Leap Year Workshop: note

Leap Year Workshop: the research opportunity.

@Department Biomedical Informatics, University of Pittsburgh

The order, title and note for the presentations. 

• Balaji Palanisamy: Protecting Time-varying Privacy with Self-emerging Data
• Kayhan Batmanghelich: An Exciting New Horizon: Medical Image Computing Meets EHR
• Daniel Mosse: OCCAM: define, run, curate, visualize experiments for your group, your class, your organization and/or the world
• Store and archive all the research data in one place
• Daqing He: Intelligent Access and Deep Representation for Medical Tasks
• Deep learning with NLP
• Rami Melhem: Distributed Graph Analytics

• big graph analysis platform

• Hochheiser, Harry: Interactive graphical tools for robust and  reproducible data interpretation

• detect and avoid bias using visualization interface
• What is the bias in the medical environment?

• Michael Becich: Towards a Pitt Data Commons

• Potential Pitt funding and grants.
• big data mail list.

• Don Taylor: How to factor industry into academic commercial translation
• Supporting from the university leadership
• UPMC is one of the commercialize example.

• Peter Brusilovsky:  Data Driven Education
• Using the proposed system in Pitt campus?
• Madhavi  Ganapathairaju: Computational and collective intelligence for translating protein interaction predictions
• Identify the highest-impact protein interaction.
• Using visualization techniques.
• Greg Cooper: technology and workforce
• Computerization and employment
• How to keep people to adopt the computerization environment?
• Richard Boyce: Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.
• A control panel to integrate the medical record and research publications.
• Idea: to put all the reading material in Google Drive and with the reading note. (Try blogger maybe?)
• Dmitriy Babichenko: Designing the Model Patient: Data-Driven Virtual Patients in Health Sciences Education”
• How to model the case? What is the effort?
• Xinghua Lu: From big data to bed side: A machine learning approach
• Personalized medical treatment.
• Cancer pathway detection
• Yu-Ru Lin: Mining Insights from Disasters Using Social Sensors
• Computational focus groups.  
• David Boone: Pipeline into computational research: educational outreach internships
• Internship for high school and undergraduate students.
• Any interested students? To be a mentor?
• UPCI academy
• Milos Haushrecht: Real-time EHR data analysis monitoring and alerting.
• Many data type presentation
• From the data that is suitable for machine learning
• Bedside medical machine learning
• Liz Lyon: Research transparency: don't just talk the talk, walk the walk
• Put transparency into the research cycle.
• Songjin Liu: Efficient exact algorithms and high-performance computing for Bioinformatics
• The NP-hard problem in biology systems and researchers.
• The approximation algorithm for NP-hard problems.