class AkshitManocha:
def __init__(self):
self.name = "Akshit Manocha"
self.role = "ML & AI Researcher"
self.education = {
"institute": "IIT Roorkee",
"major": "Chemical Engineering",
"year": "Pre-Final Year",
"cgpa": 8.30
}
self.research_areas = [
"Deep Learning",
"Computer Vision",
"Natural Language Processing",
"Brain-Computer Interface",
"Physics-Informed ML"
]
self.currently_learning = [
"Transformer Architectures",
"Reinforcement Learning",
"Multimodal AI"
]
def say_hi(self):
print("Thanks for dropping by! Let's build something amazing together!")
me = AkshitManocha()
me.say_hi()- 🧠 Deep Learning Research: Pushing boundaries in neural network architectures
- 🔬 Brain-Computer Interfaces: Working on cutting-edge EEG signal processing
- 👁️ Computer Vision: Developing intelligent visual systems
- 📊 ML Engineering: Building scalable and efficient ML pipelines
- 🤝 Open to Collaborate: Always excited about innovative AI projects!
Focus: Brain-Computer Interface & EEG Signal Processing
- 🎯 Implemented state-of-the-art EEG processing models for BCI applications
- 🏆 Achieved 97.5% accuracy using advanced parameter-efficient fine-tuning techniques
- 📊 Developed robust preprocessing pipeline for diverse EEG datasets
- 🔧 Optimized model performance through innovative architectural improvements
Ranked 46th out of 1,120 teams on Kaggle
| Course | Institution | Focus Area |
|---|---|---|
| CS229: Machine Learning | Stanford University | ML Fundamentals & Applications |
| CS224N: NLP with Deep Learning | Stanford University | Natural Language Processing |
| 6.S191: Intro to Deep Learning | MIT | Deep Learning Foundations |
mindmap
root((Akshit's Focus))
Research
Brain-Computer Interface
EEG Signal Processing
Parameter-Efficient Fine-Tuning
Learning
Transformer Architectures
Reinforcement Learning
Multimodal AI Systems
Projects
Deep Learning Applications
Computer Vision Systems
NLP Solutions
| Project | Description | Tech Stack | Highlight |
|---|---|---|---|
| 🧠 EEG-BCI System | Advanced brain-computer interface using deep learning | PyTorch, Signal Processing | 97.5% Accuracy |
| ♟️ Efficient Chess AI | Optimized chess engine for FIDE-Google challenge | Reinforcement Learning, Optimization | Top 5% Globally |
| 🔮 Coming Soon... | Stay tuned for more exciting projects! | - | - |
graph LR
A[AI Research] --> B[Deep Learning]
A --> C[Computer Vision]
A --> D[NLP]
A --> E[Brain-Computer Interface]
A --> F[Physics-Informed ML]
B --> G[Transformers]
B --> H[Neural Architecture Search]
C --> I[Object Detection]
C --> J[Image Segmentation]
D --> K[LLMs]
D --> L[Text Generation]
E --> M[EEG Processing]
E --> N[Signal Analysis]
style A fill:#9D4EDD
style B fill:#7209B7
style C fill:#7209B7
style D fill:#7209B7
style E fill:#7209B7
style F fill:#7209B7
I'm always excited to work on innovative ML/AI projects and discuss cutting-edge research. Feel free to reach out!
"The only way to do great work is to love what you do" - Steve Jobs
Currently exploring: How AI can revolutionize our understanding of the human brain! 🧠✨