Dr Guillaume HOCHARD

Associate Professor
Dr. Guillaume HOCHARD

Biography

With 15 years’ experience in artificial intelligence, he is now an associate professor at aivancity.

Passionate, meticulous and dedicated, his career has led him to specialise in the leadership, management and implementation of generative AI projects, from research through to industrialisation, including R&D and modelling.

Education & Qualifications

YearUniversityQualification/Position
2016École Polytechnique DSSP – Big Data & Machine Learning
2011Telecom ParisTech PhD – Statistics, machine learning and graphs on satellite images
2006ENSGM2 GIS – Geographical Information Systems
2005 ENSTA Bretagne Engineer – Electronics and Signal Processing

Work experience

YearPosition
2025Associate Professor at aivancity
2023 – 2024 VP of AI | Ryte.ai (AI-driven healthcare platform)
2021 – 2023 AI Product Director | Symphony AI – Retail & CPG
2017–2021 Senior Manager | Quantmetry (Capgemini Invent)
2019 – 2021 AI Expert | French National Health Authority
2011 – 2017 Head of Product, Lead Data Scientist | Sixense (Vinci)
2006 – 2010 Research Scientist | CEA (French Atomic Energy Commission)

Publications

  • "Climate Risk and Machine Learning: From Attribution to Storm Intensity Forecasting" – Hochard G., French Statistical Society, Environment and Statistics Group, 20 November 2020
  • "Short-term storm intensity forecasting: a comparison of deep learning and machine learning methods for multivariate and multimodal time series forecasting" – Hochard & Ferlande, 40th International Symposium on Forecasting, Virtual, 26–28 October 2020
  • "Improving Forecasting Performance with Topological Data Analysis" – Minervino, Hochard, Ibnouhsein & Brunel, 40th International Symposium on Forecasting, Virtual, 26–28 October 2020
  • "A deep learning approach to short-term storm forecasting" – Ferlande & Hochard, European Safety and Reliability Conference ESREL 2020 PSAM 15, 1–5 November 2020
  • "Combining statistical and machine learning methods to improve energy consumption forecasting performance" – Hochard & Blanche, 39th International Symposium on Forecasting, Thessaloniki, 16–19 June 2019
  • “Is it possible to predict background noise levels from measured meteorological data using machine learning techniques?” – Bigot & Hochard, INCE Europe, 8th International Conference on Wind Turbine Noise, Lisbon, 12–14 June 2019
  • “Cyclone Artificial Intelligence Prints Investigation” – Poster session at the AI & Climate-Ocean-Atmosphere Day, 6 February 2019, Rennes, and at the INRIA TS Days, 25–26 March 2019, Rennes
  • Co-author of the white paper “AI, explain yourself! When performance isn’t enough”, 2018
  • Member of the translation team for the book “Deep Learning” by A. Courville, I. Goodfellow and Y. Bengio, 2018
  • Ning, Zhangwei, and Guillaume Hochard. “Eyes in the Heavens: Satellite Technologies in Remote Site Monitoring.” *Geo-Strata—Geo Institute of ASCE* 21, no. 6 (2017): 50–55.
  • Satellite monitoring of structures: application of the satellite radar interferometry method for modelling, monitoring and anomaly detection – Guillaume Hochard, Fabien Ranvier (Soldata), CFBR proceedings, 2012
  • PhD thesis: Hochard, Guillaume. “Change Detection in InSAR Imagery: Processing of Long Time Series.” PhD thesis, TĂ©lĂ©com ParisTech, France, 2011.
  • Hochard, Guillaume, Renaud Binet, and Jean-Marie Nicolas. “Stable coherent area in SAR interferometry.” In 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. II-825. IEEE, 2009.

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