* indicates that this line can be assigned as a paper's topic.
| 1: | Advancing Neural Engineering Through Big Data | ||
| 1.1*: | Brain Computer Interfaces | ||
| 1.2*: | Wearable and Assistive devices | ||
| 1.3*: | Neurological Sensor Arrays and Transduction | ||
| 1.4*: | Biocompatible Interface Materials | ||
| 1.5*: | Bioelectrical Signal Processing | ||
| 1.6*: | Neuromotor and Neurosensory Modeling | ||
| 1.7*: | Bioengineering Application of Big Data | ||
| 1.8*: | Best Practices in Experimental Design | ||
| 1.9*: | Annotation and Distribution Standards | ||
| 1.10*: | Benchmarks and Open Source Tools | ||
| 1.11*: | Invited: Advancing Neural Engineering Through Big Data | ||
| 2: | Bioinformatics and Systems Biology | ||
| 2.1*: | High throughput sequencing data analysis | ||
| 2.2*: | Big data analytics in genomics and proteomics | ||
| 2.3*: | SNP/genotype/haplotype calling | ||
| 2.4*: | Biomarker discovery | ||
| 2.5*: | Modeling of disease dynamics | ||
| 2.6*: | Drug screening and effectiveness prediction | ||
| 2.7*: | Genetic network and pathway modeling and simulation | ||
| 2.8*: | Dynamics and control of genetic regulatory networks | ||
| 2.9*: | Functions of miRNA and non-coding RNAs | ||
| 2.10*: | Invited: Bioinformatics and Systems Biology | ||
| 3: | Controlled Sensing For Inference: Applications, Theory and Algorithms | ||
| 3.1*: | Sensor Management for tracking | ||
| 3.2*: | Sensor Management for detection, estimation, and classification | ||
| 3.3*: | Management of heterogeneous sensing resources | ||
| 3.4*: | Data-driven and non-parametric inference methods | ||
| 3.5*: | Information collection, processing and fusion | ||
| 3.6*: | Fundamental limits of sensing systems | ||
| 3.7*: | Applications of controlled sensing to infrastructure monitoring | ||
| 3.8*: | Controlled sensing for medical imaging | ||
| 3.9*: | Radar and surveillance applications | ||
| 3.10*: | Controlled sensing in social networks | ||
| 3.11*: | Invited: Controlled Sensing For Inference: Applications, Theory and Algorithms | ||
| 4: | Cyber-Security and Privacy | ||
| 4.1*: | Analysis and mitigation of side channels | ||
| 4.2*: | Attacks on privacy and privacy technologies | ||
| 4.3*: | Fingerprinting and watermarking | ||
| 4.4*: | Information-theoretic security | ||
| 4.5*: | Network security and intrusion detection | ||
| 4.6*: | Privacy challenges in large data | ||
| 4.7*: | Secure computation framework | ||
| 4.8*: | Traffic analysis | ||
| 4.9*: | Biometric Security, Privacy and Authentication | ||
| 4.10*: | Machine Learning in Security | ||
| 4.11*: | Invited: Cyber-Security and Privacy | ||
| 5: | Emerging Challenges in Network Sensing, Inference, and Communication | ||
| 5.1*: | Sparsity in network sensing, inference, and communication | ||
| 5.2*: | Network structure inference from noisy observations | ||
| 5.3*: | Network inference in the presence of missing data | ||
| 5.4*: | Efficient sensing of network data | ||
| 5.5*: | Energy management in networks | ||
| 5.6*: | Complex network topology | ||
| 5.7*: | Dynamics of networks | ||
| 5.8*: | Flows on networks | ||
| 5.9*: | Applications in communication networks | ||
| 5.10*: | Applications in biological networks | ||
| 5.11*: | Applications in social networks | ||
| 5.12*: | Invited: Emerging Challenges in Network Sensing, Inference, and Communication | ||
| 6: | Energy Harvesting and Green Wireless Communications | ||
| 6.1*: | Physical layer design for energy harvesting communications | ||
| 6.2*: | Signal processing for energy harvesting communication | ||
| 6.3*: | Information theory of energy harvesting communications | ||
| 6.4*: | Network theoretic approaches for energy harvesting communications | ||
| 6.5*: | Energy and message cooperation | ||
| 6.6*: | Energy efficient MIMO | ||
| 6.7*: | Design of green wireless communication systems with hybrid energy sources | ||
| 6.8*: | Heterogeneous green wireless communications systems | ||
| 6.9*: | Small cell networks and green communications | ||
| 6.10*: | Invited: Energy Harvesting and Green Wireless Communications | ||
| 7: | Graph Signal Processing | ||
| 7.1*: | Transforms for graph signals | ||
| 7.2*: | Estimation, denoising, and compression for graph signals | ||
| 7.3*: | Sparse representations of graph signals | ||
| 7.4*: | Multi-scale analysis on graphs | ||
| 7.5*: | Graph signal downsampling and simplification | ||
| 7.6*: | Uncertainty principles for graph signals | ||
| 7.7*: | Estimating graph structure from data point-clouds | ||
| 7.8*: | Graph signal processing in machine learning | ||
| 7.9*: | Applications of graph signal processing | ||
| 7.10*: | Invited: Graph Signal Processing | ||
| 8: | Information Processing in the Smart Grid | ||
| 8.1*: | Smart Grid Communication Networks | ||
| 8.2*: | Demand Side Management Systems | ||
| 8.3*: | Smart Grid Cyber-Security and Privacy | ||
| 8.4*: | Architectures and Models for the Smart Grid | ||
| 8.5*: | Smart Grid Large Data Sets: Modeling, Analysis, Communications, Compression, Storage and Security | ||
| 8.6*: | Distributed Data Processing and Decision-making in the Grid | ||
| 8.7*: | Smart Metering Networks and Data Processing | ||
| 8.8*: | Communication and Data Processing for Phasor Measurement Units | ||
| 8.9*: | Renewable and Storage Integration Challenges in Smart Grid Cyber Systems | ||
| 8.10*: | Real-Time Electricity Market Interactions | ||
| 8.11*: | Secure Power System State Estimation and Monitoring | ||
| 8.12*: | Invited: Information Processing in the Smart Grid | ||
| 9: | Information Processing over Networks | ||
| 9.1*: | Advances in network science | ||
| 9.2*: | Bio-inspired distributed processing | ||
| 9.3*: | Biological networks | ||
| 9.4*: | Distributed adaptation | ||
| 9.5*: | Distributed control mechanisms | ||
| 9.6*: | Distributed detection and inference | ||
| 9.7*: | Distributed estimation and filtering | ||
| 9.8*: | Distributed game-theoretic strategies | ||
| 9.9*: | Distributed information processing | ||
| 9.10*: | Distributed learning | ||
| 9.11*: | Distributed optimization | ||
| 9.12*: | Graphical models | ||
| 9.13*: | Signal processing over graphs | ||
| 9.14*: | Social networks | ||
| 9.15*: | Random graph representations | ||
| 9.16*: | Sparse graph representations | ||
| 9.17*: | Invited: Information Processing over Networks | ||
| 10: | Low-Dimensional Models and Optimization in Signal Processing | ||
| 10.1: | Dimensionality Reduction | ||
| 10.1.1*: | Linear dimensionality reduction and compressive sensing | ||
| 10.1.2*: | Nonlinear dimensionality reduction and manifold learning | ||
| 10.1.3*: | Subsampling, inpainting, and partial observations | ||
| 10.1.4*: | Adaptive sensing | ||
| 10.1.5*: | Active learning | ||
| 10.1.6*: | Experimental design | ||
| 10.1.7*: | Information scalability | ||
| 10.2: | Algorithms for Signal Processing | ||
| 10.2.1*: | Optimization Algorithms | ||
| 10.2.2*: | Greedy Algorithms | ||
| 10.2.3*: | Optimization Solvers | ||
| 10.3: | Signal Models | ||
| 10.3.1*: | Subspaces and unions of subspaces | ||
| 10.3.2*: | Sparsity and structured sparsity | ||
| 10.3.3*: | Low-rank matrices | ||
| 10.3.4*: | High-dimensional tensors | ||
| 10.3.5*: | Nonlinear manifolds | ||
| 10.4: | Signal Processing | ||
| 10.4.1*: | Detection and classification | ||
| 10.4.2*: | Estimation and inference | ||
| 10.4.3*: | Supervised learning | ||
| 10.4.4*: | Clustering and unsupervised learning | ||
| 10.5: | Compressive Sensing | ||
| 10.5.1*: | Compressive sensor architectures and hardware | ||
| 10.5.2*: | Computationally efficient recovery and estimation algorithms | ||
| 10.5.3*: | Practical considerations | ||
| 10.5.4*: | Distributed sensing and sensor networks | ||
| 10.6*: | Invited: Low-Dimensional Models and Optimization in Signal Processing | ||
| 11: | Low-Power Systems and Signal Processing | ||
| 11.1*: | Speech, Audio and Signal Processing | ||
| 11.2*: | Vision and Image Processing | ||
| 11.3*: | Bio-Medical Signal Processing | ||
| 11.4*: | Sensor Analytics | ||
| 11.5*: | Sensor Fusion | ||
| 11.6*: | Distributed Sensor Networks | ||
| 11.7*: | Body Area Networks | ||
| 11.8*: | Invited: Low-Power Systems and Signal Processing | ||
| 12: | Millimeter Wave Imaging and Communications | ||
| 12.1*: | Millimeter Wave Coherent Imaging and Signal Processing | ||
| 12.2*: | Holographic Millimeter-wave Imaging, Automotive Radars, and Remote Sensing | ||
| 12.3*: | Compressive Sensing in Radars and Imaging | ||
| 12.4*: | MIMO Radars | ||
| 12.5*: | Millimeter Phased Arrays | ||
| 12.6*: | Quasi-Optical Techniques | ||
| 12.7*: | THz Imaging | ||
| 12.8*: | Millimeter Wave Communication Systems and Applications | ||
| 12.9*: | Signal Processing Techniques for Impairments in Millimeter Wave Systems | ||
| 12.10*: | Invited: Millimeter Wave Imaging and Sensing | ||
| 13: | Mobile Imaging | ||
| 13.1*: | Multimedia processing on mobile devices | ||
| 13.2*: | Mobile computational photography | ||
| 13.3*: | Augmented reality | ||
| 13.4*: | Image enhancement for mobile devices | ||
| 13.5*: | Mobile visual search | ||
| 13.6*: | Mobile imaging system design | ||
| 13.7*: | Mobile image quality | ||
| 13.8*: | User experience and interaction on mobile devices | ||
| 13.9*: | Invited: Mobile Imaging | ||
| 14: | Network Theory | ||
| 14.1*: | Wireless networking | ||
| 14.2*: | Distributed signal processing | ||
| 14.3*: | Social Networks | ||
| 14.4*: | Biological networks | ||
| 14.5*: | Network information theory | ||
| 14.6*: | Network coding | ||
| 14.7*: | Distributed storage systems | ||
| 14.8*: | Multi-agent systems | ||
| 14.9*: | In-network computations | ||
| 14.10*: | Networked control systems | ||
| 14.11*: | Invited: Network Theory | ||
| 15: | New Sensing and Statistical Inference Methods | ||
| 15.1*: | Active learning and adaptive sampling | ||
| 15.2*: | Compressive-sensing-inspired systems | ||
| 15.3*: | Computational imaging systems | ||
| 15.4*: | Computational methods for "big data" | ||
| 15.5*: | Data-adaptive representation theory/Dictionary learning | ||
| 15.6*: | Distributed statistics/machine learning | ||
| 15.7*: | High-dimensional statistical inference | ||
| 15.8*: | Manifold-based signal processing | ||
| 15.9*: | New sensing paradigms in medical imaging | ||
| 15.10*: | Information processing in social networks | ||
| 15.11*: | Robust statistical inference | ||
| 15.12*: | Sensing/inference for biological processes | ||
| 15.13*: | Sensing/processing of hyperspectral data | ||
| 15.14*: | Statistical inference in graphical models | ||
| 15.15*: | Invited: New Sensing and Statistical Inference Methods | ||
| 16: | Optimization in Machine Learning and Signal Processing | ||
| 16.1*: | Models and estimation | ||
| 16.2*: | Sparsity, Low-rank and other methods in high-dimensional statistics | ||
| 16.3*: | Large-scale convex optimization: algorithms and applications | ||
| 16.4*: | Graphical models: inference, structure learning etc. | ||
| 16.5*: | Optimization for clustering, classification, regression etc. | ||
| 16.6*: | Non-convex and iterative methods | ||
| 16.7*: | Invited: Optimization in Machine Learning and Signal Processing | ||
| 17: | Signal and Information Processing in Finance and Economics | ||
| 17.1*: | Portfolio analysis: modeling and estimation of statistical dependence, sparse portfolios, robust portfolios, portfolio replication and tracking | ||
| 17.2*: | Risk analysis and modeling | ||
| 17.3*: | Term structure modeling | ||
| 17.4*: | Market microstructure analysis and order book modeling | ||
| 17.5*: | Market making and inventory management | ||
| 17.6*: | Technical analysis | ||
| 17.7*: | Algorithmic trading and optimal order execution | ||
| 17.8*: | Financial networks and systemic risk | ||
| 17.9*: | Behavioral finance and prospect theory | ||
| 17.10*: | Pricing and hedging of derivatives | ||
| 17.11*: | Smart order routing algorithms | ||
| 17.12*: | Spectrum markets | ||
| 17.13*: | Electricity markets and Smart Grid | ||
| 17.14*: | Economics of social networks | ||
| 17.15*: | Business analytics | ||
| 17.16*: | Invited: Signal and Information Processing in Finance and Economics | ||
| 18: | Software Defined and Cognitive Radios | ||
| 18.1*: | Algorithm and architecture co-optimization | ||
| 18.2*: | Platforms and architectures for SDR and CR | ||
| 18.3*: | RF/analog architectures for SDR | ||
| 18.4*: | Design methodologies and tools | ||
| 18.5*: | Baseband processing techniques | ||
| 18.6*: | Software for SDR and cognitive radios | ||
| 18.7*: | Cognitive radio technologies | ||
| 18.8*: | Dynamic spectrum access technologies | ||
| 18.9*: | Invited: Software Defined and Cognitive Radios | ||