### Tracks and Topics

Computational Science and Engineering (CSE) is a relatively new paradigm for scientific research and engineering design in which large-scale simulation, data analysis, and high performance
computing play a central role. In fact, the applications of CSE can be seen in almost all disciplines. These include the following:

**Aerospace Engineering and Mechanical Engineering**: combustion simulations, structural dynamics, computational fluid dynamics, computational thermodynamics, computational solid mechanics, vehicle crash simulation, biomechanics, trajectory calculation of satellites**Astrophysical systems****Battlefield simulations**and military gaming, homeland security, emergency response**Biology and Medicine**: protein folding simulations (and other macromolecules), bioinformatics, genomics, computational neurological modeling, modeling of biological systems (e.g., ecological systems), 3D CT ultrasound, MRI imaging, molecular bionetworks, cancer and seizure control**Chemistry**: calculating the structures and properties of chemical compounds/molecules and solids, computational chemistry/cheminformatics, molecular mechanics simulations, computational chemical methods in solid state physics, chemical pollution transport**Civil Engineering**: finite element analysis, structures with random loads, construction engineering, water supply systems, transportation/vehicle modeling**Computer Engineering**,**Electrical Engineering**, and**Telecommunications**: VLSI, computational electromagnetics, semiconductor modeling, simulation of microelectronics, energy infrastructure, RF simulation, networks**Epidemiology**: influenza spread**Environmental Biodiversty, Engineering and Numerical weather prediction**: climate research, Computational geophysics (seismic processing), modeling of natural disasters, Spatial Data Prediction: biodiversity management and conservation, role of spatial information, GIS in biodiversity monitoring, geoportals, spatial data infrastructures, global spatial data, spatial tools for natural resources management**Finance**: derivative pricing, risk management**Industrial Engineering**: discrete event and Monte-Carlo simulations (for logistics and manufacturing systems for example), queueing networks, mathematical optimization**Material Science**: glass manufacturing, polymers, and crystals**Nuclear Engineering**: nuclear blast modeling, fusion simulations**Petroleum engineering**: petroleum reservoir modeling, oil and gas exploration**Physics**: Computational particle physics, automatic calculation of particle interaction or decay, plasma modeling, cosmological simulations**Transportation**

Engineering and science problems have been solved historically using experimental testing and/or mathematical analysis. Some examples of engineering problems are fluid flows and structural properties associated with aircraft, ships, submarines, automobiles, spacecraft, jet and rocket propulsion engines, buildings and other structures. Other examples relate to electrical power generation, weather, rivers and oceans, electrical equipment, computer hardware, radar, antennas, chemical reactions and processes, fuel cells, petroleum recovery and refining, agricultural and construction equipment, refrigeration and air conditioning, air and water pollution, energy conversion and storage, and many others. Many of these problems can now be solved efficiently as computational simulations of mathematical models that represent the relevant physical phenomena arising in each problem.

The Program Committees are looking for original research contributions on a broad-range of topics related to High performance computing, Modeling and simulation,
Algorithms, Big Data Analysis and visualization, Data Science, CSE Education, Advanced Networking and Applications and Intelligent and Bio-Inspired Computing. Topics of interest include, but are not limited to the following Research and Application Tracks:

**Track 1: **High performance computing

High performance computing issues in Big Data analytics

High performance/large scale application case studies

GPU for general purpose computations (GPGPU)

Multicore and many-core
computing

Power aware computing

Cloud, distributed, and grid computing

Asynchronous numerical methods and programming

Hybrid system modeling and simulation

Large scale visualization and data management

Tools and environments for coupling parallel codes

Parallel algorithms and architectures

High performance software tools

Resilience at the simulation level

Component technologies for high performance computing

**Track 2: **Modeling and simulation

Enterprise Resource Planning

Petri
Nets

Virtual Reality and Graphical Simulations

Discrete-Event Simulation

Security/Emergency Support Tools

Plant Simulation

Domain-Specific Tools

Serious Games

Team-support Tools

Education and Training

e-Learning
Platforms

Laboratory Simulation Software

Computer Simulation Techniques

Interoperability

Collaborative Systems

Business Process Modeling

Service Value and Supply Chains

Multiscale Simulation

Performance Analysis

Biological
and Social Systems Simulation

Agent Based Modeling and Simulation

Biologically Inspired Systems Simulation

Fluid Dynamics

Environmental Modeling

Green Technologies

Crisis and Conflict Management Simulation**Track 3: N**umerical and Discrete Algorithms.

Algorithms and data structures

Algorithmic game theory

Algorithmic learning theory

Approximation Algorithms

Combinatorial Algorithms

Combinatorial Optimization

Computational Biology

Computational Complexity

Computational Geometry

Data Structures

Experimental Algorithm Methodologies

Graph Algorithms

Graph Drawing

Parallel and Distributed Algorithms

Parameterized Complexity

Sequential Algorithms

Network Optimization

Online Algorithms

Optimization Algorithms

Randomized Algorithms

Algebraic Combinatorics

Design Theory

Enumeration

Extremal Combinatorics

Graph
Theory

Topological and Analytical Techniques in Combinatorics

Probabilistic Combinatorics

Combinatorial Number Theory

Discrete Geometry

Ramsey Theory**Track 4: **Big
Data Analysis and visualization

Data collection, management and curation

Innovative approaches combining information visualization, visual analytics, and scientific visualization

Streaming methods for analysis, collection and visualization

Novel, extreme or innovative methods for understanding and interacting with data

Advanced hardware for data handling or visualization

Distributed, parallel or multi-threaded approaches

MapReduce-based and database-related methods, algorithms
or approaches

Hierarchical data storage, retrieval or rendering

Collaboration or co-design of data analysis with domain scientists

Topics in cognitive issues specific to manipulating and understanding large data

Application case studies

Industry solutions for big data

End-to-end system solutions**Track 5: **Data Science

Mathematical, probabilistic and statistical models and theories

Machine learning
theories, models and systems

Knowledge discovery theories, models and systems

Manifold and metric learning, deep learning

Scalable analysis and learning

Non-iidness learning

Heterogeneous data/information integration

Data pre-processing,
sampling and reduction

High dimensional data, feature selection and feature transformation

Large scale optimization

High performance computing for data analytics

Architecture, management and process for data science

Learning for streaming
data

Learning for structured and relational data

Intent and insight learning

Mining multi-source and mixed-source information

Mixed-type and structure data analytics

Cross-media data analytics

Big data visualization, modeling
and analytics

Multimedia/stream/text/visual analytics

Relation, coupling, link and graph mining

Personalization analytics and learning

Web/online/social/network mining and learning

Structure/group/community/network mining

Cloud
computing and service data analysis

Data warehouses, cloud architectures

Large-scale databases

Information and knowledge retrieval

Web/social/databases query and search

Personalized search and recommendation

Human-machine interaction and interfaces

Crowdsourcing and collective intelligence

**Track 6: CSE Education and Finance**

Computer Aided Education

Multimedia
and Education

Computer Science and Education Technology

Computer and Education Training

Computer and Education Management

E-learning

Knowledge Discovering

**Track 7: Advanced Networking and Applications**

Communication
Protocol and Architecture

High-speed Communication and Network

Wireless Communication and Network

Mobile Ad-hoc and Sensor Network

Low-power Network and System

Wearable Network and System

Embedded System and Networking

Internet
Technology and IP-based Applications

Network Control and Management

Network Performance, Analysis and Evaluation

Quality of Services
(QoS)

Ubiquitous/Pervasive Networks and Computing

Ubiquitous Intelligence and Smart World

Smart Object, Space/Environment and System

Innovative Networking and Applications

Social, Ethical & Other Issues of Networked World

Network and Application Hardware

**Track 8: Intelligent and Bio-inspired Computing**

Artificial Intelligence and Machine Learning

Knowledge Representation and
Reasoning

Multi-Agent Systems

Computational Neuroscience

Natural Language Processing

Evolutionary
Computation

Metaheuristics

Memetic Algorithms

Cellular Automata

Fuzzy Systems

Molecular Computing

Applications of Intelligent Computing

**Track 9: Applications of CSE**

Aerospace Engineering

Astrophysical systems

Biology and Medicine

Chemistry

Civil
Engineering

Computer Engineering

Electrical Engineering and Telecommunications

Epidemiology

Environmental Biodiversty, Engineering and Numerical weather prediction

Finance

Industrial Engineering

Material Science

Mechanical Engineering

Military Battlefield

Nuclear Engineering

Petroleum engineering

Physics

Transportation