CS&E Ph.D. Students Named Doctoral Dissertation Fellows
Six Ph.D. students working with CS&E professors have been named doctoral dissertation fellows for the 2018-2019 school year. The Doctoral Dissertation Fellowship is a highly competitive fellowship that gives the University’s most accomplished Ph.D. candidates an opportunity to devote full-time effort to an outstanding research project by providing time to finalize and write a dissertation during the fellowship year.
The award includes a stipend of $25,000, tuition for up to 14 thesis credits each semester, and subsidized health insurance through the Graduate Assistant Health Plan. More details on the award and nomination process can found on the Graduate School’s website.
CS&E congratulates the following students on this outstanding accomplishment:
Advisor: Mohamed Mokbel
A Context-Aware Crowdsourcing Framework
Crowdsourcing has been gaining a lot of popularity and has become a part of our everyday lives. This popularity can be seen by the existence of several crowdsourcing platforms, e.g., Amazon Mechanical Turk and Uber. While many crowdsourcing tasks are currently solved by asking the general crowd, users will be able to achieve better quality results by asking workers with better knowledge about the tasks. This research works toward developing a full-fledged crowdsourcing framework that enables crowdsourcing tasks to be solved by the experts, thus, resulting in more accurate results.
Advisor: Tian He
Bridging Heterogeneous Wireless Technologies in IoT with Cross-Technology Communication
Wireless network today is being crowded with diverse forms of wireless technologies, such as WiFi, ZigBee, and Bluetooth. Direct communication among these different wireless technologies was believed impossible due to their different modulation/demodulation techniques. Jiang’s dissertation explores how to build direct cross-technology communication among heterogenous wireless technologies, which will pave the way for universal network connection and a new kind of network coordination.
Advisor: George Karypis
Data-driven Methods for Course Selection and Sequencing
The average six-year graduation rate across four-year higher-education institutions has been around 60% for the past 15 years, while less than half of college graduates finish within four years. In this research, Morsy develops data-driven machine learning methods that learn from past undergraduate students' degree plans and course grades. These methods help predict what grade a student will potentially get in a future course, as well as assists students in strategizing which courses and degree pursuits he or she may be the most success.
Advisor: Shashi Shekhar
Mining Spatial Patterns from Big Data
Computational techniques to discover spatial patterns from large scale data provide critical insight in applications such as public health and public safety. Specifically, Tang’s research studies the problem of Spatial Hotspot Discovery which is one of the novel and valuable patterns in spatial data mining. His thesis proposes several new approaches to overcome the computational challenges and find interesting patterns that have been overlooked by more traditional approaches.
Advisor: Ravi Janardan
Eﬃcient Geometric Computing on Stochastic Datasets
Compared to conventional datasets, stochastic datasets are usually more expressive and can model real data more precisely. Xue’s research focuses on designing efficient algorithms for solving geometric problems on stochastic datasets and proving hardness results for such problems.
Advisor: Zhi-Li Zhang
Securing and Accelerating Networks via Virtual Network Functions
In an era of ubiquitous connectivity, various new applications, new protocols, and online services (e.g., cloud services, distributed machine learning, cryptocurrency) are constantly being created. Demands for securing and accelerating networks—whether backbone networks of Internet service providers, campus/enterprise networks, data center networks, or even satellite networks—have been growing rapidly. Zhang's thesis is centered on designing and developing new and effective virtualized network functions and systems for intelligent network processing, with the goal of enhancing security and performance of networks