NLY

Niloy Irtisam

Lecturer

niloy.irtisam@bracu.ac.bd

Address

CSE Department
4th floor, Room No # 4M121,
Brac University,
Kha 224 Bir Uttam Rafiqul Islam Avenue,
Merul Badda, Dhaka, Bangladesh

Niloy Irtisam completed his undergraduate education from the Department of Robotics and Mechatronics Engineering at the University of Dhaka. He is currently working as a Lecturer in the Department of Computer Science and Engineering at BRAC University. He is conducting research in the domains of Robotics, Deep Learning and Computer Vision. 

Journals:

[1] Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and autonomous systems, 146, 103902.

 

Conference:

[1] Irtisam, N., Ahmed, R., Akash, M. M., Abdullah, R., Sarker, S., Rahman, S., & Jamal, L. (2020, August). Pathfinder: A Fog Assisted Vision-Based System for Optimal Path Selection of Service Robots. In 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) (pp. 1-6). IEEE.

1. Dean's Award, Faculty of Engineering and Technology, University of Dhaka
2. Azfar Alam Gold Medal
3. Undergraduate Scholarship, Dhaka University
4. Luna Shamsuddoha, Chairman, Janata Bank Limited Scholarship
5. IFIC Bank Trust Fund Research Grant
6. Finalist (Top 10), Robi Datathon 2.0
7. Semi-finalist, Robotics reality show ”Esho Robot Banai” on Channel-i
8. Champion, IntraDU project showcasing: University of Dhaka
9. Champion, IntraDU Robofest: University of Dhaka
10. 1st Runners-Up, Line Following Robot competition, Mindsparks: AUST
11. 2nd Runners-Up, Line Following Robot competition, Robofiesta: BUET
12. 2nd Runners-Up, Line Following Robot competition, Techfest: DUET

Thesis

Accepting


As:

  • Supervisor
  • Co-supervisor

Level:

Undergraduate

Type:

  • Thesis
  • Project

Research Interest

Generative AI, Diffusion Models, LLMs, Robotics, Multiagent systems, Computer Vision, Machine Learning, Deep Learning, Continual/Lifelong Learning


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