Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +5 more

Tala Talaei Khoei serves as an Assistant Teaching Professor at Northeastern University, conducting interdisciplinary research at the intersection of artificial intelligence, cybersecurity, and healthcare innovation. Her academic profile reflects active engagement in developing practical AI solutions for critical real-world challenges. Her research program spans three primary domains: (1) Healthcare AI applications including pediatric pneumonia/asthma detection through respiratory sound analysis using Google's HeAR model; (2) Cybersecurity systems focused on ransomware detection, GPS spoofing countermeasures for UAVs, and IoT security frameworks; and (3) Generative AI advancements in large language models, emotional AI, and sustainable computing practices. This tripartite focus demonstrates her commitment to translating theoretical AI research into tangible societal benefits. Analysis of her 15 recent 2025 publications reveals accelerating research velocity with particular emphasis on data-efficient AI techniques for small datasets, multimodal security threat detection, and ethical implementation of generative models. Her work consistently bridges theoretical machine learning innovations with domain-specific applications, showing growing influence in both healthcare informatics and cybersecurity communities through high-impact survey papers and novel framework development.

Antonio LiottaView profile

Professor

• Machine Learning
• Intelligent Systems
• Internet of Things (IoT)
• +7 more

Antonio Liotta is a Tenured Full Professor at the Faculty of Engineering of the Free University of Bozen-Bolzano, Italy. His research focuses on machine learning, intelligent systems, and network science with applications to smart cities, IoT, and cyber-physical systems. He leads an international team advancing micro-edge intelligence and miniaturized ML, contributing breakthroughs in neural networks and network science intersections. Education details are not explicitly provided in the text, but his academic roles and publications suggest significant expertise in computer science and data science. Research Interests: Intelligent systems and networks Data-driven smart sensing Embedded machine learning Network science applications Human-in-the-loop systems Publications reflect a focus on ML for IoT, edge computing, and environmental modeling. Notable contributions include hybrid surrogate models for hydrological prediction and federated learning for healthcare. He serves as Editor-in-Chief for Springer's IoT book series. Grants and collaborations are implied through his leadership roles and international projects but not explicitly listed. His lab works on AI-driven solutions for urban science and resilient systems.

Rig DasView profile

Assistant Professor

• Brain-Computer Interface (BCI)
• Biometrics
• Artificial Intelligence (AI)
• +6 more

Rig Das is an Assistant Professor in the Computer Science and Computer Engineering Department at the University of Wisconsin-La Crosse (UWL), USA. His research focuses on Brain-Computer Interfaces (BCI), Biometrics, EEG Signal Processing, and AI, with applications in neurorehabilitation and medical diagnostics. He holds a Ph.D. in Applied Electronics Engineering from Roma Tre University, Italy, and has held roles including Research Scientist at the University of Nebraska Medical Center and Postdoctoral Researcher at Technical University of Denmark and the University of Luxembourg. Education: Ph.D. in Applied Electronics Engineering (2018) – Roma Tre University, Italy M.Tech. in Computer Science & Engineering (2012) – NERIST, India B.Tech. in Computer Science & Engineering (2007) – WBUT, India Research Interests: EEG Signal Processing for Parkinson’s Disease and Sleep Studies BCI Systems for Neurorehabilitation and Assistive Technologies Deep Learning in Biometric Identification (e.g., Finger-Vein, Facial Recognition) Medical Image and Signal Processing Grants & Projects: NIH BRAIN Initiative Grant (UH3 NS113769) – Research Scientist, UNMC (2020–25) EU H2020-ENCASE Project – Biometric Privacy Research (2016–19) UWL Faculty Research Grant ($12,200) – Parkinson’s Disease Study (2024–25) Awards: 2018 European Biometrics Research Award for PhD Thesis 2017 EUSIPCO 3MT Presentation Finalist Teaching and Advising: Current Courses: Digital Signal Processing, Programming Language Concepts, Software Design Past Roles: Instructor at UNMC Neurosurgery Dept., Assistant Professor at Assam Don Bosco University Labs & Collaboration: Developed the “Brainy Home” BCI system for smart home control Collaborates with institutions like GN Audio (Denmark) and Bar-Ilan University (Israel)

Yan WangView profile

Associate Professor

• Cyber Security and Privacy
• Internet of Things
• Mobile and Pervasive Computing
• +4 more

Yan Wang is an Associate Professor and Graduate Director in the Computer and Information Sciences Department at Temple University, College of Science and Technology. Previously, he was an Assistant Professor at Binghamton University. He holds a Ph.D. from Stevens Institute of Technology, M.S. from Rutgers University, and B.S. from Tongji University. His research focuses on Cyber Security, IoT, Mobile Sensing, and Smart Healthcare, with NSF-funded projects in Edge Computing and Trustworthy DNN frameworks. He has received multiple accolades including the AI 2000 Most Influential Scholar Award (2024, 2023, 2022) and Best Paper Awards at IEEE CNS 2018, SECON 2017, and ACM AsiaCCS 2016. Dr. Wang leads a lab conducting groundbreaking work in privacy-preserving sensing, adversarial machine learning, and wearable health monitoring. He has advised numerous students, including Zijie Tang and Zhengkun Ye, and serves on editorial boards and conference committees such as T-IFS Associate Editor and ICDCS Track Co-Chair.

Anne WarlaumontView profile

Professor

• Early vocal development
• Evolution of human vocal signals
• Word learning
• +3 more

Anne Warlaumont is a Professor in the Department of Communication at the University of California, Los Angeles (UCLA). She directs the Emergence of Communication Lab, focusing on early vocal development, the evolution of human vocal signals, and computational methods for analyzing naturalistic audio recordings of infants. Her work bridges developmental science, linguistics, and computational modeling to understand how communication emerges in humans and its evolutionary roots. Research Interests: Warlaumont’s research addresses two core questions: (1) How do speech vocalizations and communicative signals develop in early human life, particularly in typical and atypical contexts? (2) What evolutionary processes underlie the emergence of human vocal communication? Methodologically, she emphasizes machine learning, computational analysis of daylong audio recordings, and interdisciplinary collaboration. Key areas include infant vocal development, caregiver-infant interaction dynamics, and cross-cultural language exposure analysis. Labs/Teams: The Emergence of Communication Lab at UCLA develops open-source tools for analyzing child-centered audio data, including the HomeBank repository for storing and sharing long-form recordings. The lab collaborates internationally on cross-linguistic studies and computational modeling projects. Methodologies: Warlaumont’s work employs self-supervised learning models, naturalistic vocal recording analysis, and tools like VCMNet for automated vocal maturity classification. She advocates for rigorous methodological approaches to link empirical data with theoretical models in developmental science.

Björn SchullerView profile

Professor

• Health Informatics
• Artificial Intelligence
• Machine Learning
• +6 more

Prof. Björn Schuller is a Full Professor of Health Informatics at the Technical University of Munich (TUM) and holds a joint appointment at Imperial College London as Professor of Artificial Intelligence. He leads the Chair of Informatics in Healthcare at TUM and heads the Group on Language, Audio & Music (GLAM) at Imperial. His research focuses on the intersection of computer science and medicine, particularly in biosignal analysis, affective computing, and AI-driven healthcare solutions. Schuller has held academic positions at multiple institutions, including the Universities of Augsburg and Passau, and serves as a key figure in global AI and health research communities. Education: Received diploma (1999), doctorate (2006), and habilitation (2012) in electrical engineering/IT from TUM. He is a Fellow of IEEE, ISCA, ACM, and ELLIS, among others. Awards include the World Economic Forum Young Scientist (2015), ERC Starting Grant (2013), and IEEE Signal Processing Society Distinguished Lecturer (2024). Research interests span machine learning applications in healthcare, emotion recognition, and AI ethics. He has authored over 1,500 publications and secured >€15M in research funding, including ERC grants and Horizon 2020 projects. His company, audEERING GmbH, commercializes intelligent audio engineering technologies. Leadership roles include Editor-in-Chief of AI Open Journal and Field Chief Editor of Frontiers in Digital Health. He actively chairs international conferences (e.g., ACII 2025) and drives initiatives like the MPDD Challenge for depression detection.

Leonid SigalView profile

Associate Professor

• Computer Vision
• Machine Learning
• Computer Graphics
• +8 more

Leonid Sigal is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the NSERC Canada Research Chair (CRC II) in Computer Vision and Machine Learning and a CIFAR AI Chair at the Vector Institute. He also serves as an Academic Advisor to Borealis AI. His research focuses on visual understanding, including object recognition, scene analysis, motion capture, and the intersection of computer vision with machine learning and graphics. Prior roles include Senior Research Scientist at Disney Research and Adjunct Faculty at Carnegie Mellon University. Education: Ph.D. (Computer Science, Brown University, 2008), M.Sc. (Computer Science, Brown University, 2003), B.Sc. (Computer Science & Mathematics, Boston University, 1999). Research interests span computer vision challenges such as human motion analysis, activity recognition, and generative models, alongside cross-disciplinary work in robotics and psychology. Notable achievements include Best Paper Awards at WACV 2015 and AMD 2012/2006, and leadership in organizing workshops like VisStory and tutorials on 'Looking at People.' Awards include the Killam Accelerator Fellowship, NSERC DAS award, and recognition as Area Chair for CVPR 2022, ICML 2022, and IJCAI 2022. His lab advises numerous graduate students and postdocs, with active collaborations spanning industry (e.g., Disney, Borealis) and academia (e.g., CMU, Vector Institute).

Farid BoussaidView profile

Professor

• Machine Learning
• Computer Vision
• Neuromorphic Engineering
• +5 more

Farid Boussaid is a Professor in the School of Electrical, Electronic and Computer Engineering at The University of Western Australia (UWA), affiliated with the UWA Oceans Institute. He holds an MM PhD from INSA Toulouse, France, and has held roles including Head of School (2014–2017). His research focuses on smart sensors, neuromorphic engineering, and machine learning applications in computer vision and signal processing. Education: M.S. and Ph.D. from National Institute of Applied Science (INSA), Toulouse, France (1996, 1999) Postdoctoral Fellow at Edith Cowan University (2000–2001) Australian Research Council APD Fellowship recipient (2001) Research Interests: Design of low-cost smart sensing systems, neuromorphic approaches for olfactory/visual processing, and interdisciplinary work in microelectronics, gas sensors, and camera-on-chip technologies. His research addresses bio-inspired signal processing and efficient integrated circuit design. Recent Research Trends: His publications emphasize deep learning applications in 3D vision, generative models, and medical imaging, with a focus on weakly supervised learning and multimodal data integration. Awards: 2016 Citation for Outstanding Contribution to Student Learning UWA Award for Excellence in Teaching (2014) Award for Growth in Innovation and Entrepreneurship (2021) Grants & Projects: Leads initiatives like the National Australian Cardiac CT Platform and robotics with 3D vision. Involves collaborations with Tokyo University of Science and NSF-funded projects. Labs/Teams: Active in UWA’s Oceans Institute and interdisciplinary teams advancing AI-driven sensing technologies.

Anjan DuttaView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +4 more

Dr. Anjan Dutta is a Senior Lecturer in Artificial Intelligence at the University of Surrey, UK. He holds a PhD in Computer Science from the Autonomous University of Barcelona (UAB), awarded with Excellent Cum Laude and the Extraordinary PhD Thesis Award (2013-14). His research focuses on computer vision and machine learning, particularly deep multi-modal embedding, zero-shot learning, and graph neural networks. Education: PhD in Computer Science, UAB (2014) MSc in Computer Vision & AI, UAB (2010) MCA, Maulana Abul Kalam Azad University of Technology (2009) BSc Mathematics (Honours), University of Calcutta (2006) Research Interests: Deep Learning for Vision Tasks Zero-Shot and Few-Shot Learning Graph Neural Networks Multi-modal Embedding Techniques Structured Representation Learning Recent Research Trends: His work emphasizes scalable and interpretable AI systems, with notable contributions in object counting, bias reduction in neural networks, and sketch-based retrieval. Publications span top-tier venues, reflecting interdisciplinary innovation in vision and learning. Awards & Honors: Extraordinary PhD Thesis Award (UAB, 2013-14) Excellent Cum Laude PhD Award (UAB, 2014) Labs & Affiliations: Active in the Surrey Institute for People-Centred AI (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP), contributing to cross-disciplinary AI research.

Peter JancovicView profile

Senior Lecturer

• Signal Processing
• Pattern Recognition
• Speech Processing
• +3 more

Peter Jancovic is a Senior Lecturer at the University of Birmingham's School of Electronic, Electrical and Systems Engineering. He specializes in signal processing and pattern recognition, focusing on audio, speech, biomedical, and radar signals. His research has been funded by EPSRC grants and DTC projects, totaling over £1.3 million. He teaches modules such as Speech and Audio Technology, Multimodal Interaction, and Embedded Digital Signal Processing at both undergraduate and MSc levels. Education: BSc and MSc in Information Technology, Slovak University of Technology PhD in Computer Science, Queens University Belfast Research Interests: Includes speech and speaker recognition in noisy environments, biomedical data analysis, radar target classification, and audio/music pattern processing. He has published over 40 papers and 3 book chapters in top journals and conferences. Grants & Awards: Secured major EPSRC grants for speech enhancement (£470K) and DTC funding for radar research (£900K). Active in conference organization, including Interspeech 2009 and World Congress on Engineering. Advising: Supervised numerous PhD students, including joint supervision with Prof. Cherniakov and others. Current focus on interdisciplinary projects in human-computer interaction and biomedical signal analysis.

Irene AmeriniView profile

Associate Professor

• Multimedia Forensics
• Deepfake Analysis
• Machine Learning
• +5 more

Irene Amerini is an Associate Professor at the Department of Computer, Control and Management Engineering A. Ruberti, Sapienza University of Rome. She leads the Computer Vision and Multimedia Forensics Research Team at ALCORLab. Her research focuses on multimedia forensics, deepfake analysis, machine learning, adversarial machine learning, and computer vision. She has organized workshops like WiCV at CVPR 2019 and ECCV 2020, and serves on committees including IEEE Forensics and Security Technical Committee. Education: Ph.D. in Computer Engineering, Multimedia and Telecommunication (2011), University of Florence Postdoctoral Researcher at Image Forensics and Security Lab, University of Florence (2019–2022) Visiting Research Fellowship at Charles Sturt University (2018) Research Interests: Deepfake detection through RGB-depth integration and neural network fusion Adversarial robustness in AI models Monocular depth estimation for real-time applications Forensic analysis of synthetic media Awards: Endeavour Research Fellowship (2018) Best Paper Award at eForensics 2009 Highly Cited Paper (2017–2019) for copy-move attack detection work Best Paper Award SUMAC '23 (2023) Advising & Grants: She advises on AI ethics in media forensics and collaborates on projects involving secure AI systems. Her grants include Australian Government funding through the Endeavour program and EU initiatives. Labs & Teams: Leads the ALCORLab's Computer Vision and Multimedia Forensics Research Team, focusing on AI-driven solutions for digital forensics and security.

Ronald M. AartsView profile

Professor

• Ambulatory Monitoring
• Signal Processing
• Medical Devices
• +9 more

Ronald M. Aarts is a Full Professor (part-time) at Eindhoven University of Technology's Department of Signal Processing Systems, Faculty of Electrical Engineering. He holds affiliations with the Eindhoven MedTech Innovation Center (EMTEC) and EAISI Foundational. His research focuses on ambulatory monitoring, sensor systems, and signal processing for medical applications such as sleep, cardiology, and epilepsy detection. Dr. Aarts has collaborated extensively with Philips Research since 1977, contributing to over 450 publications and 250+ patents. He received his BSc and PhD in Physics from Delft University of Technology. His academic career includes supervising 19 PhD students and co-developing technologies in audio engineering (AES Fellowship 1995), signal processing (IEEE Fellow 2007), and medical device innovation. Awards include the Philips Gold Invention Award (2012) and AES Silver Medal (2010). He advises GrAI Matter Labs and operates Aarts Consultancy, emphasizing the four P’s: People, Patents, Papers, and Products. Research interests span electromagnetism, acoustics, wearable sensors, and unobtrusive monitoring systems. Notable projects include ECG-based seizure detection, preterm infant sleep classification, and non-invasive circulatory volume assessment. His work aligns with UN Sustainable Development Goals in health and innovation. Affiliations: TU/e, Philips Research, EMTEC, EAISI Foundational Education: BSc (Electrical Engineering), PhD (Physics), Delft University of Technology Grants & Industry: Over 250 patents (Philips), Philips Fellow (2003), Philips Gold Invention Award (2012/2018) Labs/Teams: Signal Processing Systems group, collaborations with Radboud University and NXP Semiconductors

Andrea CavallaroView profile

Professor

• Machine Learning
• Computer Vision
• Audio Processing
• +4 more

Andrea Cavallaro is a Full Professor and Director of the Centre for Intelligent Sensing at Queen Mary University of London (QMUL), a Russell Group university. He also serves as Editor-in-Chief of Signal Processing: Image Communication and a Fellow of the International Association for Pattern Recognition (IAPR) and the Higher Education Academy. His roles include Director of Research for the School of Electronic Engineering and Computer Science. Education: Received a Ph.D. in Electrical Engineering from the École polytechnique fédérale de Lausanne (EPFL) in 2002. Prior roles include Research Fellow at British Telecommunications (BT) and multiple editorial roles across IEEE journals. Research Interests: Focus on machine learning for multimodal perception, computer vision, audio processing, robot perception, and privacy in multimedia systems. He leads projects like CORSMAL (Collaborative Object Recognition, Shared Manipulation, and Learning) and GraphNEx (Graph Neural Networks for Explainable AI). Notable Contributions: Over 270 journal/conference papers, one monograph on video tracking (Wiley, 2011), and three edited books. Awards include the Royal Academy of Engineering Teaching Prize (2007), IEEE AVSS Best Paper Award (2009), and Turing Fellowship (2018). Labs & Affiliations: Leads the Centre for Intelligent Sensing and contributes to the Centre for Multimodal AI. Active in IEEE technical committees and international research collaborations.

Amin BeheshtiView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +5 more

Prof. Amin Beheshti is a Full Professor of Data Science at Macquarie University and Adjunct Professor of Computer Science at UNSW Sydney. He leads the Centre for Applied Artificial Intelligence, the Data Science Lab, and the Big Data Society. His research focuses on applying AI to solve real-world challenges in healthcare, cybersecurity, and industry. Beheshti holds a PhD in Computer Science from UNSW Sydney and has secured over $40 million in research funding through 40+ projects. Education: PhD in Computer Science and Engineering (UNSW Sydney, 2013) Master's and Bachelor's in Computer Science (First Class Honours) Research Interests: Artificial Intelligence Graph Neural Networks Healthcare Analytics Cybersecurity AI Ethics & Commercialization Awards & Recognition: Australian AI Award (2024): AI Academic/Researcher of the Year Macquarie Excellence Awards (2022–2023) APA Scholarship (2009–2012) Grants & Projects: 40+ research projects funded ($40M+) Leadership in Federated Learning, Healthcare AI, and Cybersecurity initiatives Labs & Initiatives: Centre for Applied Artificial Intelligence Data Science Lab Big Data Society

• Self-Organizing AI
• Machine Learning
• Text Analytics
• +3 more

Damminda Alahakoon is Professor of Business Analytics and AI at La Trobe University, where he founded and directs the Research Centre for Data Analytics and Cognition (CDAC). With dual appointments at Luleå University (Sweden) and University of Moratuwa (Sri Lanka), he bridges international AI research. His 15+ year academic career follows 10 years in industry, including roles in finance and IT across Australia, Sri Lanka, and the Netherlands. Research Impact: Developed the Growing Self-Organizing Map (GSOM) algorithm for adaptive AI systems applied in healthcare, utilities, and smart cities. Current projects include generative AI for healthcare chatbots and quantum optimization for energy systems. Industry Collaboration: Secured $6M+ in research funding for projects like AI-driven stroke rehabilitation monitoring and mining workforce analytics. Recent industry partnerships focus on operationalizing ethical AI frameworks in Australia-India tech corridors. Teaching: Leads the Master of Business Analytics program, specializing in predictive analytics curriculum development.