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ESR2

Syed Ali Hassan

Assessment of human-machine interfaces of social and collaborative robots

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Twitter Linkedin Researchgate

Syed Ali Hassan has completed his graduation in Software engineering from Mohammad Ali Jinnah University (Pakistan) and completed post-graduation in I.T. Convergence Engineering from Kumoh National Institute of Technology (South Korea).

He has been awarded a fully-funded Professor Scholarship in wens lab South Korea. He has a work experience of more than 4 years and has Developed Several AR/VR/MR and deep learning-based applications. His latest projects are revolving around computer vision and robotics.

Syed Ali Hassan is currently doing his PhD at the Bio robotics institute of Scuola Superiore Sant’Anna (Italy) as EINST4INE ESR2.

Research interests:  Augmented Reality, Virtual Reality, Mixed Reality, Deep Learning, Computer Vision and Robotics

Calogero Oddo​

Calogero Oddo

Arianna Menciassi

Supervisors

Arianna Menciassi
Calogero Oddo

Research Outcomes

Tactile sensors for Material recognition in Social and Collaborative Robots: A brief review

Contamination Detection Using a Deep Convolutional Neural Network with Safe Machine—Environment Interaction
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Blog posts

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EINST4INE ESRs

Indrė Taurosevičiūtė-Ribikauskė

Vivian Marcelino

Nelly Dux

Christina Haefliger

Jessica Fishburn

Gianlorenzo Meggio

Finn Jonas Tryggvason

Parul Chaudhary

Sébastien Martin

Chiara Mariottini – Terminated

Constanze Leeb

Nicola Felicini

Alejandra Rojas

Sophie Altrock

Philipp Jennes

EINST4INE: The European Training Network for InduStry Digital Transformation across Innovation Ecosystems

Einst4ine (at) rmit.edu.au

Privacy

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956745. Results reflect the author’s view only. The European Commission is not responsible for any use that may be made of the information it contains.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956745.

Results reflect the author’s view only. The European Commission is not responsible for any use that may be made of the information it contains.

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