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Virginia Tech’s week of commencement ceremonies is underway! The Graduate School Commencement ceremony was held Wednesday, May 8; the main ceremony is being held today, Friday, May 10; and the Washington, D.C. area ceremony will be held on Sunday, May 12.  

“Graduation is always a bittersweet time for faculty as we applaud our students’ accomplishments,” 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. “We very proud of all of them but saying good-bye is not so easy and we are always happy when they stay in touch – as many of them do — to let us know where their research is leading them.”

The following Sanghani Center students are among those who are receiving degrees:

Ph.D. Graduates

Abdulaziz Alhamadani, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research interests mainly focus on developing efficient and applicable methods of training machine learning models for real-world applications such as pandemic prediction, drug overdose crises, and crisis management in various industries. His additional research areas include natural language processing, such as text classification and building large corpora for low-resource languages, machine learning ethics, and event detection.The title of his dissertation is “Integrated Predictive Modeling and Analytics for Crisis Management.” Alhamadani has joined the Computer Science Department at Florida Polytechnic University as an assistant professor.

Lulwah AlKulaib, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. Her research focuses on social media analysis, machine learning, and natural language processing with a special interest in Arabic. The title of her dissertation is “Analyzing Networks with Hypergraphs: Detection, Classification and Prediction.” AlKulaib has joined Kuwait University as an assistant professor in computer science.

Hongjie Chen, advised by Hoda Eldardiry, has earned a Ph.D. in computer science. His research lies in the areas of graph neural networks, time-series analysis, and recommendation systems. The title of his dissertation is “Graph-based Time-series Forecasting in Deep Learning.”

Jiaying Gong, advised by Hoda Eldardiry, has earned a Ph.D. in computer science. Her research focuses on multisource machine learning and natural language processing. The title of her thesis is “Few-Shot and Zero-Shot Learning for Information Extraction.” Gong will join coreAI, eBay, in New York City, as an applied researcher.

Jianfeng He, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research focus on computer vision centers on guided image editing. He also focuses on natural language processing, studying uncertainty analysis in its applications (e.g., text classification, few-shot learning, named entity recognition, and text summarization). The title of his dissertation is “Uncertainty Estimation in Natural Language Processing,” for which he received the Department of Computer Science Achievement Award for Best Ph.D. Research. In June, He will join Amazon as an applied scientist in Seattle, Washington. 

Ola Karajeh, advised by Edward Fox, earned a Ph.D. in computer science. Her research interests are graph machine learning, natural language processing, Twitter analysis, and public health. The title of her dissertation is “Improving Text Classification Using Graph-based Methods.”

Andreea Sistrunk, advised by Naren Ramakrishnan, has earned a Ph.D. in computer science. Her research interest is in human-computing Interaction with all forms that data science takes in information processing and its impact on society. She is equally interested in education and methods leveraging the state of the art in pedagogy and andragogy in computer science, advanced math, and engineering. The title of her dissertation is “Designing Human-Centered Collaborative Systems for School Redistricting.” Sistrunk will continue her work with the Geospatial Research Laboratory in the Washington, D.C. area, as a physical research scientist.

Master of Science Degree Graduates

Cho-Ting (Amanda) Lee, advised by Naren Ramakrishnan, has earned a master’s degree in computer science. Her research interests are in data mining and machine learning, with a focus on trade data analytics and anomaly detection. The title of her thesis is “Can an LLM find its way around a spreadsheet?”

Jiayue Lin, advised by Chris North, has earned a master’s degree in computer science. His research focus is on visual analytics and artificial intelligence, with a particular interest in refining deep learning-based image projections using semantic interaction methods. The title of his thesis is “ImageSI: Semantic Interaction for Deep Learning Image Projections.” 

Daniel Palamarchuk, advised by Chris North, has earned a master’s degree in computer science. His research focuses on visualizing temporal text, document, and topic data using pre-transformer embedding methods. The title of his thesis is “Temporal Topic Embeddings with a Compass.” Palamarchuk will work as a programming teacher in the Northern Virginia area and plans on pursuing a Ph.D. 

Ramaraja Ramanujan, advised by Edward Fox, has earned a master’s degree in computer science. His research focuses on data analysis of geospatial and administrative data, conducting statistical verifications and simulations, and visual analytics. The title of his thesis is “Improving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Program.” Ramanujan will join Microsoft in Redmond, Washington, as a software engineer. 

Chia-Wei Tang, advised by Chris Thomas, has earned a master’s degree in computer science. His research focuses on the development of misinformation detection utilizing multimodal reasoning. The title of his thesis is “M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection.” Tang is joining Juniper Networks in Sunnyvale, California, as a software engineer. 

Lemara Williams, advised by Chris North, has earned a master’s degree in computer science. Her research centers around visualizing changes in projections over time. The title of her thesis is “TimeLink: Visualizing Diachronic Word Embeddings and Topics.” Williams will continue her studies and is beginning a computer science Ph.D. program in the Fall at the Washington University in St. Louis. 

Xiaona Zhou, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research focuses on the applications of data science and machine learning. The title of her master’s thesis is “Hierarchical Bayesian Dataset Selection.” Zhou will be pursuing a Ph.D. in computer science at the University of Illinois Urbana-Champaign.

Suicide, the 10th leading cause of death for adults in the United States and the third leading cause of death among kids ages 10 to 14 and young adults ages 15 to 24, is often the result of an underlying mental health condition such as depression, anxiety, or bipolar disorder. 

Motivated by a suicide mortality by state map released by the Centers for Disease Control and Prevention (CDC) on the increasing severity of mental health crisis — further exacerbated by the COVID-19 pandemic — three Ph.D. students and their advisor at the Sanghani Center for Artificial Intelligence and Data Analytics are analyzing social media in a way that can help social workers and other professionals better understand and tackle different aspects of mental health issues to help prevent suicide. Read the full story here.

While a master’s degree student in computer science at George Washington University, Lulwah AlKulaib would look for published papers in high impact factor journals and highly respected, top rated conferences matching her field of interest, machine learning.

“This is where I first learned about the Discovery Analytics Center, the research being done there, and that it was located in northern Virginia as well as in Blacksburg,” she said.

 While still at George Washington, AlKulaib had an internship, unrelated to machine learning, at the Advanced Research Institute, which, like DAC, is located at the Virginia Tech Research Center in Arlington.

“There I met other Ph.D. students and because of my research interests, they encouraged me to consider DAC as a Ph.D. option,” said AlKulaib.  She also found that Chang-Tien Lu, whose research interests were aligned with hers, was an associate director at DAC and now, he is her advisor.

“Dr. Lu is amazing at guiding us on how to approach problems and he creates a network of students who learn from and support each other,” she said. “And the resources available for students’ benefit and growth at DAC is beyond what I imagined.”

 AlKulaib’s work focuses on social media analysis, machine learning, and Natural Language Processing (NLP). NLP centers on enabling computers to understand and process human language (natural language). Machine learning builds systems that can learn from experience (existing data), she said. By combining the two, she can start building systems that can learn how to understand languages.

Currently, she is working on news classification, enabling a machine learning model to understand a news article and then labeling the article into one of two existing classes.

“This field has lots of potential and multiple areas of research and my interest lies in Arabic,” AlKulaib said. “There are 22 Arabic speaking countries that all speak the same language — called Standard Arabic — taught in school, written in religious scriptures, and used for formal communication that includes news and work documents. But each country also has at least one informal dialect used for daily communication that is a variant of Standard Arabic named after the country or city where it is spoken. Lately, dialects have started appearing in written form on social media platforms which adds to the complexity of approaching any Arabic NLP problem, but makes it more interesting too.”

Her interest in this area stemmed from her first job after graduating with a bachelor’s degree in computer science from Gulf University for Science and Technology in Kuwait. As researcher and programmer at Kuwait Institute for Scientific Research (KISR) at the Technology Applications for Special Needs section, she provided clients with Arabic assistive technology.

“While working on a tool for speech disorders, I learned a lot about NLP and its difficulties in Arabic. That was the beginning of it all,” she said.

AlKulaib collaborated on Weaponized health communication: Twitter bots and russian trolls amplify the vaccine debate, published in the American Journal of Public Health in August 2018.

At the 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, Prediction and Behavior Representation in Modeling and Simulation, she presented “Detecting and characterizing bot-like behavior on twitter.”

She and project partner, Abdulaziz AlHamdani, also a DAC Ph.D. student, are working on a paper about collecting datasets with minimized bias using Twitter. Basically, they are formulating a collection framework to ensure that the dataset has minimal bias, caused by using social media as a source for data collection. They will submit the paper to a conference in early June and she will present in November if accepted.

When she finds some free time, AlKulaib enjoys photography, traveling, socializing and meeting new people.

She is projected to graduate in 2021.