October 20 and 21, 2022
Each year, the Labex DigiCosme organizes Research Days whose objective is to present the work carried out within the teams supported by the Labex DigiCosme.
These days will also be an opportunity to discuss the future of the LabEx, beyond December 2022. From 2023, the Graduate Schools (for the University of Paris-Saclay) and the Departments (for the Institut Polytechnique de Paris) will take over from the LabEx Digicosme in scientific animation.
You are cordially invited to participate in these transition research days.
Thursday, October 20
9:00 – 9:30 Welcome
9:30 – 9:45 Opening session
9:45 – 10:45 Talk 1 Grégory Batt, Research Director – Inria and Institut Pasteur
Title : Experimental and computational methods for synthetic biology and cybergenetics
10:45 – 11:00 Coffee break
11:00 – 12:00 Presentation of doctoral students
12:00 – 13:45 Lunch – ENS Paris-Saclay Kfet
13:45 – 15:15 Round table: Digicosm beyond 2022
3:15 – 3:45 Pitch poster (M2 students and doctoral students)
15:45 – 16:45 Poster session
4:45 p.m. – 5:45 p.m. Talk 2 Sébastien Destercke, researcher at CNRS – HEUDIASYC
Title : Uncertainties in learning data
Friday, October 21
9:00 – 9:30 Welcome coffee
9:30 – 10:30 Talk 3 Jakob Hoydis Principal Research Scientist – NVIDIA
10:30 – 11:30 Presentation of doctoral students
11:30 – 11:45 Coffee break
11:45 – 12:45 Presentation of doctoral students
12:45 – 13:55 Lunch
13:55 – 14:45 Presentation of Transversal Initiatives
14:45 – 15:00 Closing speech
Grégory Batt, InBio team, Inria and Institut Pasteur, (talk 1)
Title Experimental and computational methods for synthetic biology and cybergenetics
In this talk, I will introduce synthetic biology and cybergenetics, and present recent results we obtained in these fields. Synthetic biology aims at genetically engineering cells to solve problems of biomedical or industrial interests, and cybergenetics aims at controlling natural or artificial biological processes using genetically-engineered cellular systems and automatic control. In both fields, quantitative modeling is expected to play a central role. Yet despite intense research in these fields, the gap between theory and practice remains large, and the predictive power of the models remains modest.
At InBio, we are trying to bridge this gap through the joint development of experimental and computational methods. In this talk, I will present in the first part some of our recent works on the construction of automated experimental platforms to generate high-quality data obtained in well-controlled conditions. In the second part, I will present two applications of modeling approaches that capture cellular heterogeneity to obtain quantitatively predictive models for natural and synthetic biological systems. I will conclude the presentation with perspectives on design, build, test, and learn approaches for the rational construction of synthetic biological systems.
Title : Uncertainties in learning data
Jakob Hoydis (talk 3)
Jakob Hoydis is a Principal Research Scientist at NVIDIA working on the intersection of machine learning and wireless communications. Prior to this, he was Head of a research department at Nokia Bell Labs, France, and co-founder of the social network SPRAED. He obtained the diploma degree in electrical engineering from RWTH Aachen University, Germany, and the Ph.D. degree from Supéléc, France. From 2019-2021, he was chair of the IEEE COMSOC Emerging Technology Initiative on Machine Learning as well as Editor of the IEEE Transactions on Wireless Communications. Since 2019, he is Area Editor of the IEEE JSAC Series on Machine Learning in Communications and Networks.
He is recipient of the 2019 VTG IDE Johann-Philipp-Reis Prize, the 2019 IEEE SEE Glavieux Prize, the 2018 IEEE Marconi Prize Paper Award, the 2015 IEEE Leonard G. Abraham Prize, the IEEE WCNC 2014 Best Paper Award, the 2013 VDE ITG Förderpreis Award, and the 2012 Publication Prize of the Supéléc Foundation. He has received the 2018 Nokia AI Innovation Award, as well as the 2018 and 2019 Nokia France Top Inventor Awards. He is a co-author of the textbook “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency” (2017).
He is one of the maintainers and core developers of Sionna, a GPU-accelerated open-source link-level simulator for next-generation communication systems.