Find Academic Supervisors

Simone BrugiapagliaView profile

Associate Professor

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +5 more

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

• 5G/6G wireless communications
• Internet of Things (IoT)
• Antenna design and optimization
• +7 more

Dr. Naser Ojaroudi Parchin is a Lecturer at the School of Computing Engineering and the Built Environment , Edinburgh Napier University. His work focuses on antenna systems for 5G/6G wireless networks , Internet of Things (IoT) , and microwave power transfer . Research Interests : 5G/6G antenna design IoT wireless power solutions Metamaterials and beam-steering Cognitive radio systems Recent Publications : 15 most recent works span THz phased arrays , SWIPT systems , and compact MIMO designs Key themes: beam-steering , end-fire radiation , and metamaterial integration Awards : No specific awards mentioned Advising : Supervised PhD student: Haleh Jahanbakhsh Basherlou (2023) Second supervisor for: Abubakar Suleiman (2022) Projects : Lead project: Device-based e-Assistant for People (DeAP) (2025, £7,500) Feasibility Study of Wireless Microwave Power Transfer (2022-2023, £14,999)

Paul RozbitskiView profile

Researcher

• Digital Transformation
• Low-Code/No-Code Platforms
• IT Consulting
• +3 more

Paul Rozbitski is a Researcher at the School of Business and Economics at Technische Hochschule Mittelhessen , focusing on digital transformation of small and medium-sized enterprises and public administration. Digitalization and digital transformation Impact of low-code/no-code platforms IT consulting and software implementation System modeling for urban development His 2025 publication on data-driven traffic management highlights his work in smart cities and mobility solutions. Paul’s research also intersects with bridging skills shortages via citizen developer platforms and requirements analysis for system configuration.

• Wireless Communications
• Information Theory
• UAV Networks
• +3 more

Dr. Yulin Hu serves as a Visiting Professor at RWTH Aachen University, holding the Chair of Information Theory and Data Analytics. His research program bridges theoretical foundations with practical implementations in next-generation wireless systems, with particular emphasis on UAV-aided networks and information-theoretic approaches to communication challenges. His core research interests span multiple interconnected domains: Wireless Communications (especially finite blocklength regimes) Information Theory applications in network design UAV trajectory optimization and network integration Wireless power transfer with nonlinear energy harvesting Edge computing and distributed learning systems Data analytics for network performance optimization Analysis of Dr. Hu's 2025 publication record reveals a concentrated research thrust on UAV trajectory design, where he develops joint optimization frameworks addressing energy efficiency, security, and reliability constraints. His work consistently integrates information-theoretic principles—particularly finite blocklength analysis—to solve practical challenges in ultra-reliable low-latency communications (URLLC) and wireless power transfer. A distinctive feature of his approach is the fusion of deep reinforcement learning with traditional optimization methods for dynamic network scenarios, including no-fly zone constraints and covert operations. While no specific scientific awards are documented in the available materials, his prolific output across top-tier venues demonstrates significant scholarly impact. Details regarding graduate student mentoring and research funding mechanisms remain unspecified in the current documentation. The Chair of Information Theory and Data Analytics, which Dr. Hu leads, functions as a specialized research unit focused on theoretical rigor and algorithmic innovation for wireless systems, though specific laboratory infrastructure or team composition details are not provided.

Paul BealeView profile

Professor

• Theoretical Physics
• Statistical Mechanics
• Thermodynamics
• +9 more

Paul Beale is a Professor in the Department of Physics at the University of Colorado Boulder, where he has been a faculty member since 1984 and currently serves as Chair of the Department. He holds a Ph.D. in Physics from Cornell University (1982) and a B.S. from the University of North Carolina at Chapel Hill (1977). His academic career has been centered at CU Boulder, where he advanced from Assistant to Associate and then to full Professor. Ph.D., Physics, Cornell University, 1982 B.S., Physics, University of North Carolina at Chapel Hill, 1977 Paul Beale's research is in theoretical condensed matter physics, with a focus on statistical mechanics and thermodynamics. His work spans a broad range of topics including phase transitions, critical phenomena, ferroelectrics, hysteresis, grain boundary kinetics, and Monte Carlo methods. He has made significant contributions to the exact calculation of energy distributions in the two-dimensional Ising model and developed a novel class of scalable parallel pseudorandom number generators based on Pohlig-Hellman exponentiation ciphers. His research integrates analytical theory, computational modeling, and applications in materials science and complex systems. His recent publications reflect a strong trend in computational and theoretical physics, particularly in developing robust algorithms for simulation and advancing fundamental understanding of phase behavior in soft and condensed matter systems. His work on pseudorandom number generation has implications for high-performance computing and statistical sampling. He has also contributed to physics education research, focusing on curriculum transformation and quantum mechanics instruction. Notable scientific contributions include: Co-author of the textbook Statistical Mechanics (with R.K. Pathria), a standard graduate-level reference. Development of exact methods for the 2D Ising model partition function. Innovation in scalable pseudorandom number generation for parallel computing. Paul Beale has advised numerous students and collaborated widely, though specific advisees are not listed. He has held significant administrative roles, including Director of the Honors Program and Associate Dean for Natural Sciences. He maintains active research through publications, code development, and academic leadership, with no indication of retirement. He is associated with the following labs and research groups: Condensed Matter Laboratory (former Director, 1999–2001) Statistical Physics and Computational Modeling Group (implied through research topics and code repositories)

• Building Acoustics
• Community Noise
• Machine Noise
• +4 more

Krister Larsson is a Professor of Practice and Docent at the Department of Technical Acoustics, Chalmers University of Technology. He also serves as a senior consultant at Efterklang (AFRY) and has held previous roles at SP and RISE, where he managed acoustics labs and conducted senior research. His expertise spans building acoustics , community noise , and machine noise , with significant contributions to noise reduction in urban environments and lightweight structures. Research Interests : Larsson focuses on acoustic modeling , vibroacoustic systems , thermoacoustic devices , and noise control in transportation infrastructure . His work bridges theoretical acoustics and practical applications, including sound classification standards, noise barriers, and sustainable urban planning. Scientific Awards : While no awards are explicitly listed, his extensive publication record and leadership roles in acoustics research groups underscore his professional impact. Teaching & Affiliations : He teaches foundational and advanced courses in the ACE program and Master's program in Sound and Vibration . His career includes collaborations with research institutions (SP, RISE) and industry (AFRY).

Gavrilovska LiljanaView profile

Associate Professor

• Telecommunications
• Mobile Edge Computing (MEC)
• Blockchain Technology
• +8 more

Liljana Gavrilovska is an Associate Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT) of Ss. Cyril and Methodius University of Skopje (UKIM), specializing in the Department of Telecommunications. Her work focuses on advanced telecommunications systems, including 5G/Beyond 5G networks, mobile edge computing (MEC), blockchain applications in healthcare and IoT, cognitive radio networks, and network virtualization. She leads research groups addressing challenges in wireless resource management, energy efficiency, and distributed consensus mechanisms. Her research interests span telecommunications infrastructure , blockchain for healthcare systems , IoT optimization , and network slicing . Recent work includes developing the BloHeS consensus mechanism for blockchain systems and the Shapeshifter framework for MEC latency optimization. She also investigates dynamic spectrum allocation strategies and energy-efficient WuR-based IoT protocols. Publications emphasize practical implementations like the CHEST COPD e-Health platform and the FALCON project for virtualized network resource allocation. While no specific awards are listed, her contributions to open-source solutions like eWALL and SmartWine reflect industry engagement. Advising activities and grants are not explicitly detailed in the provided texts, though her involvement in EU projects like 5G-MEC and cognitive radio initiatives suggests significant collaborative work. Current projects include system design for blockchain-based public healthcare systems and analysis of heterogeneous network performance in LTE and DVB-T coexistence scenarios. She is actively involved in the FEIT Telecommunications Institute, contributing to both academic and applied research in telecommunications engineering.

Paul ClarkeView profile

Associate Professor

• Software Engineering
• Continuous Software Engineering
• Serverless Computing
• +7 more

Paul Clarke is an Associate Professor in the School of Computing at Dublin City University (DCU), serving as Principal Investigator for the DTIF-funded Future Software Systems Architectures (FSSA) project focused on AI-driven software architectural transformation. He is a member of Lero (SFI Research Centre for Software) and represents Ireland on ISO/IEC JTC1 SC7 for software process standards, while providing industry advisory to Optimality Technologies Ltd and fourTheorem Ltd to bridge academia and practice. His research centers on continuous software engineering, serverless computing, and human aspects of development, with notable contributions to gamification in software engineering contexts and medical device software via the MDevSPICE® framework. He explores software process adaptive capability through empirical studies on occupational stress, virtual reality training environments, and blockchain applications for development integrity. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) AI/ML applications for software architecture modernization (microservices extraction, legacy system transformation), (2) gamification and virtual reality for developer training and process improvement, and (3) human factors investigations including occupational anxiety, sleep patterns, and avatar perception in metaverse environments. His work consistently employs multivocal literature reviews and empirical case studies across industrial contexts. Clarke leads the €2.1M FSSA project examining AI for monolith-to-microservices conversion and previously managed the MDevSPICE® medical device assessment framework. His industry advisory roles focus on commercializing research through Optimality Technologies (software analytics) and fourTheorem (cloud solutions), leveraging 15+ years of pre-academia experience at FINEOS, Arantech, and Logica Plc. He actively contributes to EuroSPI conference proceedings as both author and editorial board member.

• Software Development
• Internet of Things (IoT)
• Blockchain
• +7 more

Joanna F. Defranco is an Associate Professor of Engineering at the Engineering Division (Great Valley) of Pennsylvania State University. Her research focuses on software development, internet of things (IoT), blockchain, and artificial intelligence, with a particular emphasis on quality assurance, security, and practical implementations in engineering and healthcare. Research Interests Defranco's work addresses critical challenges in IoT security, digital twins for manufacturing, and AI-driven software quality analysis. She explores secure data sharing frameworks, blockchain applications in healthcare, and the evolution of low-code/no-code platforms. Her recent publications highlight interdisciplinary approaches to telemedicine, smart agriculture, and urban infrastructure. Publication Trends Her 2023-2025 publications show increasing focus on IoT integration with AI, secure healthcare data systems, and the societal implications of emerging technologies. Key themes include digital twin adoption in SMEs, generative AI's impact on software development, and cybersecurity resilience. Collaborations She collaborates with institutions like IEEE and researchers such as Paul Laplante, Joseph Voas, and Thrasyvoulos Speicher. Her work bridges engineering education with real-world applications in manufacturing, agriculture, and healthcare.

Md. Hasan TarequeView profile

Lecturer

• Software Engineering
• Gamification
• Ad-hoc Networks
• +3 more

Md. Hasan Tareque serves as an Instructor in the Department of Mathematics and Computer Science at the University of Lethbridge, Canada. Based in office C542 at University Hall, he is contactable via phone (403) 329-2473 or email mdhasan.tareque@uleth.ca. His research spans Software Engineering, Gamification, Ad-hoc Networks, and Image Processing. He develops educational games like Program Wars to teach programming concepts and cybersecurity through interactive learning. His networking work addresses routing challenges in Flying Ad Hoc Networks (UAVs) and Vehicular Ad Hoc Networks, while his image processing research focuses on contour-based face recognition and quantum-dot circuit design. Recent publications (2024) emphasize game-based learning for software engineering education and cybersecurity training, demonstrating innovative pedagogical approaches. Earlier works (2013-2019) cover ad-hoc network routing protocols, quantum-dot circuit optimization, and biometric recognition, revealing a consistent theme of applying computational solutions to education and communication systems. No scientific awards are documented in available sources. The profile provides no details regarding student advising, research grants, or laboratory affiliations. Information about educational background, research teams, or future projects remains unspecified in current documentation.

Kellyn DunnView profile

Lecturer

• Market Research
• Consumer Insights
• Brand Strategy
• +3 more

Kellyn Dunn serves as a Lecturer at the Stan Richards School of Advertising & Public Relations, The University of Texas at Austin, leveraging over 20 years of marketing expertise with specialized focus on consumer insights and strategy development for the past 13 years. She bridges academic instruction with high-impact industry practice through her concurrent role as Director of Strategy and Insights at VML. Education: Master of Arts in Advertising, Southern Methodist University Bachelor of Science in Radio-Television-Film, The University of Texas at Austin Her research expertise centers on transforming complex data into actionable business strategies through rigorous methodologies in qualitative and quantitative research, brand strategy, and user experience design. She is particularly recognized for synthesizing consumer insights into innovative solutions, exemplified by her development of Intel One Mono—a typeface enhancing coding accessibility for developers with low vision—which achieved 80,000+ GitHub downloads and industry acclaim. Awards and Recognition: Fast Company Innovation by Design Award Winner (2024) for Type Design ARF David Ogilvy Silver Award, Electronics & Tech Category (2023) ARF David Ogilvy Silver Award, Best Consumer Experience (2024) As an educator, Dunn integrates real-world applications from her industry leadership where she directs integrated market insights teams. Her certified UX Research expertise (Memorisely, 2024) informs both classroom instruction and professional practice, though no academic advising or grant activities are documented in available materials.

• Signal Processing
• Telecommunications
• Quantization
• +12 more

Jelena Nikolic is an Associate Professor in the Department of Telecommunications at the Faculty of Electronics, University of Niš, Serbia. She has been a core faculty member since earning her PhD in 2011 and was promoted to Assistant Professor in 2014 and Associate Professor in 2020. Her academic work is deeply rooted in signal processing, quantization theory, and data compression, with expanding interests in machine learning applications for communications. Research Interests: Her primary research areas include quantization , data and signal compression , approximations of the Q-function , and neural network weight compression . She applies these to telecommunications, digital signal processing, and emerging AI-driven communication systems. Her work bridges theoretical analysis with practical implementation, particularly in companding quantizers, polar quantization, and adaptive coding schemes. Publication Trends: Her recent publications (2020–2022) show a clear evolution toward integrating machine learning with classical signal processing, especially in post-training quantization of neural networks and AI-based compression. Earlier works (2013–2018) focus on theoretical foundations of quantization for Gaussian and Laplacian sources, with rigorous mathematical modeling and performance analysis. The consistent use of terms like 'companding', 'scalar quantizer', 'Gaussian source', and 'SQNR' reflects a strong foundation in information theory and digital communications. Scientific Awards: Prof. Dr. Ilija Stojanović Award (2017) – Best professional work in telecommunications Advising and Grants: She has mentored 2 master’s and 2 diploma students. She has participated in 10 major research projects funded by the Ministry of Education and Science of Serbia, Erasmus+, Norwegian Ministry of Foreign Affairs, and the Science Fund of Serbia, including projects on software-defined radio, dialogic systems, and AI-based quantization (Com-in-AI). She has also served on numerous academic committees for thesis defense, hiring, and academic evaluation across multiple institutions. Labs and Teams: While no specific lab name is mentioned, her involvement in experimental and computational work—such as laboratory exercises in acoustics, signal processing, and VoIP, as well as MATLAB-based practicals—indicates active participation in teaching and research labs at the Faculty of Electronics. She has collaborated extensively with Prof. Zoran Perić and others in the telecommunications group, forming a strong research team focused on quantization and compression.

Jan ReckerView profile

Professor

• Digital Innovation
• Technology Analysis and Design
• Digital Entrepreneurship
• +3 more

Prof. Jan Recker holds the Professorship for Information Systems and Digital Innovation at the University of Hamburg Business School. His work focuses on digital innovation, entrepreneurship, and sustainable technology solutions. He leads research in areas such as digital transformation in large organizations, digital entrepreneurship ecosystems, and the societal impact of technology disruptions. Research Interests: Technology analysis and design in the digital age Entrepreneurial ventures leveraging digital opportunities Sustainable development through digital solutions Organizational adaptation in digital transformation External enablement mechanisms for innovation Recent Publications Highlight: Exploration of societal crises as catalysts for extreme venture growth Analyzing digital disruption's strategic implications Open science initiatives for greater research transparency Advising & Grants: No specific student advisees or grant details listed in provided materials. Professional Presence: Office located at Von-Melle-Park 5, Room 3090. Contactable via +49 40 42838 2044 or jan.christof.recker@uni-hamburg.de. Active contributor to academic communities including the MIS Quarterly and Academy of Management Review.

• Information Systems & Technology
• Software Engineering
• Digital Transformation
• +4 more

Alan Megargel is a Full-time Associate Professor of Information Systems (Practice) at the School of Computing and Information Systems, Singapore Management University. He holds a Doctor of Innovation from SMU (2018). His research focuses on Information Systems & Technology, Software Engineering, and Digital Transformation, with a strong emphasis on banking technology and ESG risk assessment. Megargel teaches enterprise integration, ePayment processes, and corporate banking technology. He advises three graduate students and has published extensively on topics like blockchain in finance, NewSQL databases, and AI-driven banking solutions. Education: Doctor of Innovation, Singapore Management University, 2018 Research Interests: Banking Technology Modernization (microservices, cloud migration) AI/ML Applications in Finance (ESG risk modeling, customer-centric AI) ESG Integration in Corporate Systems Low/no-code development for digital banking His work bridges technical innovation with strategic priorities like digital transformation and sustainable finance. Article Trends: Recent work emphasizes blockchain applications, machine learning for ESG risk assessment, and architectural frameworks for modern banking systems. Early career publications focused on legacy system modernization and educational case studies like the SMU Teaching Bank project. Awards: No scientific awards explicitly listed in the profile. Advising & Projects: Research advisor to 3 graduate students Developed the SMU Teaching Bank simulation tool Contributed to LGB Bank case studies on microservices architecture Labs/Teams: Works within SMU's Information Systems research group, collaborating on FinTech, digital banking, and enterprise architecture projects.

Mahdi Jafari SiavoshaniView profile

Assistant Professor

• Machine Learning
• Optimization
• Data Science
• +14 more

Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.