Albers Laboratory

albers lab meme

Lab Mission

Develop and deploy computational machinery to positively impact health care decisions and understand systems physiology.

Collaborations and Tools

  • Data assimilation
  • Machine learning
  • Mathematical Modeling of Systems Physiology
  • Information theory
  • Statistical signal processing
  • Nonlinear time-series analysis
  • Dynamical systems
  • Complexity theory
  • Health Care Process Modeling
  • Variational Inference
  • Computational Phenotyping

George Hripcsak, Matt Levine, Melike Sirlanci, Lena Mamykina, Noemie Elhadad, Estaban Tabak, Jana de Wiljes, Inigo Urteaga, Erica Graham, Rimma Perotte, Adler Perotte, Rudy Leibel, Henry Ginsberg, Jan Claassen, Andrew Stuart, Bruce Gluckman, Jim Crutchfield, Yuzuru Sato, Will Ott, Bhargav Karamched, Peter Sottlie, Marc Moss, Brad Smith, Artie Sherman, Joon Ha, Cecilia Diniz Behn, Christine Chan, Tell Bennett, Soojin Park, Chunhua Weng, Jake Stroh, Deepak Agrawal, Jennifer Briggs, Yanran Wang, Elliot Mitchell, Christine Chan, Cecilia Low Wang, Melanie Cree-Green, Sarah Collins Rossetti, Kenrick Cato, Chris Knaplund, AZ Woldaregay, E Årsand, Gunnar Hartvigsen, PM Desai, Melissa Burgermaster.

Columbia University, Penn State, Caltech, Courant Institute (NYU), Harvard, University of Tromso (Norway), University of Postdam (Germany), UC Davis, CU Boulder, Colorado School of Mines, NIDDK, UT Houston, Caltech, Florida State, UC Davis, University of Hokkaido (Japan)

Work We Do

We develop new computational methods and models for extracting new knowledge from data collected in a clinical setting to positively impact health care decisions and understand systems physiology. These methods range from modified statistical signal processing to new data assimilation and machine learning methods to novel models of systems physiology. These efforts do not exist in vacuum. These methods are directed with to goals in mind: to extend our understanding of systems physiology and pathophysiology, and to help patients and clinicians make quantitatively-anchored decisions. In collaboration with our human-computer interaction and clinical partners, we work to develop and deploy these methods in clinical settings, including patient- and clinician centric clinical decision support settings.


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