Highlights
Innovation and Research
I authored more than 195 granted patents in
the area of machine learning and artificial intelligence. I also published many
peer-reviewed papers and book chapters. Furthermore,
I published two monograms with Springer:
my PhD thesis
(volume 93) and the
proceedings of DARS2010 (volume 83).
Product Development
I co-architected and lead the development of 4 key products at Cisco:
-
Predictive Networks (2022) is the core technology underpinning several products (e.g.,
ThousandEyes WAN Insights
,
Cisco SD-WAN Analytics
) that brought for the first time learning capabilities to enterprise networks and the Internet. Routers
proactively redirect application traffic away from paths that have a high probability of experiencing
disruptions, thereby improving significantly the application experience of the end user. See
Predictive Networks at Cisco
for further details about the technology.
-
Cisco AI Network Analytics (2019) is a cloud-based machine learning platform that provides
visibility and learns from 2500+ enterprise networks across the world. Upon its launch, the product was
the first one to perform adaptive, ML-driven network assurance. See
this blog post
for further details.
-
Cisco AI Endpoint Analytics (2020) is built on the same platform as Cisco AI Network Analytics, the
product protects organizations from ransomware with ML-based anti-spoofing technology. Using behavioral
modeling and trust analytics, we detect devices that exhibit unusual patterns of communication typical of
a rogue device attacking the network. See
this blog post
for further details.
-
Self Learning Networks (2016) is a cybersecurity product capable of zero-day attack detection by
turning every router and switch into a security agent that ingests locally NetFlow telemetry, model the
behavior of every device in the network and trigger alerts in case of anomalies. SLN uses a
hyper-distributed learning architecture capable of processing up to 100 billion events a day across 5000
routers. Now part of
Cisco Secure Network Analytics
.
Team Leadership
I built 3 engineering teams built from the ground up. This amounts to more than 50 engineers hired, coached,
and managed across a broad range of geographies and disciplines (software engineering, machine learning,
data science, cloud engineering, data visualization, front-end design).
Software Development
I have a broad expertise in managing agile teams to deliver reliable software using continuous integration
and delivery strategies, test automation, code reviews, pair programming, and cloud devops. I wrote a
popular handbook that shows why I believe that building
software that meets both customer requirements and deadlines is possible.
Customer Impact
I lead the design and development of five products at Cisco used by thousands of enterprise customers,
including many Fortune 50/500, from inception to customer delivery in production. I attended dozens of key
customer meetings over the years, both to pitch our vision and technological capabilities, and to understand
their pain points and adapt our solutions accordingly.
Technological Breadth
My 10-year tenure at Cisco allowed me to gain insights into a broad technological landscape, including
cybersecurity, smart grids, Internet of Things, wireless networks, internet architecture, endpoint
classification, and cloud computing. Prior to that, I had worked closely with micro-engineers on the design
of Micro-Electro-Mechanical Systems (MEMS) and with chemists on the self-assembly of large molecules. I also
have a general interest in the area of climate science, energy, and sustainability.
Key Skills
Every now and then, I read again Matt Might's
'What every computer science major should know'
and I realize that I will never quite live up to its standards. Yet, I found it to be an excellent North
Star for my lifelong learning plan. The list below is a compilation of topics that I have seriously used
and/or studied at some point in my career, but some might not be so fresh, especially the theoretical
part (they are marked with an asterisk).
-
Programming languages:
Python, Polars, JAX, SQL, Apache Spark, C++
-
Software engineering:
data-intensive software, distributed computing, large-scale software systems
-
Technologies:
LLMs, machine learning, statistics, networking, robotics, data science