Lehrstuhl für Angewandte Softwaretechnik
Chair for Applied Software Engineering

Sajjad Taheri                                                                                                       https://wwwbruegge.in.tum.de/lehrstuhl_1/images/linkedin.jpg
PhD candidate in computer science; researcher in machine
learning applications, in particular deep learning and convolutional
neural nets.
Email: sajjad.taheri (at) tum.de
Tel
: +49 (89) 289 18200
Technische Universität München
Institut für Informatik I1
Boltzmannstraße 3
85748 Garching b. München, Germany

 Office Hours:

  By appointment. Please contact me via email.

 Research Interests:

 Publications:

 Teaching:

 Projects:

 Theses and Guided Researches:

 Please find underneath the offered, in progress and finished theses topics. 

Offered

  

Master's Thesis
A New Approach to Classify Nuts and Washers in Overhauling Processes
Advisor
Sajjad Taheri
Author
-
Date
-
Creating a new approach to create datasets for classification of different nuts and washers
 
Master's Thesis
Detection of Damaged Small Pieces in Overhauling Processes
Advisor
Sajjad Taheri
Author
-
Date
-
Creating a model to detect damaged small pieces (screws, bolts, nuts, pins, washers, etc.) in overhauling industrial machineries, using deep learning and convolutinal neural networks
    

In Progress

  

Master's Thesis
An Experimental Two-Tiered Appeoach for an Independant and Self Learning Natural Language Processing System
Advisor
Sajjad Taheri
Author
Gopala Krishna Char Cheidu Raghavendrachar
Date
15.11.2017
Implementing a chatbot to help the users with various queries in overhauling processes, which can retrain itself when it is on online mode.
     
Master's Thesis
Creating Training Datasets for Small Pieces using 3D Modelling
Advisor
Sajjad Taheri
Author
Amr Abdelraouf
Date
15.02.2018
Studying methods to create datasets from 3D models and use them for training a classification systems for small pieces in overhauling processes
                 

Finished

 

Bachelor's Thesis
Providing Training Dataset for Automatic Recognition of Small Pieces
Advisor
Sajjad Taheri
Author
Jonas Pfab
Date
15.03.2017
Study the current approaches to get the right dataset for deep learning processes and implementation of an application for automatic data augmentation to enrich the training dataset
           
Master's Thesis
Comparison between Cloud-based and Offline Speech Recognition Systems
Advisor
Sajjad Taheri
Author
Elma Gazetic
Date
15.04.2017
Study the popular offline open-source speech recognition systems and training/tuning them in order to compare with the cloud-based solutions