From the Road

As we shift to attending and hosting a blend of live and virtual events, this dynamic continues to be a crucial element helping us to deliver on the Azafran Investment Thesis. As passionate entrepreneurs at heart, every week our team covers the road less traveled, sometimes the only VCs in the room, attending pitch events, demo days, and industry conferences for incredible applied deep tech founders and companies. Following is a sample of recent events attended by Team Azafran:

Women in Voice 2021

On the Way Up: Celebrating, amplifying & connecting global women and minority genders in voice technology

Azafran’s own Zubeyda Tebra was a panelist and judge in the Women in Voice final event of 2021, On the Way Up Pitch Finale. Women in Voice builds community, connection, and networking opportunities to get feedback, mentorship, and shape a supportive ecosystem of funding mechanisms for founders with diverse perspectives.

For more info:

ITU Events - AI for Good Discovery Series

Better Better: machine learning for improved climate models and projections

Applications of machine learning to climate modeling have witnessed a boom over the the last couple of years. Those applications range from improving physical process representation, better using massive datasets such as remote sensing information, as well as post processing of large climate mode outputs. In this talk, the ITU panel presented two major opportunities for machine learning in climate modeling:

  1. Using machine learning to better represent sub grid (i.e. smaller than the model grid) physical processes informed by high-resolution simulations or observations and

  2. Using machine learning to constrain multi-model climate model projections and to better understand the climate system.

In the first part of the talk, they showed how machine learning can be used to represent several key physical processes of the Earth system such as convection or turbulence. They also highlighted physical lessons learnt from those statistically informed parameterizations and limits of standard physically-based parameterization, such as mass flux approaches for convection, which cannot be corrected by parameter tuning alone but rather require a rethinking of the structural form of the equations as they miss some key processes. Also discussed were challenges related to extrapolation to unseen regimes and strategies to address those issues by merging machine learning with physical knowledge and physical invariances.

In the second part of the talk, they highlighted how machine learning can be used to constrain projections of future climate with observations. Those techniques perform better than multi-model means or standard emergent constraints. In addition, the use of causal discovery and causal inference can go beyond standard model evaluation and analysis approaches and help better highlighting the physical mechanisms at play.

Newchip Online Demo Week November 2021

The power of community, peer groups and the impact of strong content

The Azafran team is always excited to attend Newchip demo days and the latest virtual installment featured many great companies and some that fit the Azafran Investment Thesis which we are exploring now.

Since its inception, the Newchip Accelerator has focused on helping entrepreneurs across the globe grow their leadership and enhance their ability to execute on their business strategy by providing them access to a growing community and strong support network that prepares them to make a global impact.

For more info: