Topics:                           
 

Applications

Machine Learning
          Big Data processing and applications
          Artificial Intelligence
          Natural Language Processing
          Data mining, Information retrieval
          Computer vision, Image processing
          Pattern recognition
          Audio and speech processing
          Computational science applications
          Scientific computing applications
          E-commerce applications, Web services
          Cloud computing applications, Biomedical applications,                
               emerging applications in Healthcare, Engineering, etc.
          Social Networks and Applications

Algorithms

Parallel and Distributed Algorithms
Combinatorial and Graph Algorithms
Scheduling and Load Balancing Algorithms
Numerical Algorithms
Randomized, Approximation, and Streaming Algorithms
Locality-Aware, Power/Energy-Aware Algorithms, Optimization Algorithms
Complexity Theory
Algorithms for Big Data/Data Intensive Computing
Algorithms for Security and Privacy, Cryptography
Fault-tolerant Algorithms
Network and Peer-to-Peer Algorithms

Systems

Ad hoc, Sensor, Vehicular, Underground and Underwater Networks
Cloud, Cluster, Grid and P2P Computing, virtualization
Cryptography and Applied Mathematics
Distributed Computing
Embedded Systems and Robotics, Embedded Systems and VLSI
Multi-FPGA reconfigurable systems and architectures
Enterprise, data center, and storage-area networks
Performance evaluation of networks and distributed systems
High Performance Computing
Evolutionary Computing
Heterogeneous Computing Models and Systems
Information Security
Intelligent Systems, Next generation Internet
Parallel and Multi-core Computing
Security, Trust and Privacy
Smart phones and Security
Social Network behavior, Modeling, and Analysis, System/network-on-chip, Wireless Networking

Education

Computing and Data Science Literacy across all Science, Technology, and Social Science Disciplines,Introductory Computer Science Course Sequence
Parallel, Distributed and High Performance Computing courses,Computational Science courses
Computer Engineering and Computational Engineering courses
Curricular Issues in Computing Programs
Pedagogy for Computing courses
Systems, Networks, and Architecture courses
Programming Language and Tools
Algorithms, Automata and Discrete Math courses
Novel Elective courses, Cyber Security courses
Experience and Case Study reports
Laboratory, Projects, and Internship courses
Collaborative work and Peer learning
Integrated Multidisciplinary Curriculum
IT Entrepreneurship Education
Assessment Methodology
Employers’ Experiences with and Expectation of Graduating Students.