Dr Alireza Poshtkohi is an interdisciplinary scientist who applies computer science and mathematics to tackle grand challenges in engineering, physics and medicine. He has worked in both academia and industry in many different roles, from computer scientist, neuroscientist, lecturer and electronics engineer to software engineer, IT consultant, data-centre architect, full-stack developer and author. His research sits at the intersection of scientific machine learning, computational neuroscience, optimisation and mathematical physics/biophysics.

After generalising the classical Hodgkin–Huxley framework, Alireza is now developing a physics- and chemistry-inspired scientific machine-learning framework for data-driven model discovery that aims to provide a unified mathematical language for ion channel modelling and wider biomedical systems. This research programme seeks to capture complex receptor, enzyme and signalling dynamics in a compact and mechanistically interpretable form, and to deliver transferable modelling tools for academia and industry, enabling more predictive models for drug discovery and personalised medicine in neurological and inflammatory diseases.

As a flagship project, he is developing a comprehensive, mechanistic model of a human neuron. This work aims to provide a unifying biophysical description of neuronal computation that can support next-generation brain disease modelling and inspire new, biologically grounded AI architectures.

In parallel, he is building a unified, physics-informed framework for parallel parameter estimation in dynamical systems that can be applied across biophysics, physics and beyond. By combining geometric least-action principles with high-performance computing, this programme aims to provide a general optimisation and inference engine for stiff, multi-scale ODE and PDE models — from ion channels and cellular signalling to continuum and field theories — as a form of scientific machine learning for complex dynamical systems.

Currently, he is developing mathematical models, computational tools and simulation environments for analysing the human nervous system using molecular neurobiology. His long-term goal is to provide a unified computational framework that enables the neuroscience community to study brain diseases at the cellular level by exploiting the power of cutting-edge supercomputers.

He is also collaborating with experimental neuroscientists at the University of Reading and Michigan State University to ground his models in human data and obtain deeper, biologically realistic insight into the human brain and the enteric nervous system.

Prior to his work in computational neuroscience, Alireza spent over two decades developing parallel and distributed systems, including optimistic parallel simulation languages, Grid/Cloud middleware and spacetime-parallel solvers for stiff PDEs. This work led to contributions in IEEE TPDS, Parallel Computing and other international journals, as well as a CRC Press textbook on implementing parallel and distributed systems. These foundations now underpin his current research in scientific machine learning, where ideas from high-performance computing, numerical analysis and optimisation are being repurposed to tackle mechanistic modelling problems in neuroscience and biomedicine.

Alireza's research is inspired by the curiosity, courage, and imagination of great thinkers:

• “All great genesis have a touch of madness.”…𝗔𝗿𝗶𝘀𝘁𝗼𝘁𝗹𝗲
  “What we know is a drop, what we don't know is an ocean.”…𝗦𝗶𝗿 𝗜𝘀𝗮𝗮𝗰 𝗡𝗲𝘄𝘁𝗼𝗻
• "Nothing is too wonderful to be true, if it be consistent with the laws of nature.”…𝗠𝗶𝗰𝗵𝗮𝗲𝗹 𝗙𝗮𝗿𝗮𝗱𝗮𝘆
• “I never made one of my discoveries through the process of rational thinking.”…𝗔𝗹𝗯𝗲𝗿𝘁 𝗘𝗶𝗻𝘀𝘁𝗲𝗶𝗻
• “Nature is written in mathematical language.”…𝗚𝗮𝗹𝗶𝗹𝗲𝗼 𝗚𝗮𝗹𝗶𝗹𝗲
• “Doubt is the origin of wisdom.”…𝗥𝗲𝗻é 𝗗𝗲𝘀𝗰𝗮𝗿𝘁𝗲
• “On earth there is nothing great but man… in man there is nothing great but mind.”…𝗦𝗶𝗿 𝗪𝗶𝗹𝗹𝗶𝗮𝗺 𝗥𝗼𝘄𝗮𝗻 𝗛𝗮𝗺𝗶𝗹𝘁𝗼𝗻