Ashwin Zade
AI, Decision Optimisation & Deep Learning
Greater Bengaluru Area
14+ years of work experience
May 20242 yrs 7 mosPresent
Technical and product leader for RailMax, an optimisation SaaS platform for real-time and planning-phase rail production and maintenance operations.
Lead end-to-end algorithmic design of a simulation-based train dispatch and scheduling engine, combining mathematical optimisation, heuristics, search, and constraint-based decision logic for large operational networks.
Architect learning-augmented optimisation approach combining heuristic tree search, neural networks, and reinforcement learning to guide meet-pass decisions and reduce solution runtime by ~50%.
Design and develop high-fidelity digital twins and simulation environments for Centralized Traffic Control (CTC) and train on-board systems to validate model behaviour under stochastic and real-time operating conditions. Delivered production optimisation solutions that improved average train velocity by 10-15% and network throughput by 20-25% for a Class I North American railroad.
Mar 20222 yrs 2 mosApr 2024
Perth, WA
Building RailMax, a suite of advanced AI based products aimed to help train controllers and schedulers in real-time operations execution, deviation management and tactical scheduling
https://railsuite.com/railmax/
Oct 20192 yrs 6 mosMar 2022
Perth, Australia
Worked with Rail Logistics and Optimisation team, comprising of train controllers, planners and key rail leadership to deliver software solutions aimed at streamlining daily and weekly rail scheduling processes
Oct 20192 yrs 5 mosFeb 2022
Built end-to-end micro-simulation and optimisation pipelines for daily and weekly rail planning, supporting large operational datasets and scenario-based decision analysis. Developed MIP-based maintenance scheduling optimisers to minimise production loss subject to complex network, resource, and operational constraints. Automated simulation and analytical workflows in Python, improving model experimentation, turnaround time, and repeatability of optimisation studies. Served as industrial supervisor for a University of Sydney research collaboration on pit-to-port optimisation, reviewing and guiding simulation and MIP model formulation and experimentation. Enabled data-driven capacity assessment and operational planning by combining simulation outputs and optimisation analysis for senior rail operations leadership.
May 20145 yrs 6 mosOct 2019
Perth, Australia
Worked closely with train controllers, network specialists and supply chain schedulers to build modern AI based decision support systems for their complex problems
Delivered business needs through value adding innovative solutions
May 20145 yrs 5 mosSep 2019
Led delivery of a multi-million-dollar optimisation and decision automation platform for BHP rail operations, from algorithm development through customer deployment. Contributed to the design of the train dispatch and meet-pass optimisation engine used by train controllers for real-time operational decisions. Contributed to a multi-commodity network flow-based MIP model for train scheduling across deviation management and weekly/monthly planning horizons. Designed proof-of-concepts for rail crew optimisation, improving crew utilisation by ~5% and demonstrating the value of mathematical optimisation for operational planning. Worked with engineering, operations, and business stakeholders to validate algorithm behaviour, communicate trade-offs, and drive adoption of optimisation solutions. Led business development for optimisation initiatives, enhancements, and product ideas, connecting customer problems with feasible algorithmic and software solutions. Recipient of BHP CTO Value Award, awarded across BHP projects globally, for the RST project delivering accurate and executable schedules that enabled additional trains and generated project ROI.
May 20122 yrsApr 2014
Mumbai Area, India
Mergers & Acquisitions, Private Equity syndication as well as Corporate Debt Restructuring services to clients
Financial modelling and application of MILP techniques to financial model of corporate debt restructuring proposals to in minimise NPV loss to the lenders
Ashwin’s AI Stack
Education
B.Tech. + M.Tech., Mechanical Engineering + Operations Research
Indian Institute of Technology, Bombay
20055 yrs2010
MBA, Finance and Operations
Indian Institute of Management, Lucknow
20102 yrs2012
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