Rate My Professor Morteza Saberi

His research specializations include business intelligence, data mining, applied machine learning, and data science, focusing on developing models to identify patterns in data for informed decision-making in industries like agriculture, mining, pharmaceuticals, and retail. Examples encompass predicting abnormal milk temperature using sensors and analytics, and optimizing stock allocation based on customer preferences. Saberi has authored over 150 papers in leading journals such as Computers & Industrial Engineering, Future Generation Computer Systems, and Knowledge-Based Systems. Notable publications are 'Support vector regression with chaos-based firefly algorithm for stock market price forecasting' (Applied Soft Computing, 2013), 'ZBWM: The Z-number extension of Best Worst Method and its application for supplier development' (Expert Systems with Applications, 2018), and 'Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption' (Applied Mathematics and Computation, 2007). His scholarship has amassed over 7,000 Google Scholar citations with an h-index exceeding 27. He has garnered more than 20 awards, including Innovation Connection grants ($205,000 in 2021, $78,000 in 2019), a 2016 UNSW Postdoctoral Rector Fellowship ($300,000), recognition as Australia's leading researcher in Evolutionary Computation (2019), and multiple best paper and researcher accolades.