The Speakers

Dr. İlkay Altıntaş, a research scientist at the University of California San Diego, is the Chief Data Science Officer of the San Diego Supercomputer Center as well as a Founding Fellow of the Halıcıoğlu Data Science Institute. She is the Founding Director of the Workflows for Data Science (WorDS) Center of Excellence and the WIFIRE Lab. The WoRDS Center specializes in the development of methods, cyberinfrastructure, and workflows for computational data science and its translation to practical applications. The WIFIRE Lab is focused on artificial intelligence methods for an all-hazards knowledge cyberinfrastructure, becoming a management layer from the data collection to modeling efforts, and has achieved significant success in helping to manage wildfires. Since joining SDSC in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible. Her work has been applied to many scientific and societal domains including bioinformatics, geoinformatics, high-energy physics, multi-scale biomedical science, smart cities, and smart manufacturing. She is also a popular MOOC instructor in the field of “big” data science and reached out to more than a million learners across any populated continent. Among the awards she has received are the 2015 IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers and the 2017 ACM SIGHPC Emerging Woman Leader in Technical Computing Award. Ilkay received a Ph.D. degree from the University of Amsterdam in the Netherlands. 

Mercy Asiedu is a research scientist in Responsible AI, at Google Research. Before that she was a Schmidt Science Postdoctoral Research Fellow at MIT working on interdisciplinary research projects using machine learning methods to improve mobile ultrasound imaging, and predict breast cancer incidence from mammograms. She also worked on projects researching the use of natural language processing models to improve comprehension of health notes for breast oncology patients.

She received her PhD in Biomedical Engineering and a certificate in Global health from Duke University, supervised by Prof. Nimmi Ramanujam. Her dissertation focused on the research and development of a low-cost imaging device and machine learning algorithms to reduce barriers to cervical cancer screening. She has won several awards for her work including the Inaugural Patrick J. McGovern Tech for Humanity Changemaker Awards, the Lemelson-MIT Graduate Student Inventor Award, and Velji Emerging Leader in Global Health award.

Additionally, she is a co-founder of the Calla Health Foundation and GAPHealth Technologies. She received her bachelor’s degree in Biomedical Engineering from the University of Rochester, and high school degree from Holy Child Secondary School, Cape Coast, Ghana.

Professor Danks conducts research at the intersection of machine learning, philosophy, and cognitive science. He examines the ethical, psychological, and policy issues around AI and robotics across a range of sectors. He has also developed multiple novel causal discovery algorithms for complex types of observational and experimental data, and has done significant research in computational cognitive science. Danks received an A.B. in Philosophy from Princeton University, and a Ph.D. in Philosophy from the University of California, San Diego. He is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. 

Professor Gupta’s research interests span topics in embedded and cyberphysical systems with a focus on energy efficiency from algorithms, devices to systems that scale from IC chips, and data centers to built environments such as commercial buildings. Gupta received a Bachelor of Technology in electrical engineering from IIT Kanpur, India; a Master of Science in EECS from University of California, Berkeley; and a Ph.D. in electrical engineering from Stanford University, US. Gupta is a Fellow of the IEEE, the ACM, and the American Association for the Advancement of Science. 

Tauhidur Rahman is an Assistant Professor in HDSI at UCSD where he directs the Mobile Sensing and Ubiquitous Computing Laboratory (MOSAIC Lab). His current research focuses on building novel ubiquitous and mobile health sensing technologies that capture observable low-level physical signals in the form of an acoustic and electromagnetic wave from our bodies and surrounding environments and map them to relevant biological and behavioral measurements. Some of his notable accomplishments include a Google Ph.D. fellowship in 2016 in mobile computing, Outstanding Teaching Award 2015 from Cornell University, and a distinguished paper award from ACM IMWUT in 2021. Tauhidur received his B.S. from the Bangladesh University of Engineering and Technology, his M.S. from UT Dallas and Ph.D. in Information Science from Cornell University.

Sugihara is a theoretical ecologist who has performed foundational work in the data analysis of complex systems from fisheries to medicine to finance. He gained renown for developing, with Lord Robert May, methods for forecasting chaotic systems, providing the first example of chaos in nature with the diatom populations at Scripps Pier. He has worked with the major institutions on questions of systemic risk and on detecting early warning signs of critical transitions, including the Federal Reserve Bank of New York and The Bank of England. He also worked with fisheries to develop a currently enacted marketbased incentive plan for reducing wasteful bycatch and to improve forecasting of wild fish stocks. His current interest in neurobiology and genomics includes collaborations with the Salk Institute for Biological Studies to apply EDM to neurobiology and to problems in gene expression in cancer 

Dr. Rose Yu is an Assistant Professor at the UC San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UC San Diego. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.