The research interest of Dr. Sima is the application of pattern recognition and statistical techniques in biological data analysis. The well-developed methodologies in existing literature on classification, feature selection and error estimation can be misleading due to the uniqueness of the genomic data, which is often characterized by limited sample size and large feature dimension. Establishing these potential misuses and adapting them to applications like cancer classification, biomarker discovery and high throughput screening target identification have helped tremendously in propelling us to new discoveries.
Another major area of interests of Dr. Sima is on eliciting novel biological knowledge through mathematical and engineering approach using various genomic data. His primarily focuses on characterizing drug responses. By profiling drugs with responses of key cell components over time, much insight can be gained toward understanding their roles in cell dynamics under stress and cell’s key decision-making in the direction of apoptosis or survival. This platform also provides the opportunity for new drug functional identification, as well as an overview of effects of different drugs for particular cancel cells, therefore paving the way toward personalized medicine.
Dr. Sima received his Ph.D. degree from the department of Electrical and Computer Engineering at Texas A&M University, College Station.