Involvement in the ML community
Outreach
Rodrigo is very enthusiastic about engaging with the international machine-learning community. He combines his passions for entrepreneurship and teaching in educational events for research enthusiasts and practitioners.
The School of Machine Learning at Skoltech
In 2020, we started SMILES, the Summer School of Machine Learning at Skoltech. We aim to provide a venue for the nascent researcher in machine learning for the emerging world.
In one sentence
The School of Machine Learning at Skoltech is the leading online summer school for the emerging world’s machine learning researcher.
What?
The School of Machine Learning at Skoltech (SMILES) is an online one-week intensive course about modern statistical machine learning methods. It aims at bringing together the Machine Learning community from the CIS, Central Asia, and the Caucasus regions. SMILES presents topics that are at the core of machine learning research, from fundamentals to the state-of-the-art.
Why?
A successful machine learning researcher needs skills, network, and visibility. Most universities in the emerging world do not teach the forefront of machine learning. Only selected institutions in the US and the UK do it. Moreover, a nascent researcher needs peers to work on joint projects and get feedback on her work. SMILES provides three things, the latest knowledge in machine learning, access to a network of peers, and a venue to showcase research work.
For whom?
Courses are accessible to anyone with mathematical maturity, from advanced bachelor students to seasoned professionals interested in transitioning to machine learning research.
How?
SMILES is a fully online summer school.
When?
SMILES takes place each summer in Mid-August for one week.
What is the format?
SMILES consists of mini-courses of up to three hours covering contemporary topics of machine learning.
Who has spoken at SMILES?
| Speaker | Institution | Topic |
|---|---|---|
| Andrew Gordon Wilson | NYU | Bayesian Deep Learning and a Probabilistic Perspective of Generalization |
| Anna Goldenberg | University of Toronto | Technical obstacles to ML implementation in healthcare |
| Dimitrios Pantazis | MIT | Deep Learning in Neuroscience |
| Emtiyaz Khan | RIKEN | Deep Learning with Bayesian principles |
| Jan Peters | TU Darmstadt | Reinforcement Learning |
| Kamil Ciosek | Microsoft Research | Deep Reinforcement Learning |
| Katharina Kann | University of Colorado Boulder | Transfer Learning in NLP |
| Kun Zhang | CMU | Causality |
| Kyunghyun Cho | NYU | Natural Language Processing |
| Lior Wolf | Tel Aviv University | Transfer Learning |
| Maria Schuld | Xanadu AI | Quantum Machine Learning |
| Ruth Urner | York University | Learning Theory |
| Sinead Williamson | University of Texas at Austin | Bayesian nonparametrics |
| Suchi Saria | Johns Hopkins University | Causal inference for Personalized Decision |
| Susan Athey | Stanford Graduate School of Business | Causal Inference |
| Tamara Broderick | MIT | Variational Bayes |
| Zack Chase Lipton | CMU | Robust Deep Learning |
| Nicolò Cesa-Bianchi | Università degli Studi di Milano | Online Learning |
| Michael Bronstein | Imperial College London | Graph neural network |
| Arthur Gretton | University College London | Kernels |
| Marco Cuturi | CREST-ENSAE | Optimal Transport |
| Ulrich Bauer | TU Munich | Topological Data Analysis |
| Michel Besserve | Max Planck Institute for Intelligent Systems | ML in Neuroscience |
| Joris Mooij | University of Amsterdam | Causality |
| Isabel Valera | Max Planck Institute for Intelligent Systems | Fairness & Interpretability |
| Shimon Whiteson | University of Oxford | Reinforcement Learning |
| Yarin Gal | University of Oxford | Bayesian Deep Learning |
| Justin Solomon | MIT | 3D Deep Learning |
| Mark Girolami | University of Cambridge | Bayesian Optimization and Probabilistic Numerics |
| Francois Bachoc | University of Tolouse | Advanced methods in GP |
| Matus Jan Telgarsky | University of illinois | Mathematical Aspects of DL |
Machine Learning Summer School
The Machine Learning Summer School stretches back to 2002 when two very distinguished scientists, B. Scholkopf and A. Smola, first created it. Since then, more than 36 schools have taken place in multiple countries.
Skoltech’s Machine Learning Summer School, taking place from August 26 to September 6, 2019, drew participants from 36 countries worldwide. Initially, our MLSS attracted 800+ applications from over 40 countries. Nearly half were Ph.D. students, 21% were from industry, 23% were MSc students, and 7% postdocs from academia.
We sold a total of 250 tickets, half the participants were Russian, and 8% of these are part of the Skoltech community. Members of industry represented 40% of the participants, most of whom work for companies in Russia. German participants made up 9% of attendees, Chinese made up 5.5%, representing the most prominent international contingents. Participants and speakers hailed from world-renowned institutions such as Stanford University, Cornell University, Carnegie Mellon, Oxford University, ETH Zurich, TU Munich, and others.
MLSS is a great opportunity to meet with specialists in various machine learning fields and network with fellow students from all corners of the globe.
Participants
“I took part in the 2-week Machine Learning Summer School and it covered a very diverse set of topics by a lot of speakers. I met many interesting people from different fields.” – Jan Hendrik Lange, Max Planck Institute for Informatics
“I enjoyed the summer school very much; we had great lecturers and very varied topics. It was very useful for my research.” – Karin van Garderen, Erasmus MC (Netherlands)
“I am very happy to have had the opportunity to take part in this school. These two weeks were very intensive, the lecturers were very impressive and we had the chance to meet them and exchange contact details.” – Natalia Khanzhina, IITMO (St. Petersburg, Russia)
Speakers
“Thank you for organizing this excellent summer school! I had a very nice time.” – Shimon Whiteson, University of Oxford
“I would like to thank you for organizing my stay in Moscow. I hope to see you again in Paris!” – William Clements, Unchartech (Industrial R&D Lab, Paris)
“Many thanks again for inviting me to MLSS; it was great to visit beautiful Moscow and Skoltech. I had some great interactions with the students.” – Michael Bronstein , Imperial College London
“Thank you for the invitation, my experience here has been wonderful with interesting conferences and the opportunity to discover Moscow.” – Matus Telgarsky, University of Illinois at Urbana Champaign
Sponsor and Partner testimonials
“Thanks for organizing this great MLSS summer school! The six Swedish PhD students were thrilled and satisfied.” – Mats Hanson, Sweden AI society AI.SE
“Let me thank you for participating in the organization of the MLSS Open Community Day. In the end we had more than 500 participants, which is great! We’ve also found room for cooperation with Alexander Filippov and his IT Algorithm Laboratory.” – Danila Doroshin, Huawei
“A big thank you to the organizers! I have had a fantastic time at the school and I have learned a lot. Bolshoe spasibo!” – Johannes Oberreuter, Data Reply