You’ve been involved in so many projects at UCSD and SDSC over the past 23 years, from the WIFIRE Lab and CICORE to WoRDS and the Halıcıoğlu Data Science Institute, not to mention your MOOCs and Coursera work. How do you balance it all?

There is a lot indeed, but all these activities are synergistic of each other, so it becomes easier to balance. It takes a lot of time management and a very strong supportive interdisciplinary team. I am very grateful for my teams’ hard work every day.

Data science is expanding its profile and becoming a component of other disciplines in academia. How do you see that changing the discipline of data science itself, and should one or more aspects of it remain the same?

Data science is evolving as it integrates into various disciplines, driving evolution across all areas of work and inspiring new methods and careers. Maintaining a balance between the new applications of data science and its foundational elements is key to its continued success and trustworthiness. Interdisciplinary problem-solving approaches and ethical standards are significant aspects of data science, and they will continue to evolve along with new methods for data and knowledge management, machine learning and AI, and systems and dev ops. Overall, foundational skills including statistics, machine learning, and data engineering will always remain highly relevant and necessary. We are now seeing more integration of valuable insights with knowledge networks and generative AI in specialized areas of science, business, and society. I sincerely hope we will also see a major change in who has access to data science education and the enabling infrastructure so that everyone can participate in using data science to create impactful solutions to societal-scale problems.

Big data seems to mean different things to different people? What does the phrase mean to you in 2024?

The phrase “big data” continues to evolve, but at its core, it refers to often distributed data sets that are untraditionally large in volume and/or streaming capacity in ways that challenge structured data systems. The Vs of big data, the most prominent ones being volume, variety, velocity and veracity represent the challenges related to size, complexity, speed and integrity of big data. We now have mature solutions to these challenges which become the foundation of evolving data science and AI solutions. Ultimately, the significance of big data is how these solutions are used to generate value in form of actionable insights, explainable findings, and interpretable results. Therefore, big data in 2024 involves not just the ability to handle the challenges of data but also the technology, techniques, and strategies that are used to gain insights and drive decisions, and their ethical applicability across sectors like healthcare, government, education, and beyond.

Outside of the professional sphere, what can you share about yourself that your colleagues might be surprised to learn – any unique hobbies or stories?

I have school age kids with active schedules who I enjoy spending time with. To be honest, work and family does not leave a lot of time for regular hobbies, but I enjoy the live music and theater in San Diego whenever I get the opportunity.