Freelancer profile translated to English.
Back to original languageAbout Ilan
After studying at Polytechnique and Imperial College, I worked for 3 years at Amazon, where I optimized logistics in Europe and internationally. I now wish to start a company in the field of operational research, and I see freelance missions as a good way to better understand the concrete needs of my future clients.
If your company needs expertise in optimization, I can help you quickly develop a reliable and efficient solution, adapted to your challenges, whether they concern transport, industry, finance, or IT.
I am used to working with incomplete or ambiguous data and making it usable. On the technical side, my field of expertise is quite broad: local search, constraint programming, linear programming (LP/MIP). I also use, if necessary, artificial intelligence approaches, ranging from LLMs to reinforcement learning.
Do not hesitate to contact me, even for a simple chat. I will always be happy to discuss your problem and suggest some approaches, even if we do not work together immediately.
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- AmazonApplied Scientist IIApril 2022 - Today (4 years and 4 months)Paris, France• Created a generic in-house fast and versatile Local Search solver in Julia with a novel system of stateless invariants allowing the use of distributed computing to evaluate hundreds of moves in parallel, and reducing the search space by leveraging DeepReinforcement Learning based heuristics.• Adapted lightning-fast shortest-path algorithms from scientific literature to model complex constraints, applied the solver to Amazon's global Middle-Mile transportation network optimization (thousands of warehouses, tens of thousands of trucks,millions of packages every day).• Conceived an LSTM-based Deep Learning architecture to perform a variable-length regression to forecast arrival times and fill rates of trailers in warehouses to assess operational feasibility in what-if scenarios, improving the former model's accuracy by10 pts.• Led to turning an expensive several days, multiple-people process into an AWS-hosted CI/CD pipeline for a fully automated run of a few minutes, that generated dozens of millions of revenue (saving transportation cost and improving delivery speed)compared to the existing solution.• Acquired management experience with full-time employees and interns, officially managing one full-time employee and 2 interns; and leading full-time employees to contribute to my project.• Interacted with a great variety of teams in order to communicate about how the problem is modelled through meetings, papers,presentations and internal scientific conferences. In under a year, that led to the use of my model both in Europe and North America.
- AmazonResearch Scientist InternApril 2021 - October 2021 (6 months)London, UK• Very important ambiguity by defining the scope and leading a high-impact project involving Machine Learning and Robust Optimization applied to Labor Planning, applied to reducing the variablity of rosters for Amazon warehouse employees.• Under the supervision of Dr. Dario Paccagnan (Imperial College London), wrote a paper as a first author reviewed for IJCAI 2022 but ended up being pulled by Amazon for political reasons.• Leveraged labor plans, supply chain topology information and transportation historical data from hundreds of warehouses• Project leader: worked with 2 full-time employees (1 research scientist and 1 data scientist) to bring the project to success andwas the main point of contact for it
- POLYTECHNIQUE MONTREALResearch Scientist InternMarch 2020 - September 2020 (6 months)Montreal, QC, Canada• Created a Julia package that is a hybrid solver using Reinforcement Learning to train a Graph Convolutional Network thatworks as a variable-selection or value-selection heuristic in a Constraint Programming framework• As a first author, published a peer-reviewed paper in the 2021 CPAIOR conference (29 citations in under 3 years),proving good results on the TSPTW, the Graph-Coloring and the Knapsack problem. Received the 2020 Polytechnique's BestResearch Internship Award for this work• Source available on an active Github repository (1300+ commits, 160+ stars)
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Education
- MSc Artificial Intelligence & Machine LearningImperial College London2021Recherche Opérationnelle, Intelligence artificielle
- Engineering Degree - X 2017Ecole Polytechnique ´2021Intelligence articifielle, vision par ordinateur