Summer Research Program Lunch and Learn

Preparing a Competitive Application for Graduate School and External Fellowships

This session will provide information, examples, and strategies for how to prepare a competitive application for graduate school and external fellowships. The session will address questions such as: What should you write in the personal / research statement? What should you highlight in your resume? Should you take the GRE? Who and how should you ask for letters of recommendation? How are applications reviewed? Should you contact professors at the schools you are applying to? How important is prior participation in research? Do awards (e.g., CRA Undergraduate Research Award) matter? 

Mashfiqui Rabbi
Mashfiqui Rabbi is an assistant research professor at the Siebel School of Computing and Data Science. Previously he was a senior research scientist at Optum AI under UnitedHealth Group, the largest payer and one of the largest providers in US healthcare. Before Optum, Mashfiqui was a research scientist at Apple Health AI and a postdoctoral fellow at Harvard University, where he worked with Professor Susan Murphy. Before his postdoc, he received a Ph.D. in Information Science from Cornell University. His Ph.D. advisor was Professor Tanzeem Choudhury. Mashfiqui's Ph.D. thesis led to the creation of the MyBehavior app, the first mobile recommender system to automatically generate personalized physical activity and food suggestions from mobile phone data. In his postdoc at Harvard, Mashfiqui created the first just-in-time intervention for improving health app engagement. This engagement intervention was later adopted by multiple NIH-funded grants focusing on youth substance abuse, cancer rehabilitation, and sickle cell disease. Mashfiqui also helped establish the AI-based intervention group at Apple's Health AI team, and his sub-goal app was adopted for the customized plan feature in Apple's Fitness+ app, which is currently used by millions of iPhone users. Mashfiqui's work has also been featured in MIT Technology Review, New Scientist, the Economist, Mashable, and the NY Times. 

Gang Wang 
Gang Wang received his Ph.D. from UC Santa Barbara in 2016 (under the direction of Ben Y. Zhao and Heather Zheng), and a B.E. from Tsinghua University in 2010. After working as an Assistant Professor at Virginia Tech (2016 - 2019), he joined the University of Illinois at Urbana-Champaign in 2019. Currently, he is an Associate Professor in the Department of Computer Science and also has affiliate faculty appointments in the Department of Electrical and Computer Engineering (ECE) and the Informatics Program of the School of Information Sciences at the University of Illinois. He is a recipient of the NSF CAREER Award (2018), Amazon Research Award (2021), Google Faculty Research Award (2017), and Best Paper Awards from IMWUT 2019, ACM CCS 2018, and SIGMETRICS 2013. His projects have been covered by media outlets such as MIT Technology Review, The New York Times, Boston Globe, CNN, and ACM TechNews.

Gang Wang's research interests are Security and Privacy, Internet Measurement, and Data Mining. His work takes a data-driven approach to addressing emerging security threats in massive communication systems (social media, email services), crowdsourcing systems, mobile applications, and enterprise networks. His major contribution is a series of measurement methodologies that have revealed previously overlooked security threats, including crowdturfing activities in online social networks, security certification failures in the payment card industry, email spoofing vulnerabilities in major email providers, and the deep link usage in mobile app ecosystems. Another key contribution is his work on applying machine learning in security applications (e.g., bot detection, malware classification) to handle adversarial behaviors and concept drift.

Gang Wang's current focus is on developing robust data-driven methods (e.g., machine learning models, graph models) to enable accurate, scalable, and user-friendly security applications for malware analysis and online abuse mitigation. The key challenge Wang is tackling is to effectively engineer human-machine collaboration pipelines to improve the efficacy and efficiency of both parties against adaptive attackers in the respective fields. Wang is currently collaborating with industry partners and researchers from related fields such as HCI and AI to address these problems.

Lana Lazebnik  
I received my Ph.D. at UIUC in May 2006 under the supervision of Prof. Jean Ponce. From August 2007 to December 2011 I was an assistant professor at the University of North Carolina at Chapel Hill, and as of January 2012, I have returned as faculty to U of I. My research specialty is computer vision. The main themes of my research include scene understanding, joint modeling of images and text, large-scale photo collections, and machine learning techniques for visual recognition problems.

Current and former sources of support for my research include the National Science Foundation (under grants CCF 2348624, IIS 1718221, IIS 1563727, IIS 1228082, CIF 1302438, and IIS 0916829), Amazon Research Award, AWS Machine Learning Research Award, Microsoft Research Faculty Fellowship, Xerox University Affairs Committee Grants, DARPA Computer Science Study Group, Sloan Foundation Fellowship, Google Research Award, ARO, and Adobe.

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