About

I am a PhD student at the Computer Laboratory at the University of Cambridge (UK) under the supervision of Prof. Cecilia Mascolo.
I am currently interested in exploring how technology could be applied to study personal interactions in a workplace environment. This could help improve the work experience and boost productivity and innovation by shaping the offices in ways that stimulate, for example, serendipitous interactions between members of different teams. My research is funded by the Engineering and Physical Sciences Research Council (EPSRC) and by a Qualcomm Inc. Research Studentship in Computing.
I received my BSc and MSc cum laude in Computer Engineering from the University of Bologna (Italy). During my master I spent six months as an exchange student in Mid Sweden University (Sweden). I then completed my Master Thesis at the ABB Corporate Research Center (Switzerland) working on wireless sensor networks and lately I did an internship at Nokia Bell Labs (Ireland) focusing on privacy issues in the context of the Internet of Things.
In September/October 2016 I was a visiting researcher at Dartmouth College, hosted by Dr. Xia Zhou.
In August 2017 I joined ARM for a 14-week internship in the IoT Engineering Group.

Publications

Detecting Emerging Activity-Based Working Traits through Wearable Technology. [pdf]
Alessandro Montanari, Cecilia Mascolo, Kerstin Sailer, Sarfraz Nawaz.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). To be presented at Ubicomp 2017. Maui, Hawaii, USA. September 2017.
A Study of Bluetooth Low Energy Performance for Human Proximity Detection in the Workplace. [pdf]
[Best Paper Nominee]
Alessandro Montanari, Sarfraz Nawaz, Cecilia Mascolo and Kerstin Sailer.
IEEE International Conference on Pervasive Computing and Communications (Percom), March 2017, Hawaii, USA.
Mobile Sensing for Social Interaction Monitoring and Modelling. [pdf]
Alessandro Montanari.
PhD Forum of the IEEE International Conference on Pervasive Computing and Communications (Percom), March 2017, Hawaii, USA.
Understanding the Privacy Design Space for Personal Connected Objects. [pdf] [slides]
Alessandro Montanari, Afra Mashhadi, Akhil Mathur, and Fahim Kawsar.
Proceedings of the 30th British Human Computer Interaction Conference (British HCI 2016), July 2016, Bournemouth, UK.
Experience in deploying wearable devices for office analytics. [pdf] [slides]
Alessandro Montanari, Sarfraz Nawaz, Cecilia Mascolo, Kerstin Sailer.
Proceedings of the 1st International Workshop on Quantified Workplace. Co-located with CSCW 2016. February '16, San Francisco, USA.
Multimodal Indoor Social Interaction Sensing and Realtime Feedback for Behavioural Intervention. [pdf] [slides]
Alessandro Montanari.
Proceedings of the ACM Workshop on Wireless of the Students, by the Students & for the Students. September 2015, Paris, France.
REST assured, we manage your microgrid. [pdf]
Alessandro Montanari, Yvonne-Anne Pignolet, Ettore Ferranti.
IEEE International Conference on Smart Grid Communications (SmartGridComm), November 2014, Venice, Italy.
Demo Abstract: Plug & Play Site Management or, Why Your Solar Panel Should Be Like Your Webcam. [pdf]
Ettore Ferranti, Alessandro Montanari, Yvonne-Anne Pignolet, Igor Zablotchi.
ACM SenSys 2013. November 2013. Rome, Italy.

Patents

Privacy-Aware Personal Data Store
Montanari Alessandro, Afra Mashhadi, Aidan Boran and Fahim Kawsar.
US Patent 20,160,044,039, Published 11/02/2016

Teaching

2015 - 2017 Supervised course “Mobile and Sensor Systems”. University of Cambridge, UK.
2014 Co-supervisor of Master student Andrea Bucaletti, University of Bologna, Italy.
Main supervisor Prof. Enrico Denti.
Co-supervisor of Bachelor student Matteo Librenti, University of Bologna, Italy.
Main supervisor Prof. Enrico Denti.
2013 Co-supervisor of Bachelor student Alessio Mercurio, University of Bologna, Italy.
Main supervisor Prof. Enrico Denti.

Student Projects

If you are interested in working with me and Prof. Cecilia Mascolo on one of the following projects, feel free to contact me.

Efficient Area Surveying through Context Aware Drone Mobility

Area surveying is a costly and often difficult process if performed through human observation: road access, danger from wildlife and simply unknowns make this very hard. Drone based area surveying has the aim to inform the development of new and effective infrastructure by providing timely and fine-grained information on the terrain and area details.
This project aims at optimizing drone surveying flight paths using onboard context sensing and computation. The intuition is that areas with certain characteristics (e.g., presence of human settlements, roads, rivers, etc.) require more time for the survey while others can be completed quicker. Rather than adopting a statically defined path the drone will adapt its path based on the environment in order to achieve longer flight times and cover larger areas.
Different machine learning algorithms will be evaluated in order to find the one that best matches the requirements of low latency and resource consumption. Given that the system will mainly rely on the video feed captured during flight, particular emphasis is required on techniques able to process video streams (e.g. Deep Learning and Convolutional Neural Networks). However other sensors could potentially be included. We expect to test and validate the system with simulations as well as with real-world experiments.
The outcome of this project will constitute a building block of a larger autonomous survey system used for research in Africa in the following months.

Power Efficient Autonomous Flight for Area Surveying

Area surveying is a costly and often difficult process if performed through human observation: road access, danger from wildlife and simply unknowns make this very hard. Drone based area surveying has the aim to inform the development of new and effective infrastructure by providing timely and fine-grained information on the terrain and area details.
The aim of the project is to explore the power consumption aspects of autonomous flight and research methods to achieve long endurance flight. The project will include a first investigation to identify the most efficient aircraft’s form factor (e.g. multirotor, fixed wing or vertical take-off and landing aircraft) to support endurance flying. Subsequently, the student will devise methods to plan and execute the most efficient trajectories to survey a certain area. The control algorithms will be implemented on popular drone firmwares (e.g. Ardupilot). We expect to test and validate the system with simulations as well as with real-world experiments.
The outcome of this project will constitute a building block of a larger autonomous survey system used for research in Africa in the following months.

Get In Touch

  • Address

    Office FN01
    Computer Laboratory
    University of Cambridge
    William Gates Building
    15 JJ Thompson Avenue
    Cambridge CB3 0FD
  • Email

    alessandro dot montanari at cl dot cam dot ac dot uk