### Hi!

I'm a data scientist and manager working at Unit8, as part of a world-class team dedicated to democratising data engineering, data science and machine learning in the industry.

Before Unit8, I was a lead data scientist at Swisscom, a PhD student at EPFL, and a researcher in the Silicon Valley.

I have experience designing machine learning models and systems, and writing production-grade software in different contexts (see some projects below). My PhD thesis was nominated by the jury for the EPFL Patrick Denantes Memorial Prize. I wrote a few academic papers (some of which got best paper awards), and have a couple of (granted) patents.

## Some Projects

#### Darts: Time Series Made Easy in Python

For a long time, the time series forecasting experience in Python was not really great. I created Darts as an attempt to provide a unified and user-friendly API for time series forecasting (and more). The library puts a strong emphasis on modern machine learning techniques. Among other things, it offers the possibility to easily train models (included some of the latest and coolest competition-winning algorithms) on large time series datasets, and obtain probabilistic forecasts. I'm leading the development and contributing regularly, along with a great team of contributors.

pip install darts

#### VeGANs: Various existing GANs (and Other Generative Models) in PyTorch

When working and experimenting with GANs, I grew a little frustrated by the fact that most open-source implementations of research papers could only be used to reproduce that paper's result in a limited context. So I created VeGANs , a small library making it easier to use many different sorts of GANs (and other generative models) with PyTorch. The idea is to only provide a Generator and Discriminator PyTorch modules, and let VeGANs train them for you with a GAN algorithm of your choice. The library was then revamped and vastly improved by Thomas Neuer, a Unit8 colleague.

pip install vegans

#### Country-scale Mobility Mining Using Telco Data

At Swisscom, I was product owner and tech lead for the Mobility Insights Platform. We were transforming raw network data coming from cell towers (~ 2 Mio data points / second) into intelligible statistics about mobility in the country. I led the development and architecture of the platform, interfaced with business, and developed some key algorithms (such as for positioning and inferring paths over road and railway networks). For some time I was also in charge of the content of the Swisscom open data portal, and started a project to spot anomalies in (massive) network time series.

#### Mining Democracy

During my time at EPFL, I started a little side project whose goal was to spot patterns in voting advice applications data (such as Smartvote) and open government data. Among other things, we discovered that the Swiss Röstigraben can be quantified, that some votes' results can be predicted with very little information, and that politicians do not always optimally cover the "ideological space" of their constituents. Since then, the project has been significantly strengthened and improved by others, and more research has been done on the topic of vote prediction.

#### Distributed Resource Allocation for Wireless Networks

Over the course of my PhD, I invented, implemented and analysed new distributed algorithms for resource allocation in computer and wireless networks. Among other things, I worked on new algorithms that can reach globally optimal configurations (in some probabilistic sense) of spectrum usage, transmit durations and transmit power using only local information about their surroundings. I also used machine learning to predict the performance of wireless networks in new (unseen) configurations.

#### Graph Embedding for Scalable Routing

I proposed a new algorithm for obtaining graph embeddings in a distributed fashion. The embedding algorithm relies on spanning trees, it uses a custom metric space (with $l_{\infty}$-norm) and it can be used for geometric routing in computer networks. It requires low memory (polylogarithmic in the network size), making it scalable for Internet-scale graphs.

## Publications

• Sébastien Henri, Christina Vlachou, Julien Herzen and Patrick Thiran. EMPoWER Hybrid Networks: Exploiting Multiple Paths over Wireless and ElectRical Mediums.
in ACM CoNEXT, 2016.
• Christina Vlachou, Albert Banchs, Julien Herzen and Patrick Thiran. How CSMA/CA with Deferral Affects Performance and Dynamics in Power-Line Communications.
in IEEE/ACM Transactions on Networking (ToN), 2016.
• Christina Vlachou, Albert Banchs, Pablo Salvador, Julien Herzen and Patrick Thiran. Analysis and Enhancement of CSMA/CA with Deferral in Power-Line Communications.
in IEEE Journal of Selected Areas in Communications (JSAC), 2016.
• Julien Herzen, Flexible Spectrum Assignment for Local Wireless Networks.
PhD thesis, 2015.
• Julien Herzen, Albert Banchs, Vsevolod Shneer and Patrick Thiran. CSMA/CA in Time and Frequency Domains.
in IEEE ICNP, 2015.
• Julien Herzen, Henrik Lundgren and Nidhi Hegde. Learning Wi-Fi Performance.
in IEEE SECON, 2015.
• Christina Vlachou, Albert Banchs, Julien Herzen and Patrick Thiran. Analyzing and Boosting the Performance of Power-Line Communication Networks.
in ACM CoNEXT, 2014.
• Christina Vlachou, Albert Banchs, Julien Herzen and Patrick Thiran. On the MAC for Power-Line Communications: Modeling Assumptions and Performance Tradeoffs.
in IEEE ICNP, 2014.
(Best paper runner-up award)
• Vincent Etter, Julien Herzen (co-first author), Matthias Grossglauser and Patrick Thiran. Mining Democracy.
in ACM COSN, 2014.
(Best paper award)

Press coverage:

• Christina Vlachou, Albert Banchs, Julien Herzen and Patrick Thiran. Performance Analysis of MAC for Power-Line Communications.
in ACM Sigmetrics (poster session), 2014.
• Julien Herzen, Ruben Merz and Patrick Thiran. SAW: Spectrum Assignment for WLANs.
in ACM S3, 2013.
• Julien Herzen, Ruben Merz and Patrick Thiran. Distributed Spectrum Assignment for Home WLANs.
in IEEE Infocom, 2013.

Press coverage:

• Christina Vlachou, Julien Herzen and Patrick Thiran. Fairness of MAC protocols: IEEE 1901 vs. 802.11.
in IEEE ISPLC, 2013.
• Julien Herzen, Cedric Westphal and Patrick Thiran. Scalable Routing Easy as PIE: a Practical Isometric Embedding Protocol.
in IEEE ICNP, 2011.
• Adel Aziz, Julien Herzen, Ruben Merz, Seva Shneer and Patrick Thiran. Enhance & Explore: an Adaptive Algorithm to Maximize the Utility of Wireless Networks.
in ACM MobiCom, 2011.
• Julien Herzen, Adel Aziz, Ruben Merz, Seva Shneer and Patrick Thiran. A Measurement-Based Algorithm to Maximize the Utility of Wireless Networks.
in ACM S3, 2011.
• Julien Herzen, Adel Aziz and Patrick Thiran. Demo Abstract of Net-Controller: a Network Visualization and Management Tool.
in IEEE Infocom (demo session), 2010.

## Patents

• Julien Herzen, Albert Banchs, Vsevolod Shneer and Patrick Thiran. CSMA/CA in Time and Frequency Domains U.S. Patent No. US10,321,488 (granted)

• Julien Herzen, Ruben Merz and Patrick Thiran. Method to optimize the communication parameters between an access point and at least one client device
U.S. Patent No. US9549328B2 (granted)

• Henrik Lundgren, Julien Herzen and Nidhi Hegde. Spectrum allocation in a wireless network
U.S. and European; US9844056B2 (granted)