Call for Papers |
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Abstract Due: 7 February 2024
Author Notification: 29 April 2024 Manuscript Due: 31 July 2024 The ETAI conference provides a forum for a highly interdisciplinary community combining artificial intelligence with photonics, spintronics, microscopy, active matter, biomedicine, and brain connectivity. Importantly, this conference includes topics outside the core expertise of optics and photonics. Photonics and machine learning have become decisively interdisciplinary, and we expect additional synergy and inspiration through this open-minded approach. ETAI actively engages with industry to foster commercialization and provides networking opportunities for young and established researchers. By bringing experts from different fields and backgrounds together, ETAI provides new fundamental insights and identifies technological applications as well as commercialization opportunities. The topics covered in ETAI include but are not limited to: data acquisition and analysis through photonic subsystems, e.g., time series, images, video feature tracking, optical signal processing simulation and design of photonic components and circuits adaptive control of experimental setups through more robust and resilient feedback cycles enhanced computational microscopy using artificial intelligence alternative computing concepts such as neural networks and Ising machines to overcome the end of Moore and Dennard scaling fundamental aspects of photonic non-digital computing integrated photonics and nonlinear optical components for next generation computing spintronic sensor integration for data acquisition and photonic subsystems enhanced precision medicine, e.g., virtual tissue staining, early diagnosis, and personalized treatments artificial intelligence for analysis of brain connectivity biomimetic and neuromorphic computational architectures embodied intelligence in nature and technology evolution of adaptive behaviors in biological systems engineering collective behaviors in robotic swarms human brain haptic device interfaces physical insight and interpretability of artificial intelligence models limitations and criticism of the use of artificial intelligence. The keynote and invited presentations will provide an exciting and broad view of this interdisciplinary research effort. Abstracts are solicited on (but not restricted to) the following areas: Artificial intelligence for photonics optical system design using machine learning machine learning-based solutions to inverse problems in optics spectroscopy enhancement using machine learning. spintronics-enhanced optical system design using machine learning. Artificial intelligence for microscopy computational microscopy data-driven optical reconstruction methods digital video microscopy generation of training datasets. Artificial intelligence for optical trapping particle detection optical trap calibration feedback control. Artificial intelligence for soft and active matter data acquisition using machine learning data analysis using machine learning de-noising using machine learning reinforcement learning in physical systems dynamics of complex systems intelligent foraging navigation and search strategies. Artificial intelligence for biomedicine machine learning-enhanced optical imaging and sensing image segmentation virtual tissue staining artificial intelligence as a tool to enhance decision-making in personalized medicine and drug screening multiple-sources data structuring and combination in complex biomedical decision-making legal and ethical aspects of the use of artificial intelligence as a tool for decision-making in medicine. Neuromorphic computing next generation materials for optical nonlinearity integration of ultra-parallel photonic architectures beyond 2D substrates physical substrates for machine learning applications. Spintronics for neuromorphic computing spin-based devices for neuromorphic systems magnetic textures in neuromorphic computing. Optical neural networks learning in optical systems applications for optical neural networks scalability of optical neural networks. Spintronics in artificial intelligence materials and phenomena for spintronic applications in AI integration of spintronics with neural networks and computing architectures spintronic data processing and its impact on AI efficiencies. Autonomous robots swarming robots feedback control elaboration of sensorial inputs decision making. Biological models for artificial intelligence physical foundations of biological intelligence translation of biological models to artificial intelligence collective motion in biological populations. Machine learning to study the brain machine learning methods for image segmentation supervised and unsupervised models multi-voxel pattern analysis predictive modelling approaches. Artificial Intelligence for brain connectivity measurement of brain activity and anatomy in humans and animals structural and functional connectomics graph theoretical tools clusters and subnetwork extraction dimensionality reduction techniques to identify brain networks. Machine-brain interfaces detection of brain activity haptic devices feedback control through brain waves. Limitations of artificial intelligence the “black-box problem” of machine learning interpretability, explainability and uncertainty quantification of machine-learning models generalization power of machine-learning models model selection development of objective benchmarks. |
Credits and Sources |
| [1] ETAI 2024 : Emerging Topics in Artificial Intelligence |
