Data Science

Dan Conroy-Beam is an Associate Professor in the Department of Psychological and Brain Sciences at the University of California, Santa Barbara. He joined UCSB after receiving his PhD from the University of Texas at Austin in 2016. Dan's work combines computer simulations with behavioral data to understand how our psychologies create and navigate complex social systems. He primarily works in the area of romantic relationships (e.g., how do people choose their romantic partners? How do they navigate their relationships once initiated?), but he has also worked on topics spanning from friendship, to religion, to racial discrimination. Dan is also an enthusiastic teacher and mentor and loves empowering diverse students with the knowledge and skills to think computationally about psychology and human sociality.

Rick Zheng is currently a Statistics & Data Science major entering his fourth year. His work is driven by his passion for generating data visualizations and data-driven problem-solving. During the Summer of 2022, he was selected to be an Edison Scholar to do his research on using Natural Language Processing methods for identifying major subjects within engineering curricula. The visualized results are being cross-functionally used to analyze user preferences to generate recommendations for designing engineering courses. He is also in the process of leveraging social media text data to develop a predictive model that identifies hate messages.

Dr. Ebenezer Larnyo is a Postdoctoral Scholar at the Center for Black Studies Research at the University of California, Santa Barbara. He holds a Ph.D. in Management Science and Engineering from Jiangsu University and an MSc in Computer Science and Technology from Jiangsu University of Science and Technology, both in Zhenjiang, China. Before joining CBSR at UCSB, he was a Postdoctoral Research Fellow at the Department of Health Policy and Management at Jiangsu University, Zhenjiang, China.
 

His research broadly focuses on leveraging healthcare technologies and applied data science and analytics to improve quality of life, access to personalized healthcare, and reduce the burden of chronic diseases among the aged, people with cognitive impairment, and other socioeconomically disadvantaged groups in minority communities and Sub-Saharan Africa.
 

Some of his recent works borders on modeling the complex relationship between the actual use behavior of healthcare wearable devices and the quality of life of people with dementia and using nationally representative population-based data across low- and middle-income countries to predict how the disparities in socioeconomic status (SES) and other risk factors such as lifestyle and chronic diseases affect the cognitive functioning of adults in those countries.

Joe Incandela’s research has primarily focused on using high energy particle colliders to produce and study elementary particles that do not appear in our day-to-day life but determine the key characteristics of our universe such as why matter, but virtually no antimatter is found in nature. Joe's group is currently involved in the CMS experiment at the CERN Large Hadron Collider and the LDMX dark matter experiment to be operated at the Stanford LCLS-II Accelerator Complex. For both experiments he is developing high granularity silicon-tungsten devices that will greatly enhance accessible information to make possible searches for new particles and the ability to carry out precision measurements of their properties. Joe is an enthusiastic mentor of underrepresented minorities and first generation students in laboratory studies and data analysis in particle physics and particle detectors with his group.

My research uses advanced data science techniques to understand ecohydrology in a rapidly changing world. I am interested in how water availability is changing for both people and for plants – and how this impacts things we care about: fires, floods, droughts, ecosystem health, groundwater and rivers in the landscapes that we live in. Much of my research involves designing advanced simulation models that integrate data from multiple sources including field and lab experiments and data from remote sensing technologies. These models are ‘virtual laboratories’ that we can use to explore ‘what-if’ scenarios with best available science. In these labs, we can ask ‘what will happen to water supply from snow-dominated mountain watersheds as climate warms”, “how do different vegetation types in green infrastructure effect water and nitrogen cycles”, “how do fuel treatments influence fire severity”. And a wide range of other questions related to environmental change. A recent focus in my group has been on linking earth system models with interactive visualization tools to facilitate science transfer, learning and communication.

Michael Beyeler is an Assistant Professor in the Departments of Computer Science and Psychological & Brain Sciences. He directs the Bionic Vision Lab, an interdisciplinary group of researchers interested in the computational modeling of human, animal, computer, and prosthetic vision to elucidate the science behind bionic technologies that may one day restore useful vision to people living with incurable blindness. His group combines expertise in computer science/engineering, neuroscience, and psychology. Research projects in the lab range from predicting neurophysiological data with deep learning to building biophysical models of electrical brain stimulation, and from studying perception in people with visual impairment to developing prototypes of novel visual accessibility aids using virtual and augmented reality.