Find Academic Supervisors

• Robot Cooperation
• Human-Robot Interaction
• People Motion Behavior
• +5 more

Anaís Garrell Zulueta is an Associate Professor at the Polytechnic University of Catalonia (UPC) and a Robotics Researcher at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC) . She serves as Vice-Director of the Mobile Robotics and Intelligent Systems subline and supervises the RAIG - Mobile Robotics and Artificial Intelligence Group . PhD (2013): European Doctorate with highest honors from UPC B.S. in Mathematics (2006) from University of Barcelona Diploma d'Estudis Avançats (DEA) in Control, Vision, and Robotics from UPC Her research focuses on Human-Robot Interaction (HRI) and robot cooperation , particularly in urban environments . Key themes include: Explainable AI for robot transparency Human motion behavior prediction Socially aware navigation systems Collaborative transport robotics Autonomous last-mile delivery systems Cybernetic avatars and societal implications Recent projects (2023-2025) include: TORNADO: Foundation models for robots handling deformable objects HandIA: AI-based rehabilitation tools LENA: Lifelong navigation learning TRIFFID: First responder assistance robotics SOCIAL PIA: Cybernetic avatar modeling Scientific recognition includes: Second Prize for Best Spanish Robotics Thesis Best Paper Award Nomination (IEEE/RSJ IROS, 2009) As an advisor, she supervises: PhD students: Ferran Gebelli Guinjoan, Lavinia Hriscu, Edison Bejarano Final year projects: 8 students on topics like LLM-enhanced interaction and LiDAR SLAM systems She operates within the Institut de Robòtica i Informàtica Industrial (IRI) and collaborates with Carnegie Mellon University.

Alex DiFeliceantonioView profile

Assistant Professor

• Appetitive Neuroscience
• Food Reward
• Eating Behavior
• +7 more

Alex DiFeliceantonio, Ph.D., is an Assistant Professor at the Fralin Biomedical Research Institute at VTC and in the Department of Human Nutrition, Foods and Exercise within the College of Agriculture and Life Sciences at Virginia Tech. She is also the Interim Co-director of the Center for Health Behaviors Research, where she leads interdisciplinary efforts to understand the neurobiological basis of eating behaviors. Dr. DiFeliceantonio earned her B.S. in Psychology from Sweet Briar College and her Ph.D. in Biopsychology from the University of Michigan. She completed postdoctoral training at Yale University and the Max Planck Institute for Metabolism Research in Germany, focusing on dopamine signaling and food motivation. Her research centers on appetitive neuroscience—how the brain integrates sensory and metabolic signals to guide food choices. She uses multimodal brain imaging and behavioral methods to explore food reward, craving, and the addictive qualities of ultra-processed foods. Her lab investigates how combinations of fat and carbohydrates produce supra-additive reward responses and how these mechanisms may contribute to overeating and obesity. Her recent publications highlight a strong focus on the neural and behavioral effects of ultra-processed foods, reward circuitry, and potential parallels between food and drug addiction. She has published in high-impact journals such as Cell Metabolism , Science , and Appetite , often in collaboration with leading experts in neuroscience and metabolism. Supra-additive food reward from fat-carbohydrate combinations Neural mechanisms of ultra-processed food consumption Dopamine signaling in eating behavior Overlap between food and drug reward pathways Controlled feeding trials in young adults Implications for food addiction and public health Dr. DiFeliceantonio has received research support from the National Institutes of Health and the Seale Innovation Fund. She is actively involved in training the next generation of scientists through her lab and mentoring roles. She also contributes to public science communication through media interviews, podcasts like 'Curious Conversations,' and public lectures on brain science and eating behavior. She leads the DiFeliceantonio Lab in Roanoke, which combines neuroscience, nutrition, and psychology to address critical questions about diet, motivation, and health. Her work has been featured in major media outlets including the New York Times , National Geographic , and Yahoo News , reflecting its broad scientific and societal relevance.

Alfredo BensoView profile

Professor

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +16 more

Alfredo Benso is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. He has been continuously involved in the Doctoral College for Computer and Systems Engineering since 2008, guiding PhD education across numerous cycles. His academic roles span teaching and research leadership in bioinformatics, systems biology, and computer engineering. Research interests: Bioinformatics and computational biology Systems biology and gene regulatory networks Artificial intelligence and machine learning in biomedicine Molecular modeling and multiscale simulation Biological database systems and data integration Health informatics and public health modeling His recent publications reflect a strong trend in applying AI and machine learning to biological and medical challenges, including protein function prediction, Alzheimer’s and Multiple Sclerosis modeling, food fraud detection, and viral genome analysis. These works span journals and conferences in bioinformatics, computational biology, and biomedical engineering, demonstrating interdisciplinary innovation. Scientific Awards: BIOINFORMATICS 2014 BEST PAPER AWARD (INSTICC, United Kingdom) Advising and Grants: He supervises PhD students, including Sofia Ostellino, and leads major research initiatives such as the BIGMECH (2023–2025) and FISHUB (2016–2018) projects. He has served as Scientific Director for collaborative agreements with public administrations and as Principal Investigator on EU, national (PRIN), and regional research grants focused on bioinformatics, space systems, and reliable digital technologies. Labs and Teams: He is a key member of the SBG - System Biology Group (DAUIN) and contributes to PolitoBIOMed Lab, fostering collaborative research in biomedical engineering and computational biology.

Stace ConstantinouView profile

Senior Lecturer

• Popular Music
• Music Composition
• Electronic Music
• +16 more

Dr Stace Constantinou is a Senior Lecturer in Popular Music and Programme Leader for the Popular Music course at the University of Northampton, affiliated with the Centre for Cultural and Literary Studies within the Culture school. He holds a PhD from Kingston University and is actively engaged in creative research, supervision, and public engagement. PhD, Processes of creative patterning: a compositional approach, Kingston University (2015) His research centers on the intersection of music, technology, and artificial intelligence. He explores AI through programmatic electronic music, develops analytical tools for popular song, and investigates the adaptation of mathematical symmetry into digital signal processing. His creative work emphasizes spatial sound, immersive performance, and the materiality of the audio-object within DAW environments. Key themes include musical creativity, novelty, patterns, and the philosophical implications of AI in music. His recent creative outputs form a thematic arc exploring AI, rebellion, and human identity, most notably through The SAPIAN Trilogy and Rebel-Misfit . These works blend electronic composition with narrative depth, often inspired by thinkers like Zylinska, Bostrom, and Kurzweil. His publications and compositions reflect a consistent focus on sound as a medium for knowledge exchange and conceptual exploration. His scientific recognition includes the Ricordi Prize for Best Composition and the Schillinger Composition Prize. Ricordi Prize for Best Composition Schillinger Composition Prize He supervises two practice-based PhD students: Gemma Boaden, exploring hybrid voice practices for actors, and Michael W. Bell, researching experiential soundtracks using spatial audio technologies. He has secured internal funding for the LIPSY: Live Immersive Performance Soundscape project (2024–2025), demonstrating active grant involvement. His work extends to public engagement through podcasts, radio programs, and exhibitions with the Royal Society of Arts. Dr Constantinou is actively involved in a creative research lab focused on immersive and spatial sound, leading the LIPSY project. His work is inherently collaborative, involving partnerships with artists, film directors, and institutions like Resonance FM, Angel Studios, and The Podcast Company.

Carl AnthonyView profile

Senior Lecturer

• Microsystems Engineering
• Microsensors
• Energy Harvesting
• +12 more

Dr Carl Anthony is a Senior Lecturer and Head of Education in the Department of Mechanical Engineering at the University of Birmingham, within the School of Engineering. He has been a key figure in microsystems research and education since joining the university in 2006. Educational Background: BSc (Hons) in Physics with Optoelectronics, University of Surrey, 1993 PhD in Electrical and Electronic Engineering, Newcastle University, 2006 His research focuses on Microsystems Engineering , particularly microsensors, energy harvesting, nonlinear resonators, and bio-MEMS tactile sensors. His work bridges fundamental physics with practical engineering applications, especially in autonomous sensing systems. He has pioneered research in Focused Ion Beam microfabrication and wireless sensor powering solutions. The recent publications highlight a consistent trajectory in MEMS and microfabrication technologies , with increasing emphasis on bio-integrated sensors, energy autonomy, and advanced characterization techniques. His work spans materials, devices, and system-level integration, demonstrating interdisciplinary depth. Scientific Awards: EPSRC First Grant (2010) for developing a battery-less clockwork energy harvester for in-wheel tyre pressure sensors Carl Anthony is actively involved in research funding and supervision. He has secured competitive grants such as the EPSRC award and supervises PhD students in areas including micro energy harvesters, coupled resonators, and micro-vacuum systems. He is a member of the Energy Harvesting Network and collaborates with European consortia on bio-MEMS projects. He leads research in the MicroEngineering Group , where his team investigates dynamic behavior of micro-resonators, fabrication-induced material damage, and novel sensor architectures. The group leverages advanced tools like FIB and SEM for nanoscale engineering and characterization.

Hametner, BernhardView profile

Lecturer

• Biomedical Engineering
• Arterial Pressure Modeling
• Cardiovascular Physiology
• +3 more

Prof. Dr.techn. Bernhard Hametner is a Lecturer at the Institute of Biomedical Engineering (E363) within Technische Universität Wien (TU Wien) . His research focuses on arterial pressure dynamics, cardiovascular physiology, and biomedical signal analysis with applications in clinical diagnostics and space medicine. He leads projects involving wearable medical devices, multisensor monitoring systems, and computational models of vascular systems. Key affiliations: TU Wien's Network Lab and collaborative EU projects like VascAgeNet Expertise in pulse wave analysis, vascular aging, and translational biomedical engineering Research Interests: His work spans arterial stiffness quantification, non-invasive cardiac output estimation, and developing methods to detect early cardiovascular dysfunction through waveform analysis. He has pioneered approaches using difference equations for modeling arterial wave reflections and contributed to understanding microgravity's impact on cardiovascular systems. Publications Highlight Trends: Recent work emphasizes clinical applications of wearable devices (2023), AI-driven medical recommendation systems (2022), and space physiology studies (2020-2022). Earlier research focused on computational modeling of vascular dynamics and validation of novel diagnostic methods. Advising & Education: Supervised 8 thesis students (2016-2021) in topics like biomedical signal analysis, machine learning in healthcare, and cardiovascular modeling. Active in training the next generation of biomedical engineers through TU Wien's graduate programs. Labs/Teams: Part of TU Wien's Network Lab, collaborating with international teams on projects like the EU's VascAgeNet initiative. Engaged in multidisciplinary teams combining engineering, medicine, and data science.

Wei-Tang ChangView profile

Assistant Professor

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +4 more

Dr. Wei-Tang Chang is an Assistant Professor in the Department of Radiology at the University of North Carolina School of Medicine. His research focuses on advancing ultrahigh-resolution functional and diffusion MRI techniques, with emphasis on improving spatial and temporal resolution while reducing scan times. Key projects include submillimeter isotropic-resolution fMRI for hippocampal subfield analysis (funded by NIH R21), novel dMRI approaches to overcome resolution limits, and clinical translation of robust imaging methods resistant to motion/noise artifacts. Dr. Chang holds a PhD in Biomedical Engineering from National Taiwan University and completed postdoctoral training at the Martinos Center for Biomedical Imaging (MGH), Massachusetts General Hospital, and Singapore BioImaging Consortium (SBIC). His research innovations include SORDINO fMRI for awake rodent imaging, pPRISM diffusion MRI for submillimeter resolution, and ZTE pulse sequences for ultra-fast acquisitions. Awards include the 2011 OHBM Trainee Award for work on MEG source localization and fMRI temporal resolution breakthroughs. Current work bridges basic neuroimaging science with clinical applications, particularly in neurodegenerative disease biomarker development and rodent disease model studies. Education: PhD in Biomedical Engineering, National Taiwan University Postdoctoral Fellowships: Martinos Center for Biomedical Imaging (MGH) Singapore BioImaging Consortium (SBIC) Key Technologies Developed: ZTE pulse sequences (25 ms temporal resolution fMRI) pPRISM diffusion MRI (navigator-free submillimeter imaging) Draining-vein suppression layer-dependent fMRI Awards & Funding: NIH R21 Grant (2019) for hippocampal subfield fMRI OHBM Trainee Award (2011) Dr. Chang's translational focus involves adapting laboratory innovations for clinical use, with particular interest in Alzheimer's disease biomarkers through hippocampal network analysis and Huntington's disease models using rodent functional connectivity studies. His lab actively develops open-source MRI reconstruction algorithms and collaborates internationally on multi-center neuroimaging projects.

• Neuroscience
• Psychiatry
• Cognitive Neuroscience
• +3 more

Umit Aydin is a researcher affiliated with the University of Reading. His work focuses on neuroimaging techniques applied to developmental disorders such as ADHD and autism spectrum disorder, as well as epilepsy and sleep deprivation effects. He has contributed to studies using EEG, MEG, and fNIRS technologies to understand brain connectivity, cognitive deficits, and clinical outcomes. His research integrates genetic analyses, signal processing advancements, and multimodal neuroimaging approaches. Key areas of investigation include: Electrophysiological correlates of neuropsychiatric disorders Development of advanced neuroimaging methodologies Integration of EEG/MEG with clinical outcomes

Shlomo DubnovView profile

Professor

• Music Technology
• Audio Signal Processing
• Machine Learning for Audio
• +3 more

Shlomo Dubnov is a Professor in the Department of Music at the University of California, San Diego. With an h-index of 36 and over 4,803 citations, his research spans the intersection of music, audio processing, and machine learning, making significant contributions to music information retrieval, sound synthesis, and speech recognition technologies. Dr. Dubnov's primary research interests focus on Music Technology and Audio Signal Processing , with particular expertise in: Machine learning applications for music and audio analysis Sound texture synthesis and modeling Music improvisation systems and computational creativity Audio adversarial examples and security Pitch tracking in noisy environments Multimodal learning for audio-text alignment His recent work demonstrates a strong trend toward applying transformer architectures and contrastive learning techniques to audio processing tasks, with publications in top conferences like IEEE International Conference on Acoustics, Speech, and Language Processing. Dr. Dubnov's research bridges theoretical music concepts with practical audio engineering applications, creating innovative solutions for music generation, analysis, and security. Dr. Dubnov has established productive collaborations with researchers across disciplines, particularly with: G. Assayag (102 Publications • 2,475 Citations) - music technology and improvisation systems Taylor Berg-Kirkpatrick (31 Publications • 1,459 Citations) - machine learning applications K. Chen (18 Publications • 1,150 Citations) - audio transformer models Cheng-i Wang (21 Publications • 305 Citations) - music information retrieval His work is supported by grants focused on advancing the state-of-the-art in music technology, audio processing, and machine learning applications for creative domains. Dr. Dubnov leads research in the Music Technology laboratory at UC San Diego, where his team develops innovative systems for music analysis, synthesis, and human-computer interaction in musical contexts. The lab focuses on creating intelligent systems that can understand, generate, and interact with musical content in real-time applications.

Jeffrey GirardView profile

Assistant Professor

• Affective Communication
• Affective Computing
• Clinical Assessment
• +4 more

Dr. Jeffrey Girard serves as Assistant Professor and M. Erik Wright Scholar in the Psychology Department at the University of Kansas, directing the Brain, Behavior & Quantitative Science Program and co-leading the Kansas Data Science Consortium. His interdisciplinary research bridges psychology, computer science, and statistics to investigate emotional expression through verbal and nonverbal behavior, with emphasis on individual differences and social contexts. Dr. Girard's educational background includes: Ph.D. in Psychology (Clinical) from the University of Pittsburgh M.S. in Psychology (Clinical) from the University of Pittsburgh B.A. in Psychology & Philosophy from the University of Washington His research centers on affective communication and affective computing , examining how emotions manifest in interpersonal interactions across clinical and non-clinical populations. Key focus areas include transdiagnostic psychiatry, multimodal behavior analysis, and the development of computational tools for clinical assessment. This work integrates statistical modeling with machine learning to address questions in interpersonal functioning and mental health diagnostics. Analysis of Dr. Girard's recent publications (2023-2025) reveals strong trends in computational psychiatry, with emphasis on multimodal datasets for emotion recognition, AI-driven clinical assessment tools, and transdiagnostic approaches to psychopathology. His work consistently bridges technical innovation in affective computing with real-world clinical applications, particularly in depression assessment and psychosis research. Scientific recognition includes: M. Erik Wright Scholar Dr. Girard actively contributes to academic service through editorial roles at IEEE Transactions on Affective Computing, Clinical Psychological Science, and other leading journals. He serves on executive boards for the Society for Interpersonal Theory and Research, Society for Affective Science, and Hierarchical Taxonomy of Psychopathology society, demonstrating leadership in advancing interdisciplinary research methodologies. He directs the Affective Communication and Computing Lab, which develops computational frameworks for analyzing emotional expression in clinical contexts, and co-leads the Kansas Data Science Consortium to foster cross-departmental data science initiatives.

Luca PolloniniView profile

Associate Professor

• Biomedical Optics
• Near-Infrared Spectroscopy (NIRS)
• Brain Imaging
• +5 more

Luca Pollonini, Ph.D. , is an Associate Professor at the University of Houston within the Cullen College of Engineering , Department of Engineering Technology. He leads the Optical Bioimaging Lab , focusing on biomedical optics, cortical hemodynamics, and tissue oxygenation monitoring. His research spans wearable sensors, traumatic brain injury (TBI) studies, and early detection of pressure ulcers via diffuse optical imaging. Education: Ph.D. in Information Engineering, University of Brescia, Italy (2004) M.S. in Electrical Engineering, University of Brescia, Italy (2000) Dr. Pollonini's research integrates near-infrared spectroscopy (fNIRS) for real-time brain imaging and biomedical signal processing to detect physiological changes. His work on portable optical sensors includes applications for heart failure monitoring, astronaut fingertip injuries, and pressure ulcer prevention. Recent publications highlight his contributions to biomedical device design , TBI cortical connectivity , and cardiac rehabilitation technologies . Awards include the 2001 Best Science Thesis (Lombardy Institute), 2004 ARVO Best Poster , and 2014 Wolff Center Innovation Patent for skin-flap blood-flow monitoring. He serves as an Assistant Affiliate Member at the Houston Methodist Research Institute (since 2014) and mentors postdoctoral and graduate fellows in NSF-funded projects involving diffuse optical imaging and wearable sensor development.

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +3 more

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

• Natural Language Processing
• Automatic Text Summarization
• Sentence Generation
• +5 more

Juan-Manuel Torres Moreno is an Associate Professor (Maître de Conférences HDR HC) at the University of Avignon (UAPV), where he conducts research in Natural Language Processing at the Laboratoire Informatique d'Avignon (LIA). His academic position includes the HDR (Habilitation à Diriger des Recherches), a post-doctoral qualification in France that enables supervision of PhD students. His primary research interests focus on Natural Language Processing, with particular emphasis on automatic text summarization, sentence generation, and phrase compression algorithms. His work spans both theoretical and applied aspects of NLP, incorporating machine learning techniques and artificial intelligence approaches. His research has significant applications in multilingual processing, text mining, and information extraction systems. Torres Moreno's publication record demonstrates a consistent trajectory in advancing text summarization techniques, with recent work exploring cross-lingual approaches, multimedia content processing, and deep learning applications. His research often bridges the gap between theoretical linguistic concepts and practical implementation, with publications spanning from fundamental NLP algorithms to applied systems for video summarization, speech processing, and multilingual document analysis. He actively collaborates with researchers across multiple institutions including École Polytechnique de Montréal (with 50 joint publications), Laboratoire Informatique d'Avignon (83 publications), and Universidad Nacional Autónoma de México. His work appears in reputable journals such as Computer Speech and Language, Data and Knowledge Engineering, and Pattern Recognition Letters. Within the Laboratoire Informatique d'Avignon, Torres Moreno contributes to the Language Processing research theme, working with colleagues on projects related to multilingual information access, opinion mining, and text analysis. His research group has participated in several evaluation campaigns including DEFT (Défi Fouille de Textes) challenges, focusing on information retrieval and sentiment analysis tasks.

Francis DawsonView profile

Professor

• Energy Systems
• Power Electronics
• Signal Processing in Power Engineering
• +4 more

Professor Francis Dawson is affiliated with the Department of Electrical and Computer Engineering at the University of Toronto , under the Faculty of Applied Science and Engineering . He has been active in teaching and research since 1988, focusing on energy systems, power converters, and electromagnetic modeling. PhD in Electrical Engineering (University of Toronto, 1988) MASc in Electrical Engineering (University of Toronto, 1985) BASc in Electrical Engineering (University of Toronto, 1982) BSc in Physics (University of Toronto, 1978) His research spans static power converters , energy storage systems , signal processing in power applications, and electromagnetic compatibility . Recent publications focus on fault current limiters , digital phase-locked loops , and plasma physics , with keywords indicating strong ties to Electrical Engineering and Materials Science . Scientific accolades include: IEEE Fellow Myron Zucker Student Design Project 1st Prize (2012) K. Yamamoto Best Student Paper Award (2007) F. Farahmand 3rd Best Paper Prize (2005) He has collaborated on industrial projects in power electronics and contributed to the Power Group at the University of Toronto.

• Mass Spectrometry
• Imaging Mass Spectrometry
• MALDI Imaging
• +4 more

Raf Van de Plas is a researcher at Delft University of Technology specializing in mass spectrometry imaging and data analysis. His work focuses on developing advanced techniques for molecular analysis of biological tissues, particularly in kidney research and lipidomics. With a PhD in Mechanical Engineering, he bridges engineering principles with biomedical applications. His research interests include: Mass Spectrometry Imaging MALDI Imaging Techniques Lipid Analysis and Mapping Kidney Tissue Molecular Profiling Computational Data Analysis Biomolecular Imaging Algorithms Dr. Van de Plas has published extensively in high-impact journals with multiple 2025 publications in Science Advances, Nature Communications, Kidney International, and Analytica Chimica Acta. His work demonstrates a clear trajectory toward developing innovative computational workflows for mass spectrometry data while applying these techniques to solve critical biomedical problems, particularly in kidney pathology and lipid metabolism. His scientific contributions include: Development of advanced MALDI matrices for high spatial resolution imaging Creation of msiFlow automated analysis workflows Comprehensive lipid mapping of human kidney tissues Integration of mass spectrometry with immunofluorescence microscopy Dr. Van de Plas actively supervises research work and collaborates internationally across biomedical and engineering disciplines, with significant contributions to both methodological development and clinical applications of imaging mass spectrometry.