Hasan Jehangir
AI Engineer & Computational Neuroscience Researcher
AI Engineer with 2+ years of industry experience and extensive academic background. Specialized in developing intelligent systems at the intersection of machine learning, neuroscience, and healthcare. Core expertise includes:
- →Computational Neuroscience: Neural dynamics, connectomics, brain-inspired AI architectures
- →Brain-Computer Interfaces: Neural decoding, signal processing, real-time systems
- →Medical AI: Neuroimaging analysis, diagnostic systems, clinical decision support
- →Biomedical Signal Processing: EEG/EMG analysis, feature extraction, ML pipelines
Key Achievements
10+
Research Publications
5+
AI/ML Projects
2+
Industry Years
150+
Students Mentored
Core Research Areas
Research Focus
Computational Neuroscience
Brain-inspired AI, neural signal processing, EEG/EMG analysis
Healthcare AI
Medical imaging, clinical decision support, digital medicine
Biomedical Engineering
Wearable sensors, signal analysis, health monitoring systems
Generative AI
LLMs, RAG systems, multimodal learning applications
Research Interests & Open Topics
Open to collaboration on cutting-edge computational neuroscience research. Exploring novel approaches to understanding brain function at multiple scales.
Neural Dynamics & Brain-Computer Interfaces
Modeling neural population dynamics, decoding motor intentions, and developing non-invasive BCI paradigms
Key Topics
- •Recurrent neural network models for motor control
- •Spiking neural networks and neuromorphic computing
- •EEG-based cursor control and thought-to-action systems
- •Neural plasticity and learning-dependent changes
Connectomics & Network Analysis
Analyzing brain connectivity at multiple scales, from synaptic to whole-brain networks
Key Topics
- •Graph-theoretic analysis of brain networks
- •Functional connectivity from fMRI and EEG
- •Structural connectomics reconstruction and analysis
- •Small-worldness and network optimization
Neural Signal Processing
Advanced techniques for extracting information from complex neural recordings
Key Topics
- •Spike sorting and unit identification
- •Dimensionality reduction (PCA, t-SNE, UMAP)
- •Wavelet analysis and spectral methods
- •Real-time decoding pipelines for BCI applications
Computational Pathophysiology
Understanding disease mechanisms through computational modeling and analysis
Key Topics
- •Epilepsy dynamics and seizure prediction
- •Neurodegeneration modeling and progression
- •Alzheimer's and Parkinson's disease computational models
- •Sleep-wake cycle disruptions and neural oscillations
AI & Machine Learning in Neuroscience
Leveraging deep learning and advanced ML for neuroscience discovery
Key Topics
- •Convolutional neural networks for neuroimaging analysis
- •Transformer models for sequence prediction in neural data
- •Transfer learning from neuroimaging datasets
- •Automated detection of neural phenomena
Neurotransmitter Systems & Pharmacology
Modeling and analyzing neurotransmitter dynamics and drug effects
Key Topics
- •Dopamine, serotonin, and GABA system modeling
- •Receptor binding kinetics and synaptic transmission
- •Drug response prediction and pharmacogenomics
- •Neuromodulation and therapeutic interventions
Sensory & Motor System Neuroscience
Understanding how the brain processes sensory information and plans movement
Key Topics
- •Visual cortex processing and object recognition
- •Proprioception and somatosensory integration
- •Motor planning and trajectory optimization
- •Vestibular system and balance mechanisms
Computational Cognitive Neuroscience
Modeling cognitive processes including memory, attention, and decision-making
Key Topics
- •Working memory and prefrontal cortex dynamics
- •Attention mechanisms and saliency processing
- •Value-based decision making and learning
- •Metacognition and consciousness models
Interested in Collaboration?
I'm actively seeking research collaborations on novel computational neuroscience problems. Whether you're exploring neural dynamics, developing BCI systems, or analyzing brain data—let's connect.
Get in TouchPublications
NeuroShield: AI-Powered Wearable for Epileptic Seizure Detection
PeerJ Computer Science
Research paper on real-time seizure detection using deep learning on multimodal biomedical signals.
Body Sound Analysis for AI/ML Applications
Medium Publication
Comprehensive analysis and applications of respiratory sound processing in medical AI.
Computational Neuroscience & Digital Medicine
Ongoing Research
Exploring brain-inspired architectures for clinical decision support and patient monitoring systems.
Experience
GenAI Instructor
Wrapify Labs
Oct 2025 – Present
Teaching Generative AI, LLM applications, and prompt engineering to professionals and students.
Machine Learning Intern
NCAI AI in Healthcare Lab
2024 – 2024
Research on healthcare AI systems and signal analysis. Developed RespiraSense: Lungs disease detection models and contributed to medium articles.
Deep Learning Instructor
TCPC / DSS UETP
2024 – 2024
Teaching deep learning frameworks, neural network architectures, and capstone projects in Deep Learning.
Data Analyst
Accenture
2023
Data analysis and business intelligence. Analyzed real-world datasets and implemented predictive analytics pipelines.
Computer Science Instructor
Pioneers Group of Colleges
2023 – 2023
Taught programming, algorithms, data structures, and mentored 100+ students in technical and communication skills.
Projects
NeuroShield
AI-Powered Seizure Detection Wearable
Real-time tonic-clonic seizure detection system using multimodal biomedical signals (EMG, PPG, IMU). Implements ChronoNet architecture for temporal pattern recognition and deployed on edge devices for clinical monitoring.
Achieved 94% sensitivity in seizure detection with minimal false positives. Uses signal preprocessing, feature extraction, and neural classification.
Status: Journal Paper (Under Review)
RespiraSense
Lung Disease Prediction from Respiratory Sounds
CNN-based system for automated lung disease classification using respiratory sound analysis. Employs MFCC feature extraction and attention mechanisms for improved diagnostic accuracy.
Classifies conditions including pneumonia, asthma, and COPD. Integrated into healthcare applications for non-invasive screening.
Status: Research Project
MelanoGuard
AI-Based Melanoma Detection System
Deep learning classifier for melanoma detection from dermoscopic images using transfer learning and ensemble methods for clinical reliability.
Combines ResNet and VGG architectures with voting ensemble. Designed for dermatology clinics with interpretable predictions.
Status: Research Project
Saut-ul-Haqq
Islamic Q&A System with RAG
Intelligent question-answering system providing authentic responses to Islamic queries using the Quran and verified Islamic books. Employs RAG architecture with semantic search and LLMs.
Deployed production system handling thousands of queries. Retrieves from verified sources and generates contextually accurate answers in multiple languages.
Status: Production System
CleanCapture
Recyclable Item Detection System
Computer vision system for automated recyclable item classification using YOLO object detection with real-time processing capabilities.
Detects 20+ recyclable material types. Integrated with IoT sensors for smart waste management applications.
Status: Deployed Project
Attention-Guided Image Enhancement
Museum Artifact Restoration
Advanced computer vision project using attention-based encoder-decoder networks for restoration and enhancement of historical artifact images.
Combines domain-specific loss functions with neural attention mechanisms. Preserves fine details while removing degradation.
Status: Research Project
Skills
AI & Deep Learning
Biomedical & Signal Processing
Development & Deployment
Leadership & Achievements
Recognition and community impact
Microsoft AI Ambassador Finalist
Pakistan
Recognized as a finalist in the prestigious Microsoft AI Ambassador program for contributions to AI education and healthcare applications.
President – Data Science Society
UET Peshawar
Led the Data Science Society, organizing seminars, hackathons, and mentoring 150+ students in AI/ML technologies.
ML Lead – TCPC Community
Technical Community Engagement
Spearheaded machine learning initiatives, conducted workshops, and mentored emerging AI engineers in the tech community.
Certifications & Credentials
Professional development and specializations
Computational Neuroscience Specialization
Washington University in St. Louis
2025
Neuroscience & Neuroimaging Specialization
Johns Hopkins University
2025
AI for Healthcare
Coursera
2024
Neural Networks and Deep Learning
DeepLearning.AI
2023
Data Science Specialization
IBM
2023
TensorFlow & PyTorch Advanced
Coursera
2024