My research focuses on the overarching areas of data science and knowledge extraction from complex and large-scale datasets. In a world with ever increasing data generated both by humans and machines alike, compute systems are undergoing a fundamental shift towards data driven/intensive model of operation. This creates an unprecedented need for novel approaches in algorithms, computation, and theory with the goal of inferring critical and insightful patterns from such data. My research aims to meet this need by developing computational techniques and solving challenges originating from inter and cross disciplinary fields. Specifically, the primary goal of my research is to discover dynamic patterns (temporal or event based) in sequential data using machine learning/data mining algorithms and novel data structures. My research spans two main algorithmic areas: 1) explainable machine learning for understanding human interactions & complex processes, and 2) deep learning architectures for written language and multimodal data. A key component of my research is geared towards data science for social good, where the algorithms in the above areas are applicable to a wide range of domains such as health informatics, geo-spatial intelligence, social sciences, psychology, etc.