Teaching

Department of Computer Science, ETH Zurich, Switzerland

2021
  • Machine Learning for Healthcare. Lecture, Department of Computer Science, ETH Zurich, 261-5120-00. 5 ECTS, instructors: V. Boeva, G. Ratsch, J. Vogt
  • Data Science for Medicine. Lecture, Human Medicine Bachelor, D-HEST, ETH Zurich, 252-0868-00L. 4 ECTS, instructors: V. Boeva, G. Ratsch, J. Vogt
  • Computational Biomedicine. Lecture, Department of Computer Science, ETH Zurich, 261-5100-00. 5 ECTS, instructors: V. Boeva, G. Ratsch, N. Davidson
  • Machine Learning Seminar. Seminar, Department of Computer Science, ETH Zurich, 252-4811-00. 2 ECTS, instructors: G. Ratsch, V. Boeva
  • Data Science Lab., Department of Computer Science, ETH Zurich, 263-3300-00. Instructors: C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang

2020
  • Machine Learning for Healthcare. Lecture, Department of Computer Science, ETH Zurich, 261-5120-00. 85 enrolled students, 5 ECTS, instructors: V. Boeva, G. Ratsch, J. Vogt
  • Computational Biomedicine. Lecture, Department of Computer Science, ETH Zurich, 261-5100-00. 75 enrolled students, 5 ECTS, instructors: V. Boeva, G. Ratsch, N. Davidson
  • Machine Learning Seminar. Seminar, Department of Computer Science, ETH Zurich, 252-4811-00. 24 enrolled students, 2 ECTS, instructors: T. Hofmann, V. Boeva
  • Data Science Lab., Department of Computer Science, ETH Zurich, 263-3300-00. 28 enrolled students, 14 ECTS, instructors: C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang

2019
  • Computational Biomedicine. Lecture, Department of Computer Science, ETH Zurich, 261-5100-00. 60 enrolled students, 5 ECTS, instructors: V. Boeva, G. Ratsch, N. Davidson

Other teaching

2020
  • Guest lecture at UZH, BIO390 "Introduction to Bioinformatics"


Reaseach projects for bachelor and master students

We offer several internship projects. Bachelor students and Master students from ETH Zurich (D-INFK, D-BSSE, D-BIOL, D-MATH), but also mobility students from EPFL are welcome to apply.

Proposed topics include:

  1. Infering ploidy and purity of cancer samples [machine learning, C/C++]
  2. Neural networks for signal deconvolution in cancer transcriptomics data [machine learning]
  3. Detection of shared transcriptional programs from single cell transcriptomics data in cancer [machine learning]
  4. Multi-omics data integration for cancer patients' survival [biomedicine, data analysis, machine learning]
  5. Deconvolution of epigenetic data from mixed tumor samples [deconvolution, computational methods]
  6. Analysis of ChIP-seq data in adrenocortical carcinoma [data analysis, biology]
  7. Transcriptional heterogeneity in mesothelioma [data analysis, biology]
  8. Reconstruction of gene regulatory interactions using machine learning [machine learning]
  9. Prediction of intratumoral heterogeneity using bulk transcriptional data from cancer samples [deconvolution, machine learning]
  10. Digging deep into intratumoral heterogeneity in melanoma and neuroblastoma using scRNA-seq data [data analysis]
  11. Analysis of genomic events driving transcriptional programs in cancer [data analysis]
  12. Analysis of tumor microenvironmnet in melanoma [data analysis, biology]
To get more information, please, contact Valentina Boeva directly.