Find Academic Supervisors

• Business Analytics
• Data Science
• Crowdsourcing
• +5 more

Prof. Dr. Ivo Blohm is an Associate Professor of Information Management with a focus on Business Analytics at the Hasso Plattner Institute of Management and Digitization (HPI-St. Gallen), University of St. Gallen. His work bridges academic research and practical applications in digital transformation, with expertise spanning AI-driven decision support systems, crowdsourcing governance, and agile work practices. He holds a leadership role in advancing data science methodologies and their integration into organizational frameworks. His research interests emphasize leveraging AI and data analytics to enhance business processes, particularly through generative AI architectures, decision-making interfaces, and platform-driven innovation. Notable contributions include frameworks for conversational AI implementation, governance mechanisms for crowdfunding platforms, and strategies for internal crowd work empowerment. He has collaborated with organizations like Lufthansa to design leadership development programs for data-driven transformation. Blohm's publications (2014-2025) explore topics such as AI accountability in workplaces, hybrid human-AI creativity systems, and the socio-technical dynamics of digital work. His work often addresses ethical dimensions of emerging technologies and their implications for organizational structures. Despite his prolific output, no scientific awards are explicitly listed in the provided materials. His advising and grant activities are not detailed here, though his research collaborations indicate engagement with industry partners. He is actively involved in designing frameworks for data products (e.g., data mesh) and refining agile practices in large-scale organizations. No specific lab affiliations are mentioned, though his work often involves cross-disciplinary teams focusing on digital innovation challenges.

• Embedded Sensors
• Sensor Networks
• Distributed Systems
• +3 more

Bruno Rodrigues is an Assistant Professor of Computer Science (Embedded Sensing Systems) at the University of St. Gallen (HSG). His research focuses on embedded sensors, sensor networks, distributed systems, and network security, with additional expertise in software-defined networks and distributed ledgers. He holds a PhD from the University of Zurich (2020), a Master's from the Polytechnic School of the University of São Paulo (2016), and a Bachelor's from the University of Santa Catarina, Brazil (2013). Prof. Rodrigues' professional career includes roles as a Senior Researcher (2020–2024) and Junior Researcher (2016–2020) at the University of Zurich, where he contributed to projects like the EU-funded CONCORDIA H2020 and collaborations with Swiss innovation agencies (KTI/Innosuisse). His work spans DDoS mitigation, blockchain applications in voting systems, and energy-efficient cloud networks. Notable projects include the 'Passive Wireless Intelligence Tracking System (PasWITS)' and 'Energy-Efficiency to Clouds (E2C)' with Ericsson Research. His research portfolio includes over 50 publications on topics such as secure IoT frameworks (SHIFT, WeTrace), blockchain-based voting systems (ProvotuMN, Æternum), and cybersecurity tools like SecGrid for traffic analysis. He has pioneered solutions for privacy-preserving mobile tracking (LaFlector), real-time medical device inventory systems (PMD-Track), and decentralized financial inclusion tools (AfriBit). Prof. Rodrigues' work bridges theoretical computer science with practical applications, emphasizing sustainability, security, and innovation in distributed systems. He has led interdisciplinary projects at the intersection of blockchain technology, sensor networks, and ethical AI, with a focus on real-world implementation challenges.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +5 more

Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.

Vitor JesusView profile

Lecturer

• Cybersecurity
• Privacy Management
• Blockchain
• +2 more

Dr. Vitor Jesus is a Lecturer in Software Engineering and Cybersecurity at Aston University's School of Computer Science and Digital Technologies. His research focuses on privacy-enhancing technologies and cybersecurity frameworks for distributed systems, with particular emphasis on: Consent management architectures and standardization Blockchain-based auditing and data provenance IoT security in smart environments Privacy-preserving data sharing models As co-chair of the COnSeNT workshop series and BSI expert for ISO standards development, he contributes to international privacy frameworks. Funded projects include CyberAlarm (crowd-sourced threat intelligence) and EVBatteries4Planet (supply chain visibility).

Xiaokun YangView profile

Associate Professor

• FPGA Acceleration
• AI/ML Hardware
• System-on-Chip Design
• +2 more

Dr. Xiaokun Yang is an Associate Professor of Computer Engineering in the College of Science and Engineering at the University of Houston-Clear Lake. With extensive industry experience at AMD and CEC, his research bridges hardware design and AI acceleration. Research focuses on FPGA acceleration for AI/ML, hardware/software co-design, and IoT system architectures. Publications demonstrate innovations in neural network accelerators, hardware-efficient AI, secure SoC design, and edge computing solutions. Recent work explores ReRAM-based accelerators, blockchain-secured hardware, and federated learning systems. Awards include Best Ph.D Forum Paper at ISVLSI 2014. Education includes a Ph.D. in Electrical and Computer Engineering from Florida International University (2016) and dual M.S. degrees from FIU and Beihang University. Courses taught include Advanced Digital Systems Design, Electronics, and Senior Project. Current research projects investigate FPGA-based AI acceleration and IoT architectures.

Tarun SheelView profile

Assistant Professor

• Computational Fluid Dynamics
• Scientific Computing
• Numerical Analysis
• +3 more

Dr. Tarun Sheel is a Teaching Assistant Professor in the Department of Mathematics & Statistics at Memorial University of Newfoundland. With a PhD in Mechanical Engineering from Keio University, his research focuses on computational fluid dynamics, vortex methods, and high-performance computing techniques. His expertise includes developing accelerated vortex methods using Fast Multipole Method (FMM) and special-purpose computers like MDGRAPE systems. Current research investigates scour reduction at bridge piers using RANS turbulence modeling and bluff body flow control. Additional work spans parallel mesh generation, fluid-structure interaction, and multiphase flow simulation. Dr. Sheel's publications demonstrate consistent focus on computational acceleration techniques, with applications ranging from turbulent flow simulation to geotechnical problems like submarine landslides. His work integrates numerical analysis with hardware-specific optimizations for scientific computing. Teaching responsibilities include calculus, linear algebra, differential equations, and numerical methods across undergraduate and graduate levels at multiple institutions internationally.

• Scaled Boundary Finite Element Method (SBFEM)
• Composite Structures Analysis
• Fracture Mechanics
• +3 more

Adrian Egger is an academic affiliated with ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, part of the College of Architecture, Civil and Environmental Engineering. His research focuses on advanced numerical methods like the Scaled Boundary Finite Element Method (SBFEM), with applications in composite materials, fracture mechanics, and structural analysis. He has contributed to studies on topology optimization, stress intensity factor accuracy, and crack propagation modeling. Additionally, he has collaborated on astrophysics projects involving gamma-ray detection using novel telescope designs. Egger earned his doctoral degree from ETH Zurich in 2020 and has published extensively in journals and conferences. His work bridges computational mechanics with practical engineering challenges, emphasizing multiscale approaches and innovative numerical techniques.

Guangdong BaiView profile

Associate Professor

• Trustworthy AI
• Cyber Security
• Software Engineering
• +5 more

Associate Professor Guangdong Bai is affiliated with The University of Queensland's School of Electrical Engineering and Computer Science, specifically within the Discipline of Cyber Security and Software Engineering. He holds the academic rank of Associate Professor and is currently on leave. His research focuses on Trustworthy AI, Cyber Security, Software Engineering, and Formal Methods, with notable contributions to privacy compliance, IoT/ICS security, and adversarial machine learning defense mechanisms. Education includes a PhD from the National University of Singapore and undergraduate/master's degrees from Peking University. His work spans theoretical advancements and practical applications, including blockchain-based systems for EV charging and vaccine tracking, adversarial attack frameworks (e.g., GenDroid), and federated learning security. Research interests emphasize AI ethics, privacy-preserving techniques, and securing emerging technologies like autonomous vehicles and smart agriculture networks. Notable projects include AuSSE (autonomous vehicle security framework), CoreLocker (neural network usage control), and PAODING (neural network pruning toolkit). Publications (15 most recent) reflect expertise in adversarial machine learning, federated learning, and IoT security. His work bridges theory and practice, addressing real-world challenges in critical infrastructure and decentralized systems. Labs/teams include collaborations on Cyber Security and Software Engineering at UQ, with supervision of PhD candidates such as Yanjun Zhang (Deakin University) and Kailong Wang (Huazhong University of Science and Technology).

Chuan FohView profile

Senior Lecturer

• Computer Networks
• Wireless Communication Protocols
• 5G/6G Networks
• +6 more

Dr Chuan Foh is a Senior Lecturer at the University of Surrey, affiliated with the Institute for Communication Systems within the School of Computer Science and Electronic Engineering. His research focuses on advanced communication protocols and performance analysis across diverse networks, including 5G/6G, vehicular networks, and satellite systems. He explores topics such as network security, federated learning, and reconfigurable intelligent surfaces to enhance communication efficiency and reliability. Dr Foh has contributed to over 50 peer-reviewed publications since 2015, with recent work addressing Open RAN vulnerabilities, NOMA-enhanced 5G protocols, and AI-driven network automation. His research interests span protocol design for wireless networks (e.g., IEEE 802.11bd, V2X systems), low-latency access mechanisms, and secure edge computing architectures. Collaborations involve industry standards bodies and academic institutions. He actively publishes in top-tier venues like IEEE Transactions and Vehicular Technology Conferences, with a focus on practical solutions for emerging telecom challenges. Key contributions include scalable resource allocation in MIMO-LEO satellite networks, ML-based fault detection in cellular systems, and energy-efficient UAV communication via RIS. His work bridges theoretical advancements with real-world deployment scenarios, emphasizing network security and automation in 5G/6G ecosystems.

Katharine FraserView profile

Senior Lecturer

• Mechanical Circulatory Support Devices
• Biofluid Mechanics
• Cardiovascular Biomechanics
• +3 more

Katharine Fraser is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Therapeutic Innovation and the Centre for Bioengineering & Biomedical Technologies (CBio). Her research focuses on improving mechanical circulatory support devices for heart failure patients, with expertise in ventricular assist devices (VADs), cardiovascular fluid dynamics, and novel imaging techniques like Ultrasound Imaging Velocimetry. She collaborates with companies such as Calon Cardio-Technology Ltd and CorWave SAS, and her work supports regulatory bodies like the FDA. Her research spans three core areas: optimizing VAD design using CFD and blood damage models, investigating interactions between mechanical circulatory support and the native cardiovascular system, and developing new diagnostic methods. She leads projects funded by EPSRC and industry, including studies on pediatric LVADs, fluidized bed bioreactors, and hemocompatibility assessment platforms. Fraser has supervised doctoral students and mentors teams like Team Bath Heart, fostering engineering talent in medical device innovation. Notable awards include the John Willis Award (2018) and the 2024 Doctoral Recognition Award. Her publications address topics like hemolysis modeling, secondary blood flows in rotating systems, and personalized cardiovascular modeling for pediatric care. She actively contributes to advancing medical technology through interdisciplinary research, education, and industry partnerships.

• Fluid-Structure Interaction
• Geophysics Applications
• Immersed Boundary Methods
• +3 more

Maria Giuseppina Chiara Nestola is a Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and partially affiliated with the Department of Earth Sciences at ETH Zurich. Her work focuses on developing advanced numerical methods for fluid-structure interaction (FSI), immersed boundary techniques, and fracture network modeling in biomedical and geophysical contexts. She contributes to projects like FASTER (geothermal reservoir simulations) and HPC-PREDICT (cardiovascular prognosis), and leads software development for tools like AV-FLOW (FSI library), Parrot (fracture network modeling), and Utopia (linear algebra). Her research integrates high-performance computing with applications in geophysics (e.g., seismic hazard assessment, geothermal systems) and biomechanics (e.g., aortic valve dynamics, turbulent blood flow). Key collaborations include the Center for Computational Medicine in Cardiology and the Swiss Competence Center for Energy Research (SCCER-SoE). She holds roles as FOMICS administrator and project co-PI/co-investigator in PASC initiatives. Her technical expertise spans parallel algorithms, embedded finite-element methods, and multi-physics simulations. Current efforts emphasize real-time geothermal reservoir modeling using Multilevel Monte Carlo methods and benchmarking fracture flow simulations.

Shinjiro SuedaView profile

Associate Professor

• Computer Graphics
• Computer Animation
• Computational Biomechanics
• +4 more

Shinjiro Sueda is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads the Aggie Graphics Group. His research focuses on physically-based modeling, biomechanical simulation, and computational fabrication. He also holds an adjunct appointment at Baylor University. Education: Ph.D. in Computer Science, University of British Columbia (2011) M.S. in Computer Science, Rutgers University (2006) B.Sc. (Hons) in Computer Science, University of British Columbia (2003) Postdoctoral Fellowships: Disney Research Boston & MIT (2011–2013) Research Interests: Computer Graphics and Animation Computational Biomechanics (musculoskeletal systems, muscle dynamics) Computational Fabrication (3D printing, mechanical design) Robotics and Haptic Interaction Virtual and Augmented Reality Applications Publications: His work spans 100+ peer-reviewed articles in top venues like ACM Transactions on Graphics (SIGGRAPH), with recent focus on neural simulation techniques, deformable physics, and robotics integration. Notable contributions include REDMAX (articulated dynamics), AutoConnect (3D-printable connectors), and Simit (physical simulation language). Awards: NSF CAREER Award (2019) Disney Inventor Award (2013) Best Paper Awards (SIGGRAPH Asia 2020, SWS 2023) Teaching & Service: Teaches courses on computer graphics, animation, and computational fabrication Program Chair: Motion, Interaction & Games (2020); Eurographics (2023) Associate Editor: ACM Transactions on Graphics (2022–present) Labs: Directs the Aggie Graphics Group, collaborating with industry partners like Disney Research and MIT. Active in developing open-source tools for physical simulation (Simit) and VR educational platforms (Anatomy Builder VR).

• Intelligent monitoring and maintenance of civil infrastructure
• Optimum asset maintenance and management
• Structural reliability analysis and safe life prediction of structures and infrastructure
• +4 more

Dr. Mojtaba Mahmoodian is a Senior Lecturer in Civil and Infrastructure Engineering at RMIT University's School of Engineering, City Campus, Australia. He is a Chartered Engineer in both Civil Engineering and Asset Management with Engineers Australia. His work spans diverse infrastructure types including bridges, pipelines, railways, buildings, ports, and offshore platforms. Dr. Mahmoodian's research focuses on intelligent monitoring and maintenance of civil infrastructure, optimum asset maintenance and management, structural reliability analysis and safe life prediction of structures, and deterioration modeling of structures and infrastructures. His work integrates advanced technologies including digital twins, artificial intelligence, and machine learning to enhance infrastructure monitoring and management systems. He has developed innovative approaches for corrosion modeling, structural health monitoring, and real-time structural integrity assessment. His recent publications demonstrate a strong trend toward the integration of digital twin technology with artificial intelligence for infrastructure monitoring. The research spans civil infrastructure domains including bridges, railways, ports, and pipelines, with emphasis on real-time monitoring, predictive maintenance, and optimal sensor placement. His work shows increasing sophistication in applying machine learning techniques to accelerate finite element analysis and improve structural assessment accuracy. RMIT Vice-Chancellor's Award for Outstanding Contributions in Learning and Teaching (2019) Chartered Engineer in Civil Engineering and Asset Management (2019) Chair of special sessions at major international bridge conferences (IABMAS 2018, 2020) Conference chair for ICWSS 2017: International Conference on Water Supply Systems Kharazmi Festival Award for innovation in concrete testing (2004) Dr. Mahmoodian actively supervises Masters and PhD research students, with current projects focusing on AI-enabled health monitoring of bridge structures through digital twin technology, computer vision for damage detection, automatic condition assessments of civil infrastructure, and intelligent monitoring and maintenance systems. His research has significant practical applications for infrastructure asset management and maintenance optimization. His work involves collaboration with various research teams focusing on structural health monitoring, digital twin development, and AI applications in civil engineering. These teams work on developing innovative monitoring systems that integrate sensor networks, data analytics, and visualization tools to provide comprehensive infrastructure assessment capabilities.

• Computer Networks
• Edge Computing
• Digital Twins
• +4 more

Armir Bujari is an Associate Professor at the Department of Computer Science within the Faculty of Science and Engineering at the University of Bologna. His research focuses on advanced networking, edge computing, digital twins, and blockchain applications. He holds a scientific disciplinary sector specialization in Information Processing Systems (IINF-05/A). Key research areas include: Edge and cloud computing architectures IoT and smart systems integration Blockchain-enabled data ecosystems Urban and industrial digital twins Disaster communication protocols Recent publications emphasize innovative solutions in federated learning, QUIC protocol optimization for satellite networks, and digital twin applications in logistics and smart cities. His work bridges theoretical advancements with practical implementations in 5G/6G networks, disaster response systems, and sustainable energy grids. Office location: 1st floor, Palazzina ex-scuderie, Viale Risorgimento 2, Bologna. Reception hours are typically Mondays 3-5 PM, with flexible scheduling via email.

Bettina SpeckmannView profile

Professor

• Computational Geometry
• Geometric Algorithms
• Spatial Data Analysis
• +12 more

Bettina Speckmann is a full Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU Eindhoven), where she leads the Applied Geometric Algorithms group. She holds additional appointments as EAISI Health Professor and EAISI Foundational Professor, and is affiliated with the Data Science Center Eindhoven. Her research bridges theoretical algorithm design with practical applications in spatial computing. Her research interests lie primarily in computational geometry and geometric algorithms, with strong applications in GIScience, Smart Mobility (including moving object analysis and automated cartography), geo-visualization, visual analytics, and e-Humanities. She focuses on developing efficient algorithms and data structures for spatial data, combining rigorous theoretical methods with practical engineering for real-world impact. Her recent publications (2025) show a strong trend in geometric data processing, particularly in polycube segmentations, dual loop algorithms, density estimation for moving groups, and topological analysis using merge trees and Fréchet distances. These works reflect her interdisciplinary focus on computational geometry, visualization, and data structures. Scientific awards received include: Netherlands Prize for ICT Research (2011) NWO Vici Award (2012) PEriTiA Prize (2020) Bettina Speckmann has advised numerous students and researchers through her group and has secured major grants, including the NWO Vici. She has served in leadership roles such as PC co-chair for Graph Drawing (GD 2011), PC chair for ICALP Track A (2015), and PC co-chair for SoCG (2018). She teaches courses such as Data Structures and Heuristic Algorithms. She leads the Applied Geometric Algorithms group, which actively collaborates with industry partners like HERE Global B.V., Fugro NL Land B.V., and OCLC B.V., and contributes to UN Sustainable Development Goals in areas related to data and mobility.