Eliassi-Rad, Tina

Assistant Professor
Areas of Interest: 
Data Mining and Knowledge Discovery, Machine Learning, Artificial Intelligence, Network Science, Cyber Security, Information Retrieval and Extraction, Computational Social Science, Computational Humanities
My Story: 
At heart, I am a problem solver. I have always been interested in solving problems, particularly in mathematics, statistics, and engineering. As a child, I would browse through IEEE Computer Magazines – to which my father (an electrical engineer) subscribed – and marvel at the problems. I also learned early on from my mom that solving a problem was not enough. One must also present it correctly – in terms of understandability and aesthetics. In high school, I took a computer-programming course and was hooked! Solving problems using a computer was the right mix of mathematics and engineering for me. 

I went to the University of Wisconsin-Madison for my undergraduate education and studied computer science. I had some excellent professors. In particular, I completed a senior thesis under the supervision of Professor Anne Condon, who was an excellent mentor and got me to think about systematic investigation of harder problems like the graph-coloring problem. I continued my education at University of Illinois at Champaign-Urbana (UIUC) and worked on knowledge-based software engineering for my masters’ thesis. While at UIUC, I got married to Branden Fitelson, then a philosophy graduate student at University of Wisconsin. This prompted me to move back to the University of Wisconsin for my doctoral education. I took a class in machine learning – a branch of artificial intelligence. I found the discipline fascinating! Imagine building a computer system that improves over time through its experiences. For my dissertation, I built a computer system composed of two intelligent agents that crawled the World Wide Web and learned the kinds of pages and hyperlinks that their human master liked. It was one of the early systems that conducted personalized search.

While working late one night writing up my dissertation, I received an email inviting graduate students to attend an information session for Lawrence Livermore National Laboratory (LLNL). Any student who showed up would get free pizza and soda. Naturally, I went! At the session, I found out that LLNL – located in the San Francisco Bay Area – is a Department of Energy National Laboratory with a long history of multidisciplinary scientific research. When I was offered a staff scientist position at LLNL, I accepted it and spent nine great years there – working on machine learning and data mining algorithms for large-scale scientific simulation data, complex networks, and cyber situational awareness. LLNL’s open research environment allowed me to build strong academic collaborations and mentor great students.

In 2008, a colleague mentioned that I should consider moving to academia. It sounded like a great opportunity, with more intellectual freedom. So, I decided to make the switch. However, I had a two-body problem – at that time my husband was a tenured professor at University of California, Berkeley – and the Great Recession had just started. But, I never back down from a challenge. We went on the academic job market, received a couple of great joint offers, and decided to accept the ones from Rutgers University.

My current research is about understanding behavior in complex networks through data mining, machine learning, and artificial intelligence. I collaborate with a diverse set of people from humanities professors, to information technology experts in industry, to scientists at government labs. The mix of problems that we solve vary from making algorithms scale-up to effectively operate on big data, to improving the predictive performance of algorithms, to generating new models that capture behavior in complex interactions.

The keys to success for me in computer science have been the following. I have had a supportive group of family and friends and an enlightened husband. I have had outstanding mentors at every stage of my career. Mentors are great at helping us not sweat the small stuff and giving us perspective. I have had many excellent interdisciplinary collaborators, who bring fresh viewpoints to problems and inspire new solutions. As a professor, I know feel that I am well situated to “pay it forward”.

Transcribed from an interview and edited by Lauren Miller