Mufeed Patel

Wanderer and Wonderer.

Exploring intelligence from Neural Networks to Brain Networks

Data Scientist • Computational & Theoretical Neuroscientist

hello@mufeedpatel.com

About Me

An engineer at heart, a data scientist by profession, and a computational and theoretical neuroscientist by passion shaped by a lifelong curiosity for numbers, intelligence, and the mysteries of the brain. With a mother deeply rooted in physics and mathematics, I grew up inspired by equations, logic, and patterns, which continue to guide both my research and my exploration of the brain’s mysteries...

I was born and brought up in the Middle East Asia, where I grew up with my parents and younger sister. That early environment taught me adaptability, curiosity, and the value of education qualities that continue to shape both my personal and professional life.

Today, I am based in London, where I balance two worlds: working at Barclays Investment Bank as a Data Scientist in the Data Design & Analytics team, while being a student of Brain Sciences at University College London.

I hold a Master’s degree in Computer Engineering (specialised in Data Science & AI) from the University of Glasgow, and currently student at University College London bridges mathematics, machine learning, and brain sciences. I am particularly interested in how deep neural networks can be linked with brain networks to advance our understanding of cognition and disease. My ultimate goal is to leverage these insights to help people suffering from Alzheimer’s disease, Parkinson’s disease, schizophrenia, depression, multiple sclerosis, and anxiety. My work involves computational modelling of neural circuits, neuroimaging (fMRI, EEG), and machine learning to study functional localisation, neurochemical pathways, and oscillatory synchrony in the brain. I also extend this research to animal models (mouse and Drosophila), investigating learning, memory, and controllability of neuronal networks.

I am grateful to be guided by inspiring mentors in my academic journey Dr. Fani Deligianni (Faculty of Computing Science, University of Glasgow), Dr. Michael Kohl & Dr. Ana Carolina Bottura de Barros (Faculty of Brain Sciences, University of Glasgow, University of Oxford) and Dr. Evans (Faculty of Neuroscience, UCL),

Beyond research and data science, I love exploring the frontiers of AI, blockchain, and mathematics. I also write occasionally, with a budding interest in fantasy fiction. Outside of work, I enjoy swimming, table tennis, billiards, chess, and playing acoustic guitar, and I’m a curious reader of astrophysics, cosmology, and astronomy.

At the crossroads of engineering, data science, and brain research, I aspire to build systems that not only process information but also bring us closer to understanding and treating the most complex disorders of the human brain.

Professional and Research Skills

I work with:

• Python and R for statistical analysis, prototyping analytics solutions, and building ML models (Pandas, scikit-learn).
• Keras and PyTorch for deep learning on GPUs (recommender systems, vision, NLP).
• SQL and Excel for querying, analytics, and quick charting.
• ElasticSearch and Grafana for dashboarding and large-scale data monitoring.
• Apache Spark (Scala) and Apache Storm for ETL and feature engineering.
• Spark, Hadoop, Hive for big data pipelines.
• Tableau and QlikView for advanced data visualisation.
• Apache Kafka and Spark Streaming for real-time analytics.
• AWS and GCP for scalable cloud deployments.

Programming Languages / Analytics

Python • R • Java • C/C++ • Bash • Advanced SQL • Node.js • Tableau • IPython • Excel/VBA • LaTeX

Tools & Platforms

Apache Spark • Hadoop • Kafka • SAS • PyTorch • Apache Storm • Google Cloud Platform • Amazon Web Services

Web & Media

Django • React • Express • Flask • HTML5/CSS/JavaScript • Photoshop • Premiere Pro • Illustrator

Neuroscience Research Skills

• Computational Neuroscience – modelling neural circuits and brain dynamics.
• Neuroimaging – fMRI, EEG, and advanced signal analysis.
• Machine Learning – unsupervised learning, deep neural networks, and pattern recognition applied to neural data.
• Clinical Brain Disorders – studying Alzheimer’s, Parkinson’s, schizophrenia, depression, multiple sclerosis, and anxiety.
• Animal Models – neural analysis in mouse and Drosophila for memory, learning, and network controllability.
• Neuroengineering & Neuromodulation – exploring brain–machine links and interventions.
• Systems Neuroscience – cortico-basal ganglia interactions, synchrony, and oscillations.
• Theoretical Approaches – information flow in neuronal networks, systems biology, and brain disease dynamics.

Cups of Coffee
No. of companies I worked
Projects
No. of universities I worked

Projects

Beginning with a background in computer engineering and advancing through roles in the tech industry, I have worked across diverse fields including Data Science, Deep Neural Networks, Machine Learning, and Blockchain Technology. Over time, my curiosity led me to focus increasingly on neural networks and their parallels with brain networks using computational models to study learning, memory, and cognition. Many of my projects and works now align with my current passion for Computational & Theoretical Neuroscience, where I apply deep learning and statistical modelling to explore neurological disorders such as Alzheimer’s disease, Parkinson’s disease, schizophrenia, and anxiety. Below is a selection of projects that reflect this journey

Automated Neural Cell Extraction & Calcium Imaging Analysis with Deep Neural Networks

Research Work

End-to-end pipeline for mouse calcium imaging: segmentation, cell extraction, and activity quantification using CNN/UNet and post-hoc signal deconvolution. Image Source: AI Generated

Connectome-to-Function in the Drosophila Mushroom Body with Graph Neural Networks

Research

Build a directed synaptic graph from the hemibrain (PN→KC→MBON/MBIN incl. DANs); train GNNs to predict odor-evoked neural/behavioral responses and identify predictive microcircuits. Image Source: NY Times

Spiking Neural Networks (SNNs) for Brain-Like Computation

Method Development

Implement SNNs with LIF neurons and surrogate gradients; evaluate temporal coding and energy-efficient inference for neuromorphic tasks. Image Source: Kasabov et. al

Deep Generative Models for Neural Activity Prediction

Modeling / Simulation

VAE/Flow/Transformer models to predict population activity and reconstruct latent dynamics from fMRI/EEG/calcium traces; evaluates sample efficiency and uncertainty. Image Source: AI Generated

Graph Neural Networks for Functional Brain Network Analysis

Clinical ML

Model correlation/functional connectivity graphs (fMRI/EEG/calcium) with GNNs to detect anomalies in epilepsy and schizophrenia; extensions to disease progression and brain-state decoding. This image is a Drosophila (fruit fly) whole-brain connectome reconstruction (structural wiring) created after a decade research work. Image Source: Tyler Sloan and Amy Sterling for FlyWire/Princeton University; Dorkenwald et al., Nature, 2024

Neural Plasticity Modeling with Meta-Learning

Theory & Algorithms

Apply MAML/ANIL to emulate learning-to-learn; compare learned adaptation rules with Hebbian updates and STDP; implications for memory formation and recovery. Image Source: Kwan et. al

Cross-Species Neural Representation Learning

Representation Learning

Contrastive alignment of neural population activity across mouse → primate → human visual cortex to reveal conserved vs. species-specific representations. Image Source: Gallego et. al

Achievements

INSIGHTIST FELLOWSHIP GERMAN SCHOLAR

Scholarship

  • Management and Leadership
  • 1 Project
  • 6 Months

IET INNOVATION CHALLENGE 2017

2nd Place

24 - Hour State University Hackathon

  • Theme : Innovative Product
  • 1 Project
  • 24 Hours
Best

MAKER SUMMIT 2017

3rd Place

24 - Hour State Government Hackathon

  • Theme: Socially Useful Product
  • 1 Project
  • 24 Hours

MARTIAN - MARS MAN MISSION IDEA DEVELOPMENT

Shortlisted

IIT Bombay

  • Theme: Space Science
  • 1 Idea
  • 1 Mentor

That's Me

Roger Garfield

Mufeed Patel

Data Scientist/Neuroscientist

Wanderer and Wonderer. Engineer and friend of science

Thank You Everyone

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