We are thrilled to welcome Elena Eneva as a new Research Director at RegLab. With over 20 years of expertise in artificial intelligence, machine learning, and data science, Elena brings a wealth of experience across industry, academia, and government. She is dedicated to harnessing the power of AI to solve social problems and has led work that includes developing AI tools to identify human trafficking from online ads, improve healthcare, and better inform disaster response. Through her work, Elena has led collaborations with government agencies and nonprofit organizations, ensuring that research not only advances the science of AI, but also makes a meaningful impact on people’s lives.

Elena is deeply committed to engaging in impactful research and to training the next generation of data scientists. Previously, she directed DataLab at the University of the South, where she led programs to train some of the top US undergraduate students in applied AI through projects with nonprofit and government partners. Elena also co-founded and directed the Data Science for Social Good program at the University of Chicago, which trains aspiring data scientists to work on real-world AI, machine-learning, and data science projects designed to have a positive social impact. Elena also has extensive industry experience and most recently served as the Senior Principal AI Researcher and Lead for US AI Research and Development at Accenture Technology Labs. Elena holds an M.S. in Knowledge Discovery and Data Mining from Carnegie Mellon University and a B.A. in Computer Science from the University of the South.

With her skills, experience, and commitment to using AI to drive positive social change, Elena is a perfect addition to our team and we are grateful that she has decided to bring her talents to RegLab. Please join us in welcoming Elena – we are excited to her lead the groundbreaking work that lies ahead!

How did you chart your path to RegLab? And what inspired you to join our team?

I fell in love with Computer Science accidentally. As a brand-new college freshman at Sewanee, I was convinced I’d be an economist – someone who would work to “fix” the economy and make life better for my fellow citizens. But in the spirit of being a well-rounded person, I figured I ought to take a CS class – if for no other reason than because, the way the world was going, it might come in handy to know a little something about computers. This was all back in the ancient days, of course: Google didn’t exist yet, we browsed the web with Netscape Navigator, Python was still an unknown toddler of a language, and I had just created my first email address – at Hotmail.

So I took Intro to Programming that freshman year, and I was amazed by the elegance, power, and beautiful logic I discovered in it (Turbo Pascal, I still think about you sometimes). It was love at first sight, and I couldn’t get enough. Alongside my Econ curriculum, I took another CS class, then another, and eventually took all of the ones my college offered. In the summers I did CS summer internships, too, and the summer before my senior year something very fortuitous happened: I participated in a Machine Learning project while I was at CMU. Everything I loved about Computer Science, I found even more of in Machine Learning. It gave me a sense of limitless possibilities, exciting riddles, and power on a whole new scale. It felt like unlocking a new dimension – one where data wasn’t just information, but a natural resource or an energy source that could be used for understanding and, dare I say, even re-shaping the world. It filled me with joy and optimism for the future.

Next year I returned to CMU, this time as a grad student in the School of Computer Science, in a department that would later rename itself the “Machine Learning Department” – the first such academic department in the world. I say all this not to note my age, but to put things in perspective: I began my path when Machine Learning wasn’t trendy, when we didn’t even talk about “doing AI,” when flip phones were just starting to be invented, and when you still had to bring paper maps on a car trip. And all that wasn’t that long ago – for me, it was basically yesterday. AI has been around since the ’50s (in a way, it’s about my parents’ age), but as a field, it’s still so, so young.  

After grad school, I carried that joy and optimism with me as I started working first on applied Data Science/ML/AI and then on ML/AI research. I loved the idea of making the world run better through data-driven approaches. Want to fight fraud? Prevent a customer from quitting? Find abnormal network behavior? Yes, I could do that with the tools I could build! What a rush it was (and still is) to manage to make order out of chaos and to find meaning in an apparent mess. Along the way, I started specializing in certain domains, like health and IoT. More and more datasets became “big” data, and our compute got cheaper, more powerful, and more distributed. Machine Learning and AI were seeing breakthrough after breakthrough, the loop of data-compute-algorithms feeding on itself.

With no shortage of projects and domains to work on, I realized that what compelled me most were meaningful projects – ones that could make the world better, like those in health, education, and economic development. (Life is short, and I thought we already had enough people working on smarter sponsored search ads and faster loading of social media photos.) I wanted to focus on something different, and then we managed to do a pretty cool thing: together with some friends, we started the very obviously named “Data Science for Social Good” program at the University of Chicago. We designed it as a multidisciplinary fellowship program where aspiring data scientists work on real-world projects with social impact, in collaboration with nonprofit organizations and government agencies in the US and abroad. Want to improve traffic safety through video analytics in Jakarta? Reduce harassment of tenants in New York City? Improve proactive diabetes screening in Chicago? Yes, we could do that for you! DSSG grew into multiple programs at universities in the US and internationally, training not only our students but also our partners on how to plan for and run these “for good” Data Science/ML projects. Later, we also started a similar program called DataLab at my undergrad university, where I never did end up majoring in Economics (just minoring in it, along with math, and of course majoring in CS). Economics was never my love – CS was, ML was, social impact was.

So this is how my path led to RegLab. I wanted to do more of what I loved and, more importantly, what I believed in, and do it on a larger scale, with an interdisciplinary team of extremely bright and energetic people, partnering with government agencies at all levels. In terms of having a positive social impact, it’s hard to beat improving regulatory processes and public services. Modernizing the system for tax collection? Improving the effectiveness of public health enforcement? Using AI to protect the nation’s waterways from major polluters? Building a new model for technology to promote justice at scale? Yes, that’s what we do!

But there’s also another thing that’s very important about RegLab: remember how young the field of AI is? Its potential is undeniable, but so is the risk of getting it wrong. It’s still in its wild, wild west stage – rapid progress, incredible breakthroughs, but also deep uncertainty about the rules of the road and the pitfalls. How do we make sure AI is used ethically and equitably? How do we regulate it? How do we prevent harm? RegLab also thinks deeply about this and works on questions of AI policy and algorithmic fairness, among others.

All of this continues to sustain my joy and optimism about my work and about the future. I’m very happy to be here, and I’m looking forward to making great progress together. The stakes are high, but that’s what makes the work necessary and meaningful, and I wouldn’t want to be anywhere else. After all, I still have to make up for never “fixing” the economy – so making AI work for effective governance seems like a fair trade.