Muhammad Noor E Alam, Ph.D.

Assistant Professor of Mechanical and Industrial Engineering, Northeastern University | Faculty Affiliate, Global Resilience Institute

Boston, MA, UNITED STATES

Professor Noor-E-Alam focuses on operations and data analytics, applied to healthcare, and manufacturing systems and supply chain.

Contact

Biography

Md. Noor-E-Alam is an Assistant Professor in the Department of Mechanical & Industrial Engineering at the Northeastern University. Prior to his current role, he was working as a Postdoctoral Research Fellow at Massachusetts Institute of Technology, affiliated with Computer Science & Artificial Intelligence Laboratory (CSAIL), Operations Research Center (ORC), and Sloan School of Management. He has completed his PhD in Engineering Management in the Department of Mechanical Engineering at the University of Alberta (UofA) in 2013. His current research interests lie in the intersection of operations research and data analytics, particularly as applied to healthcare, manufacturing systems and supply chain. Before coming to the UofA, he served as a faculty member (first as a Lecturer and then as an Assistant Professor) in the Department of Industrial and Production Engineering at Bangladesh University of Engineering & Technology (BUET). He also previously received a B.Sc. and M.Sc. in Industrial and Production Engineering from BUET.

Areas of Expertise (7)

Financial Management

Total Quality Management

Data Analytics

Operations Research

Large Scale Optimization

Operations & Supply Chain Management

Project Management

Accomplishments (3)

Analytics Best Track Paper Award

2016 IEOM Detroit Conference

Postdoctoral Fellowship, Natural Sciences and Engineering Research Council of Canada

2013-2015

Izaak Walton Killam Memorial Scholarship

2009-2011

Education (3)

University of Alberta: Ph.D., Engineering Management 2013

Bangladesh University of Engineering & Technology: M.S., Industrial & Production Engineering 2006

Bangladesh University of Engineering & Technology: B.S., Industrial & Production Engineering 2004

Affiliations (2)

Institute for Operations Research and Management Sciences (INFORMS)
Institute of Industrial & Systems Engineers (IISE)

Links (2)

Articles (5)

Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets

Energy - Pergamon

Jose L Crespo-Vazquez, C Carrillo, E Diaz-Dorado, Jose A Martinez-Lorenzo, Md Noor-E-Alam

2018 A more comprehensive participation of renewable generators in the power market is being practiced in many countries. To add storage capability to these generators is also a major trend nowadays. Decisions concerning the participation in the power market have to be made several hours in advance, which is a key challenge for the renewable energy-based generators...

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

Healthcare

Md Saiful Islam, Md Mahmudul Hasan, Xiaoyi Wang, Hayley D. Germack, Md Noor-E-Alam

2018 The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic...

Primal Dual Interior Point Methods for Type-2 Second Order Cone Optimization

Arxiv

Md Sarowar Morshed, Md Noor-E-Alam

2018

A failure-dependency modeling and state discretization approach for condition-based maintenance optimization of multi-component systems

Journal of Manufacturing Systems

Mengkai Xu, Xiaoning Jin, Sagar Kamarthi, Md. Noor-E-Alam

2018 Unexpected component failures in a mechanical system always cause loss of performance and functionality of the entire system. Condition-based maintenance decisions for a multi-component mechanical system are challenging because the interdependence of individual components' degradation is not fully understood and lack of physical models. Most existing literature commonly assumes that degradation and failure of individual components within a mechanical system are independent, which could lead to inaccurate diagnostic and prognostic results. In this research, state-rate dependence denoting interaction between component health condition (degradation state) and failure rate is proposed for degradation and failure analysis for a two-component repairable system. A state discretization technique is proposed to model how health state of one component affects the hazard rate of …

Accelerated Affine Scaling Algorithms for Linear Programming Problems