Naga Venkata Sai Jitin Jami | MaD Lab

Naga Venkata Sai Jitin Jami, M. Sc.

Researcher & PhD Candidate

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room 01.016
Carl-Thiersch-Straße 2b
91054 Erlangen
Germany

Telefon: +49913185-20296
E-Mail: jitin.jami@fau.de
Webseite: https://www.mad.tf.fau.de/person/jitin-jami/

since 10/2023	Researcher and PhD Candidate Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
10/2020-09/2023	M.Sc. Computational Engineering, „Thermo and Fluid Dynamics“ Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany Majors: Artificial Intelligence, High Performance Computing Master’s Thesis: „Feasibility and Robustness of Machine Learning algorithms for calculating Locational Marginal Pricing„
09/2021-09/2023	Masters in Computational Science Università della Svizzera italiana (USI), Lugano, Switzerland Double Degree with FAU Majors: Artificial Intelligence, High Performance Computing
03/2023- 09/2023	Computational Science Werkstudent Siemens Healthineers, Erlangen, Germany
02/2023-06/2023	Teaching Assistant High Performance Computing Lab for CSE, ETH, Zürich, Switzerland
09/2022-03/2023	ERASMUS Student Università della Svizzera italiana (USI), Lugano, Switzerland
07/2022- 03/2023	MaRS Scholarship Researcher (Master’s Thesis) Università della Svizzera italiana (USI), Lugano, Switzerland Topic: Feasibility and Robustness of Machine Learning algorithms for calculating Locational Marginal Pricing
03/2022-03/2023	Research Assistant Deutsche Museum Projekt, Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
05/2021- 10/2021	Data Science Werkstudent Streem.ai, Berlin, Germany
10/2011-06/2015	B.Tech. in Aeronautical Engineering Manipal Institute of Technology, Manipal, India Majors: Computational Fluid Dynamics

SWODDYS – Smart WOund Dressing incorporating DYe-based Sensors

07/2022 – 03/2023: Masters Research Scholarship (MaRS) from Hasler Foundation.

Courses

Biomedical Signal Analysis

Seminar Advances in Deep Learning for Time Series

Year	Student	Titel
2024/25	Aditya Parikh	Effective Disjoint Representational Learning: A Systematic lnvestigation of Multi-Decoder Networks and Parameter Sharing Strategies for Anatomical Segmentation (Master’s Thesis, Co-Supervision with Fraunhofer IAIS)
2024/25	Yangbin Peng	Semi-supervised Learning for Tissue Segmentation in Wound Images (MaD-Project)
2024/25	Sleiman Sharara	Multi-Task Learning for Multi-Tissue Wound Segmentation Using a Distance Map Regularized Modified U-Net (MaD-Project)
2024/25	Alisha Mund	Superpixel segmentation in wound images using EDTRS framework (MaD-Project)
2023/24	Atheeth Naik	Deep Learning Based Wound segmentation using Local-Global Architecture (MaD-Project)