Monica Wachowicz

   STEM College

  Space Industry Hub

  Sir Lawrence Wackett Defence & Aerospace Centre

  Email: monica.wachowicz@rmit.edu.au

   Campus: Melbourne City

 GEOM2114 - Geospatial Science Major Project A and B

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Professor Wachowicz works on the synergy of streaming analytics and mapping for a world in which “intelligence” will be embedded in virtually everything around us.

Monica Wachowicz is Professor of Data Science in RMIT. She is also Associate Dean of Geospatial Science, Research Leader of Cyber and Data Analytics of the Space Industry Hub, Deputy Director of the Sir Lawrence Wackett Defence & Aerospace Centre, and the academic representative of the Information Governance Board. She is the founding Member of the IEEE Technical Sub-Committee on Big Data and Member of the editorial board of 9 scientific journals, including International Journal of Big Data Intelligence, International Journal of Internet of Things and Big Data, International Journal of Big Data Security Intelligence, and Associate Editor of the International Journal of Spatio-Temporal Data Science. She is the first woman engineer in Geomatics Engineering to be awarded an NSERC Industrial Research Chair in Canada. Dr. Wachowicz received the title of Honorary Visiting Scholar in the College of Science and Engineering, Flinders University, Australia. Her pioneering work in multidisciplinary teams from government, industry and research organizations is fostering the next generation of data scientists for innovation in green economies.

Prof. Wachowicz has collaborated in research projects with many high technology companies, such as IBM, Cisco, Orange Communications, ESRI and Siemens. Her recent collaborations include: • Cisco Systems Canada: Dr. Wachowicz is the Cisco Innovation Chair in Big Data for R&D processes centred on three core research domains: Real-Time Streaming Sampling Model, Graph-Based Mobility Data Mining and Map-as-an-Interface Design for Internet of Things.

• Hickling Arthurs Low Corporation: Research-oriented consulting assignments for Impacts and Implications of Big Data for Geospatial.
• Roam Technologies: leveraging new and innovative technologies to enhance a tourism experience in a smart city. The broader goal is to change the way local industry works with data and catalyze the adoption of technology with Roam’s partners.
• Siemens, NBPower, Emera: building a geospatial analytical platform to foster the usage of electric vehicles.
• Smart Campus Integration and Testing Lab (SCITLab) to explore digital and sustainable buildings.

Having a unique multidisciplinary background in Geomatics Engineering, Geography, and Computer Science, Monica Wachowicz specializes in machine learning to analyze data streams from the Internet of Things, with the purpose of identifying opportunities and challenges in building sustainable smart cities. Dr. Wachowicz has an extensive experience in supervising graduate students from Engineering, Geography, Computer Science, Statistics, Mathematics, Physics and Urban Planning backgrounds. The students come from Brazil, Canada, China, India, Iran, Mexico, South Korea, Spain, Tunisia, USA, Venezuela, and Vietnam. In the last six years, 52 students have been under her supervision and are currently working for RIDDL, Irving, Deloitte, Leica, IBM, Intact Financial Corporation, Shopify Inc., Santander Group or pursing their academic careers at the University College Dublin, Technical University of Madrid, Harrisburg University, Wageningen University, and Pennsylvania State University.

Streaming Analytics Mobility Behaviour Analytics Edge Computing, EdgeAI Machine Learning on Graphs, TinyML Internet of Things Visual Analytics Geospatial Intelligence

• Space + Geospatial Data Science for Social Wellbeing Smart Mobility

• Multi-Time Window Analytical Workflow for Clustering Wearable Data Streams for revealing self-quantified patterns
• A data-driven approach for energy consumption prediction of changing scheduling, optimality and scalability of EV integration in smart grids
• Matrix factorization model with local and global consistency for flow prediction in bike-sharing systems
• Evaluation of supervised and semi-supervised Machine Learning algorithms for Space Situational Awareness
• Data Science for smart campus: What smart campuses can teach us about smart cities.