Canada
Engineering Consultant in Safety, Risk, Reliability and Licensing
Nuclear Power, Petrochemical & Urban Transit
Engineering Consultant in Safety, Risk, Reliability and Licensing
Nuclear Power, Petrochemical & Urban Transit
Holder of four Chemical Engineering degrees, B.Sc (Shiraz University)., M.Sc. (Sharif University of Technology), M.A.Sc. (University of Toronto), and Ph.D (University of Toronto). Keivan’s diverse educational background, includes areas such as mathematical modelling, computer simulation of simultaneous momentum, heat and mass transfer in multicomponent distillation columns, quantitative FTIR polymer characterization, and application of Data Mining tools, Artificial Intelligence (AI) and Machine Learning techniques, combined with Computer Vision, for real-time image analysis and object detection, in manufacturing. Keivan was a seasonal instructor at the University of Toronto and the Ryerson University for few years after his PhD was completed.
Keivan has more than 25 years of engineering experience in Oil & Gas and Nuclear Power Generation, and Transportation. He has worked in refineries and petrochemical plants for 5 years, Canadian nuclear industry (CANDU) for 20 years, and he is currently working on safety certification of urban transit systems, conducting Safety, Risk and Reliability assessment (RAMS) for large and complex driverless subway systems in Canada and internationally.
Keivan has worked in Shell and Esso refineries, Ontario Power Generation Pickering and Darlington nuclear stations, Atomic Energy Canada Limited (AECL), Candu Energy (SNC-Lavalin), AMEC, as well as Thales and WSP Canada.
Keivan’s primary expertise is in advanced data analysis, including statistical data analysis, Data Mining, Artificial Intelligence (AI), Machine Learning (ML), Computer Visions, and Computational Fluid Dynamics (CFD). His thesis in application of Artificial Intelligence in real-time image analysis in polymer film manufacturing and mechatronics was published 15 years ago, when AI was not fad or a buzz word, and AI applications in engineering were mostly unnoticed. His papers in diverse areas are cited by other researchers in different fields globally.
Keivan has always worked in multidisciplinary and heavily regulated industries, and currently he is working on Safety and Reliability (RAMS) of driverless Communication-Based Train Control (CBTC) systems, developing Engineering Safety Case, conducting deterministic and probabilistic safety analyses, Quantitative Risk-Assessment (QRA), based on CENELEC standards for European and North American train operators.
Keivan is a Professional Engineer (P.Eng.), and has served as a Regional Councillors between 2018 to 2020, at the Association of Professional Engineers Ontario (PEO), a self-regulating body of professional engineers in Ontario, Canada.