معرفی
Simo Alami is a Ph.D. candidate and Tutor at Ecole Polytechnique, affiliated with the LIX computer science laboratory under the supervision of Professor Jesse Read. He teaches tutorials for the Advanced Machine Learning and Autonomous Agents course (Computer Science, Ingénieur 3A/Master1) since 2020, focusing on practical implementations of machine learning algorithms and autonomous systems.
His academic background includes undergraduate and graduate studies in Mathematics and Computer Science at Université Pierre et Marie Curie and Ecole CentraleSupélec.
- Ph.D. Candidate, Ecole Polytechnique (2020-Present)
- Master's Studies, Mathematics and Computer Science, Université Pierre et Marie Curie and Ecole CentraleSupélec
Alami's research centers on developing algorithms that learn from experience through self-learned metrics, with core expertise in Deep Learning, Inverse Reinforcement Learning, and Metric Learning. His work targets robotic applications where agents must perform novel tasks by understanding logical concepts, while also extending to energy management systems through non-invasive load monitoring. He explores how meta-learning frameworks can enable transferable reward structures across domains.
His publication record reveals a strong trajectory in distributional reinforcement learning and inverse reinforcement learning using generative models, with significant contributions to metric learning for clustering and energy disaggregation. The consistent focus on transferable methodologies across robotics and smart grid applications demonstrates his interdisciplinary approach to solving real-world problems through advanced machine learning techniques.
Scientific recognition includes:
- Nvidia Grant awarded in 2022 for research on Meta-Inverse Reinforcement Learning
- 3rd Prize at Hi Paris Hackathon (2021) for a reinforcement learning-based smart grid energy management solution
While not supervising graduate students as a Ph.D. candidate, Alami has secured competitive external funding including the Nvidia research grant. His industry collaboration with Accenta during a visiting researcher position resulted in ECML 2022 publications on non-invasive load monitoring, highlighting his ability to bridge academic research with practical engineering applications.
As an active member of the LIX laboratory at Ecole Polytechnique, Alami participates in cutting-edge computer science research while maintaining external collaborations. His visiting researcher position at Accenta demonstrates engagement with industry partners, and his organization of the IRT SystemX Electromobilité et territoires webinar in 2021 reflects leadership in knowledge dissemination within the energy and mobility research community.
