Alessandra Sala of Nokia Bell Labs discusses her work in the analytics and AI research field.
Working in research is more than simply putting on a lab coat and looking for the right scientific answer.
Research involves several different fields searching for new innovations, especially when it comes to analytics. Here, Alessandra Sala gives us an insight into her work with Nokia Bell Labs.
What is your role within Nokia Bell Labs?
I am the head of the analytics research group in Nokia Bell Labs Europe, which is globally connected into the many other Bell Labs sites, and externally influencing the scientific communities in many areas, such as web mining, social media and algorithms in general.
What education and/or other positions led you to the role you have now?
I got my PhD in computer science in Italy and then I spent five years in the US, first as a visiting student and then as a postdoc. My years at UCSB have deeply structured my technical research abilities as much as trained me to identify critical problems with potential disruptive impact.
When I joined Bell Labs, I discovered an environment where I could foster the growth of my core strengths because of the unique opportunity to interact with exceptional and inspired researchers. But that wasn’t all. In Bell Labs, I found mentors and career angels who saw in me some leadership abilities I didn’t even know I had. Their guidance and training has elevated me to become an inspired technical scientist with the mission to lead researchers to work together towards disruptive ideas, which have the potential for massive impact in our industry and in general humanity.
Can you tell us about the research you’re currently working on?
The mission of my team is to disrupt the field of data analytics by inventing new ways to sense the world at scale (anything, anywhere and anytime), create personalised interpretation of data with contextualised meaning, and realise intelligent data synthesis to enable informed decision-making.
The underlying technology is built on innovative solutions in the areas of machine learning, deep learning, compressed data structures and, in general, sophisticated algorithms for data manipulations.
What first stirred your interest in this area?
I believe I was chosen by research. My degree thesis was a research project that was made into a publication. My professor at that time thought I was fit for a PhD and he gave me the opportunity to join his team if I was able to pass a nationwide selection process, which I did.
At that time, I knew that I only had a small sample of what research really means, and today I’m delighted I went for the full experience. My research path has carved my soul so much that even when I receive extremely compelling external offers, their monetary value vanishes compared to the mental reward of staying anchored to technical research.
If there is such a thing, can you describe a typical day for you?
Among the many different working days, global v local meetings, technical v business discussions, travelling for scientific conferences or customer meetings, there are always three common elements that describe my personality and principles, which I feed into my typical day: creativity, people and mens sana in corpore sano (a healthy mind in a healthy body).
First, on the creativity side, be inspired to inspire others isn’t only a personal ambition, but it’s critical to continue to influence my teams and to impact the scientific community. My greatest inspirations raise from the technical discussions with my teams. Those moments are certainly the most rewarding for my research soul as they have the ability to recharge my batteries even in the toughest time.
Second, on the people and social aspect, let’s task a normal day in the lab. When I arrive in the lab in the morning, the first thing I do is run to my team, making that early morning human contact to feel the mood and establish that human connection. If I spot any tension, then I find a way to spend a few minutes alone with that person. Often with the excuse of an espresso coffee (as an Italian I can’t live without espresso), taking just that little emotional connection gives us an opportunity to alleviate possible tensions. I deeply care about my team members as people first.
Third, training the physique. I’m meticulous in my eating and physical training habits, but they are often disrupted by my frequent travels. Thus, I redefined my travel time routine to comply with my health needs.
I created the ‘city night walker’ persona. It is simply the habit of taking long walks after work before I turn off for the night. Those long walks have become an integral part of my travels now. They are much more than just walking; it has become my way to get inspiration from observing the beauty in the world, imagining the lives of those random people crossing my path, losing myself in random cities with random thoughts while I’m fulfilling the need for exercising.
What skills and tools do you use on a daily basis?
My researcher interests lie in one of many rapidly expanding scientific fields of these years. The discovery of new tools and solutions is part of the constant growth of the analytics and AI space. This highly dynamic environment is deeply inspiring and motivates the fast adoption of new techniques very often.
In general, the tools that are mostly part of my scope are twofold: on one side, the data processing infrastructures and data management solutions; and, on the other side, the more algorithmic aspect founded on statistical analysis, mathematical modelling and machine learning.
What applications do you foresee for this research?
In Bell Labs, we believe in augmented intelligence, which is founded on the principle of expanding human abilities instead of replacing humans with intelligent machines. The applications for augmented intelligence are ubiquitous.
For instance, it will allow us to ease the decision-making process, to accelerate our communications and to expand the creation of new concepts. By delegating the mundane tasks to intelligent machines, humans will gain time that we can spend on more fulfilling experiences.
Are there any common misconceptions about this area of research? How would you address them?
There are two diverging opinions on how AI is going to change our lives: the first sees this technology in its ability to enrich human capabilities, and the second anticipates how AI will substitute humans in more and more intelligent tasks.
The extreme incarnation of this second view aims to depict AI that would ultimately outsmart human intelligence and so, it will become a threat for humanity. Although the field of AI is making gigantic progress, the actual AI systems are still quite far from artificial general intelligence, where the intelligent machine would be much smarter than the smartest person alive.
When you first started work as a researcher, what were you most surprised to learn was important in the role?
One thing I experienced in the US that surprised me profoundly was the open collaborative approach across university departments and different teams. Possibly due to a cultural heritage in Italy, I had previously experienced a more controlled flow in how ideas were shared across teams.
In the US, I had the chance to be part of meetings and discussions with several experts from different fields, and I noticed a deep difference. The conversational flow was naturally unconstrained, the arguments constructive and the people able to debate conflicting points, with the genuine intent of finding the best solution with no prejudice.
I honestly felt that I was experiencing the optimal approach to create disruptive ideas. It also forms an empowering culture in which it becomes natural to enrich each other, with the common intent of feeling part of a bigger mission compared to what you could realise on your own.
What do you enjoy most about your career in research?
My specific role gives me the possibility to balance creativity and community spirit. The first feeds my technical and innovative side, and the second nourishes my inclination for sharing knowledge and inspiring young minds.