News featuring Ping Wang

Congratulations to Sanghani Center 2021 Summer and Fall Graduates

Virginia Tech’s Fall Commencement ceremony for the Graduate School is now underway (livestream here) and seven students from the Sanghani Center are among those receiving degrees. 

“This has been a tough year and they successfully navigated obstacles caused by the COVID19 pandemic to achieve their academic goals and we are very proud of them,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics

Following is a list of Sanghani Center 2021 summer and fall graduates:


Khoa Doan, advised by Chandan Reddy, has earned a Ph.D. in computer science.  His primary research interests lie in Machine Learning and Information Retrieval. The title of his dissertation is “Generative models meet similarity search: efficient, heuristic-free and robust retrieval”.  Doan has joined Baidu Research as a machine learning researcher. 

You Lou, co-advised by Bert Huang and Naren Ramakrishnan, earned a Ph.D. in computer science. His research areas are structured prediction, probabilistic graphical models, variational inference, and deep generative models. The title of his dissertation is “Modeling Structured Data with Invertible Generative Models.” Lou has joined Motional, a driverless technology company, as a machine learning research scientist.

Anika Tabassum, advised by B. Aditya Prakash, has earned a Ph.D. in computer science. She also earned the Urban Computing graduate certificate. For her Ph.D. research, she worked to develop explainable and domain-guided machine learning frameworks for power systems to aid decision-making for emergency management authorities. The title of her dissertation is “Explainable and Network-based Approaches for Decision-making in Emergency Management.” Tabassum has joined Oak Ridge National Laboratory in Tennessee as a postdoctoral research associate in the Discrete Algorithms Group, working on various projects related to scientific machine learning. 

Tian Shi, advised by Chandan Reddy, has earned a Ph.D. in computer science. His primary research interests lie natural language processing and machine learning. The title of his dissertation is “Novel Algorithms for Understanding Online Reviews.” Shi has joined Moody’s Analytics as a machine learning research scientist.

Ping Wang, advised by Chandan Reddy, has earned a Ph.D. in computer science. Her primary research focuses on question answering, graph mining, information extraction, and survival analysis with their applications in the healthcare domain. The title of her dissertation is “Automatic Question Answering and Knowledge Discovery from Electronic Health Records.” Wang has joined the Computer Science Department at Stevens Institute of Technology in Hoboken, New Jersey, where she is an assistant professor.  

Master’s Degree

Eman Abdelrahman, advised by Edward Fox, has earned a master’s degree in computer science. Her research interest lies in applying machine learning and natural language processing on Arabic scientific datasets such as ETDs in order to improve the accessibility to Arabic scientific data. The title of her thesis is “Improving the Accessibility of Arabic ETDs with Metadata and Classification.” She is remaining at Virginia Tech and the Sanghani Center to pursue a Ph.D. in computer science, advised by Ismini Lourentzou. 

Aarathi Raghuraman, advised by Lenwood Heath, has earned a master’s degree in computer science. Her primary research interests lie in biomedical data science and bioinformatics. The title of her thesis is “Predicting Mutational Pathways of Influenza A H1N1 Virus using Q-learning. Raghuraman has joined LexisNexis Legal and Professional in Raleigh, North Carolina, as a data scientist.

Esther Robb, advised by Jia-Bin Huang, has earned a master’s degree in computer engineering. Her primary research interests lie in reinforcement learning and data-efficient learning. The title of her thesis is “Data-Efficient Learning in Image Synthesis and Instance Segmentation.” Robb is pursuing a Ph.D. in computer science at Stanford University.

Sanghani Center Student Spotlight: Ping Wang

 Graphic is from the paper “Text-to-SQL Generation for Question Answering on Electronic Medical Records”

In 2016, Ping Wang followed her advisor, Chandan Reddy, from Wayne State University, where she received a master’s degree in computer science, to Virginia Tech and the Sanghani Center.

Her area of interest is healthcare systems, which are undergoing many changes in the era of big data.

“Advances in artificial intelligence and digitization in healthcare have enabled healthcare providers to effectively sift through tremendous amounts of medical information,” said Wang. “My first research project in this direction was about survival analysis and my advisor Dr. Reddy and other group members provided many useful suggestions and help at the initial stage. After further investigation, I found that there are still many unique challenges in the healthcare domain. I hope to leverage my expertise in data mining and machine learning to solve real-world challenges and advance healthcare applications.”

While earning her Ph.D., Wang has been located, at different time periods, in both Arlington and Blacksburg. She said she has enjoyed her experiences on both campuses, maintaining regular meetings with Reddy and other group members to discuss her research and its progress.

“The professional environment for learning and conducting research at the Sanghani Center has offered me great research and collaboration opportunities,” Wang said.

Her research is focused on developing machine learning methods that can efficiently utilize Electronic Health Records (EHRs). These records contain medical and treatment history of patients to facilitate physicians’ decision making in their clinical practice.

Wang is looking at three aspects: (1) Clinical Question Answering: How to seek answers from EHRs for clinical activity related questions posed in human language without the assistance of database and natural language processing (NLP) domain experts; (2) Survival Analysis: How to predict when a medical event will occur and estimate its probability based on prior medical history of patients; and (3) Knowledge Discovery: How to discover underlying relationships between different events and entities in structured tabular EHRs and apply NLP techniques to construct structured events and knowledge base from clinical notes.

One of the goals in clinical question answering is to develop machine learning methods that can automatically seek answers from relational tables of the EHR database for human-language questions, she said. Traditionally, doctors interact with EHR via searching and filtering functions available in rule-based systems that first turn predefined-rules (user interface) to SQL queries, which will be executed on the database to retrieve patient information.

“These systems are complicated, difficult to manage, and require special training,” Wang said. “To tackle this problem, we proposed building a Text-to-SQL Query Translation System that can automatically translate clinical activity related questions to SQL queries, so that the doctors only need to type their questions in a search box to get answers. I also created a MIMICSQL dataset for question answering on tabular EHR to simulate a more realistic setting.”

This work, “Text-to-SQL Generation for Question Answering on Electronic Medical Records” was published at The Web Conference 2020.

Most recently, Wang presented “Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks” virtually at The Web Conference 2021.

Among her other published work is “Tensor-based Temporal Multi-Task Survival Analysis,” which was in the IEEE Transactions on Knowledge and Data Engineering in 2020.

Wang plans to defend her dissertation this summer and will join the Department of Computer Science at Stevens Institute of Technology as a tenure-track assistant professor for the Fall 2021 semester.