The Data-Intensive Parallel Algorithms for AI (DIPAAI) group at UH focuses on developing parallel, scalable, algorithms, minimizing I/O, to analyze data sets for machine learning (e.g. neural networks including deep learning, transformers, clustering,classification, regression, dimensionality reduction, variable/feature selection, time series), and graph analytics (connectivity, paths, cliques, vertex neighborhood, summarization). Our goal is to develop novel algorithms working all the way from reading the data set from secondary storage, processing it in main memory exploiting modern CPUs with a small RAM footprint, without reaching computer capacity. On the other hand, we develop algorithms and data structures that can exchange and read data in diverse formats, to analyze them with flexibility and high speed (e.g. streams). In the past our research focused on parallel DBMSs (parallel SQL) and ''Big Data'' Hadoop system (MapReduce, Spark). Today we have shifted to how theory can help AI and data science. Currently, most of our cofe development uses Python, Pascal, C++ and SQL.