Read my story
Majid Farhadloo holds a Ph.D. in Computer Science from the University of Minnesota, Twin Cities, where he conducted his
doctoral research under the supervision of Prof. Shashi Shekhar. His work broadly spans the field of data science, with
a particular emphasis on developing innovative spatial data science techniques to improve clinical outcomes and support decision-making in healthcare.
Spatial data introduces unique challenges for AI and data science due to spatial autocorrelation, where nearby entities and their arrangements
influence predictions, and spatial heterogeneity, where properties vary across locations. These factors violate the IID assumption of
many machine learning models and require methods that capture spatial relationships, handle limited labeled data, and provide
spatially explainable outputs in high-stakes domains such as healthcare.
His recent work focuses on addressing these challenges by developing novel spatially lucid classification methods that leverage spatial
concepts, patterns, and relationships to uncover meaningful structures. These methods are applied to the analysis of spatial omics data
(e.g., cellular maps) to design data-driven hypotheses for creating novel immunotherapies. His broader interests also include integrating
spatial intelligence into biomedical decision support systems to inform precision medicine strategies and public health interventions.
Full Resume: 2 pages, 2025: (pdf)
