Syllabus

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Jan 27: Introduction: Why do a CS Phd? What is research?


Feb 3: Combining Applied and Basic Research: ABC Principle

Read: Introduction & Chapter 1

Read: Google Research: A Hybrid Approach Spector, A., Norvig, P., and Petrov, S., Google’s hybrid approach to research, Communications of the ACM 55, 7 (July 2012), 34-37.


Feb 10: Blending Science, Engineering & Design Blended: SED Principle

Read: Chapter 2

Read: Fraunhofer-Gesellschaft: Applied Research in Public Service http://www.fraunhofer.de/en.html


Feb 17: What science contributes: Persistence in understanding the world

Read: Chapter 3

Read: Bell Labs: Designed to Ensure Applied Research Inspired Basic Research

Read: Sebastian Seung: Role model for researcher transformation


Feb 24: What engineering contributes: Devotion to focused innovations

  • Guest: Megan Monroe (PhD graduate, now at IBM Research): The Talk Talk (Video)

Abstract: So you have to give a talk, now what? Well, it's probably too late to run, and nobody likes a hider, so your best bet is to just suck it up, and start prepping your talk. But how? What should you do first? What are you even trying to accomplish here? Prepping a talk is not only a daunting prospect, but it's really easy to get lost in the details and lose sight of the big picture. In this talk, I'll be laying out that big picture, and providing a step-by-step roadmap of how to get there. The goal is to give rookie talk-givers a better sense of direction as they navigate the shadowy abyss of prepping a talk. I'm also hoping that some of the more experienced talk-givers can chime in with some of their best tips and tricks for building a slammin' talk.

Bio: Megan Monroe completed her Ph.D. in Computer Science at the University of Maryland in 2014. She currently works in Cambridge, MA as a Research Scientist for IBM. In general, she does not enjoy describing her exploits in the third person. However, she has given a LOT of talks, and generally knows how to come out of them smelling like roses.


Read: Chapter 4

Read: CITRIS-UCalifornia: Multi-Campus Center in Public Service


March 3: What design contributes: Fresh thinking to serve human heeds

Read: Chapter 5

Read: Scientific Computing and Imaging Institute: University of Utah’s Innovation Engine


March 10: Choose actionable problems that address civic, business & global priorities

  • Guest: Amitabh Varshney (Prof & Director, UMIACS), Research Philosophy of UMIACS

Read: Chapter 6

Read: Teaching Teamwork with Real Users: Relate-Create-Donate

     *** March 17: Spring Break

March 24: Apply observation, intervention, and controlled experiments

  • Guest: Prof. Michael Hicks, How to Write a Great Paper (Slides)

Read: Chapter 7

Read: Michael Crow: President of Arizona State University


April 7: Form teams with diverse individuals & organizations

Read: Chapter 8


April 14: Test prototypes with realistic interventions

  • Guest: Prof. Rance Cleaveland, How Model-based Testing Can Have Applied and Basic Outcomes: Prove If You Can, Test If You Cannot

(and a short description of the Fraunhofer Institute)

Abstract: Current formal methods focus on mathematical proof as a means for establishing that a system is correct with respect to a formal specification. This perspective can limit the applicability of formal methods, since the development of such proofs remains a very difficult task requiring specialized expertise, even with computer assistance. This presentation argues that formal-specification approaches that support both proof and testing as V&V technologies can enhance the practical usefulness of formal methods. It then describes an approach, called instrumentation-based verification, that is intended to realize this vision.


Read: Chapter 9

Read: VRVis: A Viennese Version of Blending Disciplines


April 21: Promote adoption & measure impact

Read: Chapter 10

Read: Web Science Trust: Thought Leaders for Blended Research


April 28: How to Change?

  • Guest: Prof. Samir Khuller, Chair, Dept of Computer Science, Elizabeth Iribe Chair for CS

Abstract: This is very much a personal story of what I find exciting about research in algorithms, the innovative ideas, their impact, and a level of understanding that has emerged and the advances made in a rapidly moving field. Efficient Algorithms are a central component of Big Data Analysis and I'd like to share some "war" stories about my personal heroes in algorthms and the work they did. Slides

Read: Chapter 11

Read: Nathan Eagle: An Individual as Role Model for the ABC Principle


May 5: Recommendations for UMd

Presentations: NSF Proposal Summaries

Read: Chapter 12

Read: Evidenced-based Research for Social Programs


May 12: Course Summary

Presentations: NSF Proposal Summaries