Paul M. Aoki
		Intel Labs Berkeley 2150 Shattuck Ave., Ste. 1300 Berkeley, CA 94704-1347 USA	tel: +1-510-495-3089 fax: +1-510-495-3049 aoki@acm.org [PGP]

Overview Publications Service Personal I am a research scientist at Intel Labs Berkeley (formerly Intel Research Berkeley). Briefly, the facts of my life as are follows: SSRS Intel Labs Berkeley Intel 2006- MRS II Computer Science Laboratory PARC 1999-2005 Ph.D. Computer Science U.C. Berkeley 1993-1999 Lt. Surface Warfare U.S. Navy 1989-1992 B.S. Electrical Engineering & Computer Sciences U.C. Berkeley 1984-1988 Research Interests: I work in the areas of human-computer interaction and data management systems. I'm basically a systems guy.* On the HCI side, I design and build systems (including interaction design) and help out with the fieldwork. On the DB side, I work on middleware and server technology. * Admittedly, given what I've been doing recently, I'm saying this with some degree of irony. Human-Computer Interaction and Computer-Supported Cooperative Work My work in HCI has been highly interdisciplinary, mostly sitting in the intersection of mobile computing and human interaction. I am part of TIER, a project in which Intel is collaborating with U.C. Berkeley on the design of appropriate technology for emerging regions. Various TIER sub-projects have connections with Intel's Emerging Markets Platforms Group (EMPG). My published research on this topic is based on fieldwork in Ghana and Kenya and a field visit to Chile. I am working on a project on mobile participatory sensing called Common Sense. We are necessarily exploring some technical issues in sensing environmental air quality using mobile phones, but the key problem of interest for us as HCI researchers is to understand how and why people become engaged in citizen science and environmental activism. (Note that environmental degradation is a key concern in emerging regions.) I have collaborated for several years with PARC researchers in the sociological discipline of conversation analysis; their detailed understanding of human conversational structure have helped us understand how (technologically) minor differences in a system can have dramatic effects on interaction. ... An example of this work is a system we nicknamed Mad Hatter, a mobile audio space designed for use by small gelled social groups. This work applied machine learning techniques to produce a conferencing system that adapts to human conversation. ... As part of this work, Allison Woodruff and I analyzed some fieldwork on young adults using push-to-talk cellular radios, drawing on several perspectives in computer-mediated communication. ... Another example is Sotto Voce, a networked electronic guidebook system that delivers audio information in a way that facilitates face-to-face human interaction instead of inhibiting it. All of this work on interaction has also lead to a renewed interest in wide-area collaborative systems. I spent a lot of my time at sea sitting in front of networked tactical command-and-control consoles. Finally, I have some background in visualization: information display technology, information visualization techniques and visualization system architectures. This came through working with members of the Tioga DataSplash database visualization project at Berkeley. Data Management I recently (2007-2008) had the experience of architecting, building and deploying an asynchronous data replication system for use in a telemedicine system in Ghana. I consulted (2002-2003) for the PARC security group in a project on privacy preservation in distributed repositories of personal data. The Harland project at PARC (1999-2000) explored the programming affordances of data modelling constructs that lie between the semistructured and (dynamic) object-oriented data models. Another consideration is how such constructs can be supported efficiently. Harland was an outgrowth of the Placeless Documents project on property-based data stores.   At Berkeley, I worked for Mike Stonebraker in the database research group on a variety of topics: Database extension technologies Extensibility architectures Extensible indexing Support for selectivity estimation. Bulk-loading, reorganization, ... Support for document databases. Parallel and distributed information systems Query optimization. Web characterization.