Find Academic Supervisors

Ivana DusparicView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +6 more

Ivana Dusparic is an Ussher Associate Professor in Future Cities and the Internet of Things at the School of Computer Science and Statistics , Trinity College Dublin . She is also a Funded Investigator at the CONNECT SFI Research Centre and co-director of the Center for Doctoral Training in Artificial Intelligence . Previously, she held positions as Assistant Professor at University College Dublin (2015-2016) and Research Fellow at Trinity (2010-2015). Education: PhD in Computer Science, Trinity College Dublin (2010) Research Interests: Her research centers on leveraging Artificial Intelligence —particularly machine learning , intelligent agents , and multi-agent systems —to autonomously optimize large-scale heterogeneous infrastructures. A primary focus is on smart cities applications, including sustainable urban mobility, intelligent transport systems, autonomous car sharing, and smart energy grids. Research Projects & Labs: Surpass : Investigates how shared autonomous cars will transform cities, using Dublin as a case study. LAMP : Applies agent learning to smart grid management for renewable energy optimization. REALT : Real-time adaptive learning-based traffic control using multi-agent reinforcement learning. ClearWay : Optimizes travel-time reliability in mixed traffic using deep reinforcement learning and swarm intelligence. SATORI : Focuses on reinforcement learning in smart cities and moving networks. Grants & Funding: She has secured multiple high-profile grants, including Science Foundation Ireland (SFI) funding for CONNECT , ENABLE , and ClearWay . She has also launched initiatives like the IBM-TCD Pre-doc Fellowship in AI and the CRT in AI, offering 30+ funded PhD positions across Irish universities. Teaching & Supervision: She has taught courses such as Artificial Intelligence , Advanced Data Structures and Algorithms , and Computer Architecture at both Trinity and UCD. She actively supervises PhD students, postdocs, and interns across multiple projects.

Alfred TaudesView profile

Professor

• Operations and Supply Chain Management
• Cryptoeconomics
• Blockchain Technology
• +5 more

Alfred Taudes is a Full Professor at WU Vienna University of Economics and Business, affiliated with the Department of Information Systems and Operations Management and the Institute for Production Management. His research focuses on the intersection of Operations and Supply Chain Management, Cryptoeconomics, and Blockchain Technology, with an emphasis on Digitalization’s impact on Operations Management and Decentralized Finance. Doctoral Degree in Business Administration (WU Vienna) Magister Degree in Management Information Systems (Vienna University) PhD (Habilitation) in both fields (WU Vienna) Over his career, he has led large-scale research projects such as the WWTF-project "Integrated Demand and Supply Chain Management" and the Special Research Area "Adaptive Models in Economics and Management Science." He has also served as a visiting professor at universities in Augsburg, Münster, Essen, and Tsukuba, Japan. The recent 15 articles highlight his work in Blockchain Applications, Cryptoeconomics, and Supply Chain Resilience, with sub-fields including Smart Contracts, Decentralized Federated Learning, and Cryptographic Solutions for Operational Challenges. These publications reflect his interest in leveraging advanced technologies to address complex problems in Finance and Operations. Cooperation Officer of the Year 2013/14 (2015) Distinguished Paper Award WI 2009 (2009) VHB Best Paper Award (2009) Nomination VHB Best Paper Award (2008) Dr. Wolfgang Houska - Recognition Award (2007) His advisory and grant work spans major European research initiatives, including the Austrian Blockchain Center and projects on Decentralized Finance. He has also contributed to labs and teams focused on Blockchain and Cryptoeconomics, such as the Institute for Production Management and the WU Working Paper Series on Cryptoeconomics.

Yossiri AdulyasakView profile

Professor

• Supply Chain Analytics
• Optimization under Uncertainty
• Stochastic Optimization
• +6 more

Yossiri Adulyasak is a Full Professor in the Department of Operations and Logistics Management at HEC Montréal, where he was promoted to this position effective June 1, 2025. He also holds an appointment as Associate Professor in the Department of Computer Science and Operational Research at the University of Montreal since May 2022. Adulyasak is a member of the GERAD research center since February 2017 and holds the Canada Research Chair in Supply Chain Analytics. His educational background includes a Ph.D. in Operations and Logistics Management from HEC Montréal, where his doctoral research focused on planning systems in supply chains, and a Master of Civil Engineering in Transportation Engineering from Chulalongkorn University. Professor Adulyasak's research centers on supply chain analytics, with particular emphasis on stochastic and robust optimization under uncertainty. His work bridges operations research, artificial intelligence, and supply chain management, focusing on real-world applications in logistics, inventory management, and sequential decision-making processes. He investigates how to integrate demand forecasting with optimization frameworks and develops algorithms that can predict disruptions and recommend mitigation measures in supply chain operations. His recent publications demonstrate a strong trend toward integrating AI and machine learning techniques with traditional optimization methods to address complex supply chain challenges. The research spans multiple domains including electric vehicle routing, drone delivery, facility location under uncertainty, and pandemic response strategies. His work frequently appears in top-tier journals across operations research, transportation science, and management science. Canada Research Chair in Supply Chain Analytics (2018-present) Prix d'impact en recherche (HEC Montréal, 2024) Prix du meilleur cas de gestion (2024) 1st place for Prix de la pratique (Société canadienne de recherche opérationnelle, 2024) Best Paper Award for 2021 ('The value of aggregate service levels in stochastic lot sizing problems') Professor Adulyasak has supervised numerous doctoral and master's students, with 3 doctoral theses completed or in progress, 5 master's theses, and 37 supervised master's projects. His students have worked on diverse topics including inventory management, demand forecasting, supply chain network design, and optimization of logistics systems. He has collaborated extensively with industry partners including Bombardier, Pratt & Whitney Canada, and Hydro-Québec on research projects addressing real-world supply chain challenges. As an active member of GERAD and holding the Canada Research Chair in Supply Chain Analytics, Adulyasak leads a research group focused on developing advanced optimization techniques for supply chain applications. His team works at the intersection of operations research, artificial intelligence, and business analytics to create innovative solutions for complex decision-making problems under uncertainty.

• Biodiversity
• Vegetation Ecology
• Plant Community Analysis
• +3 more

Stephan Hennekens is a Researcher at the Vegetation and Landscape Ecology group within Wageningen University & Research. He specializes in biodiversity informatics, vegetation ecology, and habitat classification using artificial intelligence and large-scale datasets. Key research areas include European vegetation databases , indicator value systems , and AI-driven habitat mapping . Contributor to FloraVeg.EU , ReSurveyEurope , and Dutch Vegetation Database projects. Focus on climate change impacts , nitrogen deposition , and ecosystem stability through vegetation analysis. His recent work involves machine learning for EUNIS habitat classification , continental-scale vegetation modeling , and ecological risk assessment for agricultural and aquatic systems. He has led projects on Natura 2000 and bioindicator systems .

Muting HaoView profile

Research Fellow

• Computational Fluid Dynamics
• Turbomachinery
• Turbine Cooling
• +5 more

Dr. Muting Hao serves as a Research Fellow at the Oxford Thermofluids Institute (OTI) and an Associate Member of Faculty in the Department of Engineering Science at the University of Oxford. Since 2024, she has held a Career Development Research Fellowship at St John's College, focusing on advancing computational fluid dynamics (CFD) to address global energy and pollution challenges in aviation technology through fundamental research and industrial collaboration. Dr. Hao completed her DPhil in 2022 at the University of Oxford specializing in numerical methods and conjugate heat transfer for gas turbine film cooling under Rolls-Royce sponsorship. Her academic background includes BSc and MSc qualifications, though specific institutions are not detailed in available records. Her research spans numerical methods in fluid dynamics, turbomachinery, turbine cooling, conjugate heat transfer, turbofan design, large eddy simulation (LES), turbulence, and machine learning integration. She investigates unsteady flows across gas turbines, compressors, steam turbines, and nuclear reactor coolant pumps, with current emphasis on aerospace CFD applications and AI-enhanced fluid dynamics modeling for sustainable aviation solutions. Analysis of her publication record (2018-2023) reveals consistent focus on film cooling optimization, turbulence modeling, and advanced CFD techniques for turbomachinery. Recent work increasingly incorporates machine learning approaches and high bypass ratio turbofan design, reflecting strategic alignment with industry needs for efficient propulsion systems. Dr. Hao's contributions have earned significant recognition: UKRI Computing Insight UK 2023 Jacky Pallas Award The Osborne Reynolds 2024 Best Poster Award She currently leads UKRI-funded research on high bypass ratio turbofan design and maintains active collaboration with Rolls-Royce on solver development for next-generation aircraft engines. Dr. Hao mentors potential DPhil students in fundamental fluid dynamics, aerodynamics, high-order numerical schemes, and machine learning applications in CFD, emphasizing practical solutions for aviation sustainability challenges. As a core member of OTI's CFD Methodology group, Dr. Hao operates at the intersection of academic research and industrial application, leveraging Oxford's infrastructure and Rolls-Royce partnerships to advance turbomachinery efficiency through cutting-edge computational approaches and emerging AI methodologies.

Changqing ChengView profile

Associate Professor

• Complex Systems
• Nonlinear Dynamics
• Data Analytics
• +6 more

Changqing Cheng is an Associate Professor in the Department of Systems Science and Industrial Engineering at the State University of New York at Binghamton, where he also serves as a Core Faculty Member at the Binghamton Center of Complex Systems (CoCo). His primary research focuses on sensing and data-driven modeling, simulation and analytics for process monitoring, quality control and performance optimization of complex systems, with special interests in nonlinear dynamics, chaotic patterns, and recurrence behaviors. He applies these methodologies across manufacturing (optimal design, uncertainty quantification) and healthcare (data fusion, time series analysis, epidemic modeling). PhD, Industrial Engineering and Management, Oklahoma State University BS & MS, Engineering Mechanics, Dalian University of Technology, China Professor Cheng's research integrates advanced computational techniques with practical applications across multiple domains. His work on nonlinear dynamics and complex systems has led to innovative approaches for process monitoring and anomaly detection in manufacturing, as well as novel methods for healthcare analytics and epidemic modeling. His publications demonstrate a consistent pattern of high-impact research in top journals such as IEEE Transactions, Chaos, and Journal of Computational Physics, with a strong emphasis on methodological innovation and practical application. His research group has produced significant work in uncertainty quantification, deep learning applications in biomedical engineering, and network science applications to epidemic dynamics. The publications reveal a strong interdisciplinary approach that bridges engineering, computer science, and healthcare applications. Professor Cheng's work on basin stability estimation for complex systems and decomposition-enhanced deep learning for cardiac fibrillation detection represents cutting-edge contributions to their respective fields. INFORMS Data Mining and Decision Analytics Best Paper Award (2017) OSU Outstanding Doctoral Student Award OSU Distinguished Graduate Fellowship Material Handling Education Foundation Scholarship CIEADH Best Poster Award, IIE Research Conference Doctoral Colloquium Professor Cheng actively mentors PhD and Master's students, with several receiving prestigious awards including BU Distinguished Dissertation Award (2024) and Excellence in Systems Science Research Award. He serves as Associate Editor for IISE Transactions on Healthcare Systems Engineering and has received significant research funding supporting his students' work. His leadership extends to the Binghamton University Data Science Transdisciplinary Area of Excellence and the Northeast Regional Conference on Complex Systems where he has served as Program Chair for 2025. His lab maintains an active GitHub presence with code repositories for many of his publications.

• Artificial Intelligence
• Healthcare Informatics
• Sustainable Systems
• +5 more

Sabine Janzen is a Researcher at the German Research Center for Artificial Intelligence (DFKI) in the Smart Service Engineering department. Her work focuses on: AI applications in rehabilitation and personalized medicine Decision support systems for rural mobility Energy-efficient AI for sustainable data centers Research Projects: FedWell: Life-Long Federated User and Mental Modeling for Health and Well-being ESCADE: Energy-Efficient Large-Scale AI for Sustainable Data Centers INTE:GRATE: Intelligent Green Mobility in Saarland PAIRS: Privacy-Aware Intelligent Crisis Management SMARF: Cross-Manufacturer Agricultural Machine Networking Her recent publications highlight AI integration in healthcare, mobility optimization, and sustainable computing. Contact: Sabine.Janzen@dfki.de

Laura PalagiView profile

Professor

• Nonlinear Optimization
• Mixed Integer Nonlinear Programming
• Semidefinite Programming
• +7 more

Laura Palagi is a Full Professor at Sapienza University of Rome, Department of Computer, Automation, and Management Engineering "Antonio Ruberti". She has been with the institution since 1996, progressing from Researcher to Associate Professor in 2005, and Full Professor since March 2020. She currently serves as Coordinator of the PhD program in Automatic Control, Bioengineering and Operations Research (ABRO) since February 2024 and is the GEP committee representative at the DIAG Department. Her research focuses on developing and analyzing methods for solving nonlinear optimization problems with continuous and discrete variables. Key research themes include decomposition methods for training Support Vector Machines and Deep Networks, methods for Mixed Integer Nonlinear Programming (MINLP), Semidefinite Programming (SDP), standard quadratic problems (StQP), and convex quadratic constrained problems. She also develops predictive machine learning models for healthcare applications and surrogate models for engineering. Her recent publications span optimization methods, machine learning applications in healthcare, sustainability assessment, and network design. Her work demonstrates a strong integration of theoretical optimization with practical applications across multiple domains. Continuous Optimization Awards 2020 Member Best Paper Award from Journal of Global Optimization Honorable Mention from Marguerite Frank Award jury for Best EJCO Paper 2024 EURO Doctoral Dissertation Award Professor Palagi teaches multiple courses including Optimization and Decision Science, Laboratory of Decision Science, Optimization Methods for Machine Learning, and Optimization for Medical programs. She serves as thesis advisor for numerous Bachelor's and Master's students in Management Engineering, Mechanical Engineering, and Data Science programs.

Hao ChengView profile

Assistant Professor

• Artificial Intelligence for Geo-Science
• Computer Vision
• Autonomous Driving
• +4 more

Dr Hao Cheng is an Assistant Professor (from 1 Oct 2024) in the Department of Earth Observation Science, ITC Faculty, University of Twente, the Netherlands. He currently holds a Marie Skłodowska-Curie European Postdoctoral Fellowship focused on vehicle-vulnerable road user interactions for safer intelligent transportation and autonomous driving systems. Education: Ph.D. (with distinction) in Civil Engineering & Geodetic Science, Leibniz Universität Hannover, Germany, 2021 M.Sc. (with distinction) in Internet Technologies & Information Systems, joint programme of TU Braunschweig, Leibniz Universität Hannover, TU Clausthal & University of Göttingen, Germany, 2017 Research interests lie at the intersection of Artificial Intelligence and Geo-Information Science , with principal themes: Deep learning & computer vision for road-user behaviour modelling Trajectory prediction and motion forecasting for autonomous driving Interaction & safety analysis between vehicles and vulnerable road users (pedestrians, cyclists) Ethical, explainable and accessible AI for geospatial applications Graph neural networks, diffusion models and transformer architectures applied to dynamic scene understanding His recent publication portfolio (2018-2025) demonstrates a strong methodological core in deep learning and computer vision deployed across transportation safety , 3D mapping , remote sensing and human-machine interaction . A notable trend is the migration from early LSTM-based traffic modelling toward contemporary transformer, graph and diffusion frameworks that deliver diverse, controllable and interpretable predictions for real-world autonomous-driving scenes. Scientific recognition: Marie Skłodowska-Curie Actions European Postdoctoral Fellowship (MSCA) – VeVuSafety project Grants & projects: Cheng is principal investigator of the MSCA-funded VeVuSafety project (Grant 101062870) which develops learning-based models of vehicle-VRU interactions to enable safer intelligent transport systems. Labs & teams: From October 2024 he will lead research activities within the Department of Earth Observation Science at ITC, University of Twente, collaborating with the broader ITC AI-for-Geo groups and transportation-safety institutes across Europe.

Torsten HoeflerView profile

Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +2 more

Torsten Hoefler is a Professor at ETH Zurich , renowned for pioneering contributions to High-Performance Computing (HPC) and its application to Artificial Intelligence (AI) . His work on scalable network design, parallel algorithms, and Message Passing Interface (MPI) advancements has revolutionized supercomputing and AI infrastructure. Hoefler chairs key MPI working groups and co-developed foundational concepts like "3D parallelism" that underpin modern AI systems. Hoefler's research spans interconnection networks, congestion avoidance protocols (e.g., Slim Fly, PERCS), and performance modeling. His innovations in nonblocking collective operations (Iallreduce, Iallgather) power distributed deep learning frameworks. He also leads benchmarking and reproducibility initiatives that set global HPC research standards. ACM Prize in Computing (2024) : Recognized for enabling AI's computational scale through HPC breakthroughs ACM Gordon Bell Prize (2019) : For quantum transport simulations mapping transistor heat distribution Hoefler's work impacts millions via large-language model training (e.g., ChatGPT) and reduces data center cooling costs through thermal modeling. He leads teams at ETH Zurich's Scalable Parallel Computing Laboratory , translating theoretical advances into industry-adopted technologies.

Martin SchlüterView profile

Professor

• Geodesy
• Photogrammetry
• Structural Health Monitoring
• +5 more

Martin Schlüter is a Professor at the Department of Geodesy and Geoinformatics within the School of Engineering at Mainz University of Applied Sciences. His research focuses on the integration of photogrammetry, geodetic engineering, and machine learning for structural monitoring applications. Key institutional affiliations include leadership in the i3mainz research center and collaboration with the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF). His research interests span Geodesy , Photogrammetry , and Structural Health Monitoring , with recent emphasis on machine learning applications for optical measurement systems. Schlüter pioneers modular digital camera tachymeters (MoDiTa) for bridge vibration analysis, railway safety, and BIM integration. His work bridges theoretical geodetic principles with practical engineering solutions for infrastructure monitoring, emphasizing real-time data processing and sensor fusion. Schlüter leads significant research projects including BAM (Big-Data Analytics in Environmental Monitoring) and VCSA3D (Virtual Crime Scene Analysis) , securing institutional funding through Mainz University's Best Practice Research Program. His laboratory specializes in optical measurement systems, developing calibration protocols for X-ray micro-CT and laser tracking applications. Current work focuses on AI-driven structural monitoring for transportation infrastructure and heritage preservation. Professional contributions include active participation in the DGPF and editorial roles in German surveying journals. His research demonstrates consistent innovation in merging traditional geodetic methods with emerging computational techniques for precision engineering applications.

• Modularisation
• Productivity
• Artificial Intelligence
• +8 more

Dr. Rolla Monib serves as a Senior Lecturer in Construction Project Management at the University of the West of England (UWE Bristol), bringing over seven years of UK academic experience and more than a decade of industry practice overseeing large-scale infrastructure projects in the Middle East (UAE and Egypt), where she held leadership roles including Project Control Team Leader. Her academic credentials comprise: PhD in Construction Engineering from Loughborough University MSc in Construction Management B.Sc. in Civil Engineering Dr. Monib's research centers on revolutionizing construction project management through cutting-edge methodologies. Her primary expertise spans modularisation, productivity optimization, artificial intelligence integration, Building Information Modelling (BIM), and advanced project control systems. She investigates how these innovations can transform construction processes, particularly in complex infrastructure and engineering contexts, addressing critical challenges like risk mitigation in AI adoption and decision support for modular construction frameworks. Her recent publications (2024-2025) reveal a concentrated research trajectory focused on modular construction and AI applications within the industry. Key contributions include decision-support frameworks for modularisation in offshore and power plant sectors, infrastructure planning tools leveraging modularization, and comprehensive analyses of emerging AI risks with actionable mitigation strategies. This body of work demonstrates a consistent commitment to advancing construction project management through technological innovation and practical implementation.

• Cartography
• 3D Virtual Cartography
• Map Projections
• +5 more

Professor Temenoujka Bandrova is a distinguished academic at the University of Architecture, Civil Engineering and Geodesy (UASG), serving as Head of the Department of Photogrammetry and Cartography within the Geodetic Faculty since 2020. With a career spanning nearly three decades since joining UASG in 1995, she has established herself as a leading expert in cartography and geospatial technologies. Her academic leadership extends to roles including Chairman of the Academic Council of the Geodetic Faculty (2016-2020) and current member of UASG's Academic Council (2020-2024). Professor Bandrova's educational background includes: Ph.D. (1997-2001): "Symbol System for 3D Urban Settlements" at University of Architecture, Civil Engineering and Geodesy Specialization (1996): "Design of 3D Conventional Signs for Representation of Various Objects in 3D Urban Models" at Institute of Computer Graphics, Graz University of Technology, Austria Specialization (1988): "Large-scale Topographic Maps" at VIAS-Sofia Diploma Engineer in Geodesy, Photogrammetry and Cartography (1983-1988): Graduated as valedictorian from Higher Institute of Architecture and Civil Engineering (VIAS) Secondary Education (1980-1983): 8th Secondary Mathematical School, Sofia, graduated with gold medal Professor Bandrova's research spans multiple domains within cartography, with particular emphasis on 3D virtual cartography, map projections, and applications of cartography for early warning and crisis management. Her work bridges theoretical cartographic principles with practical applications in environmental monitoring, disaster response, and educational contexts. She has pioneered approaches to children's cartography and developed innovative methods for 3D map creation and visualization. Her recent research increasingly integrates artificial intelligence and big data analytics with traditional cartographic methods, exploring new frontiers in geospatial representation and analysis. Analysis of Professor Bandrova's recent publications reveals a strong focus on contemporary cartographic challenges including map projections, geospatial data infrastructure, risk mapping, and the integration of emerging technologies in cartography. Her work demonstrates consistent contributions to both theoretical advancements and practical applications, with particular attention to Bulgarian and European mapping standards. The interdisciplinary nature of her research connects cartography with environmental science, crisis management, cultural heritage preservation, and educational technology. Professor Bandrova holds significant leadership positions in the international cartographic community: Vice-President of the International Cartographic Association (2019-2023) Treasurer and Bureau Member of the International Society for Digital Earth (2019-present) Chair of the Young Scientists Committee, International Society for Digital Earth (2011-2019) Member of editorial boards for multiple international journals including "Geodesy and Cartography," "Eurasian Journal of Digital Earth," and "Geo-Spatial Information Science" As a dedicated educator and mentor, Professor Bandrova has supervised numerous doctoral students whose research focuses on thematic cartography for climate change impacts, early warning systems, disaster management, and educational applications. She has led multiple international research projects funded by EU programs including Erasmus+, Horizon 2020, and various national research initiatives. Her laboratory work includes the Cartography Laboratory at UASG (2008-2020) and contributions to the development of digital geodetic twins and 3D printed mountainous terrain with augmented reality.

Tianyin XuView profile

Associate Professor

• Cloud Computing
• Datacenter Systems
• System Reliability
• +4 more

Tianyin Xu is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), with a courtesy appointment in the Department of Electrical and Computer Engineering. He leads research in cloud and datacenter systems reliability, security, and configuration management. His work bridges theoretical foundations with practical implementations that have been adopted in real-world systems. Dr. Xu earned his PhD from UC San Diego and has held positions at Microsoft Research Asia, Facebook, and has collaborated with industry partners including VMware, Intel, and IBM. His research has been published in top-tier systems conferences including OSDI, SOSP, NSDI, ASPLOS, and EuroSys. His primary research interests focus on making computer systems reliable and secure at cloud and datacenter scale. He investigates approaches to help systems defend against software bugs, configuration errors, and design flaws. His work often combines formal methods with practical systems building to create tools that detect and prevent failures in production environments. Analysis of his recent publications shows a strong focus on cloud reliability, AI for systems, memory management, and configuration validation. His work increasingly integrates AI techniques with traditional systems approaches, particularly in using large language models for configuration validation and multi-agent systems for reliability engineering. There's also a consistent thread of work on memory systems, particularly with emerging technologies like CXL. 2025 Best Paper Award at ASPLOS'25 (cxlfork) 2025 Spotlight at ICML'25 (ITBench) 2024 Jay Lepreau Best Paper Award at OSDI'24 (Anvil) 2024 C.W. Gear Outstanding Junior Faculty Award 2022 NSF CAREER Award 2021 Facebook Academic of the Month 2016 Jay Lepreau Best Paper Award at OSDI'16 (PCheck) Dr. Xu actively advises numerous PhD, Master's, and undergraduate students, with many alumni securing positions at top tech companies and academic institutions. He serves on program committees for major systems conferences and co-organizes the UIUC Systems Research Seminar. His research is supported by NSF, VMware, Facebook, IBM, Intel, Boeing, Azure, GCP, and GitHub. He is also a co-lead of Infrastructure for Trusted and Confidential Computing at IIDAI and co-editor of SIGOPS Blog.

Mohamed Wiem MkaouerView profile

Associate Professor

• Software Engineering
• Refactoring
• Artificial Intelligence
• +7 more

Mohamed Wiem Mkaouer is an Associate Professor of Software Engineering in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the principal investigator of the SMILE (Software Maintenance and Intelligent Evolution) Research Lab, which focuses on designing intelligent algorithms to tackle software engineering problems. Dr. Mkaouer has published over 130 peer-reviewed papers in top venues including TSE, TOSEM, EMSE, ICSE, FSE, CHI, and ASE, and has secured $1.5 million in externally funded projects as PI/Co-PI. PhD in Computer and Information Science, 2016, University of Michigan MS in Computer Science, 2010, University of Geneva (Switzerland) BS in Computer Science, 2007, University of Tunis (Tunisia) Dr. Mkaouer's research spans the intersection of Software Engineering and Artificial Intelligence, with particular focus on software quality assurance, systems refactoring, and the application of search-based techniques. His work increasingly explores the integration of large language models in software development processes, examining how AI tools like ChatGPT impact developer workflows, code quality, and refactoring practices. His research often employs empirical methods, mining software repositories, and conducting large-scale studies of developer behavior. Analysis of Dr. Mkaouer's recent publications reveals a strong trend toward investigating AI-assisted software development, particularly focusing on how developers interact with large language models for code refactoring and quality improvement. His work bridges traditional software engineering concerns with emerging AI capabilities, examining both infrastructure-as-code quality and mobile application development challenges. His research methodology typically combines empirical studies of developer practices with the development of intelligent tools to support software maintenance. Recipient of 5 best-paper/presentation awards RIT GCCIS Best Emerging Researcher Award (2020) Dr. Mkaouer actively mentors a diverse group of students at multiple levels, from undergraduate to PhD candidates, through the SMILE Research Lab. His externally funded projects demonstrate the practical relevance of his research to industry needs. He also contributes significantly to the software engineering community through service on program committees for major conferences including ASE, ICSE, and ICSME, where he frequently serves in leadership roles for tracks related to software maintenance, refactoring, and AI in software engineering. The SMILE Research Lab, under Dr. Mkaouer's leadership, maintains strong industry connections and collaborates with researchers worldwide. The lab's work has practical implications for improving software quality, reducing technical debt, and enhancing developer productivity through intelligent tooling and evidence-based practices.