MUDDSAIR SHARIF

Data Analytics | AI | Digital Transformation | ML

📍 Stuttgart, Germany
🗣️ German | English (C1 Advanced)

Cover Letter: Professor of Digital Transformation

Muddsair Sharif, PhD
December 2025
Stuttgart, Germany | muddsairsharif@gmail.com

To the Search Committee,

I am writing to formally submit my application for the position of **Professor of Digital Transformation**. With a PhD in Computing and a robust track record in Deep Reinforcement Learning (DRL), Internet of Things (IoT), and High-Performance Computing, I offer a unique blend of high-level academic research and strategic industrial engineering experience.

My research focuses on the intersection of Artificial Intelligence and sustainable digital infrastructure. My work on projects like **M4_LAB** and **KNIGHT** has pioneered context-aware resource management and personalized learning frameworks, contributing directly to the intelligent evolution of urban and educational systems. My commitment to research excellence was recently recognized with the "Best Researcher Award 2025" at the Hochschule für Technik Stuttgart.

In the classroom, I believe in a "Learning-by-Doing" philosophy. My teaching portfolio covers Distributed Computing, IoT, and Software Engineering, where I specialize in curricula that bridge theoretical foundations with modern DevOps practices and AI-driven business innovation. I am eager to lead your faculty’s research initiatives, establish new laboratory environments, and foster international partnerships that prepare students for the complexities of the digital age.

Thank you for your time and consideration. I look forward to the possibility of discussing my vision for the Digital Transformation department with your committee.

Sincerely,
Muddsair Sharif, PhD

Anschreiben: Professur für Digitale Transformation

Muddsair Sharif, PhD
Dezember 2025
Stuttgart, Deutschland | muddsairsharif@gmail.com

An das Berufungskomitee,

mit großem Interesse bewerbe ich mich auf die **Professur für Digitale Transformation**. Als promovierter Informatiker mit langjähriger Erfahrung in der angewandten Forschung an der Hochschule für Technik Stuttgart sowie in der internationalen Industrie verfüge ich über die Expertise, um die Lehre und Forschung in diesem zukunftskritischen Bereich maßgeblich zu prägen.

In meiner aktuellen Forschung konzentriere ich mich auf die synergetische Verbindung von Künstlicher Intelligenz und nachhaltiger digitaler Infrastruktur. Durch Projekte wie **M4_LAB** habe ich bewiesen, dass ich theoretische Konzepte in praxisnahe Lösungen für die Stadt- und Regionalentwicklung überführen kann. Meine wissenschaftliche Exzellenz wurde kürzlich mit dem „Best Researcher Award 2025“ ausgezeichnet.

Mein didaktisches Konzept basiert auf einer engen Verzahnung von Theorie und Praxis. In meinen Lehrveranstaltungen zu verteilten Systemen, IoT und maschinellem Lernen bereite ich Studierende gezielt auf die Anforderungen der Industrie 4.0 vor. Ich beabsichtige, neue Forschungskooperationen zu etablieren und die internationale Vernetzung Ihrer Hochschule durch innovative Drittmittelprojekte und Laborinitiativen voranzutreiben.

Ich freue mich darauf, Ihnen meine Konzepte für Forschung und Lehre im Bereich der Digitalen Transformation persönlich vorzustellen.

Mit freundlichen Grüßen,
Muddsair Sharif, PhD

Professional Experience

Digital Transformation Analytics ML/AI/DRL
Jan 2023 - Present
Hochschule für Technik Stuttgart, Germany
Optimisation Researcher - High Performance Computing
Jul 2018 - Dec 2022
HfT Stuttgart & HLRS, Baden-Württemberg, Germany
Research Scientist (IoT)
Mar 2016 - Jul 2018
University of Antwerp & IMEC, Antwerp, Belgium
Researcher IoT
Mar 2016 - Jul 2018
IMEC Antwerp, Belgium
Industry Experience
Mar 2007 - Aug 2010
InfoSpan & ITSS
  • Senior Software Engineer at CROEM Inc. (InfoSpan, Dec 2009 - Aug 2010)
  • Software Engineer (ITSS, Mar 2007 - Aug 2009)

Berufserfahrung

Digitale Transformation Analytics ML/AI/DRL
Jan 2023 - Heute
Hochschule für Technik Stuttgart, Deutschland
Optimierungsforscher - Hochleistungsrechnen
Jul 2018 - Dez 2022
HfT Stuttgart & HLRS, Baden-Württemberg, Deutschland
Wissenschaftler (IoT)
Mär 2016 - Jul 2018
Universität Antwerpen & IMEC, Antwerpen, Belgien
Forscher IoT
Mär 2016 - Jul 2018
IMEC Antwerpen, Belgien
Branchenerfahrung
Mär 2007 - Aug 2010
InfoSpan & ITSS
  • Senior Softwareentwickler bei CROEM Inc. (InfoSpan, Dez 2009 - Aug 2010)
  • Softwareentwickler (ITSS, Mär 2007 - Aug 2009)

Education

PhD Computing
May 2022 - Jan 2025
Birmingham City University, Birmingham, UK

Thesis: Context-Aware optimal resource management in electric vehicle smart2charge using DRL

MSc Software Technology Specialise in Big Data Visualisation
Aug 2010 - Dec 2011
Linnaeus University Växjö, Sweden
Bachelor in Computer Science
Aug 2002 - Jul 2006
COMSATS University, Abbottabad, Pakistan

Bildung

PhD Computing
Mai 2022 - Jan 2025
Birmingham City University, Birmingham, UK

Dissertation: Kontextbewusstes optimales Ressourcenmanagement beim intelligenten Laden von Elektrofahrzeugen mittels DRL

MSc Softwaretechnologie mit Spezialisierung auf Big Data Visualisierung
Aug 2010 - Dez 2011
Linnaeus University Växjö, Schweden
Bachelor in Informatik
Aug 2002 - Jul 2006
COMSATS University, Abbottabad, Pakistan

Professional Teaching Experience

Digital Maturity Assessment & Strategy Design

Key Learning Outcomes:

  • AI-Driven Business Model Innovation: How intelligent systems reshape industry operations
  • Sustainable Digital Transformation: Environmental impact and green technology integration
  • Cross-Cultural Technology Adoption: Multinational organization transformation strategies
  • SME Digital Acceleration: Scalable transformation approaches for smaller enterprises
  • Industry 4.0 Implementation: Manufacturing and industrial digitization methodologies

Teaching Approach: Hands-on laboratory sessions combined with theoretical foundations, enabling students to process real-world consulting simulation where students solve actual enterprise transformation challenges using cutting-edge technologies, bridging hands-on technical implementation with strategic business consulting through industry partnerships and research-driven problem solving.

Distributed Computing & Data Processing

Key Learning Outcomes:

  • Mastery of Apache Hadoop ecosystem and MapReduce programming paradigm
  • Implementation of large-scale data processing pipelines using Apache Spark and PySpark
  • Understanding of distributed file systems (HDFS) and resource management (YARN)
  • Practical experience with data processing tools including Apache Pig

Teaching Approach: Hands-on laboratory sessions combined with theoretical foundations, enabling students to process real-world datasets across distributed computing clusters.

Internet of Things & Cloud Architecture

Key Learning Outcomes:

  • End-to-end IoT solution development from sensor integration to cloud analytics
  • Proficiency in AWS IoT Core services and MQTT protocol implementation
  • Serverless computing with AWS Lambda and real-time data storage using DynamoDB
  • Edge computing integration and scalable IoT architecture design

Teaching Approach: Project-based learning with students building complete IoT ecosystems, emphasizing industry-standard practices and cloud-native architectures.

Digital Media & Professional Communication

Key Learning Outcomes:

  • Advanced understanding of digital media's social impact in contemporary society
  • Multilingual professional communication competencies
  • Research methodologies in communication studies
  • Critical analysis of mediatized communication patterns

Teaching Approach: Interactive seminars combining theoretical frameworks with practical digital communication projects, emphasizing cross-cultural communication effectiveness.

Software Engineering & DevOps Practices

Key Learning Outcomes:

  • Design and implementation of continuous integration/delivery pipelines
  • Containerization strategies using Docker and Kubernetes orchestration
  • Infrastructure as Code principles and automated testing frameworks
  • Modern software development workflow optimization

Teaching Approach: Industry-simulation environment where students manage complete software lifecycle from development to production deployment.

Advanced Machine Learning & AI

Key Learning Outcomes:

  • Implementation of neural network-based decision-making algorithms
  • Policy optimization and agent training in complex environments
  • Advanced techniques including Q-learning, actor-critic methods, and policy gradients
  • Development of adaptive intelligent systems

Teaching Approach: Research-oriented coursework with students implementing state-of-the-art algorithms using PyTorch/TensorFlow, culminating in original research projects.

High-Performance Computing

Key Learning Outcomes:

  • Mastery of shared and distributed memory programming paradigms
  • Performance optimization techniques for scientific computing applications
  • Parallel algorithm design and implementation using C++/Fortran
  • Cluster computing architecture understanding and performance profiling

Teaching Approach: International collaboration with intensive laboratory work on high-performance computing clusters, emphasizing both theoretical understanding and practical implementation skills.

Educational Technology & Advanced AI

Key Learning Outcomes:

  • Development of adaptive learning platforms leveraging multiple data modalities
  • Implementation of federated learning for privacy-preserving educational data sharing
  • Multi-agent architectures for individualized tutoring systems
  • Integration of reinforcement learning in educational contexts

Teaching Approach: Cutting-edge research integration with students developing novel educational AI systems, combining deep learning, educational data mining, and adaptive algorithms.

Berufliche Lehrerfahrung

Digitale Reifegradbewertung & Strategiedesign

Wichtige Lernergebnisse:

  • KI-getriebene Geschäftsmodellinnovation: Wie intelligente Systeme Branchenoperationen umgestalten
  • Nachhaltige digitale Transformation: Umweltauswirkungen und Integration grüner Technologien
  • Interkulturelle Technologieadoption: Transformationsstrategien für multinationale Organisationen
  • Digitale Beschleunigung für KMU: Skalierbare Transformationsansätze für kleinere Unternehmen
  • Industrie 4.0 Implementierung: Fertigungs- und Industriedigitalisierungsmethoden

Lehransatz: Praktische Laboreinheiten kombiniert mit theoretischen Grundlagen, die es Studenten ermöglichen, reale Beratungssimulationen zu bearbeiten, bei denen sie tatsächliche Unternehmenstransformationsherausforderungen mit modernsten Technologien lösen und praktische technische Umsetzung mit strategischer Unternehmensberatung durch Industriepartnerschaften und forschungsgetriebene Problemlösung verbinden.

Verteiltes Rechnen & Datenverarbeitung

Wichtige Lernergebnisse:

  • Beherrschung des Apache Hadoop-Ökosystems und des MapReduce-Programmierparadigmas
  • Implementierung großskaliger Datenverarbeitungspipelines mit Apache Spark und PySpark
  • Verständnis verteilter Dateisysteme (HDFS) und Ressourcenmanagement (YARN)
  • Praktische Erfahrung mit Datenverarbeitungstools einschließlich Apache Pig

Lehransatz: Praktische Laboreinheiten kombiniert mit theoretischen Grundlagen, die es Studenten ermöglichen, reale Datensätze über verteilte Computing-Cluster zu verarbeiten.

Internet der Dinge & Cloud-Architektur

Wichtige Lernergebnisse:

  • End-to-End IoT-Lösungsentwicklung von Sensorintegration bis Cloud-Analytik
  • Kompetenz in AWS IoT Core-Diensten und MQTT-Protokollimplementierung
  • Serverloses Computing mit AWS Lambda und Echtzeit-Datenspeicherung mit DynamoDB
  • Edge-Computing-Integration und skalierbares IoT-Architekturdesign

Lehransatz: Projektbasiertes Lernen mit Studenten, die komplette IoT-Ökosysteme aufbauen, mit Schwerpunkt auf branchenüblichen Praktiken und cloud-nativen Architekturen.

Digitale Medien & Professionelle Kommunikation

Wichtige Lernergebnisse:

  • Fortgeschrittenes Verständnis der sozialen Auswirkungen digitaler Medien in der zeitgenössischen Gesellschaft
  • Mehrsprachige professionelle Kommunikationskompetenzen
  • Forschungsmethoden in Kommunikationsstudien
  • Kritische Analyse mediatisierter Kommunikationsmuster

Lehransatz: Interaktive Seminare, die theoretische Rahmenwerke mit praktischen digitalen Kommunikationsprojekten kombinieren und die interkulturelle Kommunikationseffektivität betonen.

Softwareentwicklung & DevOps-Praktiken

Wichtige Lernergebnisse:

  • Design und Implementierung von Continuous Integration/Delivery-Pipelines
  • Containerisierungsstrategien mit Docker und Kubernetes-Orchestrierung
  • Infrastructure as Code-Prinzipien und automatisierte Test-Frameworks
  • Optimierung moderner Softwareentwicklungs-Workflows

Lehransatz: Industriesimulationsumgebung, in der Studenten den kompletten Software-Lebenszyklus von der Entwicklung bis zur Produktionsbereitstellung verwalten.

Fortgeschrittenes maschinelles Lernen & KI

Wichtige Lernergebnisse:

  • Implementierung neuronaler netzwerkbasierter Entscheidungsalgorithmen
  • Richtlinienoptimierung und Agententraining in komplexen Umgebungen
  • Fortgeschrittene Techniken einschließlich Q-Learning, Actor-Critic-Methoden und Policy Gradients
  • Entwicklung adaptiver intelligenter Systeme

Lehransatz: Forschungsorientierte Kursarbeit mit Studenten, die modernste Algorithmen mit PyTorch/TensorFlow implementieren und in originalen Forschungsprojekten gipfeln.

Hochleistungsrechnen

Wichtige Lernergebnisse:

  • Beherrschung von Shared- und Distributed-Memory-Programmierparadigmen
  • Leistungsoptimierungstechniken für wissenschaftliche Computing-Anwendungen
  • Paralleles Algorithmendesign und -implementierung mit C++/Fortran
  • Verständnis der Cluster-Computing-Architektur und Leistungsprofilerstellung

Lehransatz: Internationale Zusammenarbeit mit intensiver Laborarbeit an Hochleistungs-Computing-Clustern, mit Schwerpunkt auf theoretischem Verständnis und praktischen Umsetzungsfähigkeiten.

Bildungstechnologie & Fortgeschrittene KI

Wichtige Lernergebnisse:

  • Entwicklung adaptiver Lernplattformen unter Nutzung mehrerer Datenmodalitäten
  • Implementierung von föderiertem Lernen für datenschutzschützendes Teilen von Bildungsdaten
  • Multi-Agenten-Architekturen für individualisierte Tutorensysteme
  • Integration von Reinforcement Learning in Bildungskontexten

Lehransatz: Integration modernster Forschung mit Studenten, die neuartige Bildungs-KI-Systeme entwickeln und Deep Learning, Educational Data Mining und adaptive Algorithmen kombinieren.

Publications

Journal Publications (5)

Context-Aware Optimal Resource Management in Electric Vehicle Smart2Charge
Dissertation, Birmingham City University, 2025
Sharif, Muddsair.
Smart EV charging with context-awareness: Enhancing resource utilization via deep reinforcement learning
IEEE Access 12 (2024): 7009-7027
Sharif, Muddsair, and Huseyin Seker.
Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach
IEEE Access 12 (2024): 54049-54065
Sharif, Muddsair, and Dieter Uckelmann.
Context aware-resource optimality in electric vehicle Smart2Charge application: A deep reinforcement learning-based approach
IEEE Access 11 (2023): 88583-88596
Sharif, Muddsair, Gero Lückemeyer, and Huseyin Seker.
Distributed uniform streaming framework: an elastic fog computing platform for event stream processing and platform transparency
2019
Vanneste, Simon, Jens de Hoog, Thomas Huybrechts, Stig Bosmans, Reinout Eyckerman, Muddsair Sharif, Siegfried Mercelis, and Peter Hellinckx.

Conference Publications (16)

Reinforcement Learning-based Optimization of EBike Charging Infrastructure
2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE. 2024
Sharif, Muddsair, and Huseyin Seker.
KNIGHT: Machine Learning Methods to Enhance Individualized Learning
International Conference on Artificial Intelligence in Education Technology. Singapore: Springer Nature Singapore, 2024
Sharif, Muddsair, and Dieter Uckelmann.
Wahrnehmung von verkehrslärm im innenraum
In Proceedings of DAGA. vol. 2024. 2024
Zeitler, Berndt, Jan-Philipp Kirch, Muddsair Sharif, and André Fiebig.
KNIGHT learning analytics architecture for betterment of student education
International Conference on Artificial Intelligence in Education Technology. Singapore: Springer Nature Singapore, 2023
Sharif, Muddsair, Ferdinand Munz, and Dieter Uckelmann.
ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning
ICity. Transformative Research for the Livable, Intelligent, and Sustainable City (2022): 199
Sharif, Muddsair, Charitha Buddhika Heendeniya, and Gero Lückemeyer.
iMobiliAkou: The Role of Machine Listening to Detect Vehicle using Sound Acoustics
Proceedings of the 5th International Conference on Advances in Artificial Intelligence. 2021
Sharif, Muddsair, et al.
Context-aware optimal charging distribution using deep reinforcement learning
Proceedings of the 2020 4th International Conference on Big Data and Internet of Things. 2020
Sharif, Muddsair, et al.
COaaS: Continuous Integration and Delivery framework for HPC using Gitlab-Runner
In Proceedings of the 2020 4th International Conference on Big Data and Internet of Things (BDIOT '20). Association for Computing Machinery. 5458
Muddsair Sharif, Skowronek Janto, and Gero Lueckemeyer. 2020. https://doi.org/10.1145/3421537.3421539
Demonstrating innovative technologies for the Flemish asphalt sector in the CyPats project
In IOP Conference Series: Materials Science and Engineering (Vol. 471, No. 2, p. 022031). IOP Publishing. February 2019
Van den Bergh, W., Jacobs, G., De Maeijer, P. K., Vuye, C., Armilli, S., Couscheir, K., & Sharif, M.
Context-aware distribution in constrained IoT environments
In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 437-446. Cham: Springer International Publishing, 2018
Eyckerman, Reinout, Muddsair Sharif, Siegfried Mercelis, and Peter Hellinckx.
A particle swarm optimization-based heuristic for optimal cost estimation in internet of things environment
Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things. 2018
Sharif, Muddsair, et al.
Distributed uniform streaming framework: towards an elastic fog computing platform for event stream processing
In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 426-436). Cham: Springer International Publishing. October 2018
Vanneste, S., de Hoog, J., Huybrechts, T., Bosmans, S., Sharif, M., Mercelis, S., & Hellinckx, P.
IT in road construction: Recommendations for the development of production, transport and paving data storage in the Flemish region
In Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things (pp. 143-147). October 2018
Sharif, M., Jacobs, G., Van Den Bergh, W., & Hellinckx, P.
Context-aware optimization of distributed resources in internet of things using key performance indicators
International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Cham: Springer International Publishing, 2017
Sharif, Muddsair, Siegfried Mercelis, and Peter Hellinckx.
To-DO Map-Location Focused Task Management Framework
2011
Sharif, Muddsair.
On fast recognition of isolated characters by constructing character signature database
2006 International Conference on Emerging Technologies. IEEE, 2006
Azam, Sheikh Muhammad, Zubair Ali Mansoor, and Muddsair Sharif.

Publikationen

Zeitschriftenveröffentlichungen (5)

Kontextbewusstes optimales Ressourcenmanagement beim intelligenten Laden von Elektrofahrzeugen
Dissertation, Birmingham City University, 2025
Sharif, Muddsair.
Smart EV charging with context-awareness: Enhancing resource utilization via deep reinforcement learning
IEEE Access 12 (2024): 7009-7027
Sharif, Muddsair, and Huseyin Seker.
Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach
IEEE Access 12 (2024): 54049-54065
Sharif, Muddsair, and Dieter Uckelmann.
Context aware-resource optimality in electric vehicle Smart2Charge application: A deep reinforcement learning-based approach
IEEE Access 11 (2023): 88583-88596
Sharif, Muddsair, Gero Lückemeyer, and Huseyin Seker.
Distributed uniform streaming framework: an elastic fog computing platform for event stream processing and platform transparency
2019
Vanneste, Simon, Jens de Hoog, Thomas Huybrechts, Stig Bosmans, Reinout Eyckerman, Muddsair Sharif, Siegfried Mercelis, and Peter Hellinckx.

Konferenzveröffentlichungen (16)

Reinforcement Learning-based Optimization of EBike Charging Infrastructure
2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE. 2024
Sharif, Muddsair, and Huseyin Seker.
KNIGHT: Machine Learning Methods to Enhance Individualized Learning
International Conference on Artificial Intelligence in Education Technology. Singapore: Springer Nature Singapore, 2024
Sharif, Muddsair, and Dieter Uckelmann.
Wahrnehmung von verkehrslärm im innenraum
In Proceedings of DAGA. vol. 2024. 2024
Zeitler, Berndt, Jan-Philipp Kirch, Muddsair Sharif, and André Fiebig.
KNIGHT learning analytics architecture for betterment of student education
International Conference on Artificial Intelligence in Education Technology. Singapore: Springer Nature Singapore, 2023
Sharif, Muddsair, Ferdinand Munz, and Dieter Uckelmann.
ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning
ICity. Transformative Research for the Livable, Intelligent, and Sustainable City (2022): 199
Sharif, Muddsair, Charitha Buddhika Heendeniya, and Gero Lückemeyer.
iMobiliAkou: The Role of Machine Listening to Detect Vehicle using Sound Acoustics
Proceedings of the 5th International Conference on Advances in Artificial Intelligence. 2021
Sharif, Muddsair, et al.
Context-aware optimal charging distribution using deep reinforcement learning
Proceedings of the 2020 4th International Conference on Big Data and Internet of Things. 2020
Sharif, Muddsair, et al.
COaaS: Continuous Integration and Delivery framework for HPC using Gitlab-Runner
In Proceedings of the 2020 4th International Conference on Big Data and Internet of Things (BDIOT '20). Association for Computing Machinery. 5458
Muddsair Sharif, Skowronek Janto, and Gero Lueckemeyer. 2020. https://doi.org/10.1145/3421537.3421539
Demonstrating innovative technologies for the Flemish asphalt sector in the CyPats project
In IOP Conference Series: Materials Science and Engineering (Vol. 471, No. 2, p. 022031). IOP Publishing. Februar 2019
Van den Bergh, W., Jacobs, G., De Maeijer, P. K., Vuye, C., Armilli, S., Couscheir, K., & Sharif, M.
Context-aware distribution in constrained IoT environments
In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 437-446. Cham: Springer International Publishing, 2018
Eyckerman, Reinout, Muddsair Sharif, Siegfried Mercelis, and Peter Hellinckx.
A particle swarm optimization-based heuristic for optimal cost estimation in internet of things environment
Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things. 2018
Sharif, Muddsair, et al.
Distributed uniform streaming framework: towards an elastic fog computing platform for event stream processing
In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 426-436). Cham: Springer International Publishing. Oktober 2018
Vanneste, S., de Hoog, J., Huybrechts, T., Bosmans, S., Sharif, M., Mercelis, S., & Hellinckx, P.
IT in road construction: Recommendations for the development of production, transport and paving data storage in the Flemish region
In Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things (pp. 143-147). Oktober 2018
Sharif, M., Jacobs, G., Van Den Bergh, W., & Hellinckx, P.
Context-aware optimization of distributed resources in internet of things using key performance indicators
International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Cham: Springer International Publishing, 2017
Sharif, Muddsair, Siegfried Mercelis, and Peter Hellinckx.
To-DO Map-Location Focused Task Management Framework
2011
Sharif, Muddsair.
On fast recognition of isolated characters by constructing character signature database
2006 International Conference on Emerging Technologies. IEEE, 2006
Azam, Sheikh Muhammad, Zubair Ali Mansoor, and Muddsair Sharif.

Research Projects

KNIGHT LA Framework

Personalized education through advanced multi-modal learning with federated learning.... Knight Project

M4_LAB Project

Innovation and transfer project for sustainable urban and regional development in the Stuttgart Metropolitan Region... M4_LAB Project

ARaaS (EV Charging Optimization)

Context-aware optimal charging distribution using deep reinforcement learning

COaaS (HPC CI/CD)

Continuous Integration and Delivery framework for HPC using GitLab-Runner

Smart Road Tetra Project

IoT framework interoperability for intelligent infrastructure... Road-IT Project

Forschungsprojekte

KNIGHT LA Framework

Personalisierte Bildung durch fortgeschrittenes multimodales Lernen mit föderiertem Lernen.... Knight Projekete

M4_LAB Projekt

Innovations- und Transferprojekt zur nachhaltigen Stadt- und Regionalentwicklung in der Metropolregion Stuttgart... M4_LAB Projekt

ARaaS (EV-Ladeoptimierung)

Kontextbewusste optimale Ladeverteilung mittels Deep Reinforcement Learning

COaaS (HPC CI/CD)

Continuous Integration and Delivery Framework für HPC mit GitLab-Runner

Smart Road Tetra Projekt

IoT-Framework-Interoperabilität für intelligente Infrastruktur... Road-IT Projekte

Professional Certificates & Courses

Software Architecture for the Internet of Things
2016
Coursera
Practical Machine Learning
2016
Coursera

Certificate ID: TBTCXKNTYB

Exploratory Data Analysis
2016
Coursera

Certificate ID: 9UTC62K8GT

Developing Data Products
2016
Coursera

Certificate ID: 4YF6WCVXY2

Microsoft Certified Technology Specialist
2007
Microsoft
Microsoft Valued Professional
2008
Microsoft

Professionelle Zertifikate & Kurse

Softwarearchitektur für das Internet der Dinge
2016
Coursera
Praktisches maschinelles Lernen
2016
Coursera

Zertifikats-ID: TBTCXKNTYB

Explorative Datenanalyse
2016
Coursera

Zertifikats-ID: 9UTC62K8GT

Entwicklung von Datenprodukten
2016
Coursera

Zertifikats-ID: 4YF6WCVXY2

Microsoft Certified Technology Specialist
2007
Microsoft
Microsoft Valued Professional
2008
Microsoft

Honours & Awards

Best Researcher Award
2025
University of Applied Sciences Stuttgart, Germany
Fully Funded Scholarship
2022 - 2025
Birmingham City University, Birmingham, UK
Best Paper Presentation Award
2021
BDIOT Conference

Ehrungen & Auszeichnungen

Bester Forscher Award
2025
Hochschule für Technik Stuttgart, Deutschland
Vollständig finanziertes Stipendium
2022 - 2025
Birmingham City University, Birmingham, UK
Beste Vortragspräsentation Award
2021
BDIOT Konferenz

Student Supervision

Master's Level Theses: 7

  • Supervised 7 Master's theses, including 3 from Belgium
  • Topics covering advanced AI, machine learning, IoT, and data analytics
  • Students achieved excellent outcomes with publications and industry placements

Bachelor's Level Theses: 5

  • Supervised 5 Bachelor's theses, including 2 from Belgian universities
  • Focused on practical implementations in software development and data science
  • Guided students through research methodology and technical execution

Studentenbetreuung

Masterarbeiten: 7

  • Betreuung von 7 Masterarbeiten, einschließlich 3 aus Belgien
  • Themen umfassen fortgeschrittene KI, maschinelles Lernen, IoT und Datenanalytik
  • Studenten erzielten hervorragende Ergebnisse mit Publikationen und Industriepraktika

Bachelorarbeiten: 5

  • Betreuung von 5 Bachelorarbeiten, einschließlich 2 von belgischen Universitäten
  • Schwerpunkt auf praktischen Implementierungen in Softwareentwicklung und Data Science
  • Begleitung der Studenten durch Forschungsmethodik und technische Ausführung