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Lenwood Heath, a professor in the Department of Computer Science and core faculty at the Sanghani Center, is part of a team that recently received a National Science Foundation (NSF) grant for its plant genome research project, “Unraveling the origin of vegetative desiccation tolerance in vascular plants collaborators.” Heath is collaborating with colleagues from Texas Tech University and the University of Nevada, Reno on the study.

Excessive water loss is lethal for most plants, but a minority of plants (known as resurrection plants) have a remarkable ability to survive almost complete dryness, said Heath. This ability, known as desiccation tolerance, relies upon a combination of physiological, biochemical, and molecular responses that allow the plant to preserve cell integrity in the dry state.

“In the context of climate change,” Heath said, “we feel it is important to understand how plants respond to drying out and especially important to develop the science that will allow crops to better tolerate drought.”

“It is believed that this resurrection capability depends on genes that are in all plants but lost by most over evolutionary times,” Heath said. “The aim of our project is to discover the essential differences in genetic responses between resurrection plants and drought-sensitive plants so that crops can be re-engineered to be more drought tolerant.” 

In addition to sophisticated biological experiments to measure gene response in the two kinds of plants, the project will employ machine learning techniques, led by Heath, to construct gene regulatory networks (GRNs) for comparative study.  

The grant will provide learning and professional opportunities to graduate students and postdocs at the three universities. Jingyi Zhang, a Ph.D. computer science student advised by Heath, will work with him on the project.

Long-term goals for the project include promoting conservation programs for resurrection species; providing diverse scientific workforce training and outreach activities to first-generation students and the general public; and increasing public awareness about the importance of vegetative desiccation tolerance to future crop breeding in order to tackle the effects of climate change. 

Raquib Bin Yousuf, a Ph.D. student in computer science, is exploring the capabilities of large language models to generate text from different forms of data, especially from knowledge graphs. 

A knowledge graph, he said, can be a network with various entities and their relationships on any domain. Generating the correct and helpful narrative from the knowledge graphs is an important task for the user of that domain. 

“Although my research focus is on natural language processing, I have been fortunate while at the Sanghani Center to work in some other multidisciplinary domains as well,” he said. “The excellent and diverse work of the faculty is what attracted me to the center and the exposure I have had to real-world problems in these collaborative projects has helped me to learn more and conduct better research.”

Yousuf’s first exposure to his research area was through information retrieval projects from large scale text data during his undergraduate years. 

He has also worked on knowledge extraction projects under supervision of his advisor Naren Ramakrishnan, which have involved the application of natural language processing methods on large scale scholarly articles. 

“Recently there has been a pivotal innovation in NLP in the form of the Transformer model and subsequent development of large language models,” Yousuf said. “Today’s large language models can work well, across many tasks, with little to no help at all and that has motivated me to look deep into the working nature of these state of art models for real-world applications.” 

At the 2022 SIGKDD Conference on Knowledge Discovery and Data Mining last August in Washington, D.C., he presented “Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn’t, and Future Directions.” The paper explored the use of domain adapted Transformers models as building blocks to develop and deploy an automated End-to-end Research Entity Extractor, capable of extracting technical facets from full-text scholarly research articles of a large scale dataset.

Yousuf received a bachelor’s degree in computer science and engineering from Bangladesh University of Engineering and Technology (BUET) and a master’s degree in computer science from Virginia Tech.

Projected to graduate in 2025, he hopes to continue his research as an industry professional.

 

Danfeng “Daphne” Yao, professor in the Department of Computer Science and affiliate faculty at the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, has been elevated to fellow, the highest grade of membership in the Institute of Electrical and Electronics Engineers (IEEE), for her contributions to enterprise data security and high-precision vulnerability screening. 

Following a rigorous evaluation procedure, fewer than 0.1 percent of voting members in the institute are selected annually for this career milestone. Read more here.

Hoang Anh Just has received some good news: The paper “LAVA: Data Valuation Without Pre-Specified Learning Algorithms” — on which he is first author — has been accepted as a spotlight at the 11^th International Conference on Learning Representations (ICLR) in May. He plans to travel to Rwanda to present the paper. 

Just, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, said the paper introduces a new perspective on valuating data. 

“For many current valuation methods, the valuation algorithm is based on a model learning process, which is expensive, noise-sensitive, and often impractical. To overcome such hurdles, we valuate data via optimal transport, which requires no model training,” he said. “As such, our data-centric, model-agnostic method effectively detects ‘bad’ data points in the dataset in an efficient manner.”

An interest in artificial intelligence drew him to Virginia Tech and the Sanghani Center. “I am honored to be part of an expanding community that is tackling modern AI problems and pushing the field to greater heights,” Just said.

Just’s advisor, Ruoxi Jia, influenced his research area by introducing him to data evaluation. 

“I really found it intriguing that data are used all around, but we barely know their actual value,” he said, “and this led to my work in establishing efficient and fair methods for valuating data used in machine learning models.”

Just received a bachelor’s degree in computer science and mathematics from Gettysburg College.

Projected to graduate in 2026, his goal is to become a professor who can continue research in data valuation and inspire students to conduct research in artificial intelligence.

A Virginia Tech team of  five computer science Ph.D. students at the Sanghani Center for Artificial Intelligence and Data Analytics is one of 10 university teams selected internationally to compete in the Alexa Prize TaskBot Challenge 2. The team will design multimodal task-oriented conversational assistants that help customers complete complex multistep tasks while adapting to resources and tools available to the user, such as ingredients or equipment. Read more here.

Rebecca DeSipio already knows where she is headed after graduating with a master’s degree in computer engineering this Spring. She will be joining the Charlottesville-based company GA-CCRi, an industry leader in geospatial storage, visualization, and analysis serving government and commercial clients, as a data scientist. 

In looking for a graduate program, DeSipio, who earned a bachelor’s degree in electrical engineering from the Pennsylvania State University, liked the close collaboration between the Bradley Department of Computer and Electrical Engineering and the Department of Computer Science because it allowed her to easily switch from electrical engineering to a computer science specialization, a change she knew she wanted to make.

“And at the Sanghani Center I was introduced to the world of data analytics which has provided me with endless opportunities. Because of my graduate school experience I was able to land a position in that exact area of work. I cannot think how different my career path could have been had I decided to go elsewhere for graduate school,” said DeSipio. 

“I fell in love with Blacksburg and I am beyond excited to stay relatively close and apply all that I have learned here at Virginia Tech to my career,” she said.

When she entered the master’s program, DeSipio — also a Bradley Fellow in the Bradley Department of Electrical and Computer Engineering — discussed research options with her advisor Lynn Abbott. Computer vision and machine learning piqued her interest and she was particularly drawn to biomedical applications for Parkinson’s Disease (PD) because her grandfather had been diagnosed with it. 

“When I came across publications on the use of machine learning algorithms for aiding in the diagnosis of PD by analyzing hand-drawn images, I quickly decided that I wanted to contribute to this line of research,” said DeSipeo.

Currently, she is developing a method that analyzes and rates hand tremor severity in hand-drawn spiral images via frequency features. 

“Since PD is a clinical diagnosis, the goal of my work is to help doctors diagnose and monitor PD progression and find the right medication for their patients,” said DeSipio.

Using her method, if a suspected PD patient goes to the doctor with a hand-tremor, the hand-drawn spiral test can be performed and the tremor rated. Medication can be prescribed and at each follow-up visit, the same spiral test can be performed and rated. 

“My tremor-severity rating system can allow an evaluating doctor to track the progression of the tremor and adjust medications as necessary,” she said. 

More than 275 community members, partners, and friends joined Virginia Tech and Whiting-Turner Contracting Co. on Feb. 7 to celebrate the next milestone for the Virginia Tech Innovation Campus – the topping out ceremony. The event featured a program inside the first floor of the Virginia Tech Innovation Campus construction site and the ceremonial lifting of a steel beam to the highest point – the 11th story – of Academic Building One.

“This is a significant moment for Virginia Tech, symbolizing the tremendous progress we have made on both construction and academic planning for the Innovation Campus,” said Virginia Tech President Tim Sands. “The Innovation Campus will be an important source of tech talent for the greater Washington, D.C., region — and is vital to Virginia Tech’s growing presence in the area. I look forward to 2024, when we welcome students, faculty, and the community into this remarkable building.”

The Innovation Campus will be the Northern Virginia home to the Sanghani Center for Artificial Intelligence and Data Analytics on the fifth floor, a dedicated K-12 Programs Center on the second floor, and the Boeing Center for Veterans and Families, which will be co-located with the Hokie One Stop on the second floor. Read the full story here.

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.

A strong emphasis on research, a robust commitment to sustainability, and a large international presence among its faculty served as common themes in Virginia Tech’s showing in various national and global rankings this fall.

The university wrapped up the calendar year by being ranked in a tie for No. 265 out of 2,000 universities listed in U.S. News & World Report’s Best Global Universities rankings. The list featured institutions from 95 countries.

Among the university’s 2022 research-related milestones is the partnership between Virginia Tech and Amazon to advance fields of artificial intelligence and machine learning housed in the Virginia Tech College of Engineering and led by the Sanghani Center for Artificial Intelligence and Data Analytics. Read the full story here.

Virginia Tech’s 2022 Fall Commencement ceremony takes place today.

“We wish our graduates at the Sanghani Center all the best as they receive their Ph.D. and master’s degrees and take the next step toward achieving their career goals,” 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 are the Sanghani Center’s 2022 summer and fall graduates:

Ph.D.

Nikhil Muralidhar, advised by Naren Ramakrishnan and Anuj Karpatne, has earned a Ph.D. in computer science. His research interest is in leveraging machine learning to address problems in scientific applications leveraging data and scientific theory. He also received a graduate certificate in Urban Computing. The title of his dissertation is “Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts.”  In Fall 2022, Muralidhar joined the Computer Science Department at Stevens Institute of Technology in Hoboken, New Jersey, as an assistant professor and leads the Scientific Artificial Intelligence (ScAI) Lab to develop scientific machine learning solutions incorporating data and domain knowledge in physics, fluid dynamics, cyber-physical systems and disease modeling.

Xinyue Wang, advised by Edward Fox, has earned a Ph.D. in computer science. His research focuses on web archive processing and analysis infrastructure through distributed computation. The title of his dissertation is “Large Web Archive Collection Infrastructure and Services.” Wang is joining Yahoo in San Jose, California, as a research scientist.

Master’s degree

Huiman Han, advised by Chris North, has earned a master’s degree in computer science. Her research focuses on visual analytics, interactive machine learning, and explainable artificial intelligence. The title of her thesis is “Explainable Interactive Projections for Image Data.” Huimin is joining LinkedIn in Mountain View, California, as a software engineer in Machine Learning.

Sarah Maxseiner, advised by Lynn Abbott, received a master’s degree in electrical and computer engineering. Her thesis is on assessing the quality level of hand-drawn sketches.