News featuring B. Aditya Prakash

Congratulations to our Ph.D. and master’s degree Spring graduates at the Discovery Analytics Center!

DAC graduates include (left to right): Xuchao Zhang with advisor Chang-Tien Lu in the National Capital Region; and Elaheh Raisi with Bert Huang and Yufeng Ma hooded by Ed Fox, both in Blacksburg.

The Discovery Analytics Center is pleased to announce that five of their Ph.D. and four of their master’s degree students celebrated graduation from Virginia Tech last weekend at Commencement ceremonies in Blacksburg and in the National Capital Region.

“It is always bittersweet to bid our students farewell, but we wish them all the best. We know and appreciate how hard they have worked to achieve the high goals they set for themselves and look forward to following their successful careers in academia and industry,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center.

 

Ph.D. graduates

Sorour E. Amiri, advised by B. Aditya Prakash, received a Ph.D. in computer science.
Her research interests are large-scale graph mining, data mining, and applied machine learning and the title of her dissertation is “Task-specific Summarization of Networks: Optimization and Learning.” She is joining the Google search ad team.

Minghan Chen, co-advised by Layne Watson, received a Ph.D. in computer science. Her research interest is computational cell biology and her dissertation title is “Stochastic Modeling and Simulation of Multiscale Biochemical Networks.” She joins the Computer Science Department at Wake Forest University as assistant professor.

Yufeng Ma, co-advised by Weiguo (Patrick) Fan and Edward Fox, received a Ph.D. in computer science. Ma’s research interests are computer vision, Natural Language Processing (NLP), and deep learning and his dissertation title is “Going Deeper with Images and Natural Language.” Ma is joining Verizon Media (Yahoo! Research) as a research scientist focusing on personalized recommendations.

Elaheh Raisi, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, weakly supervised learning, and computational social science and her dissertation title is “Weakly Supervised Machine Learning for Cyberbullying Detection.”

Xuchao Zhang, advised by Chang-Tien Lu, received a Ph.D. in computer science. His research interests are data mining, machine learning, and Natural Language Processing (NLP) and his dissertation title is “Scalable Robust Models Under Adversarial Data Corruption.”  Zhang joins NEC Labs America as a researcher. In that position he will work to fully understand the dynamics of big data from complex systems; retrieve patterns to profile them; and build innovative solutions to help end user managing those systems.

Master’s graduates

Raja Venkata Satya Phanindra Chava, advised by Edward Fox, received a master of engineering degree. His research interests are text summarization using deep learning and Natural Language Processing (NLP) and his project title is “Natural Language Processing Techniques for Comprehending Legal Depositions.” Chava is joining Walmart in Reston, Virginia, as a software engineer and will work on big data analysis to manage the supply chain and personalize the customer’s shopping experience.

Supritha B. Patil, advised by Edward Fox, received a master of science degree in computer science. Patil’s research interest is Natural Language Processing (NPL) and her thesis title is “Analysis of Moving Events Using Tweets.” She will be working as a software developer.

Adithya Upadhya, advised by Edward Fox, received a master’s in computer science. His research interests are machine learning and high performance computing and his project title is “A General Web Platform Summarizing Text and Documents.”

Xinfeng Xu, advised by B. Aditya Prakash, received a master’s degree in computer science. His research focused on modeling and predicting incidence and the title of his thesis is “Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases.” He also received the 2019 MS Research Award from the Department of Science. Xu is also a Ph.D. student in physics in the College of Science and will continue his research in that field.

 

 

 

 


DAC student Xinfeng Xu garners 2019 Computer Science MS Research Award

Xinfeng Xu, DAC Master’s student in computer science

Xinfeng Xu, a master’s student in the Discovery Analytics Center, received the Computer Science MS Research Award at the CS Awards Banquet last night.

The award recognizes the best MS thesis in CS with consideration to novelty of idea; quality of resulting publications; effectiveness of writing; and contributions/impact to the field overall.

Xu’s computer science research primarily focused on modeling and predicting incidence in two cases that take dynamics of propagation into account. He has defended his master’s dissertation, “Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases,” and will receive his degree from the Department of Computer Science later this month.

Aditya Prakash, Xu’s advisor, nominated him for the research award.

“This award is a nice recognition of Xinfeng’s work,” Prakash said. “His thesis offers new algorithms and models for two tough real-world problems — vulnerability of critical infrastructure systems and influenza surveillance. We are already using algorithms from his thesis in a toolkit being developed with the Oak Ridge National Laboratory for power systems, and also as part of Virginia Tech’s submission to the ongoing 2018/19 CDC FluSight challenge.”

Xu is also a Ph.D. student in physics in the College of Science and will continue his research in that field exploring the mystery of Active Galactic Nuclei (AGN). He is projected to complete the Ph.D. program in Spring 2020.


DAC Student Spotlight: Sorour Ekhtiari Amiri

Sorour Ekhtiari Amiri, DAC Ph.D. student in computer science

Sorour Ekhtiari Amiri developed an interest in machine learning during her senior year of college. After earning a bachelor’s degree in computer engineering from Beheshti University, she worked on machine learning applications while getting a master’s in computer engineering at the University of Tehran.

Amiri then decided to pursue a Ph.D. in computer science.

“I chose Virginia Tech and the Discovery Analytics Center,” Amiri said, “because of the great opportunity to collaborate with high impact researchers in the areas of data mining and machine learning.”

Amiri’s research is focused on summarizing large graphs and graph sequences based on a given task. She targets the task-based graph summarization problem, looks at various types of graphs, and uses deterministic and learning based approaches to generate high-quality graph summaries.

“Large graphs — also referred to as network — appear everywhere, as they very well capture the relation between objects,” Amiri said.

“For example, social networks, co-purchased product networks, people contact networks, and protein interaction graphs are instances of large graphs in the real world. Analyzing these graphs and solving various tasks on them has many applications in different fields such as cybersecurity, recommendation systems, sociology, and biology. However, the increasingly large size of such networks makes it challenging to visualize and analyze them, highlight their important characteristics, and perform fast computations on them,” said Amiri, whose DAC faculty advisor is B. Aditya Prakash.

Results of her research while a DAC student have been presented at a number of national and international conferences, including the IEEE International Conference on Data Mining series (ICDM); European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (PKDD); and the Association for the Advancement of Artificial Intelligence (AAAI) conference.

Her work has also appeared in journals such as the IEEE Transactions on Knowledge and Data Engineering (TDKE) and Data Mining and Knowledge Discovery (DAMI).

Amiri spent this past summer as an intern with Google’s “search ad” team, developing and training machine learning models and generating a new signal for using in search ads auction. She also analyzed machine learning models and other search ads signals.

Amiri expects to graduate in spring 2019


DAC and UrbComp actively participating at KDD 2018 with conference organization and research presentations

KDD Logo

The Discovery Analytics Center and the Urban Computing Certificate Program (funded through a National Science Foundation traineeship grant and administered through DAC) will be well represented at the 24th Annual  Association for Computing Machinery Special Interest Knowledge Discovery and Data Mining (KDD 2018) conference in London, August 19-23.

The overall theme of this year’s conference is data mining for social good.

Chandan Reddy, associate professor of computer science and DAC faculty, served as a poster co-chair for the KDD conference.

Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and DAC director, served on the senior program committee for the KDD research track.

Aditya Prakash, assistant professor of computer science and DAC faculty, served on the committee for Health Day at KDD, held in conjunction with the conference, and is one of four organizers for epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery, a Health Day workshop.

This workshop serves as a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains understudied, Prakash said.

The goal of the workshop is to raise the profile of this emerging research area of data-driven and computational epidemiology and create a venue for presenting state-of-the-art and in-progress results — in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learned in the “trenches.”

The paper, “Forecasting the Flu: Designing Social Network Sensors for Epidemics,” (B. Aditya Prakash; Naren Ramakrishnan; Huijuan Shao, K.S.M. Tozammel Hossain and Hao Wu, all DAC Ph.D. alumni; Madhav Marathe, professor of computer science and director of the Network Dynamics and Simulation Science Lab (NDSSL) at Virginia Tech; Anil Vullikanti, associate professor of computer science at NDSSL and Maleq Khan, assistant professor at Texas A&M University) will be presented at the epiDAMIK workshop by Prakash and Vullikanti.

An Urban Computing workshop is also scheduled in conjunction with KDD2018. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of the development and applications related to urban computing, present their ideas and contributions, and set future directions in innovative research for urban computing. It is particularly targeted to people who are interested in sensing/mining/understanding urban data so as to tackle challenges in cities and help better formulate the future of cities.

The following posters from DAC have been accepted for presentation at the workshop:

Additionally, a DAC alumnus, Prithwish Chakraborty, is running a third workshop taking place during the conference, Machine Learning for Medicine and Healthcare (MLMH).


B. Aditya Prakash on IEEE magazine’s list of 10 young stars to watch in artificial intelligence

B. Aditya Prakash, DAC faculty member and assistant professor of computer science.

B. Aditya Prakash, an assistant professor of computer science in the College of Engineering, is being celebrated as one of 10 young stars in the field of artificial intelligence by IEEE Intelligent Systems.

The technical magazine named Prakash, who is also a faculty member at the Discovery Analytics Center, to the prestigious AI’s 10 to Watch list for his contributions to understanding, reasoning, and mining the phenomenon of propagation over networks in diverse real-world systems.  Click here to read more about the AI’s 10 to Watch list.