• Informatics and Computational Pathology

    Informatics and Computational Pathology

  • Informatics and Computational Pathology

    Informatics and Computational Pathology

  • Informatics and Computational Pathology

    Informatics and Computational Pathology

  • Informatics and Computational Pathology

    Informatics and Computational Pathology

The fields of clinical informatics and computational pathology are changing the way medicine is practiced in every specialty.

Our department is at the forefront of this change – we are a nationally recognized translational research leader in machine learning/artificial intelligence applications and new digital imaging technologies.

We create and apply new tools to improve patient care that shape the future of pathology and laboratory medicine as well as many other clinical disciplines. We are proud of the large number of active projects, publications, inventions, and national and international invited talks by our creative and dynamic faculty.

Here are just a few of the many informatics and machine learning inventions and projects that are on-going in our department:

Our Clinical Informatics Fellowship program brings together the unique research, clinical and educational experiences at UCD Health to create the next generation of physician-informaticians.

For additional projects and more details, please check out the profiles of our faculty involved in informatics and computational pathology.

Elham Vali Betts, M.D., FASCPElham Vali Betts, M.D., FASCP

Assistant Professor


Machine Learning / Informatics-related Projects

  • Research / Quality: Active project description and collaborators
    • Acute leukemia Deep Neural Network AI prediction study: Hooman Rashidi (Pathology), Elham Vali Betts (Pathology), JP Graff (Pathology), & Denis Dwyre (Pathology)
  • Educational: Project description and collaborators
    • Histology AI Deep Neural Network differential diagnosis prediction: ( joint project with NYU and University of Colorado and UC Davis ; UC Davis collaborators are Hooman Rashidi , Elham Vali Betts, Kristin Olson, Beck, Kevin Krause, Anupam Mitra, Sameer Albahra,; University of Colorado collaborator is Antonio Galvao Neto, and NYU collaborators are Esther Adler, Cythia Loomis and Aris Tsirigos)
    • AI ML review article: Supervised machine learning overview (Hooman Rashidi , Lydia Howell, Nam Tran, Elham Vali Betts and Ralph Green)
    • Contributor to Hematology Outlines Atlas and Glossary. www.HematologyOutlines.com; Official Hematology Atlas and Glossary for several Medical School and Clinical Laboratory Scientist training programs (including UC Davis and UC San Diego Hematology Courses); Rashidi HH & Nguyen J,
    • Contributor to HemeQuiz1 iPhone and iPad App Project: Hematology QuizBank Published on Appstore: October 2017; Rashidi HH et. al.

Machine Learning – Informatics-Related Records of Inventions: Filed Patents

NA

Publications

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
    • Hooman Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology. Academic Pathology. Sept 2019. ROLE: Corresponding Author
  • Machine Learning or Informatics-related Abstracts / Posters (Published/Presented)
    • Elham Vali Betts1, Alanna Dubrovsky1, Kristin Olson1, Kenneth Beck1, Aristotelis Tsirigos2, Victoria Harnik2, Antonio Galvao Neto2, Esther Adler2, Hooman H. Rashidi1. Utility of Artificial Intelligence (AI) / Machine Learning (ML) in the Histology education arena. APC Conference July 2019.
    • E. Vali Betts1, Denis Dwyre1, Nam Ku1, John P. Graff1, and H. Rashidi. Utility of Artificial Intelligence (AI)/Machine Learning (ML) in Identifying Acute Leukemia, Chronic Lymphocytic Leukemia, and Chronic Myeloid Leukemia in Peripheral Blood Smears. APC Conference July 2019.
    •  Pathology Training for Medical Students with a Digital Image Bank (“Whitekoat”), Elham Vali Betts, Steven Russo, Kristin Olson, 2018 Scholarship of Teaching and Learning Conference, Davis, CA, November 2018
    • Evaluating the Efficacy of Whitekoat Images Curricular Software in Medical Student Education at the UC Davis School of Medicine Steven Russo, Elham Vali Betts, Kristin A. Olson MD, 2018 University of California, Davis, School of Medicine, Medical Student Research Forum, 2018
    • Use of Online Tools in Improving Medical Student and Resident Education in Anatomic Pathology, Elham Vali Betts, Hooman H. Rashidi, Kristin A. Olson, Association of Pathology Chairs, San Diego, CA, July 2018
    • Incorporating Hematology Quiz Application in Hematology Education, Elham Vali Betts, Kristin Olson , Hooman Rashidi, Association of Pathology Chairs, San Diego, CA , July 2018

Machine Learning or Informatics-related Textbooks (Published Print Versions)

NA

Recent (PAST 2 years) Machine learning & Informatics-related invited Talks at National, International and Local venues:

  • Whitekoat: A multimedia platform for teaching and quizzing, Association of Pathology Chairs Meeting, Online, 2020 

Brittany N. Dugger, Ph.D.Brittany N. Dugger, Ph.D.

Assistant Professor, Pathology and Laboratory Medicine
Co-Leader, UC-Davis Alzheimer’s Disease Research Center Neuropathology Core
Co-Leader, UC-Davis School of Medicine Machine Learning Working Group


For more information see:

News Articles


And some videos!


Machine Learning / Informatics Active Projects:

Research / Quality: active project description and collaborators

  • Machine Learning Approaches to Vascular Pathology in Dementia: A multi-institutional cohort analysis in minorities: Brittany Dugger (Pathology), Dan Mungas (Neurology), John Paul Graff (Pathology), Lee-Way Jin (Pathology), Amparo Villablanca (Cardiology), Danielle Harvey (Public health Sciences), Peter Chang (UC-Irvine), Harry Vinters (UCLA), Michael Keiser (UCSF)
  • An Enhanced UC digital pathology infrastructure: Brittany Dugger (Pathology), John Paul Graff (Pathology), Edwin Monuki (UC-Irvine), Michael Keiser (UCSF), Harry Vinters (UCLA)     
  • The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents: Brittany Dugger (Pathology), Laurel Beckett (Public Health Sciences), Charles DeCarli (Neurology), Robert Rissman (UCSD), Andrew Teich (Columbia University),
  • Deep learning models ensembling expertise of multiple neuropathologists: Daniel Wong (UCSF), Brittany Dugger (Pathology), Kirsty McAleese (Newcastle University), Julia Kofler (University of Pittsburgh), Margret Flanagan (Northwestern University), Ewa Borys (Loyola University), Michael Keiser (UCSF)
  • Ethnoracial disparities and COVID-19 testing: Brian Paciotti (CTSC Biomedical Informatics), Duke LaTran (CTSC Biomedical Informatics), Brittany Dugger (Pathology),  Kent Anderson (CTSC Biomedical Informatics),  John Paul Graff (Pathology), Pamela Reynolds (Data Science), Sergio Aguilar-Gaxiola (CTSC Center for Reducing Health Disparities)
  • Artificial intelligence for Tau Detection in Alzheimer's Disease - A Study of Braak Staging Reliability and Computational Alternatives: JC Vizcarra (Emory/Georgia Tech), David Gutman (Emory), Marla Gearing (Emory), Johnathan Crary (Mt. Sinai), Michael Keiser (UCSF), Brittany Dugger (Pathology)
  • Automated Grey And White Matter Segmentation In Digitized Human Brain Histology Slide Images: Jeffrey Lai (Electrical and Computer Engineering), Chen-Nee Chuah (Electrical and Computer Engineering), Sen-ching Cheung (Electrical and Computer Engineering), Brittany Dugger (Pathology)
  • Automatic Segmentation of CT based Liver Volumes: Thomas Loehfelm (Radiology), Brittany Dugger (Pathology), Tony Seibert (Radiology), David Johnson (Neurology), Sam Morley (CTSC Biomedical informatics).

Educational

  • An Enhanced UC digital pathology infrastructure: John Paul Graff (Pathology), Edwin Monuki (UC-I Pathology), Michael Keiser (UCSF- Pharmacy, Bioengineering & Therapeutic Sciences), Harry Vinters (UCLA-Pathology)

Machine Learning – Informatics-Related Accepted Grants

  • An Enhanced UC digital pathology infrastructure. University of California Office of the President. Grant# MRI-19-599956.
  • The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents. NIH/NIA.
  • Grant# R01AG062517
  • Machine learning Approaches to Vascular Pathology in Dementia: A multi-institutional cohort analysis in minorities. California Department of Public Health. Grant# 19-10611.

Publications

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
    • Tang Z, Chuang KV, DeCarli C, Jin LW, Beckett L, Keiser MJ, Dugger BN. Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline.  Nat Commun. 2019 May 15;10(1):2173. PMID 31092819
    • Vizcarra JC, Gearing M, Keiser MJ, Glass JD, Dugger BN, Gutman DA. Validation of Machine learning models to detect amyloid pathologies across institutions. 2020 Acta Neuropath Com. Acta Neuropathol Commun. 2020 Apr 28;8(1):59. PMID 32345363
  • Machine Learning or Informatics-related Abstracts (Published/Accepted)
    • Lai Z, Guo R, Xu W, Hu Z, Mifflin K, Dugger BN, Chuah C, Cheung SC. Automated Grey And White Matter Segmentation In Digitized A Beta Human Brain Tissue Slide Images. IEEE international conference on Multimedia and Expo.
    • Wong DR, Tang Z, Mew N, Athey J, Das S, McAleese KE, Kofler JK, Flanagan M, Borys E, Dugger BN, Keiser MJ. Deep learning models ensembling expertise of multiple neuropathologists at once achieve improved identification of amyloid neuropathologies. 96th Annual Meeting of the American Association of Neuropathologists 2020
    • Vizcarra JC, Gearing M, Keiser MJ, Glass JD, Dugger BN, Gutman DA. Validation of machine learning models to detect amyloid pathologies across institutions. 96th Annual Meeting of the American Association of Neuropathologists 2020
    • Chang P, Kart T, Chow D, Monuki E, Tang Z, Keiser M, Dugger BN. Classification of Amyloid Beta Deposition Patterns: Comparison of Customized Deep Learning Approaches. 95th Annual Meeting of the American Association of Neuropathologists 2019
    • Dugger BN, Tang Z, Kangway C, Athey J, Das S, Kofler J, McAleese K, Flannagan M, Borys E, White C, Cairns N, Keiser MJ. Amyloid beta deposit identification by multiple expert trained machine learning models. 95th Annual Meeting of the American Association of Neuropathologists 2018
    • Tang Z, Chuang K, DeCarli C, Beckett L, Jin LW, Keiser MJ, Dugger BN. An interpretable machine learning pipeline for identifying pathologies within archival human tissues. Association of Pathology Chairs Meeting 2019, Boston MA
  • Machine Learning or Informatics-related Textbooks (Published Print Version)

Recent Machine learning & Informatics-related invited Talks at National, International and Local venues:

  • 2020    Precision Medicine World Conference, San Jose, CA
  • 2020    Symposium on AI for Biomedical Imaging Across Scales, IBM Almaden Research Center, San Jose, CA
  • 2020    Soroptimist International of Metropolitan Sacramento
  • 2020    UCD Health Science Day
  • 2019    American Association of Neuorpathologist’s Annual Meeting, Atlanta, GA
  • 2019    Association of Pathology Chairs Meeting, Boston, MA
  • 2019     Alzheimer’s Disease Center National Fall Meeting, St. Louis MO
  • 2019    West/Midwest Pathology Chairs Meeting, Maui, HI 
  • 2019    Grand Rounds, Department of Neurology, University of California Davis, Sacramento CA
  • 2019    Stem Cell Seminar Series, University of California Davis, Sacramento CA
  • 2019    UC AI in Biomedicine Conference, Los Angeles, CA
  • 2019    Grand Rounds, Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento CA
  • 2019    MIND institute seminar series University of California Irvine, Irvine, CA
  • 2019    Neurofest Plenary Speaker, University of California Davis Neuroscience Program, Davis CA Presentation: https://www.youtube.com/watch?v=PE5clyisLc0&feature=youtu.be  
  • 2018    Alzheimer’s Association Northern California Alzheimer's Researchers' Symposium

John Paul Graff, D.O.John Paul Graff, D.O.

Assistant Professor


Machine Learning / Informatics Active Projects:

  • Co-Lead of UC Davis Machine Learning working group with Dr. Dugger [2017-Present]
  • COVID LDS, UC-wide COVID-19 dataset laboratory consultant [2020]
  • Developed PathSearch with Michael Erickson, capable of retrieving pathology case data over ~30 years. [2018-Present]
  • CP EPIC-Beaker Director [2019-Present]
  • Created and Developed JoltPath Grosser, an anatomic pathology case management system. [2014-Present]

Machine Learning – Informatics-Related Records of Inventions: Filed Patents:  N/A

Machine Learning – Informatics-Related Accepted Grants

  • Machine Learning Approaches to Vascular Pathology in Dementia: A multi-institutional cohort analysis in minorities Alzheimer’s Disease Program at the California Department of Public Health 04/01/2020- 06/30/2022

    • This study aims to enhance and develop machine-learning approaches to detect abnormalities in blood vessels associated with dementia using histological slides from post-mortem human brains. This is a cross institutional project involving faculty from UC-Davis, UCLA, UC-Irvine, and UCSF.
  • An Enhanced UC digital pathology infrastructure UC Office of the President            1/1/2019 12/31/2021

    • This project will create digital pathology resources for educational, consultation, collaborative, and research purposes to develop artificial intelligence and machine learning algorithms to detect pathologies within Alzheimer’s and related dementias.
  • Informatics to Support California Integrated Vital Records System (Cal-IVRS), California Department of Public Health                       06/30/2019 6/29/2020

  • Department of Pathology and Laboratory Medicine Seed Grant Machine Learning /Artificial Intelligence platform, a new tool to revolutionize practice of digital pathology. 2019-2020

Publications:

  1. Graff, J. Wu, M. The Nokia Lumia 1020 smartphone as a 41-megapixel photomicroscope.. Histopathology, 2014: DOI: 10.1111/his.12355, 2014.
  2. Jones, A. D., Graff, J. P., Darrow, M. , Borowsky, A. , Olson, K. A., Gandour‐Edwards, R. , Datta Mitra, A. , Wei, D. , Gao, G. , Durbin‐Johnson, B. and Rashidi, H. H. (2019), Impact of pre‐analytic variables on deep learning accuracy in histopathology. Histopathology. Accepted Author Manuscript. doi:10.1111/his.13844
  3. Holland L, Wei D, Olson KA, et al. Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology. J Pathol Inform. 2020;11:5. Published 2020 Feb 21. doi:10.4103/jpi.jpi_49_19

Recent Machine Learning & Informatics-related invited Talks at National, International and Local venues: (PAST 2 YEARS ONLY)

  • Association of Pathology Chairs Annual Conference 2019: Discussion Group 8: Implementing Artificial Intelligence/Machine Learning in Academic Pathology Departments
  • Western \ Midwestern Pathology Chairs Conference 2019:  Machine Learning in Action: Adventures in an Academic Pathology Department
  • UCD Clinical Translational Science Center Invited Lecture “Tools for implementing convolutional neural networks”
  • MHI Seminar Series “Using machine learning to interpret flow cytometry”

Kuang-Yu Jen, M.D., Ph.D.Kuang-Yu Jen, M.D., Ph.D.

Associate Professor, Renal Pathology


Machine Learning / Informatics Active Projects

  • Research / Quality: active project description and collaborators
    • Delayed graft function prediction Kidney transplant MILO Study: Hooman Rashidi (Pathology), Samer Albahra (Pathology), Kuang-Yu Jen (Pathology)

Machine Learning – Informatics-Related Records of Inventions: Filed Patents

N/A

Machine Learning – Informatics-Related Accepted Grants

N/A

Publications

  • Lutnick B, Ginley B, Govind D, McGarry SD, LaViolette PS, Yacoub R, Jain S, Tomaszewski JE, Jen KY, Sarder P. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nat Mach Intell. 2019 Feb;1(2):112–119. doi: 10.1038/s42256-019-0018-3.
  • Majumdar A, Jen KY, Jain S, Tomaszewski JE, Sarder P. Examining structural patterns and causality in diabetic nephropathy using inter-glomerular distance and Bayesian graphical models. Proc SPIE Int Soc Opt Eng. 2019 Feb;10956. pii: 1095608. doi: 10.1117/12.2513598.
  • Ginley B, Lutnick B, Jen KY, Fogo AB, Jain S, Rosenberg A, Walavalkar V, Wilding G, Tomaszewski JE, Yacoub R, Rossi GM, Sarder P. Computational segmentation and classification of diabetic glomerulosclerosis. J Am Soc Nephrol. 2019 Oct;30(10):1953-1967. doi: 10.1681/ASN.2018121259.
  • Lutnick B, Ginley B, Jen KY, Dong W, Sarder P. Generative modeling for label-free glomerular modeling and classification. Proc SPIE Int Soc Opt Eng. 2020 Feb;11320. pii: 1132007.
  • Border S, Jen KY, Dos-Santos WL, Tomaszewski J, Sarder P. Probabilistic modeling of diabetic nephropathy progression. Proc SPIE Int Soc Opt Eng. 2020 Feb;11320. pii: 1132014. doi: 10.1117/12.2549171.
  • Govind D*, Jen KY*, Matsukuma K, Gao G, Olson KA, Gui D, Wilding GE, Border SP, Sarder P. Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning. Sci Rep. 2020 Jul 6;10(1):11064. doi: 10.1038/s41598-020-67880-z. *co-first author

Richard Levenson, M.D.Richard Levenson, M.D.

Professor and Vice Chair, Strategic Technologies


Machine Learning / Informatics Active Projects:

  • Clinical: active project description and collaborators:
    • Renal transplant organ quality evaluation with novel microscopy and AI: Kuang -yu Jen, Farzad Fereidouni (UC Davis), Sardir Pinaki (University of NY at Buffalo), Avi Rosenberg (Johns Hopkins
    • Breast cancer prognostics using collagen detection via DUET microscopy and AI: Alexander Borowsky, Farzad Fereidouni (UC Davis)
    • Breast cancer response to adjuvant chemotherapy via collagen detection and AI-based evaluation: Farzad Fereidouni (UC Davis), Joel Saltz, Patricia Thompson-Carino (Stony Brook)
    • Brain cancer intra-operative surgical guidance: Farzad Fereidoun, Tanishq Abraham, Orin Bloch (UC Davis)
  • Research / Quality: active project description and collaborators
    • CycleGAN-based unpaired mode conversion (MUSE and FIBI to H&E): Tanishq Abraham (UC Davis); Jeremy Howard (WAMRI)
    • Conversion of qOBM (quantitative oblique back-illumination microscopy) to H&E, Tanishq Abraham (UC Davis), Francisco Robles (Georgia Tech)
    • DUET images as input into diagnostic and prognostic AI tools: Farzad Fereidouni (UC Davis); Anant Madabhushi (Case Western Reserve).
  • Educational : active project description and collaborators
    • 4 undergraduate volunteers on a variety of AI projects, including renal and breast collagen feature extraction techniques

Machine Learning – Informatics-Related Records of Inventions: Filed Patents

  • UC18-923-2PC Producing a composite image of a stained tissue sample by combining image data obtained through brightfield and fluorescence imaging modes
  • 8,639,043        Classifying image features                                         2014
  • 8,379,962        Image classifier training                                             2013
  • 8,280,140        Classifying image features                                         2012
  • 7,555,155        Classifying image features                                         2009

Machine Learning – Informatics-Related Accepted Grants

  • R33 CA202881-01 NCI IMAT Cancer histology and QC via MUSE: Sample-sparing UV surface-excitation microscopy P.I.: Richard Levenson

Publications

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
  • Machine Learning or Informatics-Related Chapters:
    • Abraham, T., Todd, A., Orringer, D.A., Levenson, R. Applications of artificial intelligence for image enhancement in pathology. In: Artificial Intelligence and Deep Learning In Anatomic Pathology. Stanley Cohen, ed. Elsevier, 2020, https://doi.org/10.1016/C2018-0-02465-2
    • McNamara, G. Lucas, J., Beeler, J.F., Basavanhally, A., Lee, G., Hedvat, C. V., Baxi, V.A., Locke, Borowsky, A, and Levenson, R. New Technologies to Image Tumors. In: Tumor Microenvironment, Cancer Treatment and Research, P. P. Lee and F. M. Marincola (eds.), 2020, ISBN 978-3-030-38862-1
  • Machine Learning or Informatics-Related Abstracts (Published/Accepted)
    • David Paredes, Prateek Prasanna, Christina Preece, Rajarsi R. Gupta, Farzad Fereidouni, Dimitris Samaras, Tahsin Kurc, Richard M. Levenson, Patricia Thompson-Carino, Joel Saltz, Chao Chen. Assessment of the Curliness of Collagen Fiber in Breast Cancer. ECCV 2020 Workshop BIC
    • Tanishq Abraham, Andrew Shaw, Daniel O'Connor, Austin Todd, Richard Levenson, Slide-Free MUSE Microscopy to H&E Histology Modality Conversion with Deep Learning
  • Machine Learning or Informatics-related Textbooks (Published Print Version)

Recent Machine learning & Informatics-related invited Talks at National, International and Local venues: (PAST 2 YEARS ONLY)

  • 2019    Mark Foundation and CMU: Accelerating Innovation at the Intersection of AI and Cancer Res.
  • 2019    UC Davis Inaugural Artificial Intelligence and machine learning symposium
  • 2018    Genentech AI Seminar Series

Kristin Olson, M.D.Kristin Olson, M.D.

Associate Dean for Curriculum and Medical Education

Professor of Pathology and Laboratory Medicine


Machine Learning / Informatics-related Projects:

  • Research / Quality: Active project description and collaborators
    • USMLE machine learning risk prediction MILO Study: Hooman Rashidi (Pathology), Kristin Olson (Pathology & Assoc. Dean of Curriculum), Samer Albahra (Pathology), Sharad Jain (Assoc. Dean of Students) & Erin Griffin (Statistician School of Medicine)
    • Effects of preanalytical variables in deep learning studies in histopathology: Hooman Rashidi (Pathology), JP Graff (Pathology), Regina Gandour Edwards (Pathology), Morgan Darrow (Pathology), Ananya Datta Mitra (Pathology), George Gao (Pathology), Kristin Olson (Pathology), Dorina Gui (Pathology), Alexander Borowsky (Pathology), Andrew Jones (Pathology)
  • Educational : Project description and collaborators
    • Histology AI Deep Neural Network differential diagnosis prediction: (joint project with NYU and University of Colorado and UC Davis; UC Davis collaborators are Hooman Rashidi, Elham Vali Betts, Kristin Olson, Kenneth Beck, Kevin Krause, Anupam Mitra, Sameer Albahra; University of Colorado collaborator is Antonio Galvao Neto, and NYU collaborators are Esther Adler, Cythia Loomis and Aris Tsirigos).

Publications:

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
    • Lorne Holland, MD, Dongguang Wei, MD, PhD, Kristin A. Olson, MD, John Paul Graff, DO, Andrew D Jones, MD, Blythe Durbin-Johnson, PhD, Ananya Datta-Mitra, MD, and Hooman H. Rashidi, MD. Effect of training set size on deep learning classification accuracy, a proof of concept of machine learning models for colon histology. Journal of Pathology Informatics. Feb 2020.
    • Andrew D Jones, MD, John Paul Graff, DO, MD, Morgan Darrow, MD, Alexander Borowsky, MD, Kristin A. Olson, MD, Kristin A. Olson, MD, Regina Gandour-Edwards, MD, Ananya Datta-Mitra, MD, Dongguang Wei, MD, PhD, Guofeng Gao, MD PhD, Blythe Durbin-Johnson, PhD, and Hooman H. Rashidi, MD. Impact of pre-analytic variables on deep learning accuracy in histopathology. Histopathology July 2019.
  • Machine Learning or Informatics-related Abstracts / Posters (Published/Presented)
    • Elham Vali Betts1, Alanna Dubrovsky1, Kristin Olson1, Kenneth Beck1, Aristotelis Tsirigos2, Victoria Harnik2, Antonio Galvao Neto2, Esther Adler2, Hooman H. Rashidi1. Utility of Artificial Intelligence (AI) / Machine Learning (ML) in the Histology education arena. APC Conference, Boston, MA, July 2019.
    • Dongguang Wei, Kristin A. Olson, Hooman H. Rashidi. Utility of Apple’s Create ML and Turi Platform in Developing an AI/ML Model in Distinguishing Colonic Adenocarcinoma from Normal Colon. UC Davis Artificial Intelligence Symposium, Sacramento, CA, October 2018.
    • Kristin Olson and Hooman H. Rashidi. Integration of technological tools in resident and fellow education: the University of California at Davis Health System experience. APC Conference, Washington DC, July 2017.

Hooman H. Rashidi M.D., MS, FASCP, FCAPHooman H. Rashidi M.D., MS, FASCP, FCAP

Vice Chair of Informatics & Computational Pathology


Machine Learning / Informatics-related Projects:

  • Clinical: Active project description and collaborators:
    • Early AKI prediction MILO study: Nam Tran (Pathology), Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) Tina Palmieri (Burn surgery), & Hooman Rashidi (Pathology)
    • Early sepsis prediction MILO Study: Hooman Rashidi (Pathology) , Nam Tran (Pathology) , Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) and Tina Palmieri (Burn surgery)
    • Delayed graft function prediction Kidney transplant MILO Study: Hooman Rashidi (Pathology), Samer Albahra (Pathology), Kuang-Yu Jen (Pathology)
    • Cardiac PET / Angio MILO Study: Hooman Rashidi (Pathology) , Thomas Smith (Cardiology) and William Wung (Cardiology)
    • Massive transfusion Protocol MILO study: Joseph Galante (Trauma Surgery), Shawn Tejiram (Trauma Surgery) & Hooman Rashidi (Pathology)
    • COVID-19 ICU predictor MILO Study: Hooman Rashidi (Pathology), Imran khan (Pathology), Gen. Aamer Ikram (NIH Pakistan: A WHO collaborating center)
    • COVID-19 ICU predictor UC COVID OMOP Data MILO Study: Samer Albahra (Pathology), Nam Tran (Pathology), Jeff Wajda (EM/Pathology & CMIO), & Hooman Rashidi (Pathology)
  • Research / Quality: Active project description and collaborators
    • USMLE machine learning risk prediction MILO Study: Hooman Rashidi (Pathology), Kristin Olson (Pathology & Assoc. Dean of Curriculum), Samer Albahra (Pathology), Sharad Jain (Assoc. Dean of Students) & Erin Griffin (Statistician School of Medicine)
    • Effects of preanalytical variables in deep learning studies in histopathology: Hooman Rashidi (Pathology), JP Graff (Pathology), Regina Gandour Edwards (Pathology), Morgan Darrow (Pathology), Ananya Datta Mitra (Pathology), George Gao (Pathology), Kristin Olson (Pathology), Dorina Gui (Pathology), Alexander Borowsky (Pathology), Andrew Jones (Pathology)
    • Acute leukemia Deep Neural Network AI prediction study: Hooman Rashidi (Pathology), Elham Vali Betts (Pathology), JP Graff (Pathology), & Denis Dwyre (Pathology)
    • Diabetes Nephropathy prediction MILO project: Maryam Afkarian (Internal Medicine), Lauren Lopez (Internal Medicine), Hooman Rashidi (Pathology)
    • TB Prediction MILO study: Imran Khan (Pathology), Luke Dang (Pathology), Samer Albahra (Pathology) and Hooman Rashidi (Pathology)
  • Educational : Project description and collaborators
    • Histology AI Deep Neural Network differential diagnosis prediction: ( joint project with NYU and University of Colorado and UC Davis ; UC Davis collaborators are Hooman Rashidi , Elham Vali Betts, Kristin Olson, Beck, Kevin Krause, Anupam Mitra, Sameer Albahra,; University of Colorado collaborator is Antonio Galvao Neto, and NYU collaborators are Esther Adler, Cythia Loomis and Aris Tsirigos)
    • Machine learning tutorials with our current clinical informatics fellow (Hooman Rashidi and Sameer Albahra)
    • AI ML review article : Supervised machine learning overview (Hooman Rashidi , Lydia Howell, Nam Tran, Elham Vali Betts and Ralph Green)
    • Hematology Outlines Atlas and Glossary. www.HematologyOutlines.com ; Official Hematology Atlas and Glossary for several Medical School and Clinical Laboratory Scientist training programs (including UC Davis and UC San Diego Hematology Courses); Rashidi HH & Nguyen J
    • Hematology Outlines iPhone & iPad Apps Projects: Hematology Atlas and Glossary
    • HemeQuiz1 iPhone and iPad App Project: Hematology QuizBank Published on Appstore: October 2017; Rashidi HH et. al.
    • Hematology Outlines Flash Cards: iPhone, iPad, android and online formats (HematologyOutlines), expected publication date November 2020; Rashidi HH, Fennel B & Nguyen J

Machine Learning – Informatics-Related Records of Inventions: Filed Patents

  • Title: SYSTEMS AND METHODS FOR AUTOMATED MACHINE LEARNING
    (MILO: MACHINE INTELLEGENCE LEARNING OPTIMIZER)
    Inventor(s):  Hooman H. Rashidi, Samer Albahra & Nam Tran
    Intellectual Property of University of California (Patent pending)
  • Title: SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED IDENTIFICATION OF SEPSIS
    Inventor(s):  Hooman H. Rashidi & Nam Tran
    Intellectual Property of University of California (Patent pending)
  • Title: SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED IDENTIFICATION OF ACUTE KIDNEY INJURY IN TRAUMA SURGERY AND BURNED PATIENTS
    Inventor(s):  Hooman H. Rashidi & Nam Tran
    Intellectual Property of University of California (Patent pending)

Publications:

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
    • Hooman H. Rashidi, MD, FASCP1*; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Thomas Blackmon, BS1; Jeffrey Wajda, DO3; and Nam K. Tran, PhD, HCLD (ABB), FACB1*. Early Recognition Of Acute Kidney Injury In Trauma Surgery And Severely Burned Patients By Artificial Intelligence: Generalization Of Machine Learning Techniques. Nature’s Scientific Reports. Jan 2020. ROLE: Corresponding Author
    • Lorne Holland, MD, Dongguang Wei, MD, PhD, Kristin A. Olson, MD, John Paul Graff, DO, Andrew D Jones, MD, Blythe Durbin-Johnson, PhD, Ananya Datta-Mitra, MD, and Hooman H. Rashidi*, MD. Effect of training set size on deep learning classification accuracy, a proof of concept of machine learning models for colon histology. Journal of Pa
    • Hooman Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology. Academic Pathology. Sept 2019. ROLE: Corresponding Author
    • Nam K. Tran, PhD, HCLD (ABB)1*, FACB; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Kelly Lima, BS1; Stephanie Falwell, BS1; Jeffrey Wajda, DO3; and Hooman H. Rashidi, MD, FASCP1*. Artificial Intelligence And Machine Learning For Predicting Acute Kidney Injury In Severely Burned Patients: A Proof Of Concept. Burns. Sept 2019. ROLE: Corresponding Author
    • Andrew D Jones, MD, John Paul Graff, DO, MD, Morgan Darrow, MD, Alexander Borowsky, MD, Kristin A. Olson, MD, Kristin A. Olson, MD, Regina Gandour-Edwards, MD, Ananya Datta-Mitra, MD, Dongguang Wei, MD, PhD, Guofeng Gao, MD PhD, Blythe Durbin-Johnson, PhD, and Hooman H. Rashidi*, MD. Impact of pre-analytic variables on deep learning accuracy in histopathology. Histopathology July 2019. ROLE: Corresponding Author
    • Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD. Novel Application Of An Automated-Machine Learning Development Tool For Predicting Burn Sepsis. (July 23rd 2020; Nature’s Scientific Reports) ROLE: Corresponding Author
    • Hooman H. Rashidi, MD, FASCP1*, Amy Makley, MD2; Tina L. Palmieri, MD, FACS, FCCM3; Samer Albahra, MD1; Julia Loegering, BS1; Lei Fang, PhD4; Kensuke Yamaguchi, PhD4; Travis Gerlach, MD5; Dario Rodriquez Jr, MSc, RRT, FAARC6; and Nam K. Tran, PhD, HCLD (ABB), FAACC. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction through an Automated Machine Learning Platform and Point-of-Care Testing. (Accepted May 2020; Archives of Pathology & Laboratory Medicine) ROLE: Corresponding Author
  • Machine Learning or Informatics-related Published Editorials
    • Green R, Hogarth MA, Prystowsky MB, Rashidi HH. The Job Market Outlook for Residency Graduates: Clear Weather Ahead for the Butterflies? Arch Pathol Lab Med. 2018 Apr;142(4):435-438.
  • Machine Learning or Informatics-related Abstracts / Posters (Published/Presented)
    • Elham Vali Betts1, Alanna Dubrovsky1, Kristin Olson1, Kenneth Beck1, Aristotelis Tsirigos2, Victoria Harnik2, Antonio Galvao Neto2, Esther Adler2, Hooman H. Rashidi1. Utility of Artificial Intelligence (AI) / Machine Learning (ML) in the Histology education arena. APC Conference July 2019.
    • E. Vali Betts1, Denis Dwyre1, Nam Ku1, John P. Graff1, and H. Rashidi. Utility of Artificial Intelligence (AI)/Machine Learning (ML) in Identifying Acute Leukemia, Chronic Lymphocytic Leukemia, and Chronic Myeloid Leukemia in Peripheral Blood Smears. APC Conference July 2019.
    • Ananya Datta-Mitra, Anupam Mitra, Guofeng Gao, Andrew D Jones, John Paul Graff, Hooman H Rashidi. The significance of low power versus high power microscopy in the training phase of various machine learning/artificial intelligence learning platforms. UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • Dongguang Wei, Kristin A. Olson, Hooman H. Rashidi. Utility of Apple’s Create ML and Turi Platform in Developing an AI/ML Model in Distinguishing Colonic Adenocarcinoma from Normal Colon UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • Gao, G; Gandour-Edwards, RF; Datta Mitra A; Jones, AD; Graff, JP; Rashidi, HH. The Predictive capability of the machine learning model in our prostate cancer project is found to be dependent on the number of images within its respective training set but with an unexpected pattern UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • Anupam Mitra, Ananya Datta-Mitra, Guofeng Gao, Andrew D Jones, John Paul Graff, Hooman H Rashidi. Comparison of various machine learning/artificial intelligence algorithms/ platforms and their predictive capabilities for ML models in diagnostic pathology subspecialties. UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • Andrew D. Jones, MD; Ananya Datta Mitra, MD; John Paul Graff, MD; Morgan Darrow, MD; Alexander Borowsky, MD; Hooman Rashidi, MD. The Significance of Image Format and Image Quality in the Predictive Capability of Machine Learning Models UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • John Paul Graff, Deepthi Buduru, Andrew Pham,  Hooman H Rashidi. Using Computer Learning to Diagnnose Acute Leukemia from Flow Cytometry. UC Davis Artificial Intelligence Symposium, Oct. 26th 2018
    • Kristin Olson and Hooman H. Rashidi. Olson. Integration of technological tools in resident and fellow education: the University of California at Davis Health System experience. APC Conference July 2017 Washington DC.
  • Machine Learning or Informatics-related Textbooks (Published Print Versions)
    • Bioinformatics Basics: Applications in Biological Science and Medicine
    • (2nd   Edition) CRC Press LLC, Boca Rotan, 2005. Hooman H. Rashidi & Lukas K. Buehler
    • Grundriss der Bioinformatik: Anwendungen in den Biowissenschaften und der Medizin Spektrum Akademischer Verlag, Heidelberg . Berlin, 2001
    • (German Translation). Hooman H. Rashidi & Lukas K. Buehler
    • Bioinformatics Basics: Applications in Biological Science and Medicine (1st edition) CRC Press LLC, Boca Rotan, 1999. Hooman H. Rashidi & Lukas K. Buehler
  • Recent (PAST 2 years) Machine learning & Informatics-related invited Talks at National, International and Local venues:
    • Yale University, Department of Pathology Grand Rounds- Live Online Talk. Automated Machine Learning & MILO (Machine Intelligence Learning Optimizer). September. 17th, 2020.
    • University of Minnesota, Department of Pathology Grand Rounds- Live Online Talk. Automated Machine Learning & MILO (Machine Intelligence Learning Optimizer). September. 17th, 2020.
    • Association of Pathology Chairs 2020 Annual Meeting- Live Online Conference/ Prerecorded Talk- Creating Shared Resources & Integrating Technology Tools in Medical Education. July 20th, 2020
    • CDC (Center for Disease Control and Prevention) ECHO Machine Learning and Hematopathology talk. April 2020. https://www.cdc.gov/labquality/echo.html
    • Radiology Grand Rounds, UC Davis, MILO (Machine Intelligence Learning Optimizer) Automated Machine Learning. March 10th 2020.
    • UCSD (University of California San Diego), Biomedical Informatics Seminar Series. Supervised machine learning in pathology and MILO Automated Machine Learning. Feb 28th, 2020
    • UC Davis health Data Science Day Conference. MILO (Machine Intelligence Learning Optimizer) Automated Machine Learning. Feb 10th, 2020
    • University of Colorado, Department of Pathology Grand Rounds, January 17th, 2020,. Don’t Concede to AI, Conquer it : An overview of AI with an emphasis on Supervised machine learning in pathology
    • Rector-Chancellor’s Special-Invited Guest Speaker at Vasile Goldis Western Universtity of Arad (Arad, Romania). Intergrating Technology into Medical Education. Nov. 5th, 2019.
    • Rector-Chancellor’s Special-Invited Guest Speaker at Vasile Goldis Western Universtity of Arad (Arad, Romania). Artificial Intelligence & Machine Learning in Medicine: the currect landscape of supervised methods. Nov. 4th, 2019.
    • New York Pathological Society Meeting, September 19th 2019, NY. Don’t Concede to AI, Conquer it : An overview of AI with an emphasis on Supervised machine learning in pathology
    • Next Generation Dx Summit August 22nd 2019, Washington DC. Burn Sepsis and Acute Kidney Injury: Unique Population and the Promise of Artificial Intelligence
    • Molecular Med TRI-CON Conference. March 13th 2019, San Francisco, CA. Artificial Intelligence (AI)/Machine Learning (ML) Expert Systems in Pathology
    • Association of Pathology Chairs (SouthEastern Conference). Feb 2nd, 2019. Amelia Island, FL. Sometimes Less Yields More: An Artificial Intelligence / Machine Learning Paradox

Nam Tran, Ph.D., HCLD (ABB), FAACCNam Tran, Ph.D., HCLD (ABB), FAACC

Professor of Clinical Pathology


Machine Learning / Informatics-related Projects:

  • Clinical: Active project description and collaborators:
    • Early AKI prediction MILO study: Nam Tran (Pathology), Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) Tina Palmieri (Burn surgery), & Hooman Rashidi (Pathology)
    • Early sepsis prediction MILO Study: Hooman Rashidi (Pathology), Nam Tran (Pathology) , Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) and Tina Palmieri (Burn surgery)
    • COVID-19 ICU predictor UC COVID OMOP Data MILO Study: Samer Albahra (Pathology), Nam Tran (Pathology), Jeff Wajda (EM/Pathology & CMIO), & Hooman Rashidi (Pathology)
  • Research / Quality: Active project description and collaborators
  • Educational : Project description and collaborators
    • AI ML review article: Supervised machine learning overview (Hooman Rashidi , Lydia Howell, Nam Tran, Elham Vali Betts and Ralph Green)

Machine Learning – Informatics-Related Records of Inventions: Filed Patents

  • Title: SYSTEMS AND METHODS FOR AUTOMATED MACHINE LEARNING
    (MILO: MACHINE INTELLEGENCE LEARNING OPTIMIZER)
    Inventor(s):  Hooman H. Rashidi, Samer Albahra & Nam Tran
    Intellectual Property of University of California (Patent pending)
  • Title: SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED IDENTIFICATION OF SEPSIS
    Inventor(s):  Hooman H. Rashidi & Nam Tran
  • Title: SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED IDENTIFICATION OF ACUTE KIDNEY INJURY IN TRAUMA SURGERY AND BURNED PATIENTS
    Inventor(s):  Hooman H. Rashidi & Nam Tran
    Intellectual Property of University of California (Patent pending)

Publications:

  • Machine Learning or Informatics-related Manuscripts (Published/Accepted Refereed manuscripts)
    • Hooman H. Rashidi, MD, FASCP1*; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Thomas Blackmon, BS1; Jeffrey Wajda, DO3; and Nam K. Tran, PhD, HCLD (ABB), FACB1*. Early Recognition Of Acute Kidney Injury In Trauma Surgery And Severely Burned Patients By Artificial Intelligence: Generalization Of Machine Learning Techniques. Nature’s Scientific Reports. Jan 2020. ROLE: Co-Corresponding Author
    • Hooman Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology. Academic Pathology. Sept 2019. ROLE: Co-Author
    • Nam K. Tran, PhD, HCLD (ABB)1*, FACB; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Kelly Lima, BS1; Stephanie Falwell, BS1; Jeffrey Wajda, DO3; and Hooman H. Rashidi, MD, FASCP1*. Artificial Intelligence And Machine Learning For Predicting Acute Kidney Injury In Severely Burned Patients: A Proof Of Concept. Burns. Sept 2019. ROLE: Principal Investigator and Co-Corresponding Author
    • Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD. Novel Application Of An Automated-Machine Learning Development Tool For Predicting Burn Sepsis. (July 23rd 2020; Nature’s Scientific Reports) ROLE: Principal Investigator and C-Corresponding Author
    • Hooman H. Rashidi, MD, FASCP1*, Amy Makley, MD2; Tina L. Palmieri, MD, FACS, FCCM3; Samer Albahra, MD1; Julia Loegering, BS1; Lei Fang, PhD4; Kensuke Yamaguchi, PhD4; Travis Gerlach, MD5; Dario Rodriquez Jr, MSc, RRT, FAARC6; and Nam K. Tran, PhD, HCLD (ABB), FAACC. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction through an Automated Machine Learning Platform and Point-of-Care Testing. (Accepted May 2020; Archives of Pathology & Laboratory Medicine) ROLE: Principal Investigator and Co-Corresponding AuthorMachine Learning or Informatics-related Published Editorials
  • Machine Learning or Informatics-related Abstracts / Posters (Published/Presented)
  • Machine Learning or Informatics-related Textbooks (Published Print Versions)

Recent (PAST 2 years) Machine learning & Informatics-related invited Talks at National, International and Local venues:

  • Next Generation Dx Summit August 22nd 2019, Washington DC. Burn Sepsis and Acute Kidney Injury: Unique Population and the Promise of Artificial Intelligence.

    Jeffery L. Wajda, D.O., M.S., FACEPJeffery L. Wajda, D.O., M.S., FACEP

    Chief Medical Information Officer and Program Director, Clinical Informatics Fellowship


    Clinical Focus

    • Emergency Medicine, IT Quality and Safety and Clinical Translation of Artificial Intelligence / Machine Learning

    Academic Appointments

    • Clinical Professor, Emergency Medicine – joint appointment Pathology and Laboratory Medicine

    Administrative Appointments

    • Chief Medical Information Officer, UCDH (2016- Present)
    • Program Director, Clinical Informatics Fellowship (2018 – Present)

    Professional Education

    • Fellowship:  UCSF, CHW Finance and Management Fellowship (2001)
    • Residency:  Rush Presbyterian / Cook County Hospital Emergency Medicine (1993)
    • Graduate Clinical Informatics Education: OHSU (2010)
    • Medical Education:  Midwestern University (1989)
    • Graduate Education:  University of Michigan., Organic and Biochemistry (1983)

    Machine Learning / Informatics Active Projects

    • Clinical: active project description and collaborators:
      • Early AKI prediction MILO study: Nam Tran (Pathology), Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) Tina Palmieri (Burn surgery), & Hooman Rashidi (Pathology)
      • Early sepsis prediction MILO Study: Hooman Rashidi (Pathology) , Nam Tran (Pathology) , Samer Albahra (Pathology) Soman Sen (Burn Surgery), Jeff Wajda (EM/Pathology & CMIO) and Tina Palmieri (Burn surgery)
      • COVID-19 ICU predictor UC COVID OMOP Data MILO Study: Samer Albahra (Pathology), Nam Tran (Pathology), Jeff Wajda (EM/Pathology & CMIO), & Hooman Rashidi (Pathology)
    • Educational : active project description and collaborators
      • Digital Health Curriculum – contributor to the MHI Graduate Program

    Publications

    • Hooman H. Rashidi, MD, FASCP1*; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Thomas Blackmon, BS1; Jeffrey Wajda, DO3; and Nam K. Tran, PhD, HCLD (ABB), FACB1*. Early Recognition Of Acute Kidney Injury In Trauma Surgery And Severely Burned Patients By Artificial Intelligence: Generalization Of Machine Learning Techniques. Nature’s Scientific Reports. Jan 2020. ROLE: Contributing Author
    • Nam K. Tran, PhD, HCLD (ABB)1*, FACB; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Kelly Lima, BS1; Stephanie Falwell, BS1; Jeffrey Wajda, DO3; and Hooman H. Rashidi, MD, FASCP1*. Artificial Intelligence And Machine Learning For Predicting Acute Kidney Injury In Severely Burned Patients: A Proof Of Concept. Burns. Sept 2019. ROLE: Contributing Author
    • Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD. Novel Application Of An Automated-Machine Learning Development Tool For Predicting Burn Sepsis. (July 23rd 2020; Nature’s Scientific Reports) ROLE: Contributing Author

    Recent Machine learning & Informatics-related invited Talks at National, International and Local venues: (PAST 2 YEARS ONLY)

    • UCSD, Use of Epic Cognitive Computing as an Intelligent Assistant to Medical Decision making, 2018
    • UC Berkeley, Rubric for Evaluation of Early Stage Digital Health Initiatives, 2020
    • San Francisco HIT Summit, Innovation in an Academic Medical center, 2019