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My name is Soumya Banerjee (first name pronounced as show-mo) and I am a senior research fellow at the University of Cambridge.
I work in explainable AI (xAI) and unconventional approaches to AI. I work at the intersection of complex systems and xAI: I take inspiration from complex systems to suggest new approaches to AI, and use AI to analyze complex systems.
If you are a Master's or PhD student and want to work on a project, please get in touch (sb2333 at cam dot ac dot uk).
In a previous life I was a researcher at the University of Oxford and Harvard University and have had the great fortune of working in India, USA, Australia and Germany.
I analyze complex problems and implement new statistical and machine learning techniques for deriving insights from large amounts of data.
My research is in the field of modelling of biological systems. I apply mathematical modelling to understand biological systems. I also apply machine learning to the field of healthcare.
I have also worked in financial and healthcare domains and am domain certified in finance and mortgage.
You can read more about my projects and skills here. I am involved in various data science projects (machine learning projects, open source data science projects) and freely share code that I write. You can also read my publications here.
My research interests are in computational immunology, computational immunogenomics and complex systems. I use techniques from computer science to solve problems in immunology and take inspiration from the immune system to solve problems in computer science.
a) Using techniques from computer science to solve problems in immunology.
I use machine learning techniques to solve problems in biology, especially immunology. The tools of my trade are ordinary differential equation (ODE) models and spatially explicit agent based models (ABMs) to understand kinetics of West Nile Virus (WNV). I use Hierarchical Bayesian non-linear mixed effects models to simulate immune response in different species.
b) Taking inspiration from the immune system to solve problems in computer science.
My work suggests how chemical signals and the physical architecture of the immune system may lead to nearly scale-invariant immune search and response. I look at how we can take inspiration from this and create human-engineered distributed systems with faster search and response characteristics.
Bio - Soumya Banerjee is a Senior Research Fellow at the University of Cambridge. He worked in industry for many years before completing a PhD in applying computational techniques to interdisciplinary topics. Over the last 18 years, he has worked closely with domain experts in finance, healthcare, immunology, virology, and cell biology. He has recently worked closely with clinicians and patients on using patient and public involvement to build trust in AI algorithms.
Alternative Bio - Soumya Banerjee has a PhD in Computer Science. He worked in Los Alamos National Laboratories, USA while he was in graduate school. Prior to graduate school, he was a software engineer working in the financial services sector for Fortune 500 clients.
His work is at the intersection of computer science and biological systems – he uses tools from computer science to study biological systems and takes inspiration from biological systems to design more efficient human-engineered systems. He is skilled in machine learning techniques and mathematical modelling using spatially explicit agent-based models and computationally tractable differential equation models.
He works closely with people from other domains, especially experimentalists and clinicians. His work has been recognized with a University of New Mexico Student Award for Innovation in Informatics in 2010.
He takes pride in writing industrial-strength software, which he attributes to years working in industry and skills honed in academia. As of January 2015, he was ranked within the top 200 worldwide on MATLAB Central (an online repository for Matlab code contributed by users all over the world).
Soumya Banerjee is a researcher in ethical artificial intelligence applied to complex systems. He applies artificial intelligence techniques to solve human problems. He is also very passionate about outreach, science communication and policy for using science for social good.My research uses data science for social good and answer questions about complex systems. Complex systems are all around us, from social networks to transportation systems, cities, economies and financial markets.
Research blurb - I analyze complex problems and implement new statistical and machine learning techniques for deriving insights from large amounts of data. I work closely with people from other domains, especially experimentalists and clinicians.
I worked in industry before completing a PhD in applying computational techniques to interdisciplinary topics. I have worked closely with domain experts in finance, healthcare, immunology, virology, and cell biology. Recently I have worked closely with clinicians and patients on using patient and public involvement to build trust in AI algorithms.
My research uses data science for social good and answer questions about complex systems. Complex systems are all around us, from social networks to transportation systems, cities, economies and financial markets. I am also very passionate about outreach, science communication.
I can be reached at - NEEL DOT SOUMYA AT GMAIL DOT COM
OR
sb2333 AT cam.ac.uk
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