Emerging Topics in Artificial Intelligence 2023

Location: San Diego Convention Center
San Diego, California, United States
20-24 August 2023

Website: Emerging Topics in Artificial Intelligence 2023

Conference Committee

Conference Chairs:
Giovanni Volpe, Göteborgs Univ. (Sweden)
Joana B. Pereira, Karolinska Institute (Sweden)
Daniel Brunner, Institut Franche-Comte Electronique Mecanique Thermique et Optique (France)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)

Program Committee:
Jonas Andersson, Volvo Car Corp. (Sweden)
Frank Cichos, Univ. Leipzig (Germany)
Margaretta Colangelo, Jthereum (United States)
Miguel C. Cornelles-Soriano, Instituto de Física Interdisciplinar y Sistemas Complejos (Spain)
Jürgen W, Czarske, TU Dresden (Germany)
Meltem Elitas, Sabanci Univ. (Turkey)
Yong Fan, Penn Medicine (United States)
Claudio Gallicchio, Univ. di Pisa (Italy)
Antoni Homs-Corbera, Cherry Biotech (France)
Pablo Loza-Alvarez, ICFO – Institut de Ciències Fotòniques (Spain)
Kathy Lüdge, Technische Univ. Ilmenau (Germany)
Carlo Manzo, Univ. de Vic (Spain)
Paula Merino Serrais, Cajal Institute (Spain)
Mite Mijalkov, Karolinska Institute (Sweden)
Armand Niederberger, Stanford Photonics Research Ctr. (United States)
Yair Rivenson, Pictor Labs (United States)
Elena Rodriguez-Vieitez, Karolinska Institute (Sweden)
Roser Sala-Llonch, Univ. de Barcelona (Spain)
Bhavin J. Shastri, Queen’s Univ. (Canada)
Guohai Situ, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (China)
Volker J. Sorger, The George Washington Univ. (United States)
Lei Tian, Boston Univ. (United States)
Mattia Veronese, Univ. degli Studi di Padova (Italy)
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)

Call for Papers

The ETAI conference provides a forum for a highly interdisciplinary community combining artificial intelligence with photonics, 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 that will be covered are:

  • 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
  • fundamental aspects of photonic non-digital computing
  • integrated photonics and nonlinear optical components for next generation computing
  • alternative computing concepts such as neural networks and Ising machines to overcome the end of Moore and Dennard scaling
  • 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.

Artificial intelligence for microscopy

  • computational microscopy
  • data-driven optical reconstruction methods
  • digital video microscopy
  • generation of training datasets.

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.

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.

Neuromorphic computing

  • next generation materials for optical nonlinearity
  • integration of ultra-parallel photonic architectures
  • beyond 2D substrates
  • physical substrates for machine learning applications.

Optical neural networks

  • learning in optical systems
  • applications for optical neural networks
  • scalability of optical neural networks.

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.