News featuring Shuo Lei

Congratulations to Sanghani Center’s 2023 Summer and Fall Graduates

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 Keithadvised 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.

Lei Zhang, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research focuses on bi-level optimization, neural architecture search, and graph neural networks. The title of his dissertation is “Bilevel Optimization in the Deep Learning Era: Methods and Applications.”

Ming Zhu, co-advised by Daphne Yao and Ismini Lourentzou, has earned a Ph.D. in computer science. Her research focus is on Machine Learning and Natural Language Processing. The title of her dissertation is “Neural Sequence Modeling for Domain-Specific Language Processing: A Systematic Approach.” Zhu has joined ByteDance in Seattle, Washington, as a research scientist.

Master’s Degree Graduates

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.

Humaid Desaiadvised by Hoda Eldardiry, has earned a master’s degree in computer science. His research focuses on enhancing the efficiency and resource utilization of Federated Learning in resource-constrained and heterogeneous environments. The title of his master’s thesis is “REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments.” Desai is joining Ellucian in Reston, Virginia, as a software engineer.

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.


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

Ph.D. student Jianfeng He 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.


Amazon-Virginia Tech Initiative showcases innovative approaches to robust and efficient machine learning

(From left) Reza Ghanadan, senior principal scientist, Amazon Alexa and the new Amazon center liaison for the Amazon-Virginia Tech initiative; Shehzad Mevawalla, vice president of Alexa Speech Recognition, Amazon Alexa; Virginia Tech President Tim Sands; Lance Collins, vice president and executive director, Innovation Campus; Julie Ross, the Paul and Dorothea Torgerson Dean of Engineering; Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Amazon-Virginia Tech initiative; and Wanawsha Shalaby, program manager for the Amazon-Virginia Tech initiative. Photo by Lee Friesland for Virginia Tech.

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.


DAC Student Spotlight: Shuo Lei

Shuo Lei, DAC Ph.D. student in computer science

A Ph.D. student in computer science, Shuo Lei is focusing her research on few-shot learning and robust model learning. She is advised by Chang-Tien Lu.

“The aim of AI is to train machines to do some of the work that people were needed to do previously,” said Lei. “The training process requires a large amount of labeled data. It is time intensive and there are significant labor costs in collecting and labeling all that data. Few-shot learning can be valuable in forwarding research because it reduces the training cost by using less labeled data to get the same – and sometimes even greater – accuracy in training results.

Lei has collaborated on two published papers that incorporate her current work: Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation,” ACM Transactions on Knowledge Discovery from Data (TKDD) 2019; and “Robust Regression via Online Feature Selection under Adversarial Data Corruption,” proceedings of the IEEE International Conference on Data Mining (ICDM), Singapore, 2018.

Lei holds bachelor’s and master’s degrees in software engineering from Beihang University in China. The opportunity to work with professors who are expert in their fields, other talented students, and the northern Virginia location attracted her to Virginia Tech and the Discovery Analytics Center.

“I think the best thing about DAC is its abundance of academic resources,” Lei said. “I really enjoy working with everyone there. Dr. Lu provides a lot of support for my research and I have also learned from my lab members. They are very nice and helpful, always willing to offer suggestions whenever I have encountered a problem.”

Lei will spend the summer months engaged in her research at DAC.

Her previous interest in spatial temporal data mining, which included resident travel pattern analysis, is reflected in a collaborative paper, “Forecasting car rental demand based temporal and spatial travel patterns,” in 2017 IEEE Ubiquitous Intelligence, Cloud and Big Data Computing, Internet of People and Smart City Innovation.

When she has free time, Lei enjoys cooking, baking, traveling, and photography.

She is projected to graduate in May 2022.