Find Academic Supervisors

Hüsnü DalView profile

Professor

• Computational micromechanics
• Multiscale and multifield problems
• Materials theory
• +3 more

Hüsnü Dal is a Professor at Middle East Technical University (METU) in Ankara, Turkey, specializing in computational mechanics of materials. His research bridges engineering and biomedical applications through advanced computational modeling techniques. Education: Bachelor's Degree, Middle East Technical University, 2001 Master's Degree, University of Stuttgart, 2005 PhD, Dresden University of Technology, 2011 Research Focus: Prof. Dal's work centers on computational micromechanics, multiscale and multifield problems, and materials theory. He investigates fracture in multiphysics media with applications in lithium-ion batteries and tissue mechanics, developing novel constitutive models for complex material behaviors under extreme conditions. His research integrates thermomechanical coupling, viscoplasticity, and data-driven approaches to solve engineering challenges in both synthetic polymers and biological systems. Publication Trends: Recent publications (2023-2025) reveal a dominant focus on data-driven constitutive modeling and phase-field fracture methods. His work spans rubber mechanics, polymeric foams, biological tissues, and battery materials, characterized by strong interdisciplinary connections between materials science, biomechanics, and computational engineering. Key themes include anisotropic hyperelasticity, thermo-viscoplastic fracture, and spatial property variations in additively manufactured materials.

• Business Intelligence
• Cloud Computing
• Software Engineering
• +7 more

Professor Farookh Hussain is a distinguished academic at the School of Computer Science , University of Technology Sydney , specializing in Artificial Intelligence , Cloud Computing , and Software Engineering . His research spans diverse sectors including agriculture, manufacturing, healthcare, and transportation. Affiliated with the Australian Artificial Intelligence Institute (AAII) , he leads impactful work in business intelligence and carbon credit systems. Key research areas: AI applications, blockchain for provenance, carbon credit analytics Active in Masters/PhD supervision and cloud computing education Research Highlights : Developed KACINO framework for carbon dynamics modeling Created hybrid cybersecurity frameworks for supply chain risk management Advanced chatbot dialogue breakdown solutions through systematic reviews Proposed hypercomplex knowledge graph recommenders Published extensively on carbon credit price prediction and blockchain storage methods Contributions to water demand forecasting and collaborative robotics adoption Grant Activities : Secured funding from Hampton Capital Asset Management , Innovation Connections , and Science and Industry Endowment Fund Projects include LLM-driven text-to-SQL conversion , blockchain for melanoma data , and AI for storm water management

• Machine Learning
• Augmented Intelligence
• Data Science
• +12 more

Simon Ruffieux is a Senior Researcher and Lecturer at the Department of Computer Science, University of Fribourg, and a member of the Human-IST Institute. He currently leads the HIP-Initiative (Human-IST x SwissPost Initiative) and coordinates academic projects related to Swiss Post. His academic roles include Lecturer and Senior Assistant , reflecting his active engagement in teaching and research. His research focuses on leveraging advanced technologies to support individuals, particularly those with special needs. Key areas include: Machine Learning and Data Science for urban systems (e.g., bike-sharing optimization) Human-Computer Interaction (HCI), especially gesture recognition and multimodal interfaces Augmented and Virtual Reality applications in rehabilitation and assistance Development of smart glasses for visually impaired users Physiological signal analysis for workload classification The 15 most recent publications reveal a strong trend in applying AI and data science to real-world challenges, particularly in assistive technologies and urban mobility. His work often involves interdisciplinary collaboration, integrating computer science with psychology, rehabilitation, and industrial applications. There is a consistent emphasis on user-centered design and real-world usability. Simon Ruffieux has not been mentioned as receiving specific scientific awards in the provided text. He has advised or collaborated with several researchers, including Nicolas Spycher, Samuel Torche, and Nicolas Ruffieux, on projects related to forecasting, AR, and gesture recognition. While no formal grant details are listed, his leadership of the HIP-Initiative suggests involvement in externally funded academic projects. His work is closely tied to the Human-IST Institute, where he contributes to interdisciplinary research in human-centered computing. He is actively involved in research teams focused on assistive technologies, gesture interaction, and data-driven urban solutions. The Human-IST Institute serves as the primary hub for his collaborative efforts, particularly through the HIP-Initiative with Swiss Post.

• Recommender Systems
• Personalization
• Data Mining
• +6 more

Alexander Tuzhilin is a prominent academic researcher in the fields of recommender systems, personalization, and data mining. His work spans over two decades, focusing on theoretical foundations and practical applications of context-aware recommendations, optimization in data validation, and temporal database systems. He has collaborated extensively with scholars like Gediminas Adomavicius, Konstantin Bauman, and Balaji Padmanabhan. Research Interests: Recommender systems, context-aware computing, temporal database design, and optimization techniques in data mining. Publications: 10+ peer-reviewed articles in journals such as Information Systems Research , Management Science , and INFORMS Journal on Computing , with a focus on algorithmic innovation and business impact. Collaborations: Worked with leading researchers in information systems and operations research, contributing to interdisciplinary advancements in eCRM and data-driven decision-making.

Wei SongView profile

Professor

• Mobile Social Networks
• Internet of Things
• Vehicular Networks
• +10 more

Dr. Wei Song is a Professor and the Coordinator of Software Engineering at the Faculty of Computer Science, University of New Brunswick (UNB) in Fredericton, New Brunswick, Canada. She has been with UNB since 2009, after completing her postdoctoral studies at UC Berkeley, and has established herself as a leading researcher in mobile networking and wireless communications. Her office is located in room ID419 and she can be reached at wsong@unb.ca. Education Ph.D. in Electrical and Computer Engineering, University of Waterloo (2003-2007) Postdoctoral Fellow, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2008-2009) Research Focus Dr. Song's research spans multiple cutting-edge areas in mobile and wireless networking, with a strong emphasis on integrating artificial intelligence and machine learning techniques. Her work addresses fundamental problems in mobile social networks, Internet of Things, vehicular networks, and mobile cloud computing. She explores how cooperative intelligence and distributed AI can enhance network performance while addressing practical constraints such as energy efficiency and user incentives. Her recent work particularly focuses on intelligent edge computing, mobile crowdsensing with deep reinforcement learning, and social-aware data dissemination through device-to-device communications. She investigates how to turn decentralized mobile "crowds" into coherent working groups and how social connections can be leveraged to improve data dissemination efficiency. Publication Trends Dr. Song's recent publications (2016-2023) demonstrate a clear evolution from traditional wireless networking to AI-driven approaches. While her earlier work focused on fundamental problems in device-to-device communications and resource allocation, her recent publications increasingly incorporate deep reinforcement learning, graph neural networks, and other AI techniques to solve complex optimization problems in mobile crowdsensing and edge computing. This shift reflects broader trends in the field toward intelligent, adaptive networking solutions. Scientific Recognition Best Paper Award from IEEE ICC (2018) UNB Merit Award (2014) Best Student Paper Award from IEEE CCNC (2013) Top 10% Award from IEEE MMSP (2009) NSERC postdoctoral fellowship (2008) Best Paper Award from IEEE WCNC (2007) Professional Service and Mentoring Dr. Song serves as Senior Member of IEEE and has held significant leadership roles, including Chair of the Joint Computer and Communications Chapter of IEEE New Brunswick Section (2014-2020). She has chaired symposia at major conferences including IEEE VTC Fall 2023, 2017, and 2016. As a supervisor, she mentors graduate students in areas including intelligent edge computing and deep learning for networking, and is currently recruiting students for Winter 2024 and Fall 2025.

• Marketing
• Digital Transformation
• Sustainability
• +3 more

Dr. Alessandro Inversini is an Associate Professor at the EHL Hospitality Business School, part of HES-SO University of Applied Sciences Western Switzerland. His primary role involves teaching and researching in the Department of Global Hospitality Business, focusing on marketing, digital transformation, and sustainability within the hospitality and tourism sectors. Educational Background: No explicit educational details provided in the text. Research Interests: Explores the transformative impact of emerging technologies (e.g., AI, ChatGPT) on hospitality operations and customer experience. Advocates for regenerative and net-positive approaches to sustainability in the hospitality industry. Investigates CSR communication strategies on social media and cross-cultural dynamics in tourism reviews. Analyzes digital media’s role in B2B relationships and customer-centric frameworks. Article Trends: Recent works emphasize AI-driven changes in tourism, ethical digital transformation, and CSR practices. Key themes include regenerative hospitality, cultural review differences, and leveraging social media for transformative learning. Awards: No scientific awards explicitly listed. Advising & Grants: Guided research on regenerative hospitality in Lebanon and transformative homestay experiences in Malaysia. Contributed to international conferences like ENTER2016 and EuroCHRIE 2024. Labs/Teams: Collaborates with interdisciplinary teams on projects involving digital tools for rural development and ethical technology integration in hospitality.

• Epidemiological Modeling
• Agent-Based Simulation
• Public Health Policy
• +3 more

Martin Bicher is a PostDoc Researcher at TU Wien, affiliated with the Department of Data Science under the Faculty of Informatics. He specializes in agent-based simulation, epidemiological modeling, and decision support systems for public health crises. His work focuses on optimizing resource allocation, vaccination strategies, and policy evaluation during pandemics. He teaches courses such as Modeling and Simulation (194.076), Modelling and Simulation in Health Technology Assessment (194.094), and Advanced Modeling and Simulation (194.056). His research is supported by projects like DynOptTestControl (2022–2026) and KLIPHA-COVID19 (2020–2021). Key research interests include agent-based modeling frameworks, integration of machine learning into simulation systems, and multi-criteria decision support for public health interventions. His publications analyze pandemic response strategies, vaccination prioritization, and the impact of environmental factors on disease spread. Recent work includes developing mathematical models for equitable disease testing, simulating vaccination strategies under supply uncertainties, and evaluating contact-tracing policies. He collaborates with interdisciplinary teams to address challenges in healthcare resource optimization and policy design. Advising two students, Bicher has mentored theses on railway simulation and delay modeling. His contributions to pandemic decision support have been featured in high-impact journals like Omega and PLoS ONE.

Sarah KaufmanView profile

Assistant Clinical Professor

• Transportation
• Urban Planning
• Urban Policy
• +7 more

Sarah Kaufman serves as Director of the NYU Rudin Center for Transportation and Assistant Clinical Professor of Public Service at New York University's Wagner Graduate School of Public Service. Her work bridges academic research and practical policy solutions for urban transportation systems, with emphasis on equity, technology, and resilience. Her research spans transportation policy, urban planning, emergency management, and climate change adaptation. Key focus areas include mobility equity (notably the "pink tax" on transportation), micromobility systems, autonomous vehicle governance, and disaster response protocols. She investigates how technology and policy can create more inclusive and resilient urban mobility networks, with particular attention to gender disparities and climate vulnerabilities. Analysis of her 15 most recent publications reveals consistent themes in transportation equity, emerging technologies, and crisis management. Her work demonstrates methodological diversity—from agent-based modeling (MATSim-NYC) to policy analysis of scooter sharing—and maintains strong practical relevance through partnerships with transportation agencies. Notable trends include the evolution of micro-mobility regulation, gender-based mobility disparities, and transportation's critical role in pandemic and extreme weather response. As Director of the Rudin Center, she leads a multidisciplinary research team that produces policy-influential reports on congestion pricing, transit innovation, and sustainable mobility. The center serves as a hub for collaboration between government agencies, industry stakeholders, and community organizations, translating academic research into actionable transportation solutions for New York City and beyond.

Corey BradyView profile

Associate Professor

• Mathematics Education
• Computational Modeling
• STEAM Education
• +5 more

Corey Brady is an Associate Professor at the Department of Teaching & Learning and Associate Dean for Research and Outreach at Southern Methodist University's Simmons School of Education and Human Development. He holds a Ph.D. in Mathematics Education from the University of Massachusetts, Dartmouth, alongside degrees in English Literature (MA) and Pure Mathematics (MS). Dr. Brady's research focuses on mathematical and computational modeling from a constructionist perspective, emphasizing collective learning and STEAM activity . His work includes design-based research on block-based programming environments and embodied participatory simulations. Recent publications (2023-2021) span journals like Frontiers in Education , Science Education , and Educational Studies in Mathematics , with sub-fields ranging from geometric transformations to disaster preparedness systems. He has received recognition including the Outstanding Paper Award at ICLS 2023 and Best Paper at ICLS 2020 . Previously, he held faculty roles at Vanderbilt and Northwestern Universities, led educational technology development at Texas Instruments, and taught at middle school to community college levels. His interdisciplinary approach bridges mathematics, computer science, and affective learning.

• Software Environments for Official Statistics
• Processing and Management of Economic Time Series Data
• Open Source Driven Data Pipelines
• +7 more

Matthias Bannert is a Lecturer at the Department of Management, Technology, and Economics at ETH Zürich, where he works at the KOF Swiss Economic Institute (Konjunkturforschungsstelle). His work focuses on the intersection of economics, software development, and data management, with particular expertise in time series analysis and official statistics. Bannert designs solutions for state-of-the-art data processing, management, and publishing of economic data and research. Bannert completed his doctoral thesis titled "Survey Based Research in Economics - Essays on Methodology, Economic Applications and Long Term Processing of Economic Survey Data" at ETH Zürich in 2016. His academic journey began when he joined KOF in late 2008, initially working as a researcher for the Business Tendency Survey group before transitioning to the institute's IT department. Dr. Bannert's research interests span several interconnected domains at the nexus of economics and data science. He specializes in developing software environments for official statistics, with particular focus on processing and managing economic time series data through open-source driven data pipelines. His technical expertise includes R programming and PostgreSQL database systems, which he applies to create robust solutions for economic data analysis. Bannert is particularly interested in survey methodology, nowcasting techniques, and the development of reproducible research workflows. His work bridges the gap between theoretical economics and practical software implementation, ensuring that economic research can leverage state-of-the-art data processing techniques. Analysis of Bannert's publication record reveals a consistent focus on the application of data science techniques to economic research problems, particularly in the domain of official statistics and survey-based economics. His work demonstrates a progression from theoretical survey methodology to practical software implementation, with increasing emphasis on real-time economic forecasting and data management systems. A distinctive feature of his research is the development of open-source R packages that make advanced economic data analysis more accessible to researchers and practitioners. As an active contributor to the R language for Statistical computing and the open source community, Bannert has developed several notable software packages including timeseriesdb, tstools, and kofdata, which are available on CRAN. These tools reflect his commitment to creating reproducible, transparent, and efficient workflows for economic data analysis. Bannert serves as a data science supervisor for multiple KOF research projects and is a co-Principal Investigator in an SNF-funded Digital Lives project in collaboration with KOF's labor market expert group. His teaching activities include "Hacking for Sciences - An Applied Guide to Programming with Data" and involvement in the Nowcasting Lab, which provides live out-of-sample forecasting and model testing capabilities for economic researchers. Dr. Bannert is affiliated with the KOF Swiss Economic Institute, where he contributes to several research groups including the KOF Macroeconomic Forecasting group and the KOF Data Science and Macroeconomic Methods group. His work at KOF bridges the institute's traditional economic research with modern data science approaches, helping to position the institute at the forefront of data-driven economic analysis.

• Spatial Databases
• Geoinformatics
• Internet of Things (IoT)
• +5 more

Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.

Pasquale ScarlinoView profile

Assistant Professor

• Quantum Computing
• Hybrid Quantum Circuits
• Semiconductor-Superconductor Interfaces
• +6 more

Pasquale Scarlino is a Tenure Track Assistant Professor in the Institute of Physics at École Polytechnique Fédérale de Lausanne (EPFL), where he founded and leads the Hybrid Quantum Circuits (HQC) Laboratory. He holds a dual appointment with the School of Basic Sciences (SB) and the Physics Section (SB-SPH), conducting research at the intersection of semiconductor and superconducting quantum technologies. His laboratory develops hybrid quantum hardware for advanced quantum information processing. His educational background includes a Master's degree in Physics from the University of Salento (Italy, 2011), where he was a student of Scuola Superiore ISUFI, followed by a Ph.D. from TU Delft (2016) in the Spin Qubits group of Prof. L.M.K. Vandersypen at the Kavli Institute of Nanoscience-Qutech. His doctoral work focused on Si/SiGe spin qubits in collaboration with the M. Eriksson Group at Wisconsin University. Scarlino's research centers on experimental quantum physics using hybrid superconductor/semiconductor devices with electrostatically defined quantum dots coupled to high-impedance microwave resonators. He investigates light-matter interactions in unconventional regimes, quantum transport in low-dimensional systems, and spin/charge qubit implementations. His work aims to merge semiconductor and superconducting platforms to expand quantum information capabilities, with applications in quantum computing, quantum optics, and analog quantum simulation. Early career achievements include establishing the first coherent interface between superconducting and semiconducting quantum systems using high-impedance resonators. His publication record shows strong focus on microwave photon-mediated interactions between quantum systems, with recent work exploring quantum acoustics, topological band engineering, and criticality-enhanced sensing. The articles demonstrate increasing specialization in hybrid quantum hardware, with a shift toward germanium-based systems and advanced resonator designs in the latest publications. Scarlino has advised eleven Ph.D. students at EPFL and teaches courses including General Physics (Electromagnetism), Solid State Systems for Quantum Information, and Introduction to Quantum Science and Technology. His teaching emphasizes experimental quantum hardware approaches and critical assessment of quantum computing platforms. The Hybrid Quantum Circuits Laboratory operates within EPFL's Institute of Physics, utilizing state-of-the-art nanofabrication facilities and cryogenic measurement setups. The team collaborates extensively with leading quantum research groups worldwide, maintaining strong ties with previous institutions including ETH Zurich, TU Delft, and Microsoft Station Q Copenhagen.

• Privacy
• Security
• Requirements Engineering
• +5 more

Travis D. Breaux is an Associate Professor in the School of Computer Science at Carnegie Mellon University, where he directs the Requirements Engineering Lab . His research bridges software engineering, privacy, security, and legal compliance, with a focus on developing formal methods to ensure software systems adhere to regulatory frameworks. He holds appointments in the Software and Societal Systems Department and directs the Masters of Software Engineering (MSE) Professional Programs. Breaux's research investigates privacy policy compliance , empirical extraction of legal requirements , and risk quantification in system design. His work employs AI, formal specification, and empirical methods to resolve ambiguities in policies and quantify privacy/security risks. Key themes include regulatory alignment, automated reasoning for compliance, and human factors in risk perception. Recent publications emphasize AI-driven requirements engineering , including LLM applications for goal modeling, legal requirement extraction, and automated question generation. His work consistently addresses the intersection of formal methods, policy analysis, and scalable compliance verification. Awards and Honors: NSF CAREER Award (2015) IEEE RE Distinguished Paper Award (2018) Distinguished Reviewer Awards (ICSE 2018, RE 2023) IEEE RE Most Influential Paper Award (Honorable Mention, 2016) Breaux advises PhD and Master's students in privacy engineering and requirements formalization. He has led NSF-funded initiatives including the Workshop on Designing Accountable Software Systems (DASS) . Current courses include Prompt Engineering and Artificial Intelligence for Software Engineering , focusing on LLM applications and AI ethics.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +3 more

Prof. Dr. Chunyang Chen is a Full Professor at the Department of Computer Science, Technical University of Munich (TUM), Heilbronn, Germany. He holds the Chair of Software Engineering & AI, serves as a core member of the Munich Data Science Institute, board member of the Heilbronn Data Science Center, and Fellow at Fortiss. He also maintains an Adjunct Professor role at Monash University, Australia. Research Focus: His work bridges Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), specializing in AI/ML, NLP, and program analysis for mobile app development, testing, and security. Key areas include LLM-assisted app development, robustness of deep learning models, and accessibility testing. Scientific Awards: Best Paper Honorable Mention in CHI 2024 Discovery Early Career Researcher Award (DECRA), Australian Research Council ACM SIGSOFT Early Career Researcher Award Facebook Research Award in Probability and Programming Dean's Award for Research Impact at Monash University Academic Leadership: He actively mentors PhD students, supervises postdocs, and leads research teams focusing on software security, automated testing, and LLM applications. His recent work explores the intersection of software security and large language models, with a special issue call for EMSE journal.

Tien N. NguyenView profile

Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +9 more

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.