Muhammad Aamir Cheema is currently an ARC Future Fellow and Associate Professor at Monash University. He was a Lecturer at Monash from Nov-2013 to Dec-2015 and Senior Lecturer from 2016-2018. Prior to this, he was a Research Fellow (2011-2013) at School of Computer Science and Engineering at The University of New South Wales (UNSW), Australia. He obtained his Ph.D. degree (2008-2011) under the joint supervision of Prof. Xuemin Lin and Prof. Wei Wang from UNSW, Australia. He received Masters by Research degree in Computer Science and Engineering from UNSW, Australia (2005-2007) under the supervision of Prof. Xuemin Lin. He completed Bachelor of Electrical Engineering from UET Lahore, Pakistan, from 2001 to 2005.

He is the recipient of 2012 Malcolm Chaikin Prize for Research Excellence in Engineering, 2013 Discovery Early Career Researcher Award, 2014 Dean's Award for Excellence in Research by an Early Career Researcher, 2018 ARC Future Fellowship, 2018 Monash Student Association Teaching Award, and 2019 Victorian Young Tall Poppy Science Award. He has also won two CiSRA best research paper awards (in 2009 and 2010), two "one of the best papers of ICDE" (in 2010 and 2012), and two best paper awards at WISE 2013 and ADC 2010, respectively. He is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE). Some of his other service roles include PC co-chair for ADC 2015, ADC 2016, ACM SIGSPATIAL Workshop ISA 2016 & 2018, and International Workshop on Social Computing (IWSC) 2017, proceedings chair for ICDE 2019 and DASFAA 2015, tutorial chair for APWeb 2017 and publicity co-chair for ACM SIGSPATIAl 2017 & 2018.

If you are interested in doing research with me, read "A guide for prospective PhD students" .

My current research interests include spatial databases, mobile and pervasive computing, computational geometry and uncertain databases. Currently, I am working on two research projects related to location-based services (LBS).

Indoor data management

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues. Some recent initiatives have started to cross this technical barrier, promising huge future opportunities for research organisations, government agencies, technology giants, and enterprising start-ups -- to exploit the potential of indoor LBS. Consequently, indoor data management has gained significant research attention in the past few years and the research interest is expected to surge in the upcoming years. This will results in a broad range of indoor applications including emergency services, public services, in-store advertising, shopping, tracking, guided tours, and much more. In this project, we are interested in developing efficient query processing techniques for indoor location data considering textual keywords associated with objects, and data uncertainty. Some additional details are given here .

This project is supported by Australian Research Council (ARC Future Fellowship FT180100140).

Location-based Social Networks

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations. The success of this project will support and enhance a wide range of applications such as law enforcement, health, national security, marketing, and advertisement.

Some additional details are given here . This project is supported by Australian Research Council (ARC Discovery Project DP180103411).

Location-based Social Networks

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations. The success of this project will support and enhance a wide range of applications such as law enforcement, health, national security, marketing, and advertisement.

Some additional details are given here . This project is supported by Australian Research Council (ARC Discovery Project DP180103411).

Dr Aamir Cheema has taught the following units in the Faculty of IT:

• FIT2004 Algorithms and Data Structures
• FIT1045 Algorithms and Programming Fundamentals in Python
• FIT1004 Data management
• FIT3107 Advanced programming for database applications