Lorena Qendro

About

I am a Ph.D. student in Computer Science at the University of Cambridge, working with Prof. Cecilia Mascolo and funded by Nokia Bell Labs. I am also a graduate student at Murray Edwards (New Hall) College and part of the Centre for Mobile, Wearable Systems and Augmented Intelligence. My research aims to investigate different techniques to efficiently and accurately provide uncertainty aware mobile sensing and health applications and apply them to provide real-world robustness against distributional shifts and adversarial attacks.

I received my BEng and MEng in Computer Engineering from the University of Bologna and completed my Master Thesis at Telekom Innovation Laboratories (T-Labs) focusing on social media analysis to understand and predict users' lifestyle. After my studies, I did an internship at Nokia Bell Labs working on using deep learning for processing mobile sensor data within device constraints (such as energy and computation).

Prior to my Ph.D I worked as a Software Engineer at Ellexus (now Altair), Connected Places Catapult, and GeoSpock.

By the way, check out my work.

Work

Publications

Early Exit Ensembles for Uncertainty Quantification. [Best Thematic Paper Award]
Lorena Qendro*, Alexander Campbell*, Pietro Liò, Cecilia Mascolo
Proceedings of Machine Learning for Health, ML4H '21, December 2021

Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data.
Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo
Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech '21, Brno, Czechia, September 2021

High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm.
Lorena Qendro*, Alexander Campbell*, Pietro Liò, Cecilia Mascolo
The Thirty-eighth International Conference on Machine Learning, Workshop on Human In the Loop Learning, ICML '21, July 2021

Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs.
Lorena Qendro, Sangwon Ha, René de Jong, Partha Maji
Proceedings of the 1st Workshop on Security and Privacy for Mobile AI (MAISP), MobiSys '21, Mars, Solar System, June 2021

The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms.
Lorena Qendro, Jagmohan Chauhan, Alberto Gil CP Ramos, Cecilia Mascolo
The Sixth ACM/IEEE Symposium on Edge Computing, SEC '21, San Jose, California, USA, December 2021

ePerceptive: energy reactive embedded intelligence for batteryless sensors.
Alessandro Montanari, Manuja Sharma, Dainius Jenkus, Mohammed Alloulah, Lorena Qendro, Fahim Kawsar
Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Sensys '20, November 2020

Uncertianty Aware Mobile Sensing.
Lorena Qendro, Jagmohan Chauhan, Cecilia Mascolo
Second UK Mobile, Wearable and Ubiquitous Systems Research Symposium, Oxford, UK, July 2019

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices.
Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, Fahim Kawsar
15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '16, Vienna, Austria, April 2016

DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. [Best Paper Award]
Nicholas D. Lane, Petko Georgiev, Lorena Qendro
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '15, Osaka, Japan, September 2015

Contact

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• GitHub

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