- Computational Health Informatics
- Applying advanced AI techniques for Precision Medicine
- Genomic Clinical Decision Support (CDS)
- Clinical Prediction Modeling
- Electronic Health Record (EHR) systems and Health Information Exchange (HIE)
- Meaningful Use, Care Coordination, and Population Health Management
- mHealth, Quantified Self, and Personal Analytics
- HL7 and IHE Health IT interoperability standards: HL7 vMR, CCDA, and FHIR.
- Machine Learning including: Bayesian Reasoning, Deep Learning, and Reinforcement Learning
- Semantic Web and Ontology Modeling: RDF, OWL2, SKOS, SPARQL, and Linked Open Data (LOD)
- Neural-Symbolic integration
- Real-time analytics with Apache Spark
- Business Rule Management Systems (BRMS) and Business Process Management (BPM)
- Natural Language Processing (NLP)
- Enterprise Architecture (TOGAF)
- Functional Domain Driven Design (DDD), Microservices, CQRS, and Event Sourcing (ES)
- Lambda Architecture: Batch and Streaming
- Functional Programming and Reactive Architecture
- Cluster and Cloud Computing
- Single Page Application (SPA) and Hybrid Mobile architectures
We use Agile and Lean principles. We program in Java, Scala, Python, R (statistical computing), XSLT3, XQuery3, and Typescript using proven practices like Test Driven Development (TDD), Continuous Delivery (CD), and Containerization. We use modern frameworks like Angular 2, JHipster, Akka, Play, and Spark.
Research and Business Analysis
Examples of completed projects include: Health Informatics and Deep Learning research, software requirements analysis, competitive analysis, and Design Ethnography. We conduct domain, business rules, and business process modeling using the Unified Modeling Language (UML), the Business Process Modeling Notation (BMPN), and the Decision Modeling Notation (DMN).