Download

EDUARDO PIGNATELLI

  1. Skills: Python, Jax, Flax, Pytorch, GPU computing, Git, Numerical methods, Predictive modelling.

  2. Skills: Line management, Risk management, Strategical thinking, Planning, C#, Python, Deep learning, Data warehouse, NoSql, Keras, Pytorch, jupyterhub.

  3. Skills: Python, Supervised learning, Unsupervised learning, NoSql, Tensorflow, Pytorch, Azure, AWS, Cloud computing, Docker.

  4. Skills: C#, JavaScript, Git, Agile development, SCRUM, UI, UX, MongoDB.

  5. Skills: Python, Visual programming, C#, MongoDB.

  6. Skills: Visual Programming, Geometrical modelling, Image processing, Rendering.

  7. Skills: Python, Numerical methods, Physics, Differential Equations, Spectral analysis, Acoustical modelling.

  1. Classes:

    • UCL-COMP0089: Reinforcement Learning

    • UCL-ELEC0136: Data Acquisition and Processing Systems

  2. Classes:

    • Machine Learning

    • Deep Reinforcement Learning

  3. Class: Computer Vision and Image retrieval

  4. Class: Technology of Architecture Studio

  1. Pignatelli, E., Ferret, J., Toni, L., 2023. A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. In submission.

  2. Ntagiantas, K., Pignatelli, E., Peters, N. S., Cantwell, C. D., Chowdhury, R. A., Bharath, A. A., 2022. Estimating Cardiac Tissue Conductivity from Electrograms with Fully Convolutional Networks. arXiv preprint arXiv:2212.03012.

  3. Wong N., Meshkinfamfard S., Turbé V., Whitaker M., Moshe M., Bardanzellu A., Dai T., Pignatelli E., Barclay W., Darzi A., Elliott P., Ward H., Tanaka R., Cooke G., McKendry R., Atchison C., Bharath A., 2022. Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies. Communications Medicine. Nature Research.

  4. Lino, M., Cantwell, C., Fotiadis, S., Pignatelli, E., Bharath, A., 2020. Simulating Surface Wave Dynamics with Convolutional Networks. In NeurIPS workshop on Interpretable Inductive Biases and Physically Structured Learning.

  5. Fotiadis, S., Pignatelli, E., Valencia, M.L., Cantwell, C., Storkey, A., Bharath, A.A., 2020. Comparing recurrent and convolutional neural networks for predicting wave propagation. In ICLR Workshop on Deep Neural Models and Differential Equations.

  6. Di Rosario, S., Pignatelli, E. and Mirra, G., 2018, May. An automated design methodology for acoustic shells in outdoor concerts. In Proceedings of the EuroNoise (Vol. 2018, pp. 2123-2130).

  7. Pignatelli, E., Mirra, G. and Pone, S., 2017, September. InFormer: designing forming actions in post-formed gridshells by means of Multi-Objective Genetic Algorithms. In Proceedings of IASS Annual Symposia (Vol. 2017, No. 17, pp. 1-10). International Association for Shell and Spatial Structures (IASS).

  8. Mirra, G., Pignatelli, E. and Pone, S., 2016, September. Computational morphogenesis and construction of an acoustic shell for outdoor chamber music. In Proceedings of IASS Annual Symposia (Vol. 2016, No. 17, pp. 1-10). International Association for Shell and Spatial Structures (IASS).

  9. Pone, S., Mirra, G., Pignatelli, E., Lancia, D. and Colabella, S., 2016, October. Specialised algorithms for different project stages in a post-formed timber gridshell design. In Proceedings of the 3rd International Conference on Structures and Architecture (ICSA) (pp. 259-266).

  10. Di Rosario, S., Parenti, B., Pignatelli, E., Mirra, G., Pone, S., 2015, October. Res, Resonant String Shell, development and design of an acoustic shell for outdoor chamber music concerts. In Proceedings of the Institute of Acoustics (Vol. 37, pp. 354-373). 9th International Conference on Auditorium Acoustics.

  11. Pignatelli, E., Colabella, S., Rosario, S.D. and Pone, S., 2015, August. A wooden acoustic shell for open-air chamber music concert. In Proceedings of IASS Annual Symposia (Vol. 2015, No. 25, pp. 1-12). International Association for Shell and Spatial Structures (IASS).

  1. Innochain Symposium, Expanding Information ModellingCopenhagen, Denmark – November 2018
    Presenting: “The BHoM – A framework for mass adoption of Computational Design”, Copenhagen, Denmark.

  2. Architectural Association, EmTec, Invited lectureLondon, UK – June 2018
    Presenting: “Generative design with active bending”

  3. Royal College of Art, Invited lectureLondon, UK – February 2018
    Presenting: “Algorithmic thinking in design"

  4. IABSE SymposiumBath, UK – April 2017
    Buro Happold Representatives Talk: “Generative design of an Acoustic Chamber for Outdoors”

  5. IASS Symposium 2015Amsterdam, Netherlands – August 2015
    Presenting : “A wooden acoustic shell for open-air chamber music concert”.

  6. University of Naples, Invited lectureNaples, Italy – March 2015
    Presenting: “A strategy for the waterfront of Naples”, Naples, Italy.

  1. Deep Learning Specialization
    Licence 55M8BYZZTGL7, Prof. Andrew Ng, Coursera

  2. Neural Networks and Deep Learning – Andrew Ng, Coursera
    Licence WJE8TMPBTAM6, Prof. Andrew Ng, Coursera

  3. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    Licence KTQFXY9DLUBS, Prof. Andrew Ng, Coursera

  4. Structuring Machine Learning Projects
    Licence XTTFC757KVLH, Prof. Andrew Ng, Coursera

  5. Convolutional Neural Networks
    Licence 8X8Z8NQS5QPB, Prof. Andrew Ng, Coursera

  6. Sequence Models
    Licence PXD3GPJWBWKF, Prof. Andrew Ng, Coursera