Shimei Pan

IJCAI Conference 2025 Conference Paper

fairGNN-WOD: Fair Graph Learning Without Complete Demographics

• Zichong Wang
• Fang Liu
• Shimei Pan
• Jun Liu
• Fahad Saeed
• Meikang Qiu
• Wenbin Zhang

Graph Neural Networks (GNNs) have excelled in diverse applications due to their outstanding predictive performance, yet they often overlook fairness considerations, prompting numerous recent efforts to address this societal concern. However, most fair GNNs assume complete demographics by design, which is impractical in most real-world socially sensitive applications due to privacy, legal, or regulatory restrictions. For example, the Consumer Financial Protection Bureau (CFPB) mandates that creditors ensure fairness without requesting or collecting information about an applicant’s race, religion, nationality, sex, or other demographics. To this end, this paper proposes fairGNN-WOD, a first-of-its-kind framework that considers mitigating unfairness in graph learning without using demographic information. In addition, this paper provides a theoretical perspective on analyzing bias in node representations and establishes the relationship between utility and fairness objectives. Experiments on three real-world graph datasets illustrate that fairGNN-WOD outperforms state-of-the-art baselines in achieving fairness but also maintains comparable prediction performance.

AAAI Conference 2023 Short Paper

Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)

• Maryam Alomair
• Shimei Pan
• Lujie Karen Chen

Data Science (DS) is an interdisciplinary topic that is applicable to many domains. In this preliminary investigation, we use caselet, a mini-version of a case study, as a learning tool to allow students to practice data science problem solving (DSPS). Using a dataset collected from a real-world classroom, we performed correlation analysis to reveal the structure of cognition and metacognition processes. We also explored the similarity of different DS knowledge components based on students’ performance. In addition, we built a predictive model to characterize the relationship between metacognition, cognition, and learning gain.

IJCAI Conference 2020 Conference Paper

Incorporating Extra Knowledge to Enhance Word Embedding

• Arpita Roy
• Shimei Pan

Word embedding, a process to automatically learn the mathematical representations of words from unlabeled text corpora, has gained a lot of attention recently. Since words are the basic units of a natural language, the more precisely we can represent the morphological, syntactic and semantic properties of words, the better we can support downstream Natural Language Processing (NLP) tasks. Since traditional word embeddings are mainly designed to capture the semantic relatedness between co-occurred words in a predefined context, it may not be effective in encoding other information that is important for different NLP applications. In this survey, we summarize the recent advances in incorporating extra knowledge to enhance word embedding. We will also identify the limitations of existing work as well as point out a few promising future directions.

IJCAI Conference 2019 Conference Paper

Social Media-based User Embedding: A Literature Review

• Shimei Pan
• Tao Ding

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e. g. , raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e. g. , combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

ECAI Conference 2016 Conference Paper

Cross-Domain Error Correction in Personality Prediction

• Isil Doga Yakut Kiliç
• Shimei Pan

In this paper, we analyze domain bias in automated text-based personality prediction, and proposes a novel method to correct domain bias. The proposed approach is very general since it requires neither retraining a personality prediction system using examples from a new domain, nor any knowledge of the original training data used to develop the system. We conduct several experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy.

IJCAI Conference 2016 Conference Paper

Improving Topic Model Stability for Effective Document Exploration

• Yi Yang
• Shimei Pan
• Yangqiu Song
• Jie Lu
• Mercan Topkara

Topic modeling has become a ubiquitous topic analysis tool for text exploration. Most of the existing works on topic modeling focus on fitting topic models to input data. They however ignore an important usability issue that is closely related to the end user experience: stability. In this study, we investigate the stability problem in topic modeling. We first report on the experiments conducted to quantify the severity of the problem. We then propose a new learning framework to mitigate the problem by explicitly incorporating topic stability constraints in model training. We also perform user study to demonstrate the advantages of the proposed method.

AAAI Conference 2016 Conference Paper

What’s Hot in Intelligent User Interfaces

• Shimei Pan
• Oliver Brdiczka
• Giuseppe Carenini
• Duen Chau
• Per Ola Kristensson

TIST Journal 2012 Journal Article

TIARA

• Shixia Liu
• Michelle X. Zhou
• Shimei Pan
• Yangqiu Song
• Weihong Qian
• Weijia Cai
• Xiaoxiao Lian

We are building an interactive visual text analysis tool that aids users in analyzing large collections of text. Unlike existing work in visual text analytics, which focuses either on developing sophisticated text analytic techniques or inventing novel text visualization metaphors, ours tightly integrates state-of-the-art text analytics with interactive visualization to maximize the value of both. In this article, we present our work from two aspects. We first introduce an enhanced, LDA-based topic analysis technique that automatically derives a set of topics to summarize a collection of documents and their content evolution over time. To help users understand the complex summarization results produced by our topic analysis technique, we then present the design and development of a time-based visualization of the results. Furthermore, we provide users with a set of rich interaction tools that help them further interpret the visualized results in context and examine the text collection from multiple perspectives. As a result, our work offers three unique contributions. First, we present an enhanced topic modeling technique to provide users with a time-sensitive and more meaningful text summary. Second, we develop an effective visual metaphor to transform abstract and often complex text summarization results into a comprehensible visual representation. Third, we offer users flexible visual interaction tools as alternatives to compensate for the deficiencies of current text summarization techniques. We have applied our work to a number of text corpora and our evaluation shows promise, especially in support of complex text analyses.

AAAI Conference 2010 Conference Paper

Constrained Coclustering for Textual Documents

• Yangqiu Song
• Shimei Pan
• Shixia Liu
• Furu Wei
• Michelle Zhou
• Weihong Qian

In this paper, we present a constrained co-clustering approach for clustering textual documents. Our approach combines the benefits of information-theoretic co-clustering and constrained clustering. We use a two-sided hidden Markov random field (HMRF) to model both the document and word constraints. We also develop an alternating expectation maximization (EM) algorithm to optimize the constrained coclustering model. We have conducted two sets of experiments on a benchmark data set: (1) using human-provided category labels to derive document and word constraints for semi-supervised document clustering, and (2) using automatically extracted named entities to derive document constraints for unsupervised document clustering. Compared to several representative constrained clustering and co-clustering approaches, our approach is shown to be more effective for high-dimensional, sparse text data.

IJCAI Conference 2007 Conference Paper

• Shimei Pan
• James Shaw

In this paper, we address a critical problem in conversation systems: limited input interpretation capabilities. When an interpretation error occurs, users often get stuck and cannot recover due to a lack of guidance from the system. To solve this problem, we present a hybrid natural language query recommendation framework that combines natural language generation with query retrieval. When receiving a problematic user query, our system dynamically recommends valid queries that are most relevant to the current user request so that the user can revise his request accordingly. Compared with existing methods, our approach offers two main contributions: first, improving query recommendation quality by combining query generation with query retrieval; second, adapting generated recommendations dynamically so that they are syntactically and lexically consistent with the original user input. Our evaluation results demonstrate the effectiveness of this approach.

AAAI Conference 2006 Conference Paper

Responsive Information Architect: Enabling Context-Sensitive Information Seeking

• Michelle Zhou
• Shimei Pan
• Vikram Aggarwal

Information seeking is an important but often difficult task especially when involving large and complex data sets. We hypothesize that a context-sensitive interaction paradigm can greatly assist users in their information seeking. Such a paradigm allows a system to both understand user data requests and present the requested information in context. Driven by this hypothesis, we have developed a suite of intelligent user interaction technologies and integrated them in a full-fledged, context-sensitive information system. In this paper, we review two sets of key technologies: context-sensitive multimodal input interpretation and automated multimedia output generation. We also share our evaluation results, which indicate that our approaches are capable of supporting context-sensitive information seeking for practical applications.

AAAI Conference 1999 Short Paper

Modeling Prosody Automatically in Concept-to-Speech Generation

• Shimei Pan
• Columbia University

My thesis emphasizes investigation and establishment of systematic methodologies for automatic prosody modeling using corpus analysis. Prosody modeling in most previous CTS systems employs handcrafted rules, with little evaluation of the overall performance of the rules. By systematically employing different machine learning techniques on a speech corpus, I am able to automatically model prosody for a given domain. Another focus of my thesis is on system architecture. There are two concerns when designing a CTS system: modularity and extensibility. The goal is to design a exible CTS system so that new prosody generators, natural language generators and speech realization systems can be incorporated without requiring major changes to the existing system. Designing a CTS system to facilitate multimedia synchronization is another focus of this research.