Find Academic Supervisors

• Jewellery Design
• Material Culture
• Digital Fabrication
• +12 more

Dr Roberta Bernabei is the Director of Doctoral Programmes at Loughborough University's School of Design and Creative Arts, with additional roles as External Examiner at the Royal College of Art's Jewellery & Metal department and Visiting Reader at Birmingham City University's Institute of Jewellery, Fashion, and Textiles. A Fellow of the Higher Education Academy since 2015, she co-founded the Journal of Jewellery Research and serves as Vice-President of the Italian Contemporary Jewellery Association (AGC). PhD in Jewellery Design from Loughborough University (thesis: 'Jewellery Theory and Practice: An investigation into emotionally invested and mnemonic jewellery through sensitising materials') BA(Hons) and MA in Jewellery Design Her research explores emotionally embedded artefacts and their impact on self-identity and well-being, blending digital fabrication (e.g., Rapid Prototyping) with traditional goldsmithing techniques. Recent projects include curating practice-based PhD outputs at the 2022 Florence Jewellery Week and co-leading the 'Craft Cultures' research cluster at BCU. Key publication themes span emotionally invested jewellery, memory studies, digital manufacturing, and design pedagogy. She advocates for the democratisation of authorship in digital jewellery production and investigates animation therapy's intersection with wearable objects. As Editor-in-Chief of the Journal of Jewellery Research , she champions interdisciplinary collaboration across design, technology, and health. Her supervisory focus includes PhD candidates exploring craft, memory, and digital fabrication.

Vahid SeydiView profile

Research Fellow

• Machine Learning
• Data Science
• Deep Learning
• +6 more

Vahid Seydi is a Research Fellow in Data Science and Machine Learning at the School of Ocean Sciences, Bangor University, UK. He previously served as an Assistant Professor (2014–2020) and award-winning Lecturer (2010–2014) at Azad University South Tehran Branch, Iran. He holds a PhD in AI (2014) from Azad University, with prior M.Sc. (2007) and B.Sc. (2005) degrees in Computer Science and AI. Education : PhD in Artificial Intelligence (2014), Azad University M.Sc. in Artificial Intelligence (2007), Science and Research University B.Sc. in Software Engineering (2005), Azad University Research Interests : Deep Learning, Domain Adaptation, Generative Models, Explainable AI, Reinforcement Learning, Optimization, and applications in marine renewable energy and environmental sustainability. Key Contributions : His work focuses on ML-driven solutions for offshore energy systems, including predictive digital twins, passive acoustic monitoring, and domain adaptation for ecological impact analysis. He has contributed to projects like the SEEC Centre and EcoWind-ACCELERATE initiative. Awards : Global Talent Endorsement (UK Royal Society, 2023) Zillow Kaggle Competition Silver Medal (2017, top 2%) Excellence in Teaching Award (Azad University, 2014–2019) Grants & Projects : Smart Efficient Energy Centre (SEEC), 2019–2023 EcoWind-ACCELERATE, 2022–2026 Morlais Demonstration Zone, 2022–2024 Labs/Teams : Active in Bangor’s Ocean Sciences research groups, focusing on ML integration with marine energy and ecological data systems.

Abir SahaView profile

Academic

• Human-Computer Interaction
• Accessibility
• Disability Inclusive Technology
• +3 more

Abir Saha is an Assistant Teaching Professor at the Khoury College of Computer Sciences, Northeastern University. Previously, they earned a PhD in Technology and Social Behavior (joint Computer Science and Communication) from Northwestern University. Their research bridges Human-Computer Interaction and Accessibility, focusing on tools to support disabled individuals in computer-mediated skilled work. Key contributions include studies on accessibility in audio production, peer-to-peer instructional communities, and inclusive streaming experiences. Education: PhD in Technology and Social Behavior (Northwestern University, 2024), with a focus on interdisciplinary work between Computer Science and Communication Studies. Research interests center on accessibility challenges in specialized domains like audio production, exploring how disabled practitioners develop expertise through community-driven learning. Methodologically, this involves qualitative studies (interviews, observations) and tool development to scaffold accessible learning. Notable achievements include a Best Paper Nomination at ASSETS 2020 for work on accessibility in audio production. Recent publications emphasize inclusive design principles for multimedia systems and assistive technologies. Current work extends accessibility research into multimodal interfaces for deaf communities and refining peer-to-peer support mechanisms in online technical spaces.

Arshdeep SinghView profile

Research Fellow

• Audio Signal Processing
• Machine Learning
• Neural Networks
• +3 more

Dr. Arshdeep Singh is a Research Fellow A at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, specializing in AI-driven audio processing. He concurrently serves as a Sustainability Fellow at the University’s Institute for Sustainability and holds the UKAN+ Early Career Acoustic Champion title (2023). His research focuses on efficient neural network architectures for audio classification, model compression, and sustainable AI applications. Singh completed his PhD at IIT Mandi, India, with prior roles including internships at Intel Bangalore and CSIR-CSIO Chandigarh, and an M.E. from Panjab University (Gold Medal recipient). Research interests include audio signal processing, CNN compression via filter pruning, and sustainable AI. Notable contributions include developing lightweight CNN frameworks for acoustic scene classification and sound event detection, with applications in smart home technologies and industrial health monitoring. His work often targets resource-constrained devices, emphasizing real-world deployability. Publications highlight innovations like graph centrality-based filter pruning and quaternion transformations for CNN efficiency. Awards include the Panjab University Gold Medal for M.E. and recognition as an Early Career Acoustic Champion. Supervision includes MSc students (e.g., Soham Bhattacharya, Bars Szegedi) and undergraduates (Kristaps Redmers, Yan-Ping Liao). His interdisciplinary projects span audio captioning, embedded hardware deployment, and dataset creation for sound event detection in residential environments.

Joydeep GhoshView profile

Professor

• Machine Learning
• Data Mining
• Web Mining
• +6 more

Joydeep Ghosh is the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin, leading the IDEAL Lab and UT-MINDS. He holds courtesy faculty positions in Biomedical Engineering, Population Health, and McCombs School of Business. Dr. Ghosh earned his B.Tech from IIT Kanpur (1983) and PhD from the University of Southern California (1988). His research focuses on machine learning, data/web mining, scalable analytics, and ethical AI, with applications in healthcare and industry. He has published over 500 papers, co-edited 20 books, and secured funding from NSF, Google, IBM, and others. Notable awards include the 2020 ICDM Research Contribution Award and 2015 IEEE Technical Achievement Award. He advises startups like CognitiveScale and teaches advanced courses in machine learning and data science, consistently recognized as 'Best Professor' by students. His lab collaborates with industry via sponsored research, addressing real-world challenges in AI and data analytics. Education: B.Tech, Electrical Engineering, IIT Kanpur (1983) Ph.D., Electrical Engineering, University of Southern California (1988) Research Interests: Predictive and Prescriptive Analytics Trustworthy AI/Explainable Machine Learning Federated Learning & Privacy-Preserving Techniques Health Informatics & Computational Phenotyping Scalable Algorithms for Big Data Grants & Industry Collaboration: Sponsored by NSF, Google, IBM, ONR, and others Co-founder/Advisor to Accordion Health, CognitiveScale, and Neonyoyo IDEAL Lab partnerships with industry for problem-driven research Labs & Teams: IDEAL Lab: Focuses on intelligent data analysis across domains UT-MINDS: Engineering Data Science and Machine Learning initiatives

Bob IannucciView profile

Adjunct Professor

• Mobile and embedded computing
• Scalable systems
• Sensor networks
• +11 more

Bob Iannucci is an Adjunct Professor in Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment at CMU Silicon Valley. He previously served as Chief Technology Officer of Nokia and Head of the Nokia Research Center (NRC), where he pioneered open innovation initiatives like 'lablets' at top universities. His research focuses on mobile/embedded systems, scalable architectures, wireless networks, and energy-efficient IoT technologies. Education: Ph.D. in Computer Science from MIT (1988), with a thesis on hybrid dataflow-von Neumann architectures. He has led engineering teams at startups and major firms like DEC/Compaq, co-founding Exa Corporation (now part of Siemens) for computational fluid dynamics. Key Projects: MoCCA (mobile computing architecture, now in Smithsonian), VIPER (drone curiosity training), PowerDué (energy-aware IoT prototyping). Awards: IDEA Gold Award (MoCCA), IPSN Best Paper (2018), Millennium Technology Prize committee member (2008). Current activities include advising on wireless networking, developing WiFi IoT devices, and an award-winning radio direction-finding iPhone app. He actively collaborates on NSF-funded projects addressing spectrum interference and emergency communication systems.

• Human-Computer Interaction
• Interactive Systems
• Cyber-Physical Systems
• +6 more

Prof. Susanne Boll is a Full Professor of Media Informatics and Multimedia Systems at the University of Oldenburg and Board Member of the OFFIS Institute. Her research focuses on human-centered interactive systems, cyber-physical systems, and digital transformation. She holds a PhD (Dr. techn.) from TU Wien and a Diploma from TU Darmstadt. Research: Her work bridges systems engineering, HCI, and computer science, emphasizing ethical technology integration. Key areas include automated vehicles, healthcare tech, Industry 4.0, and socio-technical systems. She has led over 60 projects funded by DFG, BMBF, and industry, including the DFG SPP 2199 and PIZ Center for Innovation in Care. Teaching: Teaches courses in HCI, Interactive Systems, and Digital Fabrication. Coordinates the MSc in Engineering Socio-Technical Systems, emphasizing interdisciplinary education and hands-on research. Awards & Leadership: Recipient of Best Paper Awards at CHI and MobileHCI. Serves on DFG’s Computer Science Review Board, OFFIS Board, and Germany’s AI Platform. Advocates for diversity in STEM, co-leading initiatives like the 25 in 25 gender equality strategy at OFFIS. Publications: Over 300 peer-reviewed articles with 10k+ citations (h-index 54). Recent work spans AI transparency in governance, smart home interfaces, and human-AI collaboration in workplaces.

• Artificial Intelligence
• Machine Learning
• Operations Research
• +5 more

BENATIA Amin is a Researcher-Lecturer at CESI, specializing in Artificial Intelligence, Machine Learning, and Operations Research. He holds a PhD in Computer Science from INSA de Rouen (2017), a Master's from Université Pierre et Marie Curie (2012), and an Engineer degree from Université de Biskra (2011). His research focuses on Industry 4.0, predictive maintenance, supply chain management, and big data applications in cultural heritage and industrial systems. Current projects include Smart Mobility, PFPI (Industry 4.0 demonstrator), VISTA-AR (AR/VR for tourism), and DEFI&Co (training for future industrial trades). He advises PhD students on predictive supervision and AI-driven industrial diagnostics. Key research themes: Data fusion, IoT optimization, and federated learning in industrial systems. He has published widely on wireless sensor networks, alarm management, and industrial traceability. Scientific contributions include over 20 peer-reviewed articles and conference papers, with a focus on multi-objective optimization and AI applications.

Ashley HallView profile

Professor

• Design Innovation
• Sustainability
• Globalization Design
• +6 more

Ashley Hall is a Professor of Design Innovation and Postgraduate Research Lead in the School of Design at the Royal College of Art (RCA). He also serves as Head of Programme for the MRes Healthcare Design with Imperial College. With a BA in Furniture Design from Nottingham Trent University, a Master's from RCA, and a PhD from the University of Technology Sydney, his research spans design resilience, globalization, cultural transfer, and sustainable innovation. His work includes the NEMO project, developing AI-driven ocean monitoring systems, and collaborations with Airbus, BBC, and Huawei. He has supervised over 20 PhD students and holds fellowships from the Higher Education Authority and Design Research Society. His awards include the LDOC Doctoral Award and Microsoft Research Doctoral Award. Current projects focus on ocean sustainability, design safety, and immersive technologies. Education : BA Furniture Design, Nottingham Trent University MA, Royal College of Art PhD, University of Technology Sydney Research Themes : Design for Safety & Sustainability Globalization & Cultural Transfer Experimental Design Pedagogy Ocean Conservation & Technology Grants & Partnerships : Logitech (NEMO Project) Lloyds Register Foundation (Design for Safety) EPSRC (Circular Makerspaces) Awards : LDOC Doctoral Award (2016) Microsoft Research Award (2015) AHRC Collaborative Doctoral Awards (2014–2017)

SAURO LONGHIView profile

Professor

• Automation
• Robotics
• Control Systems
• +6 more

Prof. SAURO LONGHI is a Full Professor at the Department of Information Engineering, Faculty of Engineering, Polytechnic University of Marche. His research focuses on robotics, control systems, industrial automation, and AI-driven solutions for manufacturing and cyber-physical systems. He specializes in fault-tolerant control, predictive maintenance, human-robot collaboration, and autonomous systems resilience against cyber attacks. His work bridges theoretical advancements with practical applications in Industry 4.0, smart manufacturing, and ambient assisted living. Key research interests include: Development of collaborative robotics for industrial processes Real-time anomaly detection using edge AI frameworks Resilient control architectures for UAVs and autonomous vehicles Integration of digital twins for assembly line optimization Psychophysiological interfaces for assistive robotic systems Recent publications highlight advancements in: ISO-compliant human-robot co-transport systems Lightweight neural networks for edge-based anomaly detection Cybersecurity for quadrotor drones Predictive maintenance frameworks using energy data He leads projects like Health@Home , developing smart sensor networks for elderly care. His work emphasizes human-centric design principles in automation technologies.

Leonardo GabrielliView profile

Researcher

• Audio Signal Processing
• Machine Learning
• Embedded Systems
• +3 more

Prof. Leonardo Gabrielli is a Researcher at the Department of Information Engineering (Università Politecnica delle Marche). His work focuses on audio signal processing, machine learning, and embedded systems with applications in real-time audio generation, acoustic scene analysis, and smart transportation systems. He has contributed to projects such as emergency vehicle detection systems and audio enhancement techniques for reverberant environments. His research interests include neural network-based audio processing, physical modeling of musical instruments, and sustainable IoT applications. He has developed prototypes for driver-assistance systems, packet loss concealment algorithms, and biomedical signal analysis tools using deep learning approaches. Prof. Gabrielli collaborates on datasets like A3CarScene for driving scene understanding and has explored generative models for music synthesis and medical diagnostics. His work bridges theoretical signal processing with practical embedded system implementations, emphasizing real-world applications in automotive, healthcare, and environmental monitoring domains. Key Projects: Emergency vehicle detection, smart loudspeaker equalization, music streaming optimization Lab Affiliation: Department of Information Engineering's audio processing laboratories

• Human-Computer Interaction
• End User Development
• Smart Objects
• +3 more

David Verweij is a Doctoral Researcher in Computer Science at Northumbria University's School of Computer Science. His work bridges human-computer interaction, smart object design, and end-user development. Education: Doctor of Philosophy (PhD) from Northumbria University Verweij's research focuses on: Integrating artificial intelligence into everyday design practices Developing motion-matching techniques for real-world applications Co-creating data physicalizations with households Exploring family dynamics through IoT development His collaborative work spans disciplines in computer science, design, and social sciences. Recent publications reveal strong connections to interaction techniques , multimodal interaction , and consumer technology research areas.

Adwait SharmaView profile

Assistant Professor

• Human-Computer Interaction
• Wearable Technologies
• Machine Learning
• +5 more

Dr. Adwait Sharma is an Assistant Professor in Computer Science at the University of Bath, leading the Advanced Interaction and Sensing (AIS) Lab. His research focuses on enabling always-available computing through microgesture recognition, wearable technologies, and machine learning, with applications in healthcare, robotics, and smart environments. He holds a PhD from Saarland University and a Master's from the Media Interaction Lab, Austria. Education: PhD (HCI, Saarland University), MSc (Media Interaction, Austria) Research Interests: Interaction Design, Sparse Sensing, Real-Time Recognition, E-textiles His work bridges user-centered design with data-driven insights, addressing scenarios where traditional input methods are impractical. Recent projects include domestic robotics idle-time utilization, emotion-aware in-car systems, and electrotactile feedback for surgery. He has received awards such as the People's Choice Best Demo Award (CHI'23) and holds leadership roles in top conferences like UIST and CHI. Advised students include PhD researchers in wearable computing, robotics, and automotive interaction. His lab collaborates with institutions across Europe under the EMIL project, advancing XR innovation.

Dhaval SolankiView profile

Assistant Professor

• Wearable Devices
• Biomedical Signal Processing
• Embedded Systems
• +4 more

Dhaval Solanki serves as an Assistant Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island, where he co-directs the Wearable Biosensing Lab. His work bridges engineering innovation with healthcare applications, focusing on real-world implementation of accessible medical technologies. His academic foundation includes: Ph.D. in Electrical Engineering from Indian Institute of Technology Gandhinagar (2020) M.Tech. in Electrical Engineering from Indian Institute of Technology Gandhinagar (2015) B.E. in Electronics and Communication Engineering from Gujarat Technological University (2013) Solanki's research centers on wearable health technologies that transform medical electronics into practical solutions. He develops smart textiles, biosignal processing systems, and embedded devices for continuous health monitoring, with emphasis on shifting healthcare from reactive to proactive personalized care. His human-centered design approach integrates engineering precision with clinical needs across neurological rehabilitation and chronic disease management. Analysis of his 15 most recent publications reveals dominant trends in wearable biosensing for neurological disorders (Parkinson's, stroke, ADHD, epilepsy), with significant focus on textile-based physiological monitoring systems and virtual reality rehabilitation platforms. His work consistently addresses real-world challenges like motion artifacts in clinical settings and usability for diverse patient populations. As an educator, Solanki mentors students in the Wearable Biosensing Lab while teaching electrical and biomedical engineering courses. His research is supported by federal and foundation grants targeting healthcare technology innovation, with recent projects spanning neonatal monitoring, geriatric care, and digital interventions for substance use disorders.

Rupal SrivastavaView profile

Academic

• Shape Memory Alloys
• Smart Materials
• Biomaterial Composites
• +2 more

Rupal Srivastava serves as Assistant Lecturer in the Department of Engineering Technology at South East Technological University. Her research centers on shape memory alloys, smart material composites, and vibration analysis with applications in medical devices and Industry 4.0 technologies. Research domains include: Smart material characterization Biomaterial composites for medical devices Vibration response modeling Multi-objective optimization Digital twin applications Recent publications (2021-2022) demonstrate strong focus on SMA-reinforced composites, digital twin frameworks, and wearable technology interfaces. Her Marie Curie fellowship supported advanced research in thermoelastic material behavior. She contributes to curriculum development in engineering technology and mentors students in materials innovation projects.