks using various Natural Language Processing (NLP) models. We leverage the rich textual data within the GUIDE dataset to identify these transitions. We have worked with a couple of baseline models alongside some advanced models like SpanBERT and RoBERTa to assess their effectiveness in dialogue segmentation. We further experiment with optimization techniques to refine model performance. This analysis gives some insights for the future advancements in dialogue understanding and the development of more sophisticated conversation analysis systems.
You can access the project here
Stable matching, originating from the College Admission problem by Gale and Shapley, encompasses applications ranging from kidney exchange to doctor-hospital and cadet branch matching. The Deferred Acceptance algorithm, foundational in this field, is criticized for consistently favoring one side of the market, prompting the development of fairer alternatives like Egalitarian, leximin, and Sex-Equal Stable matching. We analyzed the implications of Nash Welfare on one-to-one matching markets.
Detection of Breast Cancer based on Mammograms with Metadata Integration
Collaborators:Amrit Puhan, Dept. of IST Pennsylvania State University and Akshaya Jayant Patil, Dept. of CSE, Pennsylvania State University
Supervisor: Dr. C. Lee. Giles
Conducted an empirical research project utilizing prominent CNN models (ResNet-50, ResNet-101, DenseNet-121) and Attention-based models (DeiT, DeiT-III, PatchConvnet) for predicting breast cancer from mammogram images, specifically employing the Kaggle RSNA Mammogram dataset. The innovative angle of our approach was the integration of image metadata during the deep learning models' training process. Results showcased that this metadata incorporation notably enhanced the AUC-ROC score across all models, while also improving generalization, pointing towards a viable avenue for advancing medical diagnostic AI.
Keywords: CNN, Attention-Based Models, Breast Cancer Prediction, Mammogram Image Analysis, Metadata Integration, Deep Learning.