John S. Davis II
Information Scientist, RAND Corporation
Professional Summary
John Davis is an Information Scientist at the RAND Corporation where his research focuses
on the impact of technology on society and includes projects involving cybersecurity, Big
Data and digital privacy. He has led research projects for the Department of Homeland
Security (DHS) and the Office of the Secretary of Defense. Dr. Davis serves as a core
professor in the Pardee RAND Graduate School and co-teaches a course on Big Data for
policy researchers. He also serves as co-director of RAND’s Scalable Analytics Methods
Center where he helps shape the incorporation of Big Data techniques and practices into
RAND’s data science activities. Prior to joining RAND, John worked at IBM Research
where he led several projects in context-aware and pervasive computing that studied the
design and architecture of systems for applying machine learning to user data. He has
software development experience in production environments at WaPo Labs (an innovation
lab within the Washington Post Company) and OMB (Executive Office of the President) as
well as a startup founder. Dr. Davis holds four patents and has published extensively in peerreviewed ACM/IEEE workshops and conferences. John earned a BSEE at Howard
University and a Ph.D. in EECS at UC Berkeley in which he studied communications
theory, digital signal processing and the electronic design process of signal processing
systems.
Education and Certifications
Ph.D., Electrical Engineering and Computer Science, UC Berkeley
B.S., Electrical Engineering, Howard University
Relevant Projects
A Framework for Programming and Budgeting for Cybersecurity
When defending an organization against cyberattacks, cyber security professionals are faced
with the dilemma of selecting from a large set of cyber security defensive measures while
operating with a limited set of resources with which to employ the measures. Engaging in
this selection process is not easy and can be overwhelming. Furthermore, the challenge is
exacerbated by the fact that many cyber security strategies are presented as itemized lists with
few hints at how to position a given action within the space of alternative actions. In project,
Dr. Davis helped specify a sunburst diagram and prototype web application for organizing
menu of actions for defending against cyberattacks.
Defining Biometric Gold Standard Test Data
Currently, there is no objective standard for comparing biometric systems. A “gold standard”
test database will provide a baseline to compare the performance of different algorithms (for
modalities such as fingerprint, face, iris, palm-prints, voice, latent fingerprint) that are
proposed by industry vendors. Dr. Davis’ role in this project involves leading the analysis of
face recognition systems including development of metrics for algorithm assessment.
Collective Action in Response to Cyber Security Threats
A variety of metaphors have been used to describe and influence cyber security ecosystems.
We characterize cyber security ecosystems using an immunological metaphor, and at the core
of this characterization is the notion of a collective action in response to and anticipation of
security infections. The intent is that collective action will enable a fast, reliable and robust
response, as is the case in the human immune system. Dr. Davis’s role in this project involves
defining the importance of trust in a cyber security ecosystem as inspired by the collective
action of immunological systems.
Venture Capital Methods for Advancing Counter-IED Capabilities Development
The Joint IED Defeat Organization (JIEDDO) works with contracting firms to implement
solutions for countering improvised explosive devices (IEDs); it was determined that the
process employed by JIEDDO was not attracting sufficiently innovative firms. For this
reason, analysis was performed to determine whether government “venture capital” (GVC)
enterprises could help JIEDDO attract interest from a broader set of private companies. The
analysis included a review and model of existing GVC enterprises currently under contract
with the US government. Dr. Davis’ role involved analyzing the approach used to stimulate
innovation that is in use by several technology-oriented federal agencies including DARPA,
IARPA and NSF, as well as exploring alternative incentives for innovation.
Classification of Health-related Sensor Data through User-Assisted Machine Learning
Remote patient monitoring generates much more data than health-care professionals are able
to manually interpret. Automated detection of events of interest is therefore critical so that
these points in the data can be marked for later review. However, for some important
chronic health conditions, such as pain and depression, automated detection is only partially
achievable. To assist with this problem we developed HealthSense, a framework for real-time
tagging of health-related sensor data for analysis via machine learning algorithms. The system
uses patient input to assist with classification of interesting events (e.g., pain or itching). Due
to variations between patients, sensors, and condition types, we presume that the initial
classification is imperfect and accommodate this by incorporating user feedback into the
machine learning process. Dr. Davis served as the project lead in this research including
oversight in the system design and experimental setup.
Audio/Video Search via SaaS Transcript Alignment
The translation of speech into text via automatic speech recognition (ASR) has been pursued
for decades and is considered a grand challenge in the computer science community. In this
project we proposed a data-driven approach to speaker-independent, large vocabulary
continuous speech recognition using large quantities of publicly available textualized audio.
We informally defined textualized audio as audio or video data that is paired with a
corresponding text transcript of its spoken content. Our project involved heavy use of cloud
computing, Hadoop-based data processing and a software-as-a-service (SaaS) architecture.
Dr. Davis served as the project lead in this research and managed the system design and
implementation.
Work History
Information Scientist, RAND, Arlington, VA
2012 – Present
Engineer, Office of Management & Budget (EOP), Washington, DC
2011 – 2012
Senior Engineer, Washington Post Company, Washington, DC
2010 – 2011
Co-Founder, LingFling Inc., Arlington, VA
2007 – 2010
Research Staff Member, IBM Research, Hawthorne, NY
2000 – 2007
Publications and Presentations
Privacy Engine for Context-Aware Enterprise Application Services, 2008 IEEE/IFIP
International Symposium on Trust, Security and Privacy for Pervasive Applications (with M.
Blount, M. Ebling, W. Jerome, B. Leiba, X. Liu and A. Misra)
Opportunities for Pervasive Computing in Chronic Cancer Care, Sixth Int’l Conference on
Pervasive Computing - Pervasive 2008 (with G. Hayes, G. Abowd, M. Ebling, M. Blount, E.
Mynatt)
HealthSense: Classification of Health-related Sensor Data through User-Assisted Machine
Learning, Ninth ACM Workshop on Mobile Computing Systems and Applications HotMobile 2008 (with E. Stuntebeck, G. Abowd and M. Bount).
Context Tailor: Towards a Programming Model for Context-Aware Computing, First
International ACM Workshop on Middleware for Pervasive and Ad-Hoc Computing – 2003
(with D. Sow, M. Blount and M. Ebling)
Composing Pervasive Data with iQL, Proceedings of the Fourth Annual IEEE Workshop on
Mobile Computing Systems & Applications – 2002 (with N. Cohen, H. Lei, P. Castro and
A. Purakayastha)
Contact Information
1200 South Hayes Street, Arlington, VA 22202
703-413-1100 x5198, john_davis@rand.org