many more research areas in computer science

          

•  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