We develop quantum mechanics-based multi-tensor AI/ML,1  which, as we experimentally validated, is uniquely able to discover accurate, precise, clinically actionable, and mechanistically interpretable predictors from small-cohort, noisy, and multi-dimensional, multi-omic data.2  Orly Alter, the principal investigator, is a USTAR associate professor at the Scientific Computing and Imaging Institute3  and the Huntsman Cancer Institute at the University of Utah, a scientific advisory board member of the NCI-DOE Cancer Moonshot collaboration, and the CSO and a co-founder of Prism AI Therapeutics, Inc.4  As a genetics postdoctoral fellow at Stanford University, she invented the concept of the "eigengene" in a top 50 most cited PNAS paper of all time.5,6,7  Her Ph.D. thesis in applied physics, also at Stanford, was published by Wiley8,9,10  and is recognized as crucial to gravitational wave detection and quantum computing.11,12 

We proved that our multi-tensor algorithms,13,14  known as comparative spectral decompositions,15,16,17  (i) always converge, and (ii) almost always into a mathematically unique model, from any data types, of any number and dimensions. We showed that our models (iii) comprehensively remove batch effects and capture known biology, and (iv) correctly predict previously unknown mechanisms.18,19  Our clinical trial validation of a pattern of DNA copy-number alterations in brain tumors solved the 70 year-old problem of correctly predicting a patient's response and survival from their genome,20,21,22  and demonstrated that our predictors are (v) the most accurate and precise, (vi) clinically actionable in the general population based upon as few as 1923  patients, and (vii) consistent across federated studies and over time. Recent experiments additionally demonstrated that the models (viii) correctly predict drug targets to sensitize a tumor to treatment.24  We discovered and validated multi-omic predictors, in cancer25  and other diseases, in public data, establishing that her AI/ML is uniquely suited to personalized medicine.

    In the News

  1. Highlight: "SIAM Celebrates Mathematics and Statistics Awareness Month," Society for Industrial and Applied Mathematics (SIAM) News Blog (April 1, 2025).
  2. Mention: Top 10 most cited Proceedings of the National Academy of Sciences (PNAS) USA paper of the year 2000 and top 50 most cited PNAS paper of all time, Google Scholar (April 12, 2024).
  3. Mention: BME 6770, Genomic Signal Processing course may "be pivotal for … career," Amazon Science (April 6, 2022).
  4. Mention: Among the most shared Applied Physics Letters (APL) Bioengineering research as of 2021, APL Bioengineering (October 30, 2021).
  5. Press Release: J. Kiefer, "Genome-Wide Pattern Found in Tumors from Brain Cancer Patients Predicts Life Expectancy," American Association for the Advancement of Science (AAAS) EurekAlert! (May 15, 2020).
  6. Mention: Among the top 10 most downloaded Applied Physics Letters (APL) Bioengineering articles as of 2019, APL Bioengineering (May 14, 2019).
  7. Feature: A. J. Engler and D. E. Discher, "Rationally Engineered Advances in Cancer Research," Applied Physics Letters (APL) Bioengineering 2 (3), Special Topic: Bioengineering of Cancer preface 031601 (September 2018).
  8. Mention: Among the top 10% most cited Public Library of Science (PLoS) One articles as of 2017, PLoS One (June 30, 2017).
  9. Feature: "Ovarian Cancer: Increasing Accuracy of Diagnosis, Prognosis," USA Today 144 (2849), 8 (February 15, 2016).
  10. Feature: F. Pavlou, "Big Data, Hidden Knowledge," The Pathologist (June 15, 2015).26
  11. Feature: R. Atkins, "Calculating Cancer Cures," National Academy of Engineering (NAE) Innovation Podcast and Radio Series (April 19, 2015).27
  12. Feature: "U. Researchers' Mathematical Models Could Provide Better Ovarian Cancer Treatment, Outcomes," Salt Lake Tribune (April 15, 2015).
  13. Press Release: J. Kiefer, "New Method Increases Accuracy of Ovarian Cancer Prognosis and Diagnosis," American Association for the Advancement of Science (AAAS) EurekAlert! (April 15, 2015).
  14. Recommendation: M. Méchali, Faculty Opinions recommendation 1728974 (February 2010).
  15. Feature: S. N. Dwivedi, "Rao Conference at the Interface between Statistics and the Sciences (Hyderabad, India, December 30, 2009 – January 2, 2010), Rao Best Poster Prize," International Biometric Society (IBS) Bulletin 27 (1), pp. 6–7 (January–March 2010).
  16. Press Release: B. Rische, "Mathematical Modeling Correctly Predicts Previously Unknown Biological Mechanism of Regulation," American Association for the Advancement of Science (AAAS) EurekAlert! (October 13, 2009).
  17. Excerpt: O. Alter, International Linear Algebra Society (ILAS) Linear Algebra and Its Applications (LAA) Lecture, "Genomic Signal Processing: From Matrix Algebra to Genetic Networks," IMAGE: ILAS Bulletin 35, pp. 2–15 (December 2005).28
  18. Feature: National Research Council, Mathematics and 21st Century Biology. Washington, DC: National Academies Press (July 2005), 149 pp.
  19. Feature: M. E. Kilmer and C. D. Moravitz Martin, "Decomposing a Tensor," Society for Industrial and Applied Mathematics (SIAM) News 37 (9), (November 2004).29
  20. Feature: J. Wixon and J. Ashurst, "Genome Informatics," Comparative and Functional Genomics 4 (5), pp. 509–514 (October 2003).
  21. Citation: K. S. Thorne et al., "Noise in Gravitational-Wave Detectors and Other Classical-Force Measurements is Not Influenced by Test-Mass Quantization," Physical Review D 67 (8), article 082001 (April 2003).
  22. Commentary: L. Y. Dirix and A. T. van Oosterom, "Gene-Expression Profiling to Classify Soft-Tissue Sarcomas," Lancet 359 (9314), pp. 1263–1264 (April 2002).
  23. Feature: B. H. Ripin, "1998 Outstanding Doctoral Thesis Research in Atomic, Molecular, or Optical Physics (DAMOP) Award Finalists," American Physical Society (APS) News 7 (8), p. 5 (August–September 1998).30