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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.

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 Desai, advised 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.

Anuj Karpatne, associate professor in the Department of Computer Science in the College of Engineering has won a five-year, $595,738 National Science Foundation Faculty Early Career Development Program CAREER award to explore a unified approach for accelerating scientific discovery using scientific knowledge and data. Karpatne is also a core faculty member at the Sanghani Center for AI and Data Analytics. Read the full story here.

With advances in deep learning, machines are now able to “predict” a variety of aspects about life, including the way people interact on online platforms or the way they behave in physical environments. This is especially true in computer vision applications where there is a growing body of work on predicting the future behavior of moving objects such as vehicles and pedestrians. 

“However, while machine-learning methods are now able to match — and sometimes even beat — human experts in mainstream vision applications, there are still some gaps in the ability of machine-learning methods to predict the motion of ‘shape-shifting’ objects that are constantly adapting their appearance in relation to their environment,” said Anuj Karpatne, assistant professor of computer science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. Click here to read how Karpatne and his team will tackle this challenge in their National Science Foundation-sponsored research.

Conferences have been a big part of Arka Daw’s life as a Ph.D. student this past academic year.

Daw presented “Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling” in proceedings at the 2020 SIAM International Conference on Data Mining (SDM), and “PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics” in proceedings at the 2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

In addition, he participated in the NeurIPS ML4PS Workshop, “Physics-Informed Discriminator (PID) for Conditional Generative Adversarial Nets.”

Daw’s broader research interests include artificial intelligence and deep learning but, more specifically, his work is geared towards formulating generic ways of coupling scientific knowledge with conventional deep learning approaches. 

The research he presented at the SIAM conference involved predicting the temperature of lakes at different depths. 

“Our proposed solution included a specialized design of a seq-to-seq model where a specific physics-driven inductive bias was infused directly into the model architecture,” said Daw, who is advised by Anuj Karpatne, “It demonstrated that combining scientific knowledge with deep learning models can not only improve their generalizability but also provide meaningful uncertainty estimates.”

During his undergraduate studies in electronics and telecommunication engineering at Jadavpur University, India, Daw had the opportunity to work on retinal artery-vein classification during a research internship at the Pattern Recognition and Image Analysis group at University of Muenster, Germany. 

“This is when I realized the immense potential of deep learning in solving real-world problems and decided to pursue higher studies in the broader field of artificial intelligence,” he said. 

Daw said he was drawn to Virginia Tech due to its eminence in world-class research and exemplary work of faculty in the Department of Computer Science and at the Sanghani Center.

“I am exceedingly fascinated by Dr. Karpatne’s approach to solving real-world scientific problems and how he works towards shaping the emerging field of science-guided machine learning,” he said. “I am very fortunate to have him as my advisor.”

Daw said that being a student at the Sanghani Center has provided him the opportunity to work with students and faculty of diverse backgrounds and research focus.  

“Everyone is always very supportive, really fun to work with, and provides great advice when you need it,” he said.

Daw is projected to graduate in Spring 2023.

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:

Badour AlBahar, a Ph.D. student in electrical and computer engineering, is a computer vision intern at Adobe Vision group in San Jose, California. She is working on human reposing and animation. Her advisor is Jia-Bin Huang.

Sikiru Adewale, a Ph.D. student in computer science, is a software development engineer intern at Amazon Web Service in Seattle, Washington. He is working on data transfer and storage on the AWS snowball device. His advisor is Ismini Lourentzou.

Vasanth Reddy Baddam, a Ph.D. student in computer science, is an research intern at Siemens in Princeton, New Jersey. He is working on contributing to industrial research projects on leveraging machine learning to analyze multi-agent reinforcement learning (MARL) algorithms and implement them. His advisor is Hoda Eldardiry. 

Subhodip Biswas, a Ph.D. student in computer science, is working on Bayesian optimization techniques for automated machine learning (AutoML) and robust artificial intelligence systems as part of the Journeyman Fellowship he received from the DEVCOM Army Research Laboratory (ARL) Research Associateship Program (RAP) administered by the Oak Ridge Associated Universities (ORAU). His advisor is Naren Ramakrishnan.

Jie Bu, a Ph.D. student in computer science, is a research intern at Carbon 3D in Redwood City, California. He is working on artificial intelligence-powered computational geometry. His advisor is Anuj Karpatne.

Si Chen, a Ph.D. student in computer engineering, is a research intern at InnoPeak Technology in Seattle, Washington. She is working on research on model privacy protection. Her advisor is Ruoxi Jia.

Kai-Hsiang Cheng, a master’s degree student in computer science, is an intern at GTV Media Group in New York City. He is working on the content management system of the media’s platform. His advisor is Chang-Tien Lu.

Riya Dani, a master’s degree student in computer science, is a software engineer intern at Microsoft. She is working on web application developments under Azure. Her advisor is Ismini Lourentzou.

Debanjan Datta, a Ph.D. student in computer science, is an intern on the Amazon Web Services team at Amazon in Seattle, Washington. He is working on time series characterization and classification.  His advisor is Naren Ramakrishnan.

Arka Daw, a Ph.D. student in computer science, is an applied scientist intern at Amazon Web Services Lambda Science Team in Seattle, Washington.  He is working on developing an automated causal machine learning framework for setting up experiments and estimating causal effects from observational data. His advisor is Anuj Karpatne.

Yue Feng, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. She is working on a 3D computer vision project. Her advisor is Jia-Bin Huang.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern at Google in Cambridge, Massachusetts. He is working on creating video panoramas using a cellphone. His advisor is Jia-Bin Huang.

Jianfeng He, a Ph.D. student in computer science, is an intern at Tencent AI Lab in Seattle,Washington. He is working on research about multi-modal dialogue with mentors Linfeng Song and Kun Xu. His advisor is Chang Tien-Lu.

Taoran Ji, a Ph.D. student in computer science, is an intern at Moody’s Analytics in New York City. He is working on analyzing credit and financial data for the global financial markets, which will drive algorithmic improvements in Moody’s Analytics core machine learning and artificial intelligence-driven products. His advisor is Chang-Tien Lu.

Adheesh Juvekar, a Ph.D. student in computer science, is a machine learning and natural language processing intern at Deloitte & Touche LLP. He is working on automatically extracting relevant information from transactional invoices using state of the art deep learning techniques. His advisor is Edward Fox.

M. Maruf, a Ph.D. student in computer science, is a machine learning engineering intern at Qualcomm GNSS/location team in Santa Clara, California. He is applying machine learning techniques to hybrid technology fusion for navigation/positioning in mobile, wearable, automotive, and micro-mobility applications. His advisor is Anuj Karpatne.

Nikhil Muralidhar, a Ph.D. student in computer science, received an Applied Machine Learning Summer Research Fellowship at Los Alamos National Lab in Los Alamos, New Mexico, to work with researchers on physics-informed machine learning for modeling adsorption equilibria in fluid mixtures. His advisor is Naren Ramakrishnan. 

Makanjuola Ogunleye, a Ph.D. student in computer science, is an application support engineer intern at Northwestern Mutual in Milwaukee, Wisconsin. His duties include coding, testing, and implementing complex programs from user specifications. He is also performing client data analysis to support engineering technology to improve and facilitate customer success. His advisor is Ismini Lourentzou.

Nishan Pokharel, a master’s degree student in computer science, is a software engineering intern at Capital One in Mclean, Virginia.  He is working on network infrastructure automation. His advisor is Chris North

Avi Seth, a master’s degree student in computer science, is serving as a graduate team leader this summer for Virginia Tech’s Data Science for the Public Good program. The group works on projects that address state, federal, and local government challenges around today’s relevant and critical social issues. His advisor is Ismini Lourentzou.

Mia Taylor, a master’s degree student in computer science, is a software development intern at Amazon Web Services in Seattle, Washington. Her team is working with Comprehend AutoML which allows customers to build customized natural language processing models using their own data. Her advisor is Lifu Huang.

Yiran Xu, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. He is working on 3D human reconstruction and video generation/manipulation. His advisor is Jia-Bin Huang.

Shuaicheng Zhang, a Ph.D. student in computer science, is a natural language processing (NLP) research intern at Deloitte in New York City. He is part of the Audit and Assurance AI innovation team, working on open information extraction on internal control files to help auditors effortlessly process these files. His advisor is Lifu Huang.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at Waymo in Mountainview, California. He is working on the perception problem for self-driving cars.  His advisor is Jia-Bin Huang.