Alessandro Muscoloni  Ph.D.

    Assistant Professor, Tsinghua Laboratory of Brain and Intelligence (THBI)
    Center for Complex Network Intelligence (CCNI)
    Tsinghua University, Beijing, China

Dr. Muscoloni is Assistant Professor at THBI and is involved in several projects at the Center for Complex Network Intelligence. Current research integrates notions from machine learning and network science in order to develop models of network organization, for analysis and prediction in complex physical systems, such as brain networks. He contributes to theoretical aspects of the research and he deals with the computational realization, such as algorithmic design, implementation and optimization for big data analysis.

Degrees

Computer Science, Technische Universität Dresden, Germany, Ph.D., 2019

Bioinformatics, University of Bologna, Italy, M.S., 2016

Computer Engineering, University of Bologna, Italy, B.S., 2014

Previous Appointments

Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, China, Assistant Professor, 2020 - current

Biotechnologisches Zentrum (BIOTEC), Technische Universität Dresden (TUD), Germany, Postdoctoral Researcher, 2019 - 2020

Selected Publications

Geometrical inspired pre-weighting enhances Markov clustering community detection in complex networks
Durán C*, Muscoloni A*, Cannistraci CV [*joint first authors]
Applied Network Science, 2021 (accepted)

Modular gateway-ness connectivity and structural core organization in maritime network science
Xu M, Pan Q, Muscoloni A, Xia H, Cannistraci CV
Nature Communications, 2020

Navigability evaluation of complex networks by greedy routing efficiency
Muscoloni A, Cannistraci CV
Proceedings of the National Academy of Sciences, 2019

A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
Muscoloni A, Cannistraci CV
New Journal of Physics, 2018

Leveraging the nonuniform PSO network model as a benchmark for performance evaluation in community detection and link prediction
Muscoloni A, Cannistraci CV
New Journal of Physics, 2018

Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Muscoloni A*, Thomas JM*, Ciucci S, Bianconi G, Cannistraci CV [*joint first authors]
Nature Communications, 2017

Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
Narula V, Zippo AG, Muscoloni A, Biella G, Cannistraci CV
Applied Network Science, 2017

Functional brain network topology discriminates between patients with minimally conscious state and unresponsive wakefulness syndrome
Cacciola A, …, Muscoloni A, Cannistraci CV, Bramanti P, Calabrò RS, Anastasi GP
Journal of clinical medicine, 2017

Real-time tracking with an embedded 3D camera with FPGA processing
Muscoloni A, Mattoccia S
International Conference on 3D Imaging (IC3D), 2014

Contact Information

Email: alessandro.muscoloni@gmail.com; muscoloni@mail.tsinghua.edu.cn

Office: Room 902, Chengfu Road 160, Haidian District, Beijing