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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.

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 Karwande, advised 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 Li, advised 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 Taware, advised 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.”  

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.

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.”

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.

How has online sleuthing successfully replaced wanted posters?

Researchers within the Virginia Tech Department of Computer Science answered this question by studying the crowdsourced online investigation that followed the Jan. 6, 2021, insurrection at the U.S. Capitol.

Tianjiao “Joey” Yu and Kurt Luther collaborated on the project with Ismini Lourentzou, assistant professor of computer science and a core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, and Sukrit Venkatagiri, a postdoctoral researcher at the University of Washington. Read the full story here.

Virginia Tech and Amazon gathered for a Machine Learning Day held at the Virginia Tech Research Center — Arlington on April 25 to celebrate and further solidify their collaborative Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning.  

Announced last year, the initiative — funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s campus in Blacksburg and at the Innovation Campus in Alexandria — supports student- and faculty-led development and implementation of innovative approaches to robust machine learning, such as ensuring that algorithms and models are resistant to errors and adversaries, that could address worldwide industry-focused problems. Read full story here.

In her research at the Sanghani Center, Ph.D. student Amarachi Blessing Mbakwe is trying to develop advanced artificial intelligence methodologies for better medical imaging and clinical decision-making.

Her passionate drive to improve healthcare systems that could save millions of lives worldwide stems from personal experience. With the deaths of two close family members in her home region in Nigeria, Mbakwe witnessed firsthand the devastating consequences of delayed disease detection, poor treatment management, and a shortage of healthcare professionals. 

Targeted intervention can improve healthcare access for everyone and mitigate the disparities in clinical care often faced by underrepresented populations and minorities, said Mbakwe, who is advised by Ismini Lourentzou.

“By developing an AI algorithm that can accurately and quickly analyze chest x-rays, my research can help reduce the time and effort required for radiologists to interpret medical imaging tests which, in turn, can help ensure timely patient treatment or adjustment of treatments, especially in regions with a shortage of radiologists,” she said.

Mbakwe has published papers and articles in various journals and conferences. She presented a collaborative paper, “CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays,” at the 2022 Medical Image Computing and Computer Assisted Intervention Society conference in Singapore and, this spring, at the Computing Research Association  2023 CRA-WP Grad Cohort Workshop for IDEALS in Hawaii and the 2023 Grad Cohort Workshop for Women.

CheXRelNet incorporates local and global visual features, utilizes inter-image and intra-image anatomical information, and learns dependencies between anatomical region attributes via graph attention to accurately predict disease progression for a pair of chest x-rays.

“I was attracted to Virginia Tech’s Department of Computer Science and the Sanghani Center because I wanted to conduct impactful research that benefits society and they provided me with the perfect platform to achieve my goals,” Mbakwe.

She said that the outcome of her research is not only applicable in healthcare but could also extend further to other applications in fairness and finance. Last summer she had the opportunity to intern at JPMorgan Chase & Co as an AI research associate and will be returning for a second internship this summer.

Mbakwe earned a bachelor’s degree in mathematics from Nnamdi Azikiwe University, Anambra State, Nigeria, and a master’s degree in computer science and quantitative methods from Austin Peay State University in Clarksville, Tennessee.

Projected to graduate in 2024, she aspires to become a researcher in an industrial research lab and eventually also assume the position of visiting/adjunct professor.

Makanjuola Ogunleye, a Ph.D. student in computer science at the Sanghani Center for Artificial Intelligence and Data Analytics, has been awarded a Black Students in Technology Scholarship from Cadence Diversity in Technology Scholarship Programs.

Ogunleye, a member of the Perception and LANguage (PLAN) research lab, is one of eight students pursuing technical degrees at universities across the country who were selected to receive the scholarship based on their impressive academic records, work in the community, leadership potential, and recommendations from professors. He is advised by Ismini Lourentzou, an assistant professor in the Department of Computer Science.  Read full story here.

Assistant Professor Ruoxi Jia in the Bradley Department of Electrical and Computer Engineering and core faculty at the Sanghani Center for Artificial Intelligence and Data Analyitics at Virginia Tech has received an National Science Foundation (NSF) Faculty Early Career Development (CAREER) award to investigate fundamental theories and computational tools needed to measure the value of data. Read full story here.