Yeremia G. Adhisantoso, M.Sc.
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
phone: +49 511 762-19588
fax: +49 511 762-5333
office location: room 1312

Yeremia Gunawan Adhisantoso studied electrical engineering at the Leibniz University Hannover, with a specialization in computer engineering. During his studies, he worked on research projects such as video compression, deep learning, and electrical design automation. After graduation, he joined the Institut für Informationsverarbeitung at Leibniz University Hannover, where he is currently working as a research assistant towards his PhD.

He is an active member of the MPEG working groups of ISO/IEC JTC 1/SC 29/WG 8, where he contributes to the MPEG-G standards (ISO/IEC 23092).

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Research Interest:

  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Predictive Health Management
  • Data Compression of Genomic Data
  • Medical Information Processing

If you are looking for a student project related to my research, please click here.

Other activities

Supervised Theses

Predictive Health Management:

  • Data-Driven Predictive Maintenance for Syringe Production Line
  • Untersuchung von Ensemble Learning fuer die Zustandsueberwachung von Zahnriemenantrieben
  • Reference-Based Deep Metric Learning for Remaining Useful Lifetime Prediction of Printing Screen
  • Normalizing Flows for Prediction of Degradation
  • Variational Bayes Methods for Prediction of Degradation

Data Compression of Genomic Data:

  • Search in Compressed Domain for Genomic Data
  • Codierung numerischer genomischer Annotationen
  • High-Efficiency Contact Matrix Compression

Machine Learning in Bioinformatics:

  • Sparse Neural Network for Polygenic Risk Score Prediction
  • Resolution Enhancement of Hi-C Data Using Generative Adversarial Network-based Deep Super Resolution Models
  • Resolution Enhancement of Hi-C Data Using Autoencoder-based Deep Super Resolution Models
  • Low-Rank Matrix Completion via Semidefinite Programming for 3D Genome Reconstruction