Our professionals

Dr. Mikel Hernáez

Director, Computational Biology and Translational Genomics program Group.
Leader, Machine Learning for Biomedicine Group
Expert in: Machine Learning; Computational Biology; Information theory; Single cell omics; Genomic data compression.
Preferential dedication: our group develops and apply machine learning models to help understand the physiopathology of cancer.

Be part of: Cima Universidad de Navarra
INDEX H 18

Professional career

  • 2020- Director, Computational Biology and translational genomics Program, CIMA University of Navarra, Spain
  • 2020- Group Leader, Machine Learning for Biomedicine, CIMA University of Navarra, IdiSNA, Spain
  • 2020- Adjunct Professor, Genetics and Biochemistry Department, University of Navarra, Spain 
  • 2018-2019- Executive Director, CompGen Initiative, University of Illinois, USA
  • 2017-2019- Director, Computational Genomics, Carl R. Woese Institute for Genomic Biology, University of Illinois, USA
  • 2013-2016- Postdoctoral Researcher, Stanford University, USA
  • 2013- Head of Research, Enigmedia, Inc, Spain
  • 2012-2013- Lecturer, Universidad de Navarra, Spain
  • 2012- PhD in Electrical Engineering, TECNUN University of Navarra, Spain
  • 2009- BSc/MSc in Telecommunication Engineering, TECNUN University of Navarra, Spain

RESEARCH AREAS

Dr. Hernaez has had highly interdisciplinary research training in the last years. From training in Information Theory during his PhD (2009-2012), followed by his training in Computational Biology during his postdoc at Stanford University (2013-2016, funded by a Stanford Data Science Initiative fellowship); to his previous position as Director of Computational Genomics the Carl R. Woese Institute for Genomic Biology (IGB) at the University of Illinois (UIUC), USA; where he had ample experience working on biology-centered interdisciplinary projects.

In 2020 he moved back to Spain to lead the Computational Biology Program at the Center for Applied Medical Research (CIMA), University of Navarra. These positions have allowed him to develop clear expertise on the application of computational and statistical methods to solve biological problems as evidenced by his critical involvement in multiple projects published in the most prestigious peer-reviewed journals (Since 2019, Dr. Hernaez authored over 20 peer-reviewed articles in highly relevant journals (D1 and Q1)).

AREAS OF INTEREST

  • Elucidating transcriptional rewiring on hematological malignancies via Machine Learning.
  • Reverse Engineering the Human Cancer Transcriptome with Applications to Prostate Cancer.
  • Prediction of cellular composition and molecular makeup of the neurovascular unit using machine learning methods.
  • Reducing the footprint of Genomic Information.
     

Activity

As an educator

Dr. Hernaez has ample educational experience. While at Stanford and UIUC, he advised both master and undergraduate students, all interactions resulting in publications in top journals. In addition, at UIUC, he was adjunct lecturer of the Electrical and Computer Engineering department were he thought in ECE365 (Data Science and Engineering) and ECE398BD (Making Sense of Big data), as well as several lectures on biomedical data science at the MEng and summer courses. At the University of Navarra, he is currently co-chairing the Master in Data Science from the University of Navarra, where he teaches two courses and he is actively supervising master students. 

Finally, he has organized more than 10 workshops and special sessions on the top conferences on Computational Biology (ISMB; ECCB; Stanford Compression forum); and he is a reviewer in the top journals of Computational Biology (Nature Biotech., Nature Comms., Bioinformatics).

As a researcher

  1. Uncovering resistance mechanism to CAR-T cells via deep learning in scRNA-seq for improved therapies (iMMprove), La Caixa Foundation (CIMA, CUN, Weizmann Institute), 1M, 01/01/2025-01/01/2028, co-PI (CIMA)
  2. CRCNS: Deep Learning to Discover Neurovascular Disruptions in Alzheimer’s Disease, AC23_2/00016, National Institutes of Health (NIH) – ISCIII (CIMA, NYU, Mayo Clinic, Univ. of Minnesota), 1.5M, 01/09/2023-31/10/2028, PI
  3. Navarra European Digital Innovation Hub (IRIS), #101083411 European Commission, (ADITECH, Camara Comercio, AIN, UPNA, UNAV), 4.7M, 01/01/2023 – 31/12/2025. WP leader
  4. Machine Learning methods for translational biomedicine, Contrato Ramon y Cajal, Ministerio de Ciencia e Innovación. (CIMA). 01/10/2022- 01/10/2026. PI.
  5. Non-invasive cancer diagnosis using machine learning-based characterization of circulating tumor cells (DeepCTC). Proyectos de Transición Ecológica y Transición Digital, Ministerio de Ciencia e Innovación. (CIMA). 01/12/2022- 30/11/2024. Co-PI.
  6. Mecanismos para la gestión segura y eficiente de la información genómica adaptada a laboratorios clínicos: Aspectos Traslacionales. Ministerio de Ciencia e Innovación. (CIMA). 01/10/2021- 01/10/2024. 39.680 €. PI.
  7. Elucidating Transcriptional Rewiring on Hematological Malignancies via Computational Methods. Becas Marie S. Curie, Individual Fellows, European Research Council. (CIMA). 01/04/2020-01/04/2022. 175.000 €. PI.
  8. Novel Methods to Elucidate Abiraterone Resistance Mechanisms Using RNA- Seq Data and Xenograft Models from CRPC Patients. USA Department of Defense. (CIMA y Clinica Mayo (USA)). 01/05/2020-01/05/2023. 750.000 €. Co-PI.
  9. Quantization and Compressive Learning Methods for Omics Data. SVSF (Chan-Zuckerberg Initiative). (University of Illinois). 01/03/2018-31/08/2019. 100.000 €.  PI
  10. Bringing digital era formats to genomic information. College of Engineering, University of Illinois, State of Illinois. (University of Illinois). 01/02/2018- 01/03/2020. 150.000 €. Co-PU
  11. Mayo Grand Challenge Project Mayo Clinic. Mayo Clinic. (University of Illinois). 15/08/2017- 15/08/2019. 1.438.316 €. Co-PI.
  12. Genomic Compression: From Information Theory to Parallel Algorithms. National Institutes of Health (NIH). IP: Olgica Milenkovic, Tsachy Weissman. (Stanford University and University of Illinois). 01/06/2015- 31/05/2018. 344.000 €. Task leader.
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Recognitions

  • 2022- ISO/IEC Excellence Award, In recognition to the contribution to the Standardization of Genomic information.
  • 2023-2027- Ramon y Cajal Fellowship (Early Investigator Fellowships), Spanish Ministry of Science and Innovation.
  • 2020-2022- Marie S. Curie Fellowship, European Research Council.
  • 2014-2016- Postdoctoral Fellowship, Stanford Data Science Initiative (SDSI).
  • 2010-2012- PhD Fellowship, University of Navarra Alumni.
  • 2009-2010- Masters’ Thesis Fellowship, Telefonica R+D (largest telecommunication operator in Spain).
More information

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Scientific organizations

  • Spanish society of Bioinformatics and Computational Biology (SEBiBC)
  • International Society of Computational Biology (ISCB)
  • UNE (Spanish National Standardization Body)

Patents

  • J Voges, M Hernaez, J Ostermann, Method for encoding and decoding of quality values of a data structure, US Patent App. 16/3f41,307

  • S Chandak, KS Tatwawadi, T Weissman, I Ochoa, M Hernaez , Systems and Methods for Compressing Genetic Sequencing Data and Uses Thereof, US Patent App. 16/545,751

Latest scientific publications

More information