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.
Having the opportunity to apply state-of-the-art machine learning models to bioinformatics problems as an undergraduate motivated M. Maruf to take a deep dive into machine learning and deep learning as a Ph.D. student in computer science at Virginia Tech which he chose because of its exemplary research and top-notch facilities.
“Dr. Anuj Karpatne’s unique view towards solving real-world problems fascinated me to explore more knowledge-infused machine learning,” Maruf said of his advisor at the Sanghani Center.
With privacy a growing concern, Si Chen, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering is using machine learning to study potential attacks and defenses against machine learning models.
She was attracted to this area of research because it is important and practical in real-world settings.
“For example,” said Chen, “if a company trains a medical diagnosis model on a training set containing sensitive information, an attacker may be able to infer the training set’s knowledge even if he or she only has access to the model. Our job is to research better attack algorithms that can aid development of defense techniques.”
Working toward a Ph.D. in computer science, Muntasir Wahed is delving into self-supervised learning, adversarial training, and out-of-distribution detection.
“Suppose we train a machine learning classifier to help medical diagnosis of a disease X given an X-ray,” Wahed said. “We collect a large dataset of X-rays for both positive and negative samples of the disease X. However, after we deploy the classifier in real life, it encounters confusing X-rays that have features not seen in any of the X-rays in the training samples. In such cases, it would be unreliable to classify the samples as positives or negatives. Instead, we would like to have a mechanism to recognize that these samples are so far unseen, or in other words, out-of-distribution.”
With restrictions to working in physical office space still in effect, graduate students at the Sanghani Center are working remotely this summer for companies, labs, and programs from coast to coast. Students are not only gaining real-world experience from internships and other opportunities but, in many cases, they are also able to advance their own research interests.
Following is a list of Sanghani Center students and the work they are doing:
“We are extremely proud of our graduates who achieved their
goals despite more than a year of a pandemic that upended much of their lives,”
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. “When everything went virtual, they continued
to attend classes, meet with their advisors, conduct research, present papers
at conferences, and work at internships — all
testament to their perseverance and a good barometer of their future success .”
A multidisciplinary faculty team
has garnered the Virginia Tech 2021 Alumni Award for Outreach Excellence for
developing and administering the Urban Computing (UrbComp) program that
trains graduate students in the latest methods of analyzing massive datasets to
study key issues facing urban populations while emphasizing ethical and
societal issues for practicing responsible data science.
The award, announced today by the university, accompanied by a $2,000 monetary award, is funded through the university’s Alumni Association and managed and administered by the Commission on Outreach and International Affairs.
Her area of interest is healthcare
systems, which are undergoing many changes in the era of big data.
“Advances in artificial intelligence and digitization in healthcare have enabled healthcare providers to effectively sift through tremendous amounts of medical information,” said Wang. “My first research project in this direction was about survival analysis and my advisor Dr. Reddy and other group members provided many useful suggestions and help at the initial stage. After further investigation, I found that there are still many unique challenges in the healthcare domain. I hope to leverage my expertise in data mining and machine learning to solve real-world challenges and advance healthcare applications.”
Nurendra Choudhary was an applied science intern with the Amazon Search Team in Palo Alto, California, last summer where he worked on representation learning of products by leveraging the heterogeneous relations between them.
Jia’s research interest broadly spans the areas of machine learning, security, privacy, and cyber-physical systems. Her recent work focuses on building algorithmic foundations for data markets and developing trustworthy machine learning solutions. Towards that end, she and her group work on a range of projects, including data valuation and quality management, data privacy, active data acquisition, adversarial machine learning, and explainable machine learning.