I am a data science, AI and ML researcher, specializing in a number of applied areas:

• Empirical Software Engineering:
  
  In our DECAL research group we develop theory and methods (based on statistical models and data science) to aid in the understanding of and improving the software engineering processes in complex, distributed and collaborative socio-technical environments (mostly OSS projects). We are finding that the social component is necessary for understanding the technical contributions and vice-versa. UC Davis is ranked highly in USA in Software Engineering by csrankings.org.


• AI in Health:
  
  I also direct the AI in Health Lab, which was started in 2022. There we conduct research on problems in medicine that can benefit from AI/ML approaches including Gen AI. We collaborate closely with medical researchers and doctors from UC Davis Health on the topics of modeling recurrence of irregular heart rythms following treatment, identifying abnormalities in radiological images and video, and using large language models in document analysis. Our lab's expertise is multimodal AI and synthetic data.


• Systems Biology and Gene Networks in Plants:
  
  In the past, with plant biologists from the US Forest Service and UC Davis we studied interesting properties of plants and mutants and mapped them to the underlying biochemistry linking their genes among each other. In particular, we conducted RNA-seq, ChIP-seq, and other experiments, and then integrated the large-scale genomic data sets coming out of them into biological models of plant growth.


• Applied Network Theory:
  
  I have done more theoretical work on understanding the topological structure and characterizing complex networks in various domains, including the modeling of biological, software and social networks.
  


• Data Mining And Algorithms:
  
  In the past I worked on consensus clustering algorithms and biological data integration and analysis methods.