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Dr Nathanael L Baisa

Job: Senior Lecturer in Artificial Intelligence

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: »ÆÉ«²Ö¿â, The Gateway, Leicester, LE1 9BH

T: N/A

E: nathanael.baisa@dmu.ac.uk

 

Personal profile

Dr. Nathanael L. Baisa is currently a Senior Lecturer in Artificial Intelligence (AI) and Programme Leader of BSc AI in the School of Computer Science and Informatics, »ÆÉ«²Ö¿â (»ÆÉ«²Ö¿â). Before joining »ÆÉ«²Ö¿â in June 2021 as a Lecturer in AI, he was a senior research associate at Lancaster university (Oct 2020 – May 2021), a research scientist at Oosto (Feb 2019 – Aug 2020) and a research fellow at university of Lincoln (Oct 2017 – Dec 2018), all in computer vision and machine learning, where he participated as a main researcher in many research projects funded by ERC (under EU’s horizon 2020), EPSRC and Innovate UK.

 

Dr. Nathanael received his PhD degree in Electrical Engineering (with focus on Computer Vision and Machine Learning) from Heriot-Watt university, UK, in 2018 and his MSc degree in Computer Vision and Robotics (VIBOT) with distinction from three universities (European Erasmus Mundus): Burgundy University in France, Girona University in Spain and Heriot-Watt University in the UK, in 2013. He also received his BSc in Electrical Engineering with great distinction from Mekelle University, Ethiopia, in 2008.

His research interests include computer vision and machine learning, with current research emphasis on deep learning and Bayesian algorithms for solving computer vision (both 2D and 3D) problems such as object detection, recognition, tracking, scene understanding using multimodal learning (vision, language, etc.), generative AI (e.g. image synthesis), etc. for different applications such as intelligent video surveillance, biometrics, autonomous driving, robot perception, healthcare, etc.

Research group affiliations

Institute of Artificial Intelligence (IAI)

Publications and outputs

Research interests/expertise

  • Computer Vision
  • Machine Learning
  • Deep Learning
  • Multimodal Learning (e.g. vision-language)

Areas of teaching

  • Computer Vision
  • Machine Learning
  • Deep Learning
  • Applied AI

Qualifications

  • PhD - Electrical Engineering (Computer Vision and Machine Learning)
  • MSc - Computer Vision and Robotics
  • BSc - Electrical Engineering 

Courses taught

  • Applied AI (CSIP5403)
  • Intelligent Robotics (IMAT2721)
  • Introduction to Computer Vision (CTEC5604)
  • Machine Learning (CTEC5602)
  • Artificial Neural Networks and Deep Learning (IMAT5235)
  • NLP based on Deep Learning (IMAT5118)
  • Introduction to C++ (IMAT1907)

Honours and awards

  • Elsevier Reviewer Recognition Award.
  • Full scholarship for my PhD amounting around £90,000 payable in three years.
  • European Erasmus Mundus Scholarship amounting €42,000 payable in two years to support my MSc.
  • Distinction for MSc 
  • Great Distinction for BSc

Membership of external committees

  • Member of technical program committee of many conferences.

 

Membership of professional associations and societies

  • Fellow of Higher Education Academy (FHEA)
  • Member IEEE (MIEEE)
  • Member of the UKRI Talent Peer Review College (in progress)

Professional licences and certificates

  • Deep Learning Specialization Coursera Course Certificate lectured by Andrew Ng from Stanford University, USA, in 2018.
  • Machine Learning Specialization Coursera Course Certificate lectured by Andrew Ng from Stanford University, USA, in 2016.

Current research students

  • Ahmad Lawal (2nd supervisor)   
  • Aiden Morris (2nd supervisor)   
  • Asmau Lawal (1st supervisor)   

Professional esteem indicators

  • Reviewer of many world-renowned journals and conferences in my field including, but not limited to:
    • IEEE Transactions on Circuits and Systems for Video Technology
    • IEEE Transaction on Image Processing
    • IEEE Transaction on Multimedia
    • Journal of Visual Communication and Image Representation (Elsevier)
    • Signal Processing (Elsevier)
    • International Conference on Pattern Recognition (IEEE)
    • International Conference on Information Fusion (IEEE), etc.

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