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Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges of mHealth Academics

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Page 1: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

Rajesh K. Gupta

Department of Computer Science and EngineeringUniversity of California, San Diego

mHeath Summit, NIH 2014

A Perspective on Challenges of mHealth Academics

Page 2: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

A Story

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1947

1971 1981An archetype of a discipline, not the only one. Embedded. CPS.Quantum Computing, Molecular Computing, Cybernetics…An archetype of a discipline, not the only one. Embedded. CPS.Quantum Computing, Molecular Computing, Cybernetics…

Page 3: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

That brings us to today

1. Effect of emerging knowledge on academic institutions– Slow moving institutions…and not just a corporation

of ideas

2. Growing intersection with life sciences– A challenge of millennials era: new fangled

knowledge domains, emergent MD/PhDs.– A whole host of new areas: cBIO, qBIO, mHealth,

Systems <Biology, Medicine>

3. Whence mHealth?

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Page 4: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

CS @ Physical & Life Sciences

• Computing and Data are truly at the intersection of physical and life sciences– From knowns of physics to unknowns of life

• Semi without Maxwell’s equations, Fluid dynamics w/o Navier-Stokes or Chemistry w/o periodic table…

– Known methods of CS fail us– Emergent domain of “Data Sciences”

• Our physical realities find expression and reproduction in virtual through data– Data quality seen very differently.

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Page 5: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

Near Home: Computational to Quantitative Biology

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Programs are run by people in specific departments where they are evaluated for their success or not by their departmental colleagues.

Programs are run by people in specific departments where they are evaluated for their success or not by their departmental colleagues.

In the meantime:Neurosciences at

UCSD have thrived.

In the meantime:Neurosciences at

UCSD have thrived.

Page 6: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

Computational versus Quantitative

• cBIO– Computation applied to genomics, omics…– Really Systems Biology and Bioinformatics– “Can you teach my students all Biology in a course or

two?”

• qBIO– Predictive modeling of cellular regulatory systems– Really Dynamical Systems– “Can you teach my students all Computer Science in a

course of two?”

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Page 7: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

mHealth: From excitement of discovery to academic discipline

•Formation of a core community of researchers and practitioners

•Core concepts, skills that are agreed by this community to be essential

– De facto will not work

•Enable strong peer evaluation system– To help academic evaluation process to

succeed.

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Page 8: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

Data QUALITY IS CRITICALData can bridge the gap between physical and health sciences, but

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Page 9: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

Data and Culture

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http://goo.gl/HJKvJl

Power output (kcal/min) = VO2 / ( fraction of VO2 by fat*100 kcal/O2 mole + fraction of VO2

by carb*120 kcal/O2 mole)

VO2max = 43.23 mL O2/kg/min => 3.3 L O2/minO2: 22.393 L/mole0.1474 mole/min

Power output (kcal/min) = VO2 / ( fraction of VO2 by fat*100 kcal/O2 mole + fraction of VO2

by carb*120 kcal/O2 mole)

VO2max = 43.23 mL O2/kg/min => 3.3 L O2/minO2: 22.393 L/mole0.1474 mole/min

Page 10: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

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Page 11: Rajesh K. Gupta Department of Computer Science and Engineering University of California, San Diego mHeath Summit, NIH 2014 A Perspective on Challenges

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5 cyclists, 2 hours5 cyclists, 2 hours