phd in computer science: July 2010

computer security
Uses of randomness in complexity theory and algorithms
Efficient algorithms for finding approximate solutions to NP-hard problems (or proving that they don't exist)
Cryptography.
Computer Architecture
Cryptography, Complexity Theory, Randomness and Computation.
machine learning,graphical models,computational statistics,information retrieval,natural language processing
Design and analysis of algorithms, particularly approximation algorithms, on-line algorithms and efficient algorithms for dealing with large volumes of data.
Computational Geometry, Sublinear Algorithms, Clustering, Discrepancy Theory, Lower Bounds
Computer architecture and organization;
architecturally-focused performance analysis.
Computer Music;
Simulation of Musical Acoustics and Voice;
Real-Time Expressive Computer Control, Human-Computer Interfacing.
Visualization, pervasive computing, software engineering
Computer security;
network software;
technology law and policy.
computer graphics, animation.
operating systems, distributed systems, computer networks, programming languages, computer architecture.
Distributed systems, security, networking, applied cryptography
Structural bioinformatics and computer graphics.
Networked Systems, Operating Systems, Hardware/Software Interfaces
application-specific languages, document preparation, user interfaces, software tools, programming methodology
Search and retrieval of information;
data mining, particularly clustering;
combinatorial algorithms
Computer vision, human vision, machine learning.
Parallel architectures and systems;
distributed systems;
operating systems.
Network/Web servers, operating systems, high-performance applications.
Networked systems, communication protocols, operating systems.
: Data networks, network measurement, network management, routing protocols, network troubleshooting.
computer graphics; acquisition of 3D shape, reflectance, and appearance of real-world objects
Machine learning
Finding efficient algorithms for fundamental practical problems by studying algorithms at all levels through the design-analysis-implementation cycle;
dynamic graphical simulations of algorithms in operation.
Parallel computing systems and applications:
parallel applications and their implications for software and architectural design;
system software and programming environments for multiprocessors. Special interest in applications of computing in computational biology, especially protein structure determination and simulating the immune system
computational molecular biology, as well as its interface with machine learning and algorithms.
auctions, agent-based simulation, soliton computing.
Data structures;
graph algorithms
combinatorial optimization;
computational complexity;
computational geometry;
parallel algorithms.
Bioinformatics;
analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks);
algorithms for integration of data from multiple data sources;
visualization of biological data;
machine learning methods in bioinformatics.
Programming languages, type systems, compilers, data processing and security.
design, analysis, and implementation of algorithms;
combinatorial optimization;
graphs and networks.
computational approaches to analysis of large-scale genomics data sets

phd in computer science

Sunday 18 July 2010

many more research areas in computer science

VARIETY OF COURSES AND RESEARCH AREAS IN COMPUTER SCIENCE

share ideas on research