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Hi! My research interests are in:
- Information and
Communication Technologies for Development (ICT4D),
and specifically Spoken Language Technologies for Development (SLT4D), which
is the term we coined for our own subfield of ICT4D: finding ways to use
spoken language technologies (like automatic speech recognition, speech
synthesis, and human-machine dialog systems) to aid socio-economic
development around the world.
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Our current project, Polly, uses telephone-based viral
entertainment to reach low-literate people in Pakistan, familiarizing them with
speech interfaces and then introducing them to development-related
services. This system has been in
continuous use since May 2012, reaching over 90,000 users and fielding over two
million phone calls – track Polly’s
progress live!
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A previous project, HealthLine, investigated the
use of a telephone-based automated dialog system for access to healthcare
information by low-literate community health workers in Pakistan.
- Modeling the evolution of viral
epidemics. As part of the
multi-university MIDAS research program,
we use machine learning, large scale simulations, network analysis and
stochastic process theory to try to answer research questions such as:
- How, and to what
extent, can the evolution of infectious diseases like Influenza be predicted?
- How, and to what extent, is the evolution of viral disease like Influenza affected by public health interventions such as vaccination, antiviral drug use, school closures, travel restrictions, etc.
We model the spread of epidemics in the population as well
as the evolution of the virus itself, such as changes in its virulence,
pathogenicity, drug resistance, or antigenicity (immune escape).
- Project GATTACA: Computational molecular virology. Retroviruses like
HIV and RNA viruses like Influenza evolve at a much higher rate than DNA
life forms. This is a formidable challenge to vaccine design and
antiviral drug development, but is also an opportunity to observe
evolution as it happens. We use the fast growing databases of
viral sequences to build descriptive and generative models of viral
molecular evolution. We also use them to infer viral envelope
properties and suggest potential antigenic targets that cannot easily
mutate away. In collaboration with virologists/immunologists, we try to correlate isolate sequence composition to
important biological properties of the isolate, such as pathogenicity,
infectivity and neutralizability. Along the way we design and
develop visualization
tools for multiple sequence alignments (MSAs) and other biological
sequence data.
Publications
Teaching
: 10-601 Machine Learning, 11-761 Language and Statistics, 11-700 LTI Colloquium.
Current Graduate Students: David Farrow (CompBio), Ali Raza (LTI), Peter Schulam (LTI), Chuang Wu. (Comp Bio),
Graduated PhD
Students:
Jahanzeb Sherwani (CSD), Yong Lu (CSD), Dan Bohus (CSD), Stefanie Tomko (LTI), Jerry
(Xiaojin) Zhu (LTI, MLD), Lin Chase (RI).
Past Post-docs: Andy Walsh (computational virology), Xiaojin Wang (machine learning), Stan F. Chen (language modeling), Pierre DuPont (language modeling).