Biography
Dr. Vinod Kumar Chauhan is a Postdoctoral Research Assistant in Healthcare with CHI Lab and currently, working on PARADISE project. He is developing predictive deep learning based algorithms for post-operative atrial fibrillation in patients undergoing cardiac surgery using ICU data.
Before joining CHI lab, Vinod was a Research Associate in Industrial Machine Learning at Institute for Manufacturing, Department of Engineering, University of Cambridge UK. At Cambridge, he applied network science, machine learning and mathematical modelling to solve industrial problems. He also won (shared with one another) Institute for Manufacturing Research Execellence Award 2021.
He did his PhD from Panjab University Chandigarh, India and worked on developing optimisation algorithms to solve large-scale machine learning problems.
Most Recent Publications
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022), Computers & Industrial Engineering, 108928-108928
Continuous Patient State Attention Models
Chauhan VK, Thakur A, O’Donoghue O, Rohanian O & Clifton DA (2022)
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Exploitation of material consolidation trade-offs in a multi-tier complex supply networks
Chauhan VK, Alomari M, Arney J, Parlikad AK & Brintrup A (2022)
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022)
Research Interests
AI for Healthcare
Current Projects
PARADISE: Developing predictive deep learning based algorithms for post-operative atrial fibrillation in patients undergoing cardiac surgery using ICU data.
Research Groups
Related Academics
Most Recent Publications
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022), Computers & Industrial Engineering, 108928-108928
Continuous Patient State Attention Models
Chauhan VK, Thakur A, O’Donoghue O, Rohanian O & Clifton DA (2022)
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Exploitation of material consolidation trade-offs in a multi-tier complex supply networks
Chauhan VK, Alomari M, Arney J, Parlikad AK & Brintrup A (2022)
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022)
Most Recent Publications
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022), Computers & Industrial Engineering, 108928-108928
Continuous Patient State Attention Models
Chauhan VK, Thakur A, O’Donoghue O, Rohanian O & Clifton DA (2022)
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Exploitation of material consolidation trade-offs in a multi-tier complex supply networks
Chauhan VK, Alomari M, Arney J, Parlikad AK & Brintrup A (2022)
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Chauhan VK, Mak S, Parlikad AK, Alomari M, Casassa L et al. (2022)
