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Virginia Tech’s virtual university commencement will livestream tonight, Friday, May 14, at 6:15 p.m., and degrees will be conferred at this time.

“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 .”

Following is a list of Sanghani Center graduates:

Ph.D.

Prashant Chandrasekar, advised by  Edward Fox, is receiving a Ph.D. in computer science. His research interest lies in digital libraries. The title of his dissertation is “Continuously Extensible Information Systems: Extending the 5S Framework by Integrating UX and Workflows.” Chandrasekar will join the University of Mary Washington as an assistant professor in computer science.

Kaiqun Fu, advised by Chang-Tien Lu, is receiving a Ph.D. in computer science. His research interests lie in spatial data mining, machine learning, and graph neural networks, with a focus on social media analysis in intelligent transportation systems and smart cities. The title of his dissertation is “Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors.” Fu will join South Dakota State University as assistant professor in August 2021.

Yen-Cheng Lu, advised by Chang-Tien Lu, is receiving a Ph.D. in computer science. His research interests lie in anomaly detection and probabilistic modeling. The title of his dissertation is “Relational Anomaly Detection: Techniques and Applications.” Lu will be continuing his career as a software engineer at Amazon Alexa AI.

Sneha Mehta, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. Her research interests are data mining and deep learning, especially for natural language processing applications. The title of  her dissertation is “New Methods for Event Detection and Extraction from News Articles.” Mehta will join Twitter as a machine learning researcher in July.

Sathappan Muthiah, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. His areas of interest include forecasting, machine learning, information retrieval, and topic detection and tracking (TDT). The title of his dissertation is “Design and Maintenance of Event Forecasting Systems”.  Muthiah has joined eBay as an applied researcher.

Reza Sepasdar, advised by Anuj Karpatne, is receiving a simultaneous master’s degree in computer science and Ph.D. in civil engineering. (His master’s co-advisor is Maryam Shakiba). His research interests lie in the intersection of AI and computational mechanics. Sepasdar’s master’s thesis is entitled “A Deep Learning Approach to Predict Full-Field Stress Distribution in Composite Materials.” He will defend his Ph.D. dissertation, “Micro-mechanical Behavior of Fiber-reinforced Composites using Finite Element Simulation and Deep Learning,” this summer.

Sirui Yao, advised by Bert Huang, is receiving a Ph.D. in computer science. Her research interests include machine learning, recommender systems, and fairness. The title of her dissertation is “Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems.” Yao will join Google in June 2021 as a machine learning engineer.

Master’s degree

John Aromando, advised by Edward Fox, is receiving a (coursework only) master’s degree in computer science. His research interests include natural language processing and information retrieval, particularly analyzing design specs written in natural language and synthesizing a machine-understood language via relevant descriptions.

Mohi Beyki, advised by Edward Fox, is receiving a master’s degree in computer science. His research interests are in deep learning, health care, and software engineering. The title of his thesis is “Synthetic Electronic Medical Record Generation using Generative Adversarial Networks.”  Beyki will be joining Google as a software engineer this summer.

Yi-Chun Chang, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research interest is in using social media analytics to detect threats. The title of his thesis is “RISECURE: Metro Incidents And Disruptions Detection Using Social Media And Graph Convolution.”  He will join Walmart Global Tech as a software engineer in July.

Po-Han Chen, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research focuses on using data from social media to help solve real-world problems. The title of his dissertation is “Metro Security Incidents And Threat Detection Using Social Media.” He will be joining Bloomberg as a software engineer this summer.

Yi Huang, advised by Jia-Bin Huang, is receiving a masters of engineering degree. His research interests lie in computer vision and machine learning. The title of his master’s project is “Cross-Domain Context-aware 3D Hand Pose Estimation.” Huang will join Qualcomm as a computer vision research engineer.

Kulendra Kumar Kaushal, advised by  Naren Ramakrishnan, is receiving a master’s degree in computer science. His research interests lie in the field of natural language processing and information extraction. The title of his thesis is “Information Extraction of Technical Details From Scholarly Articles.” Kaushal will be joining Bloomberg as a software developer.

Prathamesh Kalyan Mandke, advised by Anuj Karpatne, is receiving a masters of engineering degree. His research interests lie in machine learning and computer vision. The title of his master’s project is “Fluorescent Image Reconstruction in Shape Controlled Cell Migration using Deep Learning.”  Mandke will join Qualcomm AI Research as a machine learning software engineer in July.

Ashkan Nazari, advised by Lenwood Heath, is receiving a master’s degree in computer science. His research interests lie in artificial intelligence, deep learning, and cloud-based intelligence systems analysis. Nazari has also worked toward a Ph.D. in mechanical engineering. He will join the Silicon Valley-based luxury electric vehicle start-up Lucid Motors as a senior data scientist, working on developing intelligent battery initiatives.

Ioannis Papakis, co-advised by Anuj Karpatne and Abhijit Sarkar, is receiving a master’s degree in computer science. His research interests lie in machine learning, computer vision, robotics, and signal processing. The title of his thesis is “A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow.”  Papakis also won first place in the 2021 Paul E. Torgersen Graduate Student Research Excellence Awards MS poster presentation category. Starting in July, he will be employed by Bertrandt US, Inc., working at Audi in Santa Clara, California, as an advanced driver-assistance systems engineer.

Arya Shahdi, co-advised by Anuj Karpatne and Bahareh Nojabaei, is receiving a master’s degree in computer science. His research interests lie in forecasting and geospatial modeling and analysis. The title of his thesis is “Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction.” Shahdi is a supply chain data scientist at Lowe’s Companies, Inc. 

Aarohi Sumant, advised by Edward Fox, is receiving a master’s degree in computer science. Her research focuses on deep learning and machine learning application, specifically in natural language processing. The title of her thesis is “Improving Deposition Summarization using Enhanced Generation and Extraction of Entities and Keywords.” Sumant will join Amazon as a software development engineer in July.

Omer Zulfiqar, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research interests lie in social media event detection and natural language processing. The title of his thesis is “Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution.” He will join Walmart Labs as a software engineer in June.

A national pandemic that forced the closing of physical offices has not stopped graduate students at the Discovery Analytics Center from working remote internships at companies, research laboratories, and other institutions across the country. For many students, summer internships help further their own research as they gain real world experience.

Following is a list of DAC students and the work they are doing for the next few months:

Chidubem Arachie, a Ph.D. student in computer science, is a research intern at Google Research in Mountain View California. He is working on generative modeling for 3D shapes. His advisor is Bert Huang.

John Aromando, a Ph.D. student in computer science, is an intern at Graf Research in Blacksburg, working on utilizing natural language processing to support the software verification process. His advisor is Edward Fox.

Hongjie Chen, a Ph.D. student in computer science, is a data science research intern at Adobe in San Jose, California. He is on the Cloud Technology Team, researching cloud resource allocation strategy. His advisor is Hoda Eldardiry.

Nurendra Choudhary, a Ph.D. student in computer science, is an applied science intern with the Amazon Search Team in Palo Alto, California. He is working on representation learning of products by leveraging the heterogeneous relations between them. His advisor is Chandan Reddy.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern at Google in Mountain View, California. He is working on improvements to the portrait mode on the Google Pixel phone. His advisor is Jia-Bin Huang.

Akshita Jha, a Ph.D. student in computer science, is a research intern in the Interdigital AI Lab in Palo Alto, California. Her work involves building interpretable natural language processing models. Her advisor is Chandan Reddy.

Prerna Juneja, a Ph.D. student in computer science, is an intern at the Information Science Institute at the University of Southern California with Emilio Ferrara, assistant research professor and associate director of Applied Data Science in the Department of Computer Science. She is investigating the spread of COVID-19 related conspiracy theories on Twitter. Her advisor is Tanushree Mitra.

You Lu, a Ph.D. student in computer science, is a research intern at NEC Labs America in Princeton, New Jersey, working on sequence labeling for signals in fibers. His advisor is Bert Huang.

Shruti Phadke, a Ph.D. student in computer science, is doing a research internship with James Pennebaker, a professor in the Department of Psychology at the University of Texas at Austin. She is studying online communities, their social processes, and behaviors. Her advisor is Tanushree Mitra.

Aarathi Raghuraman, a master’s degree student in computer science, is an intern at GlaxoSmithKline (GSK), working with the Digital, Data, and Analytics team to maximize process yield in upstream biopharm manufacturing. She is advised by Lenwood Heath.

Esther Robb, a master’s degree student in electrical and computer engineering, is a research intern at Google working with a team in San Francisco on reinforcement learning. Her advisor is Jia-Bin Huang.

Mandar Sharma, a master’s student in computer science, is working as a machine learning intern with Toyota Motors North America, specifically the Toyota Racing Development (TRD) branch, to help NASCAR drivers make better decisions when they are racing. His advisor is Naren Ramakrishnan.

Aarohi Sumant, a master’s student in computer science, is an intern at Amazon. She is working with the Kindle Marketing Team to develop machine learning techniques for book recommendations based on cross user activities as well as single-user activities on different Amazon platforms. Her advisor is Edward Fox.

Afrina Tabassum, a Ph.D. student in computer science is a data science intern in the Data Science for The Public Good (DSPG) program at the Biocomplexity Institute’s Social and Decision Analytics Division (SDAD) at the University of Virginia. She is working on projects that address state, federal, and local government challenges around critical social issues relevant in the world today. Her advisor is Hoda Eldardiry.

Mia Taylor, a senior undergrad in computer science, is interning at Amazon Web Services in the Route 53 (DNS) service. Her advisor is Hoda Eldardiry.

Sirui Yao, a Ph.D. student in computer science, is an intern at Google, working on tag prediction for recommender systems through learning items and tags embeddings. Her advisor is Bert Huang.

Shengzhe Xu, a Ph.D. student in computer science, is interning at Facebook Ads Core ML, working on attention-based time sequential embedding aggregation. Xu’s advisor is Naren Ramakrishnan.

Ming Zhu, a Ph.D. student in computer science, is interning at Amazon. She is an applied scientist intern for Amazon Alexa AI, working on conversational query representation learning. Zhu’s advisor is Chandan Reddy.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is working on learning with less/weaker annotations at Google. His advisor is Jia-Bin Huang.

Sirui Yao studies the biases of recommender systems.

“A recommender will often suggest different courses to male and female college students because based on historical data, there are differences in course preference between these two groups,” said Yao, a Ph.D. student in computer science at the Discovery Analytics Center.

“Over-leveraging this gender-based pattern encourages stereotypes and creates an even bigger —and undesirable — gap between demographic groups, especially in areas actively encouraging equality, such as engineering,” she said.

Yao’s work proposes methods for measuring, analyzing, and mitigating unfairness in recommender systems. She was awarded a 2018-2019 Deloitte Foundation Data Analytics Fellowship in the amount of $10,000 to fund her research.

Her advisor, Bert Huang, introduced Yao to this topic three years ago. “I realized this is a very critical yet fairly unexplored area in machine learning, so I wanted to focus on it and make whatever contributions I can to fill this vacuum,” she said.

“Being a DAC student means I am always informed about exciting data science projects and surrounded by people who have a lot of expertise, passion, and creativity in data analytics,” Yao said. “Such an environment encourages me to learn more and do more.”

Yao works in Huang’s Machine Learning Laboratory and the two have collaborated on research, including “On the Need for Fairness in Financial Recommendation Engines,” which Yao shared at the NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy.

She presented “Beyond Parity: Fairness Objectives for Collaborative Filtering” at the main NeurIPS 2017 conference and  “New Fairness Metrics for Recommendation that Embrace Differences” at the KDD 2017 Workshop on Fairness, Accountability, and Transparency.

This past summer, Yao interned at Google Brain in New York City, where she worked on a research project that designs a trajectory simulation and analysis framework for studying the long-term dynamics of recommender systems. This research has been submitted to WWW 2020: The Web Conference.

Yao earned a bachelor of science degree in computer science and technology from the Harbin Institute of Technology in China and is projected to graduate from Virginia Tech in December 2020.

A number of graduate students at the Discovery Analytics Center have opted for internships and jobs at companies and national laboratories across the country this summer as a way of both benefiting their own research and gaining real world experience.

Following is a list of where they are for the next few months:

Aman Ahuja, a Ph.D. student in computer science, is an applied scientist intern at Amazon in Palo Alto, California. He is on the Amazon Search Team, researching product search techniques. His advisor is Chandan Reddy.

Tyler Chang, a Ph.D. student in computer science, has begun a six-month appointment at Argonne National Laboratory in Chicago, Illinois. He is one of 70 graduate students who received an appointment from the U.S. Department of Energy (DOE) Office of Science Graduate Student Research (SCGSR) to work on his thesis. The goal is to produce a portable multi-objective optimization software which Argonne could utilize in the future. Chang’s advisor is Layne Watson.

Jinwoo Choi, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America, San Jose, California, working on domain adaptation for video. Choi’s advisor is  Jia-Bin Huang.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern on a video completion project at Facebook in Seattle, Washington. He is working on an algorithm that synthesizes missing regions of videos. His advisor is Jia-Bin Huang.

Liuqing Li, a Ph.D. student in computer science, is on the Content Science team at Yahoo! Research in Sunnyvale, California, working on document recommendation through reinforcement learning. His advisor is Edward Fox.

Sneha Mehta, a Ph.D. student in computer science, is a data science intern at the Netflix headquarters in Los Gatos, California. She is researching novel methods to improve machine translation for subtitles. Her advisor is Naren Ramakrishnan.

Shruti Phadke, a Ph.D. student in computer science, is an intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. She is working on developing scalable machine learning and Natural Language Processing (NLP) algorithms to detect public sentiment in news and social media. Her advisor is Tanushree Mitra.

Esther Robb, a master’s degree student in electrical and computer engineering, is a research intern at Google in Mountainview, California, where she is working on facial recognition. Her advisor is Jia-Bin Huang.

Alexander Rodriguez, a Ph.D. student in computer science, is a research intern at WalmartLabs in Sunnyvale, California. His advisor is B. Aditya Prakash.

Dhruv Sharma, a master’s student in computer science, is working at Kitware, Inc., in Carborro, North Carolina. As a research and development intern, Sharma’s work includes some medical image processing/machine learning tasks; mining EHR data for prediction of risk, procedure outcome, or other events; and analyzing training needs of healthcare providers. He is advised by Chandan Reddy.

Tian Shi, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where he is working on machine comprehension and question-answering on clinical notes in the healthcare domain. His advisor is Chandan Reddy.

Shih-Yang Su, a Ph.D. student in electrical and computer engineering, is a research intern at Borealis AI in Vancouver, Canada, working on graph convolution for structural prediction. His advisor is Jia-Bin Huang.

Deepika Rama Subramanian, a master’s student in computer science, is a mobility intern at Lam Research, Fremont, California, working on designing and developing an end-to-end mobile application for field engineers at Lam Research. Her advisor is Tanushree Mitra.

Anika Tabassum, a Ph.D. student in computer science, is a research intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, where she will be applying data mining and visualization skills in two U.S. Department of Energy (DOE) projects: “Reynolds Landing Research” and “North American Energy Resilience Model.” Her advisor is B. Aditya Prakash.

Sai Sindhura Tipirneni, a master’s student in computer science, is working in the Quantum Computing Lab at Oak Ridge National Laboratory in Oak Ridge, Tennessee. Her advisor is Chandan Reddy.

Ping Wang, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where she is working on question answering on electronic medical records using Natural Language Processing (NLP) techniques. Wang’s advisor is Chandan Reddy.

Sirui Yao, a Ph.D. student in computer science, is a research intern at Google AI in New York City, where she is studying noise and bias in dynamic recommender systems. Her advisor is Bert Huang.

Ming Zhu, a Ph.D. student in computer science, is at Amazon in Seattle, Washington. She is an applied scientist intern for Amazon Comprehend Medical, working on Natural Language Processing on medical corpora using deep learning. Zhu’s advisor is Chandan Reddy.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America in San Jose, California, where he is working on unsupervised scene structure learning. His advisor is Jia-Bin Huang.

Jonathan Baker and Sirui Yao, Ph.D. students at the Discovery Analytics Center, and Leanna Ireland, a National Science Foundation research trainee in the Urban Computing (UrbComp) Certificate program administered through DAC, have each been awarded a Deloitte Foundation Data Analytics Fellowship in the amount of $10,000 to fund their research.

Baker, also a National Science Foundation research trainee in the UrbComp program, is a math major advised by Mark Embree, professor of mathematics, associate director of the Virginia Tech Smart Infrastructure Laboratory, and DAC faculty.

Yao is a computer science major advised by Bert Huang, assistant professor of computer science and DAC faculty.

Leanna Ireland, a sociology major, is advised by James Hawdon, professor and director of the Center for Peace Studies.

The three are among five graduate students — selected from applications received from across five colleges at Virginia Tech — to receive this interdisciplinary fellowship in support of the university’s Data and Decisions Destination Area vision.

A committee consisting of four members of Data and Decisions and three representatives from Deloitte chose the fellowship recipients for 2018-2019.

Baker’s project was motivated by disasters like the 1995 collapse of a large department store in Seoul, South Korea, which killed 500 people and injured 1400. In spite of the fact that a few hours before the collapse, occupants began to feel vibrations from the air conditioning system throughout the building, no evacuation was ordered.

A building equipped with vibration sensors and software could prevent such a disaster in several ways. First, by monitoring the global vibrations of the building, software should be able to automatically detect even small amounts of structure damage so that repairs can be conducted long before evacuation becomes necessary. Second, once vibrations indicate that they building is in danger of collapse, the system could trigger an alarm, just as smoke detectors may automatically signal evacuation. Lastly, the building could use vibrations to help occupants respond intelligently to an ongoing evacuation in response to any emergency. Foot-traffic vibrations can also be used to estimate the locations of occupants and calculate real-time evacuation routes that minimize crowding, help prevent stampeding, and ensure that the building is emptied as quickly and safely as possible. The building may also be able to detect circumstances that would make some exits unavailable and adapt its evacuation directions accordingly.

By triggering the alarm and giving evacuation instructions, a smart building takes the role of emergency personnel: the building itself is the first responder. The goal of this project is to develop algorithms that this kind of intelligent building would require.

Yao’s project is focused on recommender systems.

Recommender systems play an important role in supporting human decision making. However, it is important to be aware of the potential impact of applying such technology, especially to areas that involves humans. Fairness is a crucial aspect to be taken into account. Since recommender systems are trained on data collected from the real world, which already has a long history of human bias, such data can be severely contaminated and historical biases passed on or reinforced through recommender models. It is unethical to make recommendations that constantly favor one group over the others. More concretely, unfair treatment of users would cause poor user experiences and could lead to legal trouble.

Yao’s research proposes to establish methods for measuring, analyzing, and mitigating unfairness in recommender systems. The goals are threefold: (1) to quantify and evaluate unfairness; (2) to identify the causes of unfairness; (3) to promote fairness. The success of this research will have significant impact on the wide-reaching technology of recommender systems and the many aspects of society they affect.

Ireland’s research involves crime-fighting and crime-control mobile and web applications that the general public can, for example, use to submit tips and/or share photos directly to the police.

Official crime statistics are often patchy and can be plagued by missing data, biased reporting and other measurement aliments. Crowdsourcing data can account for some of these limitations in official and self-reported crime data sources, such as lagged, incomplete, or often skewed data. However, there is also some apprehension that crowdsourced data-sources could include false-reports, trolls, and the misidentification of offenders. Relatedly, minority voices could be under-represented.

To address the potential differences in crowdsourced-policing and official policing initiatives, Ireland will investigate how the crowd-sourced initiative called the French Quarter Task Force (FQTF), colloquially known as the “Uber for cops,” impacts official crime reports. And, does success of the FQTF lead to greater community engagement, and if so, how, if at all, does the FQTF affect biased reporting? With advantages and disadvantages in both types of data, drawing from both formal and crowd-sourced data could present a clearer picture of the occurrence of crime in society, suggesting the need to include all data sources in criminological research.

The other two Virginia Tech students receiving the Deloitte Fellowship are Kaveh Kelarestaghi, a civil engineering major, and Long Xia, a business information technology major.

“A special thank you to Deloitte for initiating this interdisciplinary fellowship for our graduate students and for supporting the Data and Decisions Destination Area vision,” said Robin Russell, a member of the Data and Decisions Stakeholders Committee and chair of the Deloitte Foundation Data Analytics Fellowship Selection Committee, in a letter announcing the recipients. “We look forward to seeing the results of the research projects and engaging with these talented students.”

The Data and Decisions Destination Area seeks to advance the human condition and society with better decisions through data and to be a global destination for data analytics and decision sciences, integrating across all Destination Areas and Strategic Growth Areas of the university.