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 led 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).
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).
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 also actively contributes to organising conferences as 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.
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).
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.
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.
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 non-technical backgrounds (such as those enrolled in business administration and computer science conversion courses).
Dr Mahmud is responsible for the following modules:
- Big Data and its Infrastructures (Postgraduate – on-campus and online delivery)
- Practical Machine Learning Methods for Data Mining (Postgraduate – online delivery)
- Data Analysis (Undergraduate)
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 the 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:
- Senior Postdoctoral Research Fellow at the NeuroChip Lab of Department of Biomedical Sciences, University of Padova, Padova, Italy from 03/2015 to 03/2018.
- Marie-Curie Research Fellow at the Theoretical Neurobiology & Neuroengineering Lab of Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium from 02/2013 to 01/2015.
- Postdoctoral Research Fellow at the NeuroChip Lab of Department of Human Anatomy & Physiology, University of Padova, Padova, Italy from 01/2011 to 01/2013.
- Assistant Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Bangladesh from 09/2011 to 01/2016.
- Research Assistant at the NeuroChip Lab of Department of Human Anatomy & Physiology, University of Padova, Padova, Italy from 01/2008 till 12/2010.
- Staff Scientist at the Molecular Stamping S.r.l., Trento, Italy from 06/2007 till 01/2008.
- Lecturer of Computer Science & Engineering and Administrator of Microsoft IT Academy at the Daffodil International University, Dhaka, Bangladesh from 02/2006 till 10/2011.
- Lecturer of Computer Science & Engineering and .NET Curriculum Coordinator at the Prime University, Dhaka, Bangladesh from 03/2005 till 01/2006.
- Andre Luis Debiaso Rossi topic: Analysis of Clinical Data using Machine Learning for Disease Prediction; currently at Universidade Estadual Paulista, Brazil;
- Cosimo Ieracitano topic: Application of Advanced Deep and Reinforcement Learning and Explainable Artificial Intelligence (AI) Techniques for Processing of Physiological Data of Subjects Affected by Autism Spectrum Disorder (ASD) and Development of a Novel Multimodal AI-System for Supporting of People with ASD; 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.
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 applied DL-based methods to analyse data from different biological sources.
- Advanced Machine Learning for Crowd Analysis: This project aims to develop cutting-edge multisource crowd data analysis tools 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 microns) 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: This project aims 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 the neural transmission mechanisms in detail. In this project, we develop detailed models of neural networks suitable for electroceutical therapy and neuromuscular junction mimicking 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.
Dr Mahmud’s external activities include discharging editorial responsibilities for prominent academic journals and organising important scientific conferences.
Grant reviewer for:
- Engineering and Physical Sciences Research Council (EPSRC) the UK.
- Biotechnology and Biological Sciences Research Council (BBSRC) the UK
- Ministry of Education, University and Research (MIUR) Italy.
- São Paulo Research Foundation (FAPESP) Brazil.
- Human Frontier Science Program (HFSP) France.
- Qatar Foundation (QF) Qatar.
- Fellow of the Higher Education Academy (UK).
- Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
- Senior Member of the Association for Computing Machinery (ACM).
- Professional Member of the British Computer Society (BCS).
- Member of the International Association of Engineers (IAENG).
- Section Editor (Big Data Analytics) Cognitive Computation (Springer-Nature) (2011 - present).
- Regional Editor (Europe) of Brain Informatics (Springer-Nature) journal (2018 - present).
- Associate Editor of Frontiers in Neuroscience (Frontiers) journals (2020 - present).
- Associate Editor of IEEE Access (IEEE) (2017 - 2021).
- Editorial board member of Big Data Analytics (BioMed Central, Springer-Nature) (2017 - 2021)
- Guest editor of Applied Sciences, Brain Informatics, Cognitive Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, and Sensors.
- Referee of many renowned journals from computing, neuroscience, and health informatics disciplines.
Keynotes and invited talks (selected, since 2019):
- Keynote Speaker at the International Conference on Innovative Development on Virtual and Rapid Prototyping (IDVRP2022), Dehradun, India, May 14-15, 2022.
- Keynote Speaker at the 1st Human-Centric Smart Computing (ICHCSC2022), New Delhi, India, 27-29 Apr. 2022.
- Keynote Speaker at the 2nd International Conference on Machine Learning, Internet of Things and Big Data (ICMIB2021), Sarang
- Keynote Speaker at the 3rd International Conference on Applied Machine Learning and Data Analytics (AMLDA2021), Vardhawan, India, 16-17 Dec. 2021.
- Keynote Speaker at the 2021 Annual Meeting of Affect, Personality and the Embodied Brain (APE2021), Nottingham, UK, 20-22 Sept. 2021.
- Keynote Speaker at the 2nd Global Conference on Artificial Intelligence and Applications (GCAIA 2021), Jaipur, India, 8-10 Sept. 2021.
- Keynote Speaker at the International Workshop on Recent Advancement on Electronics and Computer Intelligence (IWRAECI2021), Sambalpur, India, 26-30 Apr. 2021.
- Keynote Speaker at the 5th International Conference on Information and Communication Technology for Intelligent Systems (ICICTIS2021), Ahmedabad, India, 23-25 Apr. 2021.
- 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. of India (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 Markets and Markets Neuroscience R&D Technologies Conference, London, UK, 3-4 Oct. 2019.
Conference organisation (selected):
- General chair of the 15th International Conference on Brain Informatics (BI2022), July 15-17, 2022, Padova, Italy.
- General chair of the 2022 International Conference on Artificial Intelligence and Informatics (AII2022), September 1-3, 2022, Reggio Calabria, Italy.
- General chair of the 2nd International Conference on Trends in Electronics and Health Informatics (TEHI2022), December 7-9, Puebla, Mexico.
- Symposium chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2022), December 4-7, 2022, Singapore.
- General chair of the 14th International Conference on Brain Informatics (BI2021), September 17-19, 2021, Padova, Italy.
- General chair of the 2021 International Conference on Artificial Intelligence and Informatics (AII2021), July 30-31, 2021, Nottingham, UK.
- Symposium chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2021), December 5-8, 2021, Orlando, Florida, USA.
- General chair of the 13th International Conference on Brain Informatics, September 18-20, 2020, Padova, Italy.
- Programme chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2020), December 1-4, 2020, Canberra, Australia.
- Coordinator of Local arrangement committee of the IEEE World Congress on Computational Intelligence (IEEE-WCCI2020), July 24-29, 2020, Glasgow, UK
- Programme co-chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2019), December 6-9, 2019, Xiamen, China.
- Workshop chair of the 12th International Conference on Brain Informatics, December 13-15, 2019, Hainan, China.
- Publicity chair of the 11th International Conference on Brain Informatics, December 7-9, 2018, Arlington, Texas, USA.
- Special Session chair of BI2018 (Texas, USA; Dec. 7-9), IEEE-EMBC 2015 (Milano, Italy; Aug. 25-29), IEEE CICARE 2015 (Cape Town, South Africa; Dec. 7-10), IEEE CICARE 2014 (Orlando, USA; Dec. 9-12), IEEE CICARE 2013 (Singapore; Apr. 15-19), ICONIP2012 (Doha, Qatar; Nov. 12-15).
- Track chair of IEEE MECBME 2018 (Neuroengineering track, Mar. 20-30), IEEE R10HTC 2017 (Healthcare and Biomedical Engineering track, Dec. 21-23), IEEE CICARE 2015 (All tracks, Dec. 7-10).
- Publication chair of BICS 2015 (Hefei, China; Dec. 11-13).
- Local organiser of CSN School on Neurotechniques at NeuroChip Lab of University of Padova, Italy (2011, 2014, 2015, and 2016 editions).
- International Advisory Committee member of ICEEICT 2018 (Dhaka, Bangladesh; Sept. 13-15), ICEEICT 2015 (Dhaka, Bangladesh; May 21-23), ICEEICT 2014 (Dhaka, Bangladesh; Apr. 10-12), E2IC2 2015 (Coimbatore, India; Dec. 18-19.
- PC member of ICTP - since 2015, ICONIP - since 2012, IEEE-CICARE - since 2013, BICS - since 2013.
- Steering Committee member of IEEE-CICARE 2016 (Athens, Greece; Dec. 06-09).
Sponsors and collaborators
Since 2008, Dr Mahmud has been funded largely by the European Commission through the following projects:
- E-AID (GBP ~36k through NTU Strategic Research Fund): Early Detection of Alzheimer’s Disease using Multimodal Data and Explainable Artificial Intelligence.
- 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 developing 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 characterising a stable biomarker for early detection of Alzheimer’s disease.
- Prof Michele Giugliano from the SISSA, Italy on developing novel cloud-based neuroinformatics tools.
- Prof Amir Hussain from the Edinburgh Napier University, the UK on developing 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 developing low-cost and wireless assistive/rehabilitation devices.
- Prof Roland Thewes from the Technical University of Berlin, Germany on developing 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 developing next-generation brain informatics tools.
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):
- D.V. Christensen, et al. (2022). 2022 roadmap on neuromorphic computing and engineering, Neuromorphic Computing and Engineering, 2022, [Online First].
- G.S. Lalotra, V. Kumar, A. Bhatt, T. Chen, M. Mahmud. (2022). iReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network, Security and Communication Networks, vol. 2022, article no. 9149164.
- A. Paul, A. Basu, M. Mahmud, M. S. Kaiser, and R. Sarkar. (2022). Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays, Neural Computing and Applications, 2022, [Online First].
- I. Kumar et al. (2022). Dense Tissue Pattern Characterization Using Deep Neural Network’, Cognitive Computation, 2022, [Online First].
- A. AlArjani, M. T. Nasseef, S. M. Kamal, B. V. S. Rao, M. Mahmud, and M. S. Uddin. (2022). Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics’, Arabian Journal for Science and Engineering, 2022, [Online First].
- M. Fabietti, M. Mahmud, and A. Lotfi. (2022). Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning’, Brain Informatics, vol. 9, no. 1, article no. 1.
- B. Deepa, M. Murugappan, M. Sumithra, M. Mahmud, and M. S. Al-Rakhami. (2022). Pattern Descriptors Orientation and MAP Firefly Algorithm based Brain Pathology Classification using Hybridized Machine Learning Algorithm’, IEEE Access, vol. 10, pp. 3848-3863.
- M. Biswas, M.H. Tania, M.S. Kaiser, R. Kabir, M. Mahmud, A.A. Kemal. (2021). ACCU3RATE: A mobile health application rating scale based on user reviews, PloS One, vol. 16, no. 12, article no. e0258050.
- S. Zaman et al. (2021). Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey’, IEEE Access, vol. 9, pp. 94668–94690.
- S. W. Yahaya, A. Lotfi, and M. Mahmud. (2021). ‘Towards a data-driven adaptive anomaly detection system for human activity’, Pattern Recognition Letters, vol. 145, pp. 200–207.
- G. Varone et al. (2021) Real-time artifacts reduction during TMS-EEG co-registration: A comprehensive review on technologies and procedures, Sensors (Switzerland), vol. 21, no. 2, pp. 1–23.
- A. K. Singh, G. Sahonero-Alvarez, M. Mahmud, and L. Bianchi. (2021). ‘Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data’, Frontiers in Neuroinformatics, vol. 15, article no. 699840.
- A. K. Singh, A. Kumar, M. Mahmud, M. S. Kaiser, and A. Kishore. (2021). COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier’, Cognitive Computation, [Online First].
- M. Shamim Kaiser, M. Mahmud, et al. (2021). iWorksafe: Towards Healthy Workplaces during COVID-19 with an Intelligent Phealth App for Industrial Settings, IEEE Access, vol. 9, pp. 13814–13828.
- M. S. Satu et al. (2021). TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets, Knowledge-Based Systems, vol. 226, article no. 107126.
- M. S. Satu et al. (2021) Short-term prediction of covid-19 cases using machine learning models, Applied Sciences (Switzerland), vol. 11, no. 9, article no. 4266.
- N. B. Prakash, M. Murugappan, G. R. Hemalakshmi, M. Jayalakshmi, and M. Mahmud. (2021). Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation’, Sustainable Cities and Society, vol. 75, article no. 103252.
- M. J. A. Nahian et al. (2021). Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features’, IEEE Access, vol. 9, pp. 39413–39431.
- H. Mukherjee et al. (2021). Automatic Lung Health Screening Using Respiratory Sounds, Journal of Medical Systems, vol. 45, no. 2, article no. 19.
- A. Hossaini, D. Valeriani, C. S. Nam, R. Ferrante, and M. Mahmud. (2021). A Functional BCI Model by the P2731 working group: Physiology, Brain-Computer Interfaces, vol. 8, no. 3, pp. 54–81.
- T. Ghosh et al. (2021). Artificial intelligence and internet of things in screening and management of autism spectrum disorder, Sustainable Cities and Society, vol. 74, article no. 103189. pp. 1-23.
- M. Fabietti et al. (2021). SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals, Brain Informatics, vol. 8, no. 1, article no. 14.
- M. Biswas, M. H. Tania, M. Shamim Kaiser, R. Kabir, M. Mahmud, and A. A. Kemal. (2021). ACCU3RATE: A mobile health application rating scale based on user reviews, PLoS ONE, vol. 16, no. 12, article no. e0258050.
- M. H. A. Banna et al. (2021). Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction, IEEE Access, vol. 9, pp. 56589–56603.
- M. Mahmud, M. S. Kaiser, M. McGinnity, and A. Hussain. (2020). Deep Learning in Mining Biological Data, Cognitive Computation, pp. 1–33, Dec. 2020, doi: 10.1007/s12559-020-09773-x [epub ahead of print].
- R. L. S. Sharpe, M. Mahmud, M. S. Kaiser, and J. Chen. (2020). Gamma entrainment frequency affects mood, memory and cognition: an exploratory pilot study, Brain Informatics, vol. 7, no. 1, p. 17, Nov. 2020.
- G. Rabby, S. Azad, M. Mahmud, K. Z. Zamli, and M. M. Rahman. (2020). TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique, Cognitive Computation, vol. 12, no. 4, pp. 811–833, Jul. 2020.
- M. B. T. Noor, N. Z. Zenia, M. S. Kaiser, S. A. Mamun, and M. Mahmud. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimers disease, Parkinsons disease and schizophrenia, Brain Informatics, vol. 7, no. 1, p. 11, Oct. 2020.
- Z. Ju, L. Gun, A. Hussain, M. Mahmud, and C. Ieracitano. (2020). ‘A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications’, Applied Sciences, vol. 10, no. 19, Art. no. 19, Sep. 2020.
- A. Leparulo, M. Mahmud, E. Scremin, T. Pozzan, S. Vassanelli, and C. Fasolato. (2020). Dampened Slow Oscillation Connectivity Anticipates Amyloid Deposition in the PS2APP Mouse Model of Alzheimer's Disease, Cells, vol. 9, no. 1, Art. no. 1, Jan. 2020.
- L. Farah, A. Hussain, A. Kerrouche, C. Ieracitano, J. Ahmad, and M. Mahmud. (2020). A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System, IEEE Access, vol. 8, pp. 163225–163237, 2020..
- N. Dey, V. Rajinikanth, S. J. Fong, M. S. Kaiser, and M. Mahmud. (2020). Social Group Optimization–Assisted Kapurs Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images, Cognitive Computation, vol. 12, no. 5, pp. 1011–1023, Sep. 2020.
- L. Chen, J. Yan, J. Chen, Y. Sheng, Z. Xu, and M. Mahmud. (2020). An event based topic learning pipeline for neuroimaging literature mining, Brain Informatics, vol. 7, no. 1, p. 18, Nov. 2020.
- M. H. A. Banna, K. A. Taher, M. S. Kaiser, M. Mahmud, M. S. Rahman, A.S.M.S. Hosen, G. H. Cho (2020). Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges, IEEE Access, vol. 8, pp. 192880–192923, 2020.
- F. I. Adiba, T. Islam, M. S. Kaiser, M. Mahmud, and M. A. Rahman. (2020). Effect of Corpora on Classification of Fake News using Naive Bayes Classifier, International Journal of Automation, Artificial Intelligence and Machine Learning, vol. 1, no. 1, Art. no. 1, Oct. 2020.
- S. W. Yahaya, A. Lotfi, and M. Mahmud. (2020). A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living, Applied Soft Computing, vol. 83, p. 105613, Oct. 2019.
- S. Azad*, N. E. A. C. Nordin, N. N. A. Rasul, M. Mahmud*, and K. Z. Zamli. (2020). A Secure Hybrid Authentication Scheme Using Passpoints and Press Touch Code. IEEE Access, vol. 7, pp. 166043–166053, 2019. [* Co-senior authors]
- M. Asif-Ur-Rahman, F. Afsana, M. Mahmud*, M.S. Kaiser*, M.R. Ahmed, O. Kaiwartya, A. James-Taylor. (2019). Towards a Heterogeneous Mist, Fog, and Cloud based Framework for the Internet of Healthcare Things. IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4049–4062. Doi: 10.1109/JIOT.2018.2876088 [* Co-senior authors]
- A. Aliyu, A.H. Abdullah, N. Aslam, A. Altameem, R.Z. Radzi, R. Kharel, M. Mahmud, S. Prakash, U.M. Joda. (2018). Interference-aware Multipath Video Streaming in Vehicular Environments. IEEE Access, Vol. 6, pp. 47610-47626. Doi: 10.1109/ACCESS.2018.2854784
- M. Mahmud*, M.S. Kaiser*, M.M. Rahman, M.A. Rahman, A. Shabut, S. Al Mamun, A. Hussain. (2018). A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications. Cognitive Computation, Vol. 10, No. 5, pp. 864–873. Doi: 10.1007/s12559-018-9543-3 [*: equal contributors.]
- M. Mahmud*, M.S. Kaiser*, A. Hussain, S. Vassanelli. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 6, pp. 2063 - 2079. Doi: 10.1109/TNNLS.2018.2790388 [*: equal contributors.]
- F. Afsana, M.A. Rahman, M.R. Ahmed, M. Mahmud*, M.S. Kaiser*. (2018). An Energy Conserving Routing Scheme for Wireless Body Sensor Nanonetwork Communication. IEEE Access, Vol. 6, pp. 9186-9200. Doi: 10.1109/ACCESS.2018.2789437 [*: Co-senior authors.]
- M.S. Kaiser, K. Lwin, M. Mahmud, D. Hajializadeh, T. Chaipimonplin, A. Sarhan, M. A. Hossain. (2018). Advances in Crowd Analysis for Urban Applications through Urban Event Detection. IEEE Transactions on Intelligent Transport Systems, Vol. 19, No. 10, pp. 3092 - 3112. Doi: 10.1109/TITS.2017.2771746
- R. Fontana, M. Agostini, E. Murana, M. Mahmud, M. Rubega, G. Sparacino, S. Vassanelli, C. Fasolato. (2017). Early Hippocampal Hyperexcitability in PS2APP Mice: Role of Mutant PS2 and AP. Neurobiology of Aging, Vol. 50, pp. 64-76. Doi: 10.1016/j.neurobiolaging.2016.10.027.
- S. Vassanelli, M. Mahmud. (2016). Trends and Challenges in Neuroengineering: Towards ‘Intelligent’ Neuroprostheses through Brain-‘Brain Inspired Systems’ Communication. Frontiers in Neuroscience, Vol. 10, art. no. 438. Doi: 10.3389/fnins.2016.00438.
- M. Mahmud, S. Vassanelli. (2016). Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-art and Challenges. Frontiers in Neuroscience, Vol.10, art. no. 248. Doi10.3389/fnins.2016.00248.
- M.S. Kaiser, Z. Chowdhury, S. Al Mamun, A. Hussain, M. Mahmud. (2016). A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cognitive Computation, Vol. 8, No. 5, pp. 946-954. Doi: 10.1007/s12559-016-9398-4.
- M. Mahmud, R. Pulizzi, E. Vasilaki, M. Giugliano. (2014). QSpike Tools: a Generic Framework for Parallel Batch Preprocessing of Extracellular Neuronal Signals Recorded by Substrate Microelectrode Arrays. Frontiers in Neuroinformatics, Vol. 8, art. no. 26. Doi: 10.3389/fninf.2014.00026.
- M. Mahmud, A. Bertoldo, et al. (2012). SigMate: A Matlab–Based Automated Tool for Extracellular Neuronal Signal Processing and Analysis. Journal of Neuroscience Methods, Vol. 207, No. 1, pp. 97-112. Doi: 10.1016/j.jneumeth.2012.03.009.
My research contributes to the UN SDG
My research supports several United Nations Sustainable Development Goals including - Goal 2: Zero Hunger, Goal 3: Good Health and Well-Being, Goal 4: Quality Education, and Goal 11: Sustainable Cities and Communities.