Dr. Fofrich’s research centers on understanding the causes and solutions to anthropogenic global environmental change. He is particularly interested in climate change attribution, helping vulnerable communities from existing and projected environmental changes, and finding ways to mitigate anthropogenic influences on the Earth system. At UCLA, his work analyzes the trade-offs associated with biogenic climate change mitigation pathways. While climate change is one of our generation’s most pressing environmental issues, many climate pathways that successfully avoid 2oC rely upon diverting large areas of land from native ecosystems and agricultural production to climate change remediation. This is of particular concern in the tropical regions, given the potential for biodiversity and carbon storage loss in addition to the disruption of regional food systems. Additionally, due to the biophysical constraints governing plant growth, numerous biogenic climate solutions cannot thrive under specific environmental conditions, particularly in regions where they exist at the edge of their climatological niche. As a result, the viability of the tropics for biogenic energy production is expected to decline under future warming scenarios and thus warrants further investigation.

About Dr. Fofrich

Dr. Robert Fofrich was born in East Los Angeles and is an alumnus of West Los Angeles College. Currently, he is a UC President’s Postdoctoral Fellow at the Institute of the Environment and Sustainability and works under the supervision of Dr. Elsa Ordway and Dr. Thomas Smith within the Department of Ecology and Evolutionary Biology. Before joining UCLA, Dr. Fofrich joined the Climate Impact lab and was briefly a postdoctoral scholar in the Department of Earth and Planetary Sciences at Rutgers University. Dr. Fofrich received his Ph.D. in 2022 from the Department of Earth System Science at the University of California, Irvine, where his research focused on energy and agricultural systems as they relate to climate change mitigation and adaptation. He has also served as a researcher at NASA-JPL and the Center for Environmental Biology in Orange County, California. His passion for the natural environment and a profound commitment to underserved communities steered his decision to study ways to mitigate anthropogenic environmental damages and protect vulnerable populations from these changes.

Dr. Fei-Man Hsu’s research is primarily focused on computational genomics and epigenomics related to different biological or medical issues such as viral impact to the host epigenomes, transcriptomic alterations in different tissues regarding physiological training, and the change in the patients’ epigenomes that have been through organ transplant.  Dr. Hsu is also developing alignment-free bioinformatic tools to discriminate DNA which could be applied to cancer detection or other disease screening.

About Dr. Hsu

Dr. Hsu is a postdoctoral fellow at the Pellegrini lab in the Department of Molecular, Cell, and Developmental Biology at UCLA. With a Ph.D. from the University of Tokyo, Dr. Hsu specializes in bioinformatics. She holds a M.S. degree in Molecular and Cellular Biology, and a B.S. degree in Life Science from National Taiwan University.  

Dr. Alec Linot studies the modeling, control, and stability of chaotic dynamical systems. Chaotic dynamical systems are deterministic systems where small perturbations to the system result in dramatically different dynamics over time. Two common examples include weather forecasting and turbulence. Understanding how to model (or predict) these systems is of vital importance, for example, simply knowing when extreme weather events will occur gives people time to prepare for said events. Controlling turbulent flows also presents a major opportunity to reduce the world’s energy consumption by mitigating turbulent drag. It is important that we study the stability of these controlled systems to understand if the control can be maintained.

The major challenge associated with the modeling, control, and stability of these systems is the massive computational cost required for these large-scale systems. As such, his research focuses on reducing this computational cost by creating accurate low-dimensional models, by using fast reinforcement learning tools, and by developing analytical solutions for evaluating stability. 

About Dr. Linot

Dr. Alec Linot works as a postdoctoral researcher with Professor Kunihiko (Sam) Taira in the Mechanical and Aerospace Engineering Department at UCLA. Prior to working with Prof. Taira, he received a BS in Chemical Engineering from Kansas State University, and a Ph.D in Chemical and Biological Engineering from the University of Wisconsin – Madison. In his Ph.D he developed machine learning techniques for modeling and controlling turbulent flows.

Dr. Luo aims to develop effective algorithms based on statistics and machine learning theories to tackle the problems in bioinformatics, data mining and dynamical systems. With the advances of sensor and network technologies, the exploration and analysis of massive interacting dynamic systems have become essential. In this project, he and his mentor Prof. Yizhou Sun, aim to develop an efficient and effective graph ODE framework, which can model and simulate the underlying dynamics from abundant temporal and structured data for various real-world tasks. He hopes that the proposed GraphODE framework offers valuable insights into the mechanics of physical systems, enhancing the understanding of complex phenomena such as ocean circulation and climate variability. 

About Dr. Luo

Dr. Luo is a postdoctoral researcher at the Department of Computer Science of UCLA. Previously, he received a B.S. degree in Mathematics from Nanjing University, Nanjing, China, in 2017 and a Ph.D. degree in the School of Mathematical Sciences from Peking University, Beijing, China in 2022. His research interests include machine learning on graphs, dynamical systems, statistical models and AI for Science.

Dr. Suh has been focusing on the applications of generative models in tabular data modalities. Specifically, he is working on designing a fake tabular data synthesizer based on a diffusion model. However, it is a challenging task to build such synthesizers as the diffusion model is designed for continuous variables, whereas tabular data often contain heterogenous features. He aims to circumvent this problem by incorporating the idea of auto-encoder, projecting the original tabular data into continuous latent space. Moreover, he is currently working on extending the idea so that the model also works for generating tabular data with temporal dependencies between rows.

About Dr. Suh

Dr. Suh is a Postdoctoral Scholar working with Dr. Guang Cheng in the Department of Statistics and Data science in UCLA. He obtained his Machine Learning Ph.D. degree at Stewart School of Industrial & Systems Engineering, Georgia Tech in Dec. 2022, and earned a M.Sc. degree in Statistics at Georgia Tech in 2018. Prior to Georgia Tech, he received a B.Sc. degree from Korea University in 2015, majoring in Industrial Engineering. 
For more information about Namjoon visit his website: Namjoon Suh (google.com)