Shazia Tabassum

I am a researcher at LIAAD, INESC TEC Porto. I have successfully concluded a Ph.D. in Informatics Engineering at FEUP, University of Porto under the supervision of Prof. João Gama. I also hold the degrees of Bachelor’s and Master’s in Computer Applications from Kakatiya University (KU) in India. My research focus is on Machine Learning, Big Data Science, Networked Data Streams, Evolving Graphs, and Social Network Analysis. I was an Organizing Committee member of Discovery Challenge at EPIA’2017, High Velocity Mobile Data Mining Workshop at IEEE MDM’2016 and DSIE’2015.

Domains

Artificial Intelligence and Decision Support Artificial Intelligence

Projects

HfPT

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

Publications

privy: Privacy Preserving Collaboration Across Multiple Service Providers to Combat Telecom Spams

Azad, MA;Bag, S;Tabassum, S;Hao, F;

2020

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING

Social Network Analysis of Mobile Streaming Networks

Tabassum, S;

2016

2016 17th IEEE International Conference on Mobile Data Management (MDM)

Processing Evolving Social Networks for Change Detection Based on Centrality Measures

Pereira, FSF;Tabassum, S;Gama, J;de Amo, S;Oliveira, GMB;

2019

Studies in Big Data

Social Network Analysis in Streaming Call Graphs

Sarmento, R;Oliveira, M;Cordeiro, M;Tabassum, S;Gama, J;

2016

Studies in Big Data

View all publications

Supervised theses

Fulfillment Optimization in Online Retail

Sérgio Vasconcelos Castro

D - 2024

UP-FEUP

Centres

Artificial Intelligence and Decision Support

Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.