Hi, I’m Muhammad Huzyafa Khokhar, Founder of Excelleve and a visionary in AI, Brain-Computer Interfaces (BCI), and robotics, dedicated to transforming healthcare and assistive technology.

Featured Work

🚀 Thought-to-Speech BCI – Restoring speech for individuals with paralysis.
🤖 Advanced Robotics & AI – High-performance robotic systems and intelligent automation.
🧠 Medical AI Innovations – AI-driven diagnostics.

My Journey

Inspired by Dr. Asim Karim, a blind professor in assistive technology, and Conor Russomanno, CEO of OpenBCI, I ventured into BCI research to revolutionize not just speech recovery but broader applications in brain-computer interaction. With a passion for AI and robotics, I founded Excelleve, a BCI-driven assistive technology company, to push the boundaries of innovation and create life-changing solutions.

Skills & Expertise

🔹AI & BCI Research

• Non-invasive BCI systems

• EEG signal processing

• Neuro-linguistic modeling

• Computational neuroscience

🔹Advanced Robotics & IoT

• Mobile robotics & AI-powered automation

• Embedded systems & sensor integration

• Robotic perception & real-time control systems

• Electronics hardware integration

🔹IoT & Embedded Systems

• Development with Arduino, Raspberry Pi, and microcontrollers

• Applications across diverse domains, including automation and intelligent systems

🔹Circuit Design & PCB Development

• Designed and developed custom PCBs for robotic and embedded systems

🔹Generative AI & LLMs

• Fine-tuning large language models (LLMs)

• Dataset curation & prompt engineering

• Model evaluation & transformer optimization

• AI-driven automated text generation

🔹Medical AI & Imaging

• AI-powered medical diagnostics

• Deep learning for disease classification

🔹Leadership & Entrepreneurship

• Founder of Excelleve (Assistive Tech & BCI)

• Founder & Head of R&D Lab (Robotics Lab)

• Robotics Mentor (Guided students in national competitions)

🔹Industry Experience

• Technology Assistant at the Interactive Media Lab, NYU Shanghai

Featured Projects

🧠 Thought-to-Speech BCI

🔹 Objective: Restoring speech for individuals with motor impairments.
🔹 Technology Used: EEG, Deep Learning, Signal Processing.
🔹 Impact: Developing non-invasive solutions for speech restoration with broader applications in Brain-Computer Interfaces (BCI).

🩺 Medical AI & Imaging

🔹 Harmful Brain Activity Detection

• Applied deep learning on EEG data to classify neurological disorders, enabling early diagnosis and detection.

🔹 Medical Image Segmentation

• Developed AI-driven models to detect and classify medical anomalies in MRI, CT scans, and X-ray imaging.

🔹 Disease Classification from Medical Imaging

• Built AI models to analyze and classify radiological scans, improving diagnostic accuracy.

🔹 AI-Powered Disease Prediction

• Created predictive AI models using patient data to detect critical diseases early and support medical decision-making.

🤖 Advanced Robotics & IoT

🔹 Robotics & Automation

• Designed and implemented high-precision robotic systems, winning awards in national and international robotics competitions.

🔹 Embedded Systems & Control

• Engineered robotic solutions integrating PID control, sensor networks, AI-based automation, and Arduino-VEX communication.

🔹 IoT & Smart Systems

• Developed IoT-driven solutions using microcontrollers and embedded systems across various applications.

🔹 Circuit Design & PCB Development

• Designed and developed custom PCBs for robotic and IoT-based applications.

🔹 Robotics Mentorship & R&D Leadership

• Led student teams in national robotics competitions, mentoring them in advanced robotics, embedded systems, and mechatronics.

🔥 Generative AI & Large Language Models (LLMs)

🔹 Fine-Tuning LLMs for Specialized Applications

• Fine-Tuned and optimized LLMs and transformer-based architectures for domain-specific AI tasks and knowledge modeling including solving Olympiad-level mathematical problems, classifying human vs. AI-generated responses.

🔹 Automated RLHF in LLM Training

• Developed an LLM to automate Reinforcement Learning from Human Feedback (RLHF), reducing human intervention and improving LLM fine-tuning.

🔹 AI-Driven Workflow Automation

• Built AI models that enhance productivity, automate tasks, and optimize real-world applications.