Abstract
Consider three different worlds of poor network connectivity:

• Scenario 1: A user in Africa uses a cheap mobile device with voice and SMS as the only data connectivity channel (140 bytes per message and each SMS costs money).
• Scenario 2: A university in India has good connectivity which is shared simultaneously by 400 users. (Per user share = 2 Kbps)
• Scenario 3: A school in Kenya has a computer but no Internet.

In this talk, I will describe a range of techniques we have developed to enhance information access in these three scenarios of poor connectivity. In Scenario 1, we have built an entire SMS-based protocol stack for mobile applications being used in India, Mexico and Ghana as well as a live SMS search engine in Kenya. We are also rolling out a data-over-GSM voice stack to support data connectivity over cellular voice.

In Scenario 2, I will describe why some of the fundamentals of network protocols break down in these regimes and why we need a completely new Web architecture for these types of networks. We have deployed early versions of our system in a few schools and universities in India, Kenya.

In Scenario 3, I will describe how we can use vertical search engines to deliver a vertical slice of the Web in a hard-disk and provide an offline searchable and browse-able Internet. This system has been used in schools in India and Kenya as an educational tool for students and teachers.

This is joint work with several others with Jay Chen being a primary leader for many of these projects.

Lakshminarayanan Subramanian is an Assistant Professor in the Courant Institute of Mathematical Sciences at NYU.

His research interests are in the areas of networks, distributed systems and computing for development. He co-leads the Networks and Wide-Area Systems(NeWS) group (which investigates software solutions for distributed systems, wireline and wireless networking, operating system, security and privacy, technologies and applications for the developing world) and the CATER Lab at NYU ( which focuses on developing and deploying low-cost, innovative technology solutions to some of the problems in developing regions in terms of communication, healthcare and microfinance).

Recently, he has co-established a new Center for Technology and Economic Development (CTED) at NYU Abu Dhabi which brings together students from several disciplines (CS, economics, healthcare, education, policy). He is the recipient of several awards including the  NSF CAREER Award (2009), IBM Faculty Award (2009, 2010) and C.V. Ramamoorthy Award. He has been at the forefront of several technological innovations for development that have been used in several countries around the world.

Why are some governments better able to reform public service delivery than others? The increased availability of information and communication technologies marks an important opportunity for politicians to improve the quality of public services. However, state-level experiences with eGovernment in India display significant variations in the ability of governments to successfully adopt these technologies to provide benefits to citizens. Based on her studies of one-stop, computerized public service centers in sixteen Indian states, Jennifer will discuss the characteristics of states associated with differences in policy outcomes and present original survey evidence on the effects of technology-based reforms on the quality of public services.

Jennifer Bussell is an Assistant Professor of Public Affairs at the Lyndon B. Johnson School of Public Affairs at the University of Texas, Austin. She received a Ph.D. in Political Science from the University of California, Berkeley. Her research concerns the political economy of development and governance with an emphasis on the role of formal and informal institutions--such as federalism, coalition politics, and corruption--in shaping policy outcomes. She is particularly concerned with the politics of government technology adoption for the benefit of poor citizens in developing countries, with an emphasis on India.

Abstract
I will begin this talk with a short discussion of the function of warning in the US national security community, and the analytic methodology used by US intelligence agencies (in 1941 and since) to address the problem of warning. I will then present a formal model for crisis warning consisting of a Partially Observable Markov Decision Process (POMDP) intended to assist an intelligence analyst in deciding when to issue an alert to a foreign policy principal decision maker such as the President. The lead time demanded by the principal is a key element in the model. I will spend the remainder of the talk illustrating this warning model in the context of the brewing crisis in the Pacific from July to December 1941, and present results from test runs of the model using historical raw intelligence data from that period. While a probabilistic approach to warning is not a new idea, this research addresses three outstanding issues left unresolved from past efforts to develop such an approach:

1. The need to process multiple dependent signals in a manner that is combinatorially feasible;
2. Incorporation of the time dimension in which intelligence data is received into the inference, and the effect of dynamics on a warning decision where a finite horizon is imposed;
3. Consideration of the fact that the analyst serves as an advisor to the principal decision maker but is not completely aware of the principal’s preference set.

Together with my thesis advisor, Prof Elisabeth Pate-Cornell, I am currently writing a paper that covers the presented material, and I hope to incorporate feedback from this presentation into the paper. Because the paper is currently a work in progress, I am not distributing it at this time.

David Blum attends Stanford University, where he is a 3rd year Ph.D. student in the Department of Management Science & Engineering as well as a U.S. Department of Defense SMART Scholar. He is currently developing a probabilistic model of national security crises, with the goal of improving crisis early warning. His interests also include targeting in counter-terrorism, signatures of WMD proliferation, and models of decisions made by adversarial actors as games with incomplete information. He is a graduate intern in the Counter-Proliferation Operations-Intelligence Support program at Lawrence Livermore National Laboratory.

Between 2004 and 2008 David worked at the U.S. Department of Defense as an operations research analyst. He deployed twice to Iraq, in 2007 and 2008, where,  as member of Multi-National Corps Iraq, he provided direct analytic support to conventional and special operations units. He received his Master's degree from MIT in political science, concentrating in security studies, and his Bachelor's degree from Columbia University in history and physics.