Naga Venkata Sai Jitin Jami

Naga Venkata Sai Jitin Jami, M. Sc.

Researcher & PhD Candidate

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

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

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

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
2023/24 Atheeth Naik

Deep Learning Based Wound segmentation using Local-Global Architecture
(MaD-Project)