My research interests center around Large Scale Data Processing systems.
Limitations of existing systems show up only when exposed to scale.
Though topics below do not seem like it (neither do my bookmarks:-)), I am
working towards solutions to certain problems in this area.

---

Data Streams: Querying on datastreams is a new and fascinating field.
Unlike traditional QP engines (which assume data is finite & bounded), in this
model data flows as a continous data stream. Application require queries to
run continously (as opposed to one time queries) and so the response is
real-time. One of main limitations of the current engines is the in ability
to answer ad-hoc queries efficiently.
This is a fascination field with wide range of application and intersting
problems to solve. There are already some companies which have implemented
stream databases. The use cases are as many and some of them might even be
valid!
Interesting Companies in this space
Coral8 - (STREAMS project at Stanford)
Streambase - (Aurora project at MIT)
Celequest .The company I work for.

---

Virtualization : As the volume for computing resources
grows, it is essential to virtualize them to ease maintainability.
Theoretically any resource can be virtualized. CPU, memory, disk,
database servers etc. Virtualization means to "abstract out things"
A set of N disks can be made to appear as one through a virtualization
layer
Interesting Companies in this space
Too many to list. I worked for 3pardata.
3pardata made high performance next generation storage systems. The SAN systems had the highest throughput and had the best TCO in terms of ease of use and maintainability.

---

Bioinformatics : There are fascinating problems to be solved
when operating with large volumes of data, or when trying to make sense
of data with lots of missing pieces.
Interesting Compaines in this space

---