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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.”  

Jie Bu, a Ph.D. student in computer science, has been interested in machine learning since he was an undergraduate in communications engineering at Harbin Institute of Technology, China. There he was introduced to the Random Forests (a machine learning model) and genetic algorithms which, Bu said, still hold great fascination for him.

In his current research at the Sanghani Center, Bu uses machine learning for physical applications. 

“We have been exploring how we can use machine learning to help fluid dynamics and quantum physics. From knowledge of science developed over the centuries, we are seeking to find out how machine learning models can be made more interpretable and generalizable,” said Bu.

“Sometimes generating simulation data is very slow so we are looking at the possibility of using machine learning to accelerate the simulation,” he said. “Machine learning  is very powerful and can be used to greatly benefit science discovery.” 

Bu is also interested in improving the efficiency of deep learning in a number of ways, including better model architecture and network pruning. 

A paper with his advisor, Anuj Karpatne focuses on model architecture. Bu presented their work on “Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs” last Spring in proceedings of the 2021 SIAM International Conference on Data Mining (SDM). They developed a new class of quadratic residual networks offering better accuracy, parameter efficiency, and convergence speed for solving forward and inverse problems in physics involving partial differential equations (PDEs).

At the Sanghani Center, Bu said, he has been able to team up and “meet with a lot of brilliant minds.” At the 2021 Neural Information Processing Systems (NeurlPS) conference in December, he presented “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM),” collaborative work with his advisor and other Ph.D. students at the Sanghani Center that uses network pruning.

Bu was also on the research team for the paper, “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly,” published both in proceedings at SDM 2020 and in Big Data Journal. 

Projected to graduate in summer 2023, Bu would like to continue his research in an industry aligned with his research direction.

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.