The Sanghani Center is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
(From left) Chang Lu, the Fred W. Bull Professor of Chemical Engineering; Daphne Yao, professor of computer science; and Xiaoting Jia, associate professor in the Bradley Department of Electrical and Computer Engineering. Photo by Peter Means for Virginia Tech.
An estimated one in six people suffer from a brain disorder worldwide, according to the American Brain Foundation. Current research has provided some insight into cell-communication inside the brain, but there are still a lot of unknowns surrounding how this crucial organ functions. What if there was a comprehensive map that took into consideration not just the biology of the brain, but the specific location where the biology occurs?
Researchers in the College of Engineering have developed a powerful, cost-effective method to do just that.
Chang Lu, the Fred W. Bull Professor of Chemical Engineering, has been leading a research project that could be groundbreaking for brain research. The newly published article in the journal Cell Reports Methods features interdisciplinary research along with faculty in two additional departments within the College of Engineering:
Poster presentation at CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling workshop during the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CPVR)
Medha Sawhney earned a bachelor’s degree from the Manipal Institute of Technology in India, where she majored in electronics and communications engineering, with a minor in data science. When considering a graduate degree in computer science, Sawhney was drawn to Virginia Tech and the Sanghani Center by a research-focused environment that offered opportunities to learn from and work with professors well known in their respective fields of research which interconnected well with her own.
“A research-focused environment makes it easy to concentrate on your work by providing interesting and challenging research projects; professors who guide you in every way; and funding opportunities via grants from organizations like the National Science Foundation,” she said. “And most professors – even if they are not your direct advisor — are extremely approachable to guide you or discuss problems.”
Sawhney entered the university as a master’s degree student but is now pursuing a Ph.D. She is advised by Anuj Karpatne.
Having worked in the domain of computer vision since her undergraduate years, Sawhney’s current research is at the intersection of computer vision and mechanobiology.
Two projects — supported by the NSF — predict the behavior and mechanics of human as well as bacteria cells. One of them involves predicting the force exerted by cells in order to be able to predict their movement using traction-force microscopy images collected in the field of mechanobiology.
“The physics knowledge that we are integrating in our machine learning methods includes phenomenological models of cell and bacteria migration and knowledge of the mechanical forces governing interactions between cells and fiber backgrounds,” she said.
The second project involves tracking the movement of bacteria cells to predict and also study the characteristics of their motion such as their velocity, their stickiness, and other such measures. This study is directed towards cancer research.
“The resolution of microscopy and the dense fibrous environment the bacteria is in makes it challenging to differentiate the bacteria in an image by just looking at it since the bacteria sometimes merges with the 3D media or goes inside,” she said. “We use artificially-generated motion and temporal features of the microscopic bacteria images as input to the machine learning model to be able to identify and track them.”
Sawhney gave a poster presentation of her work, “Detecting and Tracking Hard-to-Detect Bacteria in Dense Porous Backgrounds,” at a CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling workshop during the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CPVR) last fall.
This was preliminary work for the paper, “MEMTrack: A deep learning-based approach to microrobot tracking in dense and low-contrast environments,” which will be published in an upcoming volume of the Advanced Intelligent Systems journal.
Sawhney will also be presenting her work on bacteria tracking at the first Workshop on Imageomics during the Association for the Advancement of Artificial Intelligence (AAAI 24) conference next week.
She is also serving on the program committee for the workshop.
Projected to graduate in 2026, her ideal job would be one that offers challenges and in which her work would have an impact on society.
The Cloud-based Distributed Radio Frequency Machine Learning project team members at work. Photo by Isabella Rossi for Virginia Tech.
A student-led research team is working with Amazon to advance use cases for machine learning within the cloud for wireless communication applications.
The Virginia Tech National Security Institute is collaborating with Amazon Web Services (AWS) to give 11 undergraduate students and a graduate research assistant experience deploying state-of-the-art machine learning algorithms in the cloud for distributed radio frequency spectrum sensing through the Emerging Technology Research Fellowship. The fellowship aims to improve the performance of radio frequency spectrum sensing algorithms by leveraging multiple sensors collaborating through the cloud.
Graphic is from the paper “Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport”
Ahmed Aredah’s graduate school experience is not a typical one as he is simultaneously pursuing two graduate degrees in different majors. He is a master’s degree student in the Department of Computer Science advised by Hoda Eldardiry, assistant professor and core faculty at the Sanghani Center, and is also a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, advised by Hesham Rakha with Eldardiry serving on his dissertation committee.
“The multidisciplinary approach at the Sanghani Center aligns perfectly with my dual-degree aspirations, allowing me to bridge the gap between civil engineering and computer science,” Aredah said. “Advanced research facilities and extensive networking opportunities have further enriched my academic experience.”
His research area is centered on energy optimization in transportation. He is part of a team at the Virginia Tech Transportation Institute that developed NeTrainSim, a network train simulator that explores ways to make train operations more energy efficient.
“A significant contribution from our work has been the study and proposal of different powertrain technologies to enhance train infrastructure in the United States. Thanks to the robust methodology we have employed, our findings can be expanded to other regions/countries,” Aredah said.
His interest in energy optimization for railway systems was sparked by a combination of factors. “The real-world impact of creating more efficient, sustainable transport solutions resonated with my desire for meaningful work,” Aredah said. “And the interdisciplinary nature of the field offers a unique technical challenge that appealed to my problem-solving instincts.”
Aredah earned a bachelor’s degree and a master’s degree in civil engineering from German University in Cairo, Egypt, and nanodegrees in data science and machine learning from Udacity.
After graduating with his Ph.D. (currently projected for 2025), Aredah said that he is open to exploring any opportunity that allows him to leverage his skills.
“At my core, I am a problem solver, passionate about applying my knowledge to real-world challenges. Whether that means continuing research to push the boundaries of what’s possible or working in an industrial setting to implement practical solutions, I am eager to find a role where I can make a meaningful impact,” he said.
(From left) Chang-Tien “C.T.” Lu, Naren Ramakrishnan, and Dimitrios Nikolopoulos. Photo illustration by Peter Means for Virginia Tech.
Chang-Tien “C.T.” Lu, Dimitrios Nikolopoulos, and Naren Ramakrishnan, all faculty in the Department of Computer Science, have been elected to the 2024 class of fellows in the Institute of Electrical and Electronics Engineers (IEEE).
To be named a fellow, IEEE members must demonstrate significant contributions to their field, show evidence of technical accomplishments and realization of significant impact to society, and a record of service to professional engineering societies, among other criteria.
Fewer than 0.1 percent of voting members in the institute are selected annually for this career milestone, according to IEEE.
A recent project from the Virginia Tech Transportation Institute provides new insights to the impact of freight trains using alternative fuel sources. Virginia Tech photo
Researchers at the Virginia Tech Transportation Institute recently created the nationwide multi-train simulation software, NeTrainSim, to show the impacts of a countrywide shift away from diesel.
A U.S. map shows counties where residents could (blue) or could not (pink) receive local-specific information about environmental justice issues. Photo courtesy of Junghwan Kim.
Virginia Tech researchers have discovered limitations in ChatGPT’s capacity to provide location-specific information about environmental justice issues. Their findings, published in the journal Telematics and Informatics, suggest the potential for geographic biases existing in current generative artificial intelligence (AI) models.
Virginia Tech’s 2023 Fall Commencement ceremonies take place today. The Graduate School Commencement Ceremony will be held in Cassell Coliseum at 1:30 p.m. and will be live-streamed.
“We celebrate our Summer and Fall graduates who have worked so hard to achieve their graduate degrees,” 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. They deserve all the congratulations coming their way and we wish them all the best as they embark on their new journeys.”
The following Sanghani Center students are among those who are receiving degrees:
Ph.D. Graduates
Aman Ahuja,advised by Edward Fox, has earned a Ph.D. in computer science. His research focused on document understanding, search and retrieval, and question-answering to improve the accessibility of long PDF documents, such as books and dissertations. His dissertation, “Analyzing and Navigating Electronic Theses and Dissertations” was awarded the 2023 Innovative Student Thesis Award by the Networked Digital Library of Theses and Dissertations (NDLTD). Ahuja has joined DocuSign in Seattle, Washington, as an applied scientist.
Arka Daw, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research centers around the emerging field of science-guided machine learning, where machine learning models are integrated with scientific knowledge (or physics) to ensure better interpretability and generalizability while enforcing scientific consistency. The title of his dissertation is “Physics-informed Machine Learning with Uncertainty Quantification.” Daw is joining Oak Ridge National Lab (ORNL) in Knoxville, Tennessee, as a Distinguished Staff Fellow.
Chris Grubb, advised by Leanna House, has earned a Ph.D. in statistics. His research focuses on developing a statistical learning method of population synthesis that allows for propagation of uncertainty from sample data into synthetic populations of agents. The title of his dissertation is “Inference for Populations: Uncertainty Propagation via Bayesian Population Synthesis.” Grubb has joined Virginia Tech’s Center for Biostatistics and Health Data Science in Roanoke, Virginia, as a research scientist.
Whitney Hayes, co-advised by Ashley Reichelmann and Naren Ramakrishnan, has earned a Ph.D. in sociology. Her research focus is on identity. The title of her dissertation is “Enhancing Identity Theory Measurement: A Case Study in Ways to Advance the Subfield.” Hayes also received a graduate certificate in urban computing offered through the Sanghani Center. She has joined Elevate, a climate action nonprofit based in Chicago, Illinois, and works remotely as a research analyst.
Brian Keith, advised by Chris North, has earned a Ph.D. in computer science. His research focuses on how to represent, extract, and visualize information narratives to aid analysts in their narrative sensemaking process. The title of his dissertation is “Narrative Maps: A Computational Model to Support Analysts in Narrative Sensemaking.” Keith has joined the Catholic University of the North in Chile as an assistant professor in the Department of Computing and Systems Engineering.
Shuo Lei, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. Her research focuses on few-shot learning and domain adaptation.The title of her dissertation is “Learning with Limited Labeled Data: Techniques and Applications.” Lei has joined Sony Research in San Jose, California, as a research scientist.
Nikhil Abhyankar, advised by Ruoxi Jia, has earned a master’s degree in electrical and computer engineering. His research focus is on machine learning privacy and security. The title of his master’s thesis is “Data Centric Defenses for Privacy Attacks.” Abhyankar has joined the Virginia Tech Department of Computer Science to pursue a Ph.D.
Chongyu He, advised by Edward Fox, has earned a master’s degree in computer science. His research primarily revolves around the application of advanced deep learning techniques for cell organelle segmentation in high-resolution microscopy images. The title of He’s master’s thesis is “Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data.”
Junho Oh, advised by Lynn Abbott, has earned a master’s degree in Computer Engineering. His research focus is machine learning. The title of Oh’s master’s thesis is “Estimation of Global Illumination using Cycle-Consistent Adversarial Networks.”
Akash Sonth, advised by Abhijit Sarkar and Lynn Abbott, has earned a master’s degree in computer engineering. His research focus is on the application of machine learning in driver safety and intelligent transportation. The title of his master’s thesis is “Enhancing Road Safety through Machine Learning for Prediction of Unsafe Driving Behaviors.” Sonth has joined the Aspen Technology office located in Bedford, Massachusetts, as a data scientist.
Surendrabikram Thapa,co-advised by Anuj Karpatne and Abhijit Sarkar, has earned a master’s degree in computer science. His research focus is multimodal learning, computer vision, and natural language processing applications. The title of his master’s thesis is “Deidentification of Face Videos in Naturalistic Driving Scenarios.” Thapa also received a graduate certificate in data analytics offered by the Sanghani Center. He has joined the Virginia Tech Transportation Institute (VTTI) as a research faculty.
“Curious Conversations” is produced by the Virginia Tech Office of Research and Innovation.
Ismini Lourentzou joined Virginia Tech’s “Curious Conversations” to chat about artificial intelligence (AI) and machine learning related to personal assistants, as well as her student team’s recent experience with the Alexa Prize TaskBot Challenge 2.
Ahuja defended his dissertation this past summer and is currently an applied scientist at DocuSign in Seattle, Washington. His advisor was Edward Fox.
The organization’s annual award supports student efforts to transform the genre of the dissertation through the use of innovative research data management techniques and software to create multimedia Electronic Theses and Dissertations (ETDs). It includes a cash award and travel scholarship funds to attend a future ETD Symposium.
Following is an excerpt from the email Ahuja received from the chair of the NDLTD Awards Committee notifying him of this honor:
“Your thesis, “Analyzing and Navigating Electronic Theses and Dissertations,” provides a technical framework to expand the access to the content of millions of published theses, like yours, which are constrained in their usability and usefulness by the portable document format. Current digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier and accessible. Your research and dissertation directly addresses these concerns in creative and beneficial ways.”
