Discussions on higher education issues, universitywide priorities frame quarterly board meeting; final design for Mitchell Hall approved

Lee Learman, dean of the Virginia Tech Carilion School of Medicine, leads members of the Virginia Tech Board of Visitors on a tour of the facility in Roanoke during the board’s August meeting. Photo by Ryan Anderson for Virginia Tech.

The Virginia Tech Board of Visitors held its latest quarterly full-board meeting Sunday through Tuesday at the W.E. Skelton 4-H Educational Conference Center in Wirtz and at the Fralin Biomedical Research Institute at VTC in Roanoke.

Following an orientation session Sunday morning, board members engaged in a retreat to discuss issues facing Virginia Tech and higher education in general. To begin the retreat, three experts led a conversation on generative artificial intelligence (AI) and its impact on higher education and society more broadly: Naren Ramakrishnan, Virginia Tech’s Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for AI and Data Analytics; Scott Hartley, co-founder and managing partner of Everywhere Ventures, a pre-seed venture capital firm, and best-selling author of “The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World”; and Rishi Jaitly, professor of practice and Distinguished Humanities Fellow at Virginia Tech, where he leads the Institute for Leadership in Technology. Read full story here.


Innovation Campus solidifies plans for faculty recruitment, research areas of focus, and curriculum

Supported through a three-year seed grant from Fralin Life Sciences Institute, a group of 14 interdisciplinary researchers led by Peter Vikesland will develop wireless sensor networks to survey microbial threats to water quality. Photo by Ryan Young for Virginia Tech.

Atop a new wave of support from the Fralin Life Sciences Institute, Peter Vikesland, the Nick Prillaman Professor of Civil and Environmental Engineering, is leading a research team in creating wireless sensor networks to survey microbial threats to water quality and to enable operational control and provide real-world feedback for public transparency. The project, Technology-enabled Water Surveillance and Control, reflects the “one water” concept that views water quality as important to our society, economy, and environment and requires an integrated approach to policy planning and implementation.

Lenwood Heath, professor of computer science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, will develop algorithms for locating sensors and designing networks for optimal benefit. Read full story here.


¿Qué piensas? Graduate School program provides platform for idea-sharing with Latin American visitors

A group from the University of San Francisco d’Quito in Ecuador explored the research taking place at the Fralin Biomedical Research Institute at VTC in Roanoke. The visit was part of an educational exchange organized by Virginia Tech’s Graduate School. Photo by Leigh Anne Kelley for Virginia Tech.

Academics from the University of San Francisco d’Quito in Ecuador were hosted by Aimée Surprenant, dean of the Graduate School, as part of the Future Professoriate Group from Latin America.

Among those they heard from on campus was Naren Ramakrishnan, the Thomas L. Phillips Professor of engineering at Virginia Tech, founder and director of the Sanghani Center for Artificial Intelligence and Data Analytics and director of the Amazon-Virginia Tech Initiative in Efficient and Robust Machine Learning. Read full story here.


Children’s National Hospital, Virginia Tech unite to advance AI for pediatric health

Subha Madhavan, vice president and head of clinical artificial intelligence/machine learning with biopharmaceutical company Pfizer, stressed the need to use artificial intelligence methods to understand children’s health at a meeting of scientists and innovators led by Children’s National Hospital and the Virginia Tech Sanghani Center for Artificial Intelligence and Data Analytics. The brainstorming took place on the Children’s National Research & Innovation Campus in Washington, D.C.

“Start by determining the problem you desire to solve, then decide on the technology to solve it,” said Subha Madhavan, vice president and head of clinical artificial intelligence/machine learning with global biopharmaceutical company Pfizer. 

Madhavan was the keynote speaker at AI for Pediatric Health and Rare Diseases, an inter-institutional meeting of scientists and innovators co-led by Children’s National Hospital and the Virginia Tech Sanghani Center for Artificial Intelligence and Data Analytics to discuss the potential of artificial intelligence (AI) to understand pediatric health.

The pressing issue at the gathering at the Children’s National Research & Innovation Campus in Washington, D.C., involved tackling diseases, particularly cancer, in children, an area that suffers from limited treatment options and inadequate research compared with diseases affecting adults.  Read full story here.


Sanghani Center graduate students gain real-world experience while working at companies and labs from coast to coast

Ph.D. student Jianfeng is an applied scientist intern at Amazon AWS in Seattle, Washington

Summer offers an opportunity for graduate students at the Sanghani Center to gain real-world experience in their research focus areas by working at major companies and labs across the country. This year these include places like Amazon AWS in Seattle, Washington; JPMorgan Chase & Co in New York City;  the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts; Bosch in Pittsburgh, Pennsylvania; and the Intel Lab in Santa Clara, California.  

Following is a list of Sanghani Center students – where they are and what they are doing:

Satvik Chekuri, a Ph.D. student in computer science, is a natural language processing research intern working remotely with a Deloitte Audit and Assurance Data Science team in New York City. The team’s research focuses on the intersection of knowledge graphs and Large Language Models (LLMs) in the financial domain. His advisor is Edward A. Fox.

Hongjie Chen, a Ph.D. student in computer science, is a research scientist intern at Yahoo Research in Sunnyvale, California, working remotely with the advertising team. His advisor is Hoda Eldardiry.

Humaid Desaia master’s degree student in computer science, is a software engineer intern at Ellucian in Reston, Virginia, working remotely. He is contributing to Ellucian’s SaaS-based solutions using React.js, Node.js, and AWS cloud technologies. His advisor is Hoda Eldardiry.

Jianfeng He, a Ph.D. student in computer science, is an applied scientist intern working onsite at Amazon AWS in Seattle, Washington, where he is researching text summarization. His advisor is Chang-Tien Lu.

Adheesh Juvekar, a Ph.D. student in computer science, is an applied scientist intern working on generative artificial intelligence onsite at Amazon in Boston, Massachusetts. His advisor is Ismini Lourentzou.

Myeongseob Ko, a Ph.D. student in electrical and computer engineering, is a machine learning research intern onsite at Bosch in Pittsburgh, Pennsylvania, where he is working on a diffusion model. His advisor is Ruoxi Jia.

Shuo Lei, a Ph.D. student in computer science, is a graduate research intern onsite at Intel Labs in Santa Clara, California. She is working on developing a new few-shot learning method for multi-modal object detection to lower the effort of human annotation, training effort, and domain adaptation while meeting accuracy requirements for industrial usage. Her advisor is Chang-Tien Lu.

Wei Liu, a Ph.D. student in computer science, is a business intelligence intern at Elevance Health in Indianapolis, Indiana, working remotely with the data analysis team. Her advisor is Chris North.

Amarachi Blessing Mbakwe, a Ph.D. student in computer science, is an artificial intelligence research associate intern at JPMorgan Chase & Co in New York City, working onsite. She is conducting research on natural language processing-related problems that involve applying Large Language Models (LLMs) in finance. Her advisor is Ismini Lourentzou.

Makanjuola Ogunleye, a Ph.D student in computer science is a data scientist intern at Intuit, working onsite with the company’s AI Capital team in Mountain View, California. He is contributing to key machine learning products. His advisor is Ismini Lourentzou.

Mandar Sharma, a Ph.D. student in computer science, is a Ph.D. software engineering intern at Google AI in Kirkland, Washington, where he is working onsite on integrating state-of-the-art in natural language processing to the services provided by Google’s Cloud AI platforms. His advisor is Naren Ramakrishnan.

Ying Shen, a Ph.D. student in computer science, is a research intern onsite at Apple in New York City, where she is working on diffusion models. Her co-advisors are Lifu Huang and Ismini Lourentzou.

Afrina Tabassum, a Ph.D. student in computer science, is a research intern at Microsoft in Redmond, Washington, working onsite. She is co-advised by Hoda Eldardiry and Ismini Lourentzou.

Chiawei Tanga master’s degree student in computer science, is a software engineer intern onsite at Juniper Network in Sunnyvale, California. His work involves creating a simulator designed to emulate the data output from wired network devices such as routers and switches. This strategic initiative facilitates system scalability testing for developers and significantly mitigates the financial impact associated with the procurement of physical hardware. His advisor is Chris Thomas.

Muntasir Wahed, a Ph.D. student in computer science, is a research intern onsite at IBM Research Almaden Lab in San Jose, California, working on the development and application of foundation models. His advisor is Ismini Lourentzou.

Zhiyang Xu, a Ph.D. student in computer science, is an applied scientist intern onsite at Amazon Alexa in Sunnyvale, California, where he is working on improving dialogue systems. His advisor is Lifu Huang.  

Raquib Bin Yousuf, a Ph.D. student in computer science, is among 25 students from 19 colleges chosen to attend the Washington Post Engineering class in Washington, D.C., this summer. He is working with state of the art artificial intelligence systems to develop new technology for the Washington Post. His advisor is Naren Ramakrishnan.

Yi Zenga Ph.D. student in computer engineering, is a research scientist intern onsite at Meta in Menlo Park, California, working on artificial intelligence fairness, finding ways to make state of the art AI systems more robust and responsible. His advisor is Ruoxi Jia.

Jingyi Zhang, a Ph.D. student in computer science, is a graduate intern working remotely with Amgen’s Computational Biology Group within Clinical Biomarkers & Diagnostics in Thousand Oaks, California. She is taking an active role in developing a data and analytics platform as well as participating in prostate therapeutic area translational computational biology. Her advisor is Lenwood Heath.

Shuaicheng Zhang, a Ph.D. student in computer science, is a summer intern onsite at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts, where he is conducting research on the generative graph foundation model. His advisor is Dawei Zhou.

Xiaona Zhou, a Ph.D. student in computer science, is a University Research Association Sandia Graduate Summer Fellow at Sandia National Labs in Livermore, California. She is onsite working on anomaly detection in time series data. Her advisor is Ismini Lourentzou.


Innovation Campus solidifies plans for faculty recruitment, research areas of focus, and curriculum

A rendering of the Innovation Campus Academic Building One, opening in fall 2024.

In his biannual presentation to the Virginia Tech Board of Visitors, Lance Collins, vice president and executive director of the Virginia Tech Innovation Campus, updated the board this month on progress with the Virginia Tech Innovation Campus faculty recruitment, research areas of focus, and curriculum development.

Collins said the Innovation Campus faculty are strong collaborators, bringing with them established relationships with business, the tech industry, and government. He highlighted faculty-led centers and initiatives, such as the Sanghani Center for Artificial Intelligence and Data Analytics directed by Naren Ramakrishnan and an up-and-coming entrepreneurship track led by Angelos Stavrou with support from local investors, as strengths of the Innovation Campus community. Read the full story here.


Congratulations to Sanghani Center’s 2023 Spring Graduates

Virginia Tech’s 2023 Commencement ceremonies are underway culminating with the university commencement in Blacksburg on Friday, May 12, and commencement in the Washington D.C. area on Sunday, May 14.

“Once again we have come to that bittersweet time when we say farewell to our graduating students at the Sanghani Center and wish them continued success as they take the next step in meeting their long-term 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. “We take pride in their hard work and accomplishments and in knowing that they are well prepared to meet real-world challenges.”

The following Sanghani Center students are among the 284 Ph.D. and 1,205 master’s students receiving degrees this Spring.

Ph.D. Graduates

Badour AlBahar, co-advised by Jia-Bin Huang and Lynn Abbott, has earned Ph.D. in electrical and computer engineering. Her research interests lie in computer vision and computer graphics and more specifically, image synthesis. The title of her dissertation is “Controllable Visual Synthesis.” AlBahar is joining Kuwait University in Kuwait City, Kuwait, as an assistant professor.


Jonathan Baker
advised by Mark Embree, has earned a Ph.D. in math. His research interests lie in spectral theory in linear dynamics and control, passive source localization, and machine learning. The title of his dissertation is “Vibrations of mechanical structures: source localization and nonlinear eigenvalue problems for mode calculation.” Baker also received the graduate certificate in Urban Computing.


Jie Bu
, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research interest lies in machine learning, particularly in science-guided machine learning, representation learning, and network pruning. The title of his dissertation is “Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads.” Bu is joining Apple in Cupertino, California, as a machine learning engineer. 

Nurendra Choudhary, advised by Chandan Reddy, has earned a Ph.D. in computer science. His research focus is learning representations for knowledge graphs and natural language by utilizing auxiliary information such as relational structures. The title of his dissertation is “Multimodal Representation Learning for Textual Reasoning over Knowledge Graphs”. Choudhary is joining Amazon in Palo Alto, California, as an applied scientist II.

Mohannad Elhamod, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research interest is in machine learning in general and, more specifically, in knowledge-guided machine learning. The title of his dissertation is “Understanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapes.” He also received a Graduate Student of the Year Award from the Virginia Tech Graduate School and was one of three speakers at the Graduate School commencement ceremony. Elhamod is joining Questrom School of Business at Boston University in Boston, Massachusetts, as a clinical assistant professor.

Melissa Tilashalski, co-advised by Leanna House and Kimberly Ellis, has earned a Ph.D. in industrial systems engineering. Her research focus is urban computing. The title of her dissertation is “Influence of Customer Locations on Heuristics and Solutions for the Vehicle Routing Problem.” Tilashalski is joining Johns Hopkins University in Baltimore, Maryland, as a lecturer.

Master’s Degree Graduates

Hirva Bhagat, co-advised by Lynn Abbott and Anuj Karpatne, has earned a master’s degree in computer science. Her research focus is on improving driver gaze estimation for driver safety applications. The title of her thesis is “Harnessing the Power of Self-Training for Gaze Point Estimation in Dual Camera Transportation Datasets.” Bhagat will be joining Goldman Sachs in Dallas, Texas, as an analyst in the company’s Risk Engineering Division. 


Elizabeth Christman
, advised by Chris North, has earned a master’s degree in computer science. Her research interests lie in data analytics and finding ways to visualize and explore big data. The title of her master’s thesis is “2D Jupyter: Design and Evaluation of 2D Computational Notebooks.” Christman is joining Leidos in Bethesda, Maryland, as a software engineer.

Rebecca DeSipio, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. Her research focuses on machine learning and deep learning methods for image classification. The title of her thesis is “Parkinson’s Disease Automated Hand Tremor Analysis from Spiral Images.” She will be joining GA-CCRi in Charlottesville, Virginia, as a data scientist. 

Yogesh Deshpande advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research is focused on exploring and implementing non-invasive techniques to retrieve human body parameters specifically on the usage of computer vision and deep learning methods to address the scope of human authentication based on iPPG signals. The title of his master’s thesis is “Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos Using an Attention Network.”

Dhanush Dinesh, advised by Edward Fox, has earned a master’s of engineering degree in computer engineering. His research focus is on developing infrastructure on the cloud to support the processing of large datasets. The title of his thesis  is “Utilizing Docker and Kafka for Highly Scalable Bulk Processing of Electronic Thesis and Dissertation (ETDs).” Dhanush has joined Citibank in Irving, Texas, as a senior DevOps engineer.

Hulya Dogan, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research interests are social media analysis, machine learning, and natural language processing. The title of her thesis is “Narrative Characteristics in Refugee Discourse: An Analysis of American Public Opinion on Afghan Refugee Crisis After the Taliban Takeover.”  Dogan is joining Moog Inc. in Blacksburg, Virginia, as a data analyst and will continue her Fellowship with the CDC in Atlanta in the division of Health Informatics. 

Naveen Gupta, advised by Anuj Karpatne, has earned a master’s degree in computer science. His research interest lies in the physics guided machine learning field. The title of his thesis is “Solving Forward and Inverse Problems for Seismic Imaging using Invertible Neural Networks.”  Gupta is joining Hughes Communication in Germantown, Maryland, as an MTS 3 – software engineer.


Sahil Hamal is advised by Chris North, has earned a master’s degree in computer science. His research focus is visual analytics and explainable artificial intelligence. The title of his master’s thesis is “Interpreting Dimensions Reductions through Gradient Visualization.” Hamal also received the Paul E. Torgersen Research Excellence Award.

Meghana Holla, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research focuses on machine learning and deep learning applied to natural language processing and multimodal problems at the intersection of language and vision. The title of her thesis is “Commonsense for Zero-Shot Natural Language Video Localization.” Holla also received the Paul E. Torgersen Research Excellence Award. She is joining Bloomberg LP in New York City as a machine learning engineer.


Sanjula Karanam
, advised by Danfeng (Daphne) Yao, has earned a master’s degree in computer engineering. Her research focuses on detecting ransomware and benign files on a Windows machine using their behavioral aspects, more specifically dynamic function calls made by a file during execution. The title of her thesis is “Ransomware Detection Using Windows API Calls and Machine Learning.”

Gaurang Karwandeadvised by Ismini Lourentzou, has earned a master’s degree in electrical and computer engineering. His research focus is in the field of artificial intelligence and its applications in healthcare, more specifically medical imaging and precision medicine. The title of his master’s thesis is “Geometric Deep Learning for Healthcare Applications.” Karwande is joining VideaHealth, Inc. in Boston, Massachusetts, as a machine learning engineer.

Fulan Li, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research focuses on extracting PPG signals from human face video using machine learning models. The title of his master’s thesis is “A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos.”


Xiaochu Liadvised by Lifu Huang, has earned a master’s degree in computer science. His research focus is deep learning-based natural language processing and information extraction, especially in entity linking and event extraction in the biomedical domain. The title of his thesis is “Joint Biomedical Event Extraction and Entity Linking via Collaborative Training.”

Javaid Akbar Manzoor, advised by Edward Fox, has earned a master’s degree in computer science. His research focus is on exploring how to use deep learning to segment long scientific documents into chapters. The title of his thesis is “Segmenting Electronic Theses and Dissertations By Chapters.”  Manzoor has joined Lightcast in Boston, Massachusetts, as a data scientist. 

Avi Seth, advised by Ismini Lourentzou, has earned a master’s degree in computer science. His research is focused on active learning and generative models. The title of his thesis is “Data Sharing and Retrieval of similar Manufacturing Processes.”

Aditya Shah, advised by Edward Fox, has earned a master’s degree in computer science. His research focus is on using Large Language Models (LLMs) for different downstream applications. The title of his master’s thesis is “Leveraging Transformer Models and Elasticsearch to Help Prevent and Manage Diabetes through EFT Cues.” Shah is joining Capital One Headquarters in McLean, Virginia, as a senior data scientist.

Rutuja Tawareadvised by Naren Ramakrishnan, has earned a master’s degree in computer science. Her research is focused on analyzing the behavior of transformers when they deal with math problems, specifically in a few-shot setting. The title of her thesis is “A Study of Pretraining Bias and Frequencies in Language Models.”  


Class of 2023: Mohannad Elhamod and Alli Rossi-Alvarez named Graduate Students of the Year

Alli Rossi-Alvarez (at left) and Mohannad Elhamod, both in the College of Engineering, were named the 2023 Graduate Students of the Year by the Virginia Tech Graduate School. Photo by Peter Means for Virginia Tech.

Two graduate students in the College of Engineering have been recognized by the Virginia Tech Graduate School for their exemplary work inside and outside the classroom.

Mohannad Elhamod in computer science and Alli Rossi-Alvarez in industrial and systems engineering both received the 2023 Graduate Student of the Year award. This award recognizes students for their character, service, outstanding contributions, and academic achievements. 

Elhamod is also a student at the Sanghani Center for Artificial Intelligence and Data Analytics, advised by Anuj Karpatne, core faculty at the center. Read full story here.


Sanghani Center Student Spotlight: Longfeng Wu

Graphic is from the paper “Towards High-Order Complementary Recommendation via Logical Reasoning Network”

An interest in finding some unknown patterns from existing data influenced Longfeng Wu’s research focus. Wu, who is advised by Dawei Zou, is pursuing her doctoral degree in computer science working on symbolic reasoning and trustworthy graph learning. 

“I am focused on exploring the reasoning process and developing more reliable and trustworthy models in the real world,” Wu said. “Considering that current knowledge graphs are massive and incomplete, symbolic reasoning over graphs could deduct new facts from existing data through representation learning. For example, in recommendation systems, the representation of products could reflect the relationships between them.”

She presented “Towards High-Order Complementary Recommendation via Logical Reasoning Network” at the IEEE International Conference on Data Mining (ICDM-2022) this past November. 

Wu received a bachelor’s degree in information and computing science and a master’s degree in information science, bothfrom Nanjing Agricultural University, China. In choosing a university for her Ph.D. she was attracted to Virginia Tech for its outstanding computer science program, distinguished professors, and collaborative atmosphere.

“I am honored to be part of the Sanghani Center community where the guidance and support of professors allow and encourage me to do the work that I find interesting and meaningful,” Wu said. 

Projected to graduate in Spring 2026, Wu said her long-term goal is to continue her current research in some capacity. “Artificial Intelligence will be widely adopted in the future and can extensively promote social development and enhance social welfare. I would like make a contribution to this process.”


Lifu Huang receives NSF CAREER award to lay new ground for information extraction without relying on humans

Lifu Huang. Photo by Peter Means for Virginia Tech.

Considering the millions of research papers and reports from open domains such as biomedicine, agriculture, and manufacturing, it is humanly impossible to keep up with all the findings.

Constantly emerging world events present a similar challenge because they are difficult to track and even harder to analyze without looking into thousands of articles. 

To address the problem of relying on human effort in situations such as these, Lifu Huang, an assistant professor in the Department of Computer Science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is researching how machine learning can extract information without relying on humans.  Read the full story here.