Mining, Indexing and Visualizing Big Data in Clinical Decision Support Systems

(September 2017 to August 2022)

Today, almost all human activities generate and/or demand to store and process massive, diverse and complex data, either in scientific, academic, enterprise and even leisure pursuits – a scenario that has being called the “big data era”. Health-related and human activities are in the core of those activities, as they both produce big data and can take advantage of the technological storm, improving the behavior of everyone whose decision is increasingly guided by the information extracted from all these big data. In a clinical environment, for example, the Electronic Health Records (EHR) is the anchor to develop information extraction strategies. In this proposal, we aim at integrating novel, scalable database support, image processing, graph-based analysis, and visual analytics methods to leverage large amounts of EHR and repositories of clinical data to gather valuable and significant information for decision-making. The size and complexity large-scale databases offer great challenges when they need to be processed in terms both of applying analysis techniques and to support the development of subsequent applications for practical tools. However, it also embodies a cornucopia of opportunities to create algorithms and methods able to display smart and relevant information related to either a particular city or institution, coping strategic government decisions with the demands and benefits of big data. In this project we will develop methods and algorithms that will ultimately be materialized in a modular platform to be made available to the area community.

Quick Announcements

Post-doctoral Fellowships

There are postdoctoral fellowships available inside the thematic project "Mining, Indexing and Visualizing Big Data in Clinical Decision Support Systems - (MIVisBD)", funded by São Paulo Research Foundation – FAPESP.
We are looking for candidates with innovative ideas to understand and ride the path between computer science and medical systems in the project scenario, willing to work in the threshold between both fields. The candidates must have a recent PhD degree in Computer Science, Computer Engineering, Computer Physics, or related areas.
This opportunity is open to candidates of any nationalities. The selected candidate will receive a FAPESP’s Post-Doctoral fellowship in the amount of R$ 7,174.80 monthly and a research contingency fund, equivalent to 15% of the annual value of the fellowship, which should be spent in items directly related to the research activity.
The research will be developed at the Laboratory of Databases and Images (LaBDI) with the Computer Science Department – Institute of Mathematics and Computer Science, University of Sao Paulo at Sao Carlos, Brazil. LaBDI is well funded and aim at developing cutting edge research and experimental strategies that result in publications of high impact articles. The laboratory has an excellent infrastructure and a multidisciplinary team to collaborate in the study ( More information about the research project is at

Essential Duties and Responsibilities:

  • Development of methods and algorithms for data management regarding complex data and similarity queries;
  • Development of innovative algorithms for dealing with large volume of medical images and video data;
  • Design of new feature extraction algorithms for semantic development of content-based image/video retrieval;
  • Design of novel algorithms of machine learning to specific medical applications;

Required Knowledge, Skills, and Abilities:
  • Ph.D in computer science, computer engineering, computational math, bioinformatics or related discipline is required;
  • Strong programming skills are essential;
  • Proven ability for disseminating research results by writing manuscripts and giving academic presentations;
  • Candidates must be willing to travel in order to disseminate results and communicate with other scientists;
  • Must be able to work closely and communicate effectively with research colleagues;
  • Fluency in English, both written and spoken.

Preferred Knowledge, Skills, and Abilities:
  • Ability to interact effectively with cross-discipline scientists and technical staff;
  • A good understanding/experience of or enthusiastic attitude to image processing, visualization, databases and machine learning in the context of medical applications.

The fellowship is for 24 months and can be renewed. The candidates must send a message to Professor Agma J. M. Traina ( with the subject “Seleção para bolsa Pos-Doc: Projeto MiVisBD”. Please send your application stating the CV, two letters of recommendations and a short statement of research interest in pdf format.