PhD · Computer Vision & Artificial Intelligence
Multimedia University, Malaysia
Open to research collaboration
COMPUTER VISION · DEEP LEARNING · ARTIFICIAL INTELLIGENCE
Dr. Fahmid Al Farid
Faculty Lecturer (Assistant Professor)
Faculty of Computer Science & Informatics
Berlin School of Business & Innovation (BSBI), Berlin, Germany
Researcher and educator working at the intersection of computer vision, deep learning and applied AI, with interests spanning medical imaging, smart farming, robotics, explainable AI, federated learning and intelligent industrial systems.
AI · CV · ROBOTICS
Research · Teaching · Innovation
140+Publications · public research profile
19h-index · reported 2026
1,300+ResearchGate citations · Sep 2026
12+Years academic & research experience
01 · ABOUT
AI research with
real-world impact
Dr. Fahmid Al Farid is a Faculty Lecturer (Assistant Professor) at BSBI in Berlin. His professional experience spans Germany, Malaysia, South Korea and Bangladesh, including postdoctoral research at Multimedia University and research positions at Sungkyunkwan University and the University of Ulsan.
He completed his PhD at Multimedia University in 2023 with a specialization in Computer Vision & Artificial Intelligence, an MS in Computer Engineering at the University of Ulsan, and a BS in Computer Science & Engineering at the University of Chittagong. His research emphasizes practical, interpretable and efficient AI systems.
02 · EXPERIENCE
Academic & professional experience
2024–Present Faculty Lecturer (Assistant Professor), Berlin School of Business and Innovation (BSBI), Germany
2023–2025 Post-Doctoral Scientist, Multimedia University, Malaysia
2022–2023 Chief Technology Officer (CTO), INLEADS IT Solution Sdn Bhd
2016–2022 Graduate Research Assistant (PhD) & Tutor, Multimedia University, Malaysia
2015 Research Assistant, Sungkyunkwan University, Republic of Korea
2013–2015 Research Assistant, University of Ulsan, Republic of Korea
03 · EDUCATION
Academic qualifications
MS · Computer Engineering
University of Ulsan, Republic of Korea
BS · Computer Science & Engineering
University of Chittagong, Bangladesh
04 · RESEARCH
Research interests
Computer Vision
Image/video analysis, object detection, gesture and activity recognition, visual intelligence.
Medical AI & XAI
Medical imaging, disease classification, federated learning and interpretable deep learning.
Smart Farming
Crop disease recognition, precision agriculture, Agricultural IoT and lightweight vision models.
Robotics & Intelligent Systems
Robot vision, human–robot interaction, industrial fault diagnosis and applied intelligent automation.
05 · SELECTED WORK
Recent publications
YOLOv8-Based Fire and Smoke Detection Using CLIP-Guided Dual-Teacher Knowledge Distillation
View paper →DepressNet: An Interpretable Machine Learning Framework for Depression Severity Stratification Using Psychometric Data
View paper →Bangladeshi Traffic Sign Recognition Using the Lightweight BanTrafficNet Deep Learning Approach
View paper →Bridging the Performance–Efficiency Gap: A Resource-Efficient Hybrid DL-ML Framework for Jackfruit Disease Detection
View paper →Human activity recognition under low-light conditions using frozen CLIP with lightweight adaptation
View paper →An integrated evaluation protocol for adversarial robustness, generalization, and explanation stability in URL-based phishing detection
View paper →DiffWaferNet: A lightweight differential attention-based network for wafermap defect classification
View paper →An ROI-Guided Optimized Machine Learning Framework for Orange Disease Recognition with Feature Selection and Explainability
View paper →06 · OPEN RESEARCH
Code, datasets & reproducibility
Explore verified public repositories, notebooks, datasets and project resources associated with my publications. Resources include medical AI and federated learning, wafer-defect classification, Bangla OCR, traffic-sign recognition, crowd analytics and interpretable machine learning.
Reproducibility first. External repositories are linked to their original maintainers and co-authors, with publication attribution. This keeps the research trail transparent while respecting code ownership and licensing.
07 · TEACHING & MENTORSHIP
From foundations to intelligent robotics
Teaching emphasizes applied learning, reproducible experimentation and research-led projects—from deep-learning pipelines and image analysis to robotic perception and intelligent systems.
08 · RECOGNITION
Awards & academic milestones
2025 Gold Medal & Silver Medal — iNVENTX, Malaysia
2024 Gold Medal — iNVENTX, Malaysia · Keynote Speaker, ICIERI
2023 Postdoctoral Fellowship — Multimedia University
2021 Best Paper Award — 7th International Visual Informatics Conference · Bronze Medals, I-RIA
Patent Co-inventor, PCT/MY2020/050081 (WO2021045608A1)
09 · ACADEMIC PROFILES
Research identity & publication records
For the most current publication list and research metrics, please consult the linked academic profiles.
10 · CONNECT
Research, supervision & collaboration
I welcome scholarly collaboration in computer vision, deep learning, medical AI, smart agriculture, robotics, explainable AI and related areas.
Institutional: fahmid.alfarid@berlinsbi.com
Gmail: fahmid.farid@gmail.com