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Mufti Mahmud

Mufti Mahmud

Associate Professor

Department of Computer Science; Computing and Informatics Research Centre; Medical Technologies Innovation Facility

Staff Group(s)
Computer Science

Role

Leader

Dr Mufti Mahmud is an Associate Professor of Cognitive Computing at the Department of Computer Science of Nottingham Trent University (NTU).  Dr Mahmud was appointed to the USET, University Shadow Executive Team, in 2022, providing specialist input to the University Executive Team and Vice-Chancellor on strategic policy and direction matters related to Equality, Diversity & Inclusion (EDI). Particularly, he shadows the Pro Vice-Chancellor (Research and Innovation) in matters related to the development of the research environment at NTU and its five strategic research themes from the EDI viewpoint. He is the Coordinator of the Computer Science and Informatics (B11) Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group (ISRG) and the Cognitive Computing & Brain Informatics (CCBI) research group. He is also an active member of the Computing and Informatics Research Centre (CIRC) and the Medical Technologies Innovation Facility (MTIF). He is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility Committee and serves as an independent end-point assessor for the Level 6 BSc (Hons) in Digital & Technology Solutions Professional Degree Apprenticeship, and an expert of the online master's degree in computer science. He leads the teaching of the Big Data and its Infrastructures (Postgraduate – on-campus and online delivery) module. He is a Fellow of the Higher Education Academy, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Computing Machinery (ACM), and a professional member of the British Computer Society (BCS).


Performer

Dr Mahmud has been listed among the top 2% cited scientists worldwide in computer science (2020) (data source: from the PLoS biology paper, click here) and has been the winner of the 2021 Vice-Chancellor’s Outstanding Research Award for Early Career Researchers.  As per Scopus, Dr Mahmud holds the highest number of publications among the academics from universities in Nottinghamshire (the University of Nottingham and Nottingham Trent University) in the computer science domain between 01/2018 and 03/2022. Also, by Scopus, he has been ranked as the third top during the same period among computer science academics from universities in the East Midlands region (i.e., De Montfort University, Loughborough University, Nottingham Trent University, University of Derby, University of Leicester, University of Lincoln, University of Northampton, and the University of Nottingham).


Editor/Organiser

Dr Mahmud is a section editor (big data analytics) of the Cognitive Computation journal, regional editor (Europe) of the Brain Informatics journal and associate editor (neuroprosthetics) of the Frontiers in Neuroscience journal. He is also heavily involved in organising conferences including the general chair of the 13th/14th/15th International Conference on Brain Informatics (BI), the 2021/2022 International Conference on Applied Intelligence and Informatics (AII), the 2nd International Conference on Trends in Electronics and Health Informatics (TEHI2022), the symposium chair of the IEEE Symposium in Computational Intelligence in Healthcare and e-Health (IEEE CICARE) since 2016, the coordinator of the local organising committee chair of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020. Dr Mahmud will serve as one of the general chairs along with Dr Maryam Doborjeh and Prof Micheal Witbrock of the 31st International Conference on Neural Information Processing (ICONIP 2024), the annual conference of the Asia Pacific Neural Network Society (APNNS), during 9-13 December 2024 in Auckland, New Zealand.


Volunteer

Dr Mahmud also serves as the vice-chair of the intelligent system application technical committee of the IEEE Computational Intelligence Society (CIS), member of the IEEE CIS task force on intelligence systems for health, member of the IEEE Region 8 humanitarian activities subcommittee, secretary of the IEEE UK and Ireland CIS chapter, publications chair of the IEEE UK and Ireland Industry Applications chapter, project liaison officer of the IEEE UK and Ireland special interest group on humanitarian technologies, social media and communication officer of the British Computer Society's Nottingham and Derby chapter, and an active contributor and member of several IEEE standard development teams including Unified Terminology for Brain-Computer Interfaces (P2731), Clinical IoT Data and Device Interoperability with TIPPSS (P2933), Reporting Standards for In Vivo Neural Interface Research (P2794), and Open Mobile Health Standards (P1752).


Visionary

Dr Mahmud’s research vision is to contribute towards a secure, smart, healthy and better world to live in. In today's digitised world, converting the ever-expanding amount of raw data to smart data, and building predictive, secure and adaptive systems aiming at personalised services are essential and challenging, which require cross-disciplinary and multi-stakeholder collaborations. Towards these goals, Dr Mahmud conducts problem-driven `Brain Informatics' research, where he works with problem domain experts to find multidisciplinary solutions to real-world problems. Dr Mahmud’s research involves Computational-, Health- and Social- sciences, and uses Neuroscience, Healthcare, Applied Data Science, Computational Neuroscience, Big Data Analytics, Cyber Security, Machine Learning, Cloud Computing, and Software Engineering; and plans to develop secure computational tools to advance healthcare access in low-resource settings.


Researcher

Dr Mahmud's research is funded by several funding bodies, including European Commission. Recently funded projects under the Erasmus+ programme include:

  • AI-TOP: An AI Tool to Predict Engagement and ’Meltdown’ Events in Students with Autism.
  • DIVERSASIA: Embracing diversity in ASIA through the adoption of Inclusive Open Practice.

Career overview

Dr Mahmud is highly experienced in blended learning and online learning in higher education. He has led the MSc in Data Analytics for Business and the Data Analytics strand of the Online MBA courses and has several years of experience in developing content for asynchronous learning of online postgraduate (level - 7) courses in the field of (big) data analytics and machine learning. As a competent educator and a frequent speaker in events with heterogeneous audiences, Dr Mahmud is well versed in teaching technical subjects like big data infrastructure and machine learning to students from a non-technical background (such as enrolled in business administration and computer science conversion courses).

Dr Mahmud is responsible for the following modules:

Dr Mahmud is involved in teaching the following modules:

  • Final Year Project (Undergraduate)
  • Foundations of Computing & Technology (Undergraduate)
  • Information & Database Engineering (Undergraduate)
  • Major Project (Postgraduate)
  • Practical Project Management & Professional Development (Undergraduate)
  • Research Methods (Postgraduate)
  • System Analysis & Design with Professional Development (Undergraduate)

Also, Dr Mahmud has taught the following:

  • Neural Signal Processing, Scientific Computing using MATLAB at Postgraduate level;
  • Software Engineering, Software Design and Development, Object-Oriented Programming, Structured Computer Programming with C/C++, Professional Programming with C#.NET, Data Structures, Computing Systems, Computer Graphics, Computer Networking, Data & Tele Communication at Undergraduate level; and
  • Developing Windows Applications using Microsoft .NET Platform (C#), Developing Web Applications using Microsoft .NET Platform (ASP.NET & C#), and Web Database Programming using SQL Server 2000 at a professional level.

Prior to joining NTU Dr Mahmud served as:

Research areas

Postdoctoral researchers

  • Andre Luis Debiaso Rossi topic: Analysis of Clinical Data using Machine Learning for Disease Prediction; currently at Universidade Estadual Paulista, Brazil;
  • Cosimo Ieracitano topic: Understanding Neural & Neuromuscular Behaviour through Modelling; currently at Università degli Studi Mediterranea di Reggio Calabria, Italy;
  • Junali Jasmine Jena topic: Multimodal learning for mental health condition detection;  currently at Kalinga Institute of Industrial Technology, India;
  • Jyoti Sekhar Banerjee topic: Explainable machine learning for workplace stress detection; currently at Bengal Institute of Technology, India;
  • Md Akbar Hossain topic: Explainable machine learning for early detection of Alzheimer’s disease; currently at Manukau Institute of Technology, New Zealand;
  • Md Ekramul Hossain topic: Explainable machine learning for detecting lung cancer from Electronic Health Records; currently at University of Sydney, Australia;
  • Mohammod Abdul Motin topic:  Multimodal learning for early detection of Parkinson's disease; currently at Rajshahi University of Engineering and Technology, Bangladesh;
  • Muhammed Jamshed Alam Patwary topic: Multimodal learning for Attention Deficit Hyperactivity Disorder detection; currently at International Islamic University Chittagong, Bangladesh;
  • Nagaraj V Dharwadkar topic: Securing sensitive healthcare data using steganography; currently at Rajarambapu Institute of Technology, India;
  • Pawan Kumar Singh topic: Multimodal learning systems for the detection of stress and depression; currently at Jadavpur University, India;
  • Rajesh Kaluri topic: Blockchain-based secured system for healthcare services; currently at Vellore Institute of Technology, India;
  • Subarimalai Manikandan topic: PPG-based system for non-invasive health monitoring; currently at Indian Institute of Technology Palakkad, India;
  • Suman Lata Tripathi topic: FPGA implementation of explainable machine learning algorithms; currently at Lovely Professional University, India;
  • Tanu Wadhera topic: Explainable machine learning for early detection of Autism; currently at Indian Institute of Information Technology Una, India;
  • Thippa Reddy Gadekallu topic: Federated learning for secured healthcare services; currently at Vellore Institute of Technology, India.

Current Doctoral students

  • Marcos Ignacio Fabietti (main supervisor with Prof. A. Lotfi as co-supervisor), topic: Analysis of healthcare big data using machine learning for disease monitoring and management.
  • Faiza Guerrache (Co-supervisor with Prof D. Brown as the main supervisor), topic: Spatial and temporal environment pollution impact analysis on people's wellbeing.
  • Zakia Turabee (Co-supervisor with Prof D. Brown as the main supervisor), topic: Neuroadaptive human-computer interface for people with Autism.
  • Sepehr Shirani (Co-supervisor with Prof S. Sanei as the main supervisor), topic: EEG signal processing using machine learning for deep brain stimulation of the Epileptic brain.
  • Teena Rai (Co-supervisor with Dr Jun He as the main supervisor), topic: Machine learning augmented selection of eligible people for lung cancer screening using electronic primary care data.

Recently Completed Doctoral students

  • Oluwatamilore O. Orojo (main supervisor with Dr J. Teppar and Prof. M. McGinnity as co-supervisors), thesis: Optimizing sluggish state-based neural networks for effective time-series processing.
  • Salisu W. Yahaya (Co-supervisor with Prof A. Lotfi as the main supervisor), topic: User-Centric Anomaly Detection in Activities of Daily Living.

Prospective students may obtain detailed information on opportunities to carry out postgraduate research towards an MPhil / PhD from the NTU Doctoral School.

Research areas

Dr Mahmud’s research expertise and interest revolve around the following:

  • Advanced Machine Learning for Biological Data Analysis: Recent research in Deep and Reinforcement Learning, and their combination promise to revolutionize Artificial Intelligence. And, multimodal data from various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/ Body]-Machine Interfaces) are piling up which requires novel data-intensive machine learning techniques. In this project, we have been applying DL based methods to analyse data coming from different biological sources.
  • Advanced Machine Learning for Crowd Analysis: This project aims in developing cutting-edge tools for multisource crowd data analysis to develop a real-time automated transportation system for smart city applications.
  • Distributed Biosignal Analysis Framework: With the rapidly growing brain data, scientists require automated and intelligent tools to infer meaningful conclusions from them which, for a normal desktop PC, is becoming increasingly difficult. This project aims to harness the powers of distributed and cloud computing for the job.
  • Early Detection of Brain Network Dysfunction in Alzheimer’s Disease (AD) & Mild Cognitive Impairment (MCI): AD & MCI are characterized by altered brain network activity which is currently detected at a mature stage. In this project, we aim to characterize a stable biomarker detectable at the disease onset to facilitate their early diagnosis and treatment. Sophisticated frequency-based analyses, of in vivo LFP from genetically modified mouse models at different pathological stages, are applied to detect network dysfunctions.
  • High-Resolution Brain-Chip Interfacing: This project aims to develop high-resolution CMOS based neuronal probes to acquire neuronal signals at (sub-)cellular resolution (7.5-100 micron) across different brain structures.
  • Neuroinformatics Tools for Extracellular Neural Signal Processing & Analysis: Automated analysis of multi-channel neural data has always been a huge challenge, especially when they are recorded in a low SNR setup. Under this umbrella project, we develop novel tools for the automated analysis of multi-channel brain signals.
  • Neuroprosthetics & Rehabilitation Engineering: The aim of this project is to reduce rehabilitation costs and to provide independent mobility to the severely disabled through automatic and smart assistive devices controllable with bio-signals (e.g., EEG and EMG). These signals are mainly triggered by imagination, physical activities, and even just by looking at the way to follow. Also, fuzzy-based controllers are optimized to operate with minimal numbers of channels.
  • Secure Cyber-Physical Systems and Internet of Things: Rapid popularity of the Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel healthcare data to better assess health conditions, diagnose diseases, and devise treatments. This project aims to ensure secure and reliable data communication between end-to-end devices supported by current IoT and cloud infrastructure through trust management as well as the development of novel secure IoHT frameworks.
  • Understanding Neural & Neuromuscular Behaviour through Modelling: To design appropriate therapy and realistic assistive devices for neurodegenerative disorders we need to understand in detail the neural transmission mechanisms. In this project, we develop detailed models of neural networks suitable for electroceutical therapy and neuromuscular junction mimicking the neurobiological phenomena.
  • Wireless Sensor Network for Healthcare Application: With the rise of service automation, access to digitized data, and growing network speed, this project aims to apply wireless sensor networks to provide e-healthcare solutions.

External activity

Dr Mahmud’s external activities include discharging editorial responsibilities for prominent academic journals and organising important scientific conferences.

Grant reviewer for:

Membership:

Editorial service:

Keynotes and invited talks (selected, since 2019):

  • Keynote Speaker at the 10th Annual International Conference of Information and Communication Technology (ICICT2020), Virtual, 13-15 Nov.  2020.
  • Keynote Speaker at the ICT Academy Global Technology Forum - A conference by the Govt.  ofIndia (GTF-2020), Virtual, 14-18 Oct.  2020.
  • Keynote Speaker at the 14th IEEE International Conference on Application of Information and Communication Technologies (AICT-2020), Tashkent, Uzbekistan, 7-9 Oct.  2020.
  • Keynote Speaker at the International Conference on Intelligent and Smart Computing in DataAnalytics (ISCDA’2020), Vaddeswaram, India, 3-5 Oct.  2020.
  • Keynote Speaker at the BGC Trust Summer School, Chittagong, Bangladesh, 29 Aug.  2020.
  • Keynote Speaker at the Third International Conference on Soft Computing and Signal Processing(ICSCSP-2020), Hyderabad, India, 20-22 Aug.  2020.
  • Keynote Speaker at the International COVID-19 Congress (ICC’20), Dhaka, Bangladesh, 9-10 Aug.2020.
  • Keynote Speaker at the Information and Communication Technology for Intelligent Systems (ICTIS2020), Ahmedabad, India, 15 May 2020.
  • Keynote Speaker at the International Conference of Advanced Computing and Informatics(ICACIn’20), Casablanca, Morocco, 13-14 Apr.  2020.
  • Keynote Speaker at the IEEE Symposium Series on Computational Intelligence (IEEE-SSCI2019), Xiamen, China, 6-9 Dec.  2019.
  • Plenary Speaker at the 5th Annual MarketsandMarkets Neuroscience R&D TechnologiesConference, London, UK, 3-4 Oct.  2019.

Conference organisation (selected):

Sponsors and collaborators

Since 2008, Dr Mahmud has been funded largely by the European Commission through the following projects:

  • DIVERSASIA (Euro 1M through H2020, Erasmus+): Embracing diversity in ASIA through the adoption of Inclusive Open Practice.
  • AI-TOP (Euro ~460k through H2020, Erasmus+): An AI Tool to Predict Engagement and ’Meltdown’ Events in Students with Autism.
  • ML-SELECT (GBP ~36k through MTIF, NTU): Machine Learning Augmented Selection of Eligible People for Lung Cancer Screening using Electronic Primary Care Data.
  • Ramp (Euro 362k through FP7, ICT-2013.9.6 FET Proactive, 612058): A new biohybrid architecture of natural and artificial neurons endowed with plasticity properties was developed. Communication between artificial and natural worlds was established through new nano- and micro transducers to directly interface the network of neurons in culture to an artificial CMOS-based counterpart.
  • NeuroAct (Euro 948k through FP7, PEOPLE-2011-IAPP Marie-Curie Action, 286403): Novel tools and platforms were developed to advance current understanding of the molecular and microcircuit bases for nervous system (dys)function and perform high-throughput in-vitro drug screening.
  • Realnet (Euro 643k through FP7, ICT-2009.8.8 FET Proactive, 270434): Specific imaging techniques to record from multiple neurons in the cerebellar network was developed. From the data, a realistic real-time model of the cerebellum was obtained and connected to robotic systems under closed-loop conditions. Using "adaptable filter theory", sensorimotor control and cognitive systems were investigated.
  • CyberRat (Euro 372k through FP7, ICT-2007.8.3 Bio-ICT convergence, 216528): An innovative brain-chip interface, with CMOS chip featuring a large-scale matrix of stimulation and recording microelectrodes integrated at high-spatial-density (~1000 sites at <10-micron separation), was developed. I was in charge of processing and analysis of the data recorded from these chips.
  • Euro ~57k from UNIPD, IT for the `Development of novel software tools to study neuronal populations Activity recorded using high-resolution multi-site neuronal probes' (03/2015-02/2017).
  • Euro ~39k from UNIPD, IT for the `Use of capacitors and transistors for recording & stimulation of neuronal activity in the cortex and deep nuclei of rat brain' (01/2011-12/2012).
  • Euro ~67k from Fondazione CARIPARO for `Developing novel neuronal signal processing and analysis tools' (01/2008-12/2010).

Dr Mahmud’s external collaborators include (in alphabetical order):

  • Prof Karl Andersson from the  Lulea University of Technology, Sweden on the development of next-generation pervasive and mobile computing systems.
  • Prof Michela Chiappalone from the University of Genoa, Italy on the expiration of machine learning in understanding neuronal functionalities before and after brain damage.
  • Prof Cristina Fasolato from the University of Padova, Italy on the characterisation of a stable biomarker for early detection of Alzheimer’s disease.
  • Prof Michele Giugliano from the SISSA, Italy on the development of novel cloud-based neuroinformatics tools.
  • Prof Amir Hussain from the Edinburgh Napier University, the UK on the development of intelligent signal analysis tools for [e/m]Health applications.
  • Prof Shariful Islam from the Deakin University, Australia on developing novel digital health tools.
  • Prof M. Shamim Kaiser from the Jahangirnagar University, Bangladesh on the development of low cost and wireless assistive/rehabilitation devices.
  • Prof Roland Thewes from the Technical University of Berlin, Germany on the development of microelectronic devices.
  • Prof Stefano Vassanelli from the University of Padova, Italy on the development of novel high-resolution brain-chip interfacing and smart neuroengineering systems for distributed artificial intelligence
  • Prof Ning Zhong from the Maebashi Institute of Technology, Japan on the development of next-generation brain informatics tools.

Publications

Note:

The list below shows selected Journal publications only. It excludes book chapters and conference proceeding papers. For a complete list of publications - please consult my Google Scholar profile.

Journal articles (selected):

See all of Mufti Mahmud's publications...