Find Academic Supervisors

Marzieh AminiView profile

Associate Professor

• Computer Vision
• Sensor Fusion
• Autonomous Vehicles
• +7 more

Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.

Roberto Martinez-MaldonadoView profile

Associate Professor

• Learning Analytics
• Artificial Intelligence in Education
• Collaborative Learning
• +3 more

Roberto Martinez-Maldonado is an Associate Professor in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. He holds a PhD in Human-Computer Interaction and Educational Data Mining from the University of Sydney. His research focuses on Learning Analytics, Artificial Intelligence in Education, and Collaborative Learning, with applications in healthcare and classroom settings. Prior to Monash, he worked at the Connected Intelligence Centre (CIC) and as a lecturer at the University of Technology, Sydney. His academic journey includes a Master's in Information Technology from Universidad Tecmilenio and a Bachelor's in Computer Systems Engineering from Instituto Tecnológico de Mérida. Key research projects include developing analytics dashboards for healthcare simulations, teamwork analytics for professional education, and AI-driven tools for reflective practice. His innovations include the HuCETA framework and the Data Storytelling editor, which enhance educational data visualization and teacher-student interaction. He has received multiple awards, including the 2022 Best Student Paper and 2020 Best Paper Award. His work contributes to UN Sustainable Development Goals related to quality education. He supervises PhD students in Multimodal Teamwork and Classroom Analytics. Roberto is actively involved in academic conferences, serving on program committees for LAK and AIED. Media engagements include ABC Radio interviews discussing classroom design and AI in education.

Mehrtash Tafazzoli HarandiView profile

Associate Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +3 more

Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.

Neashan MathavanView profile

Lecturer

• Biomechanics
• Bone Biology
• Regenerative Medicine
• +3 more

Dr. Neashan Mathavan is a Lecturer in the Department of Health Sciences and Technology at ETH Zürich, affiliated with the Institut für Biomechanik . His research focuses on musculoskeletal biomechanics, aging-related bone deterioration, and spatial omics approaches to study fracture healing and mechanoregulation. He has pioneered work on mouse models of premature aging (e.g., PolgA mice) to investigate sex-specific mechanisms of bone regeneration and frailty. Key areas include spatial transcriptomics, osteocyte function, and the role of mechanical loading in musculoskeletal repair. Dr. Mathavan’s research integrates advanced imaging techniques (e.g., spatial μProBe, super-resolution spatial transcriptomics) with biomechanical testing to elucidate molecular and structural changes in aging bones. His recent studies emphasize the interplay between mechanical signals and molecular pathways in bone regeneration, particularly in contexts like osteoporosis and osteoarthritis. He has also developed novel osteochondral explant models to study cartilage-bone crosstalk in osteoarthritis. His publications span 2009–2025, with a focus on translational studies linking mechanobiology to clinical outcomes. Notable contributions include investigating the efficacy of BMP-7 and zoledronate therapies in bone regeneration, as well as the role of IL-1β in osteochondral tissues. His work has implications for personalized therapies targeting musculoskeletal aging and degenerative diseases. Dr. Mathavan supervises PhD students like Riyin Tay, who explored palliative care for advanced dementia patients. He collaborates on grants involving biomechanical modeling, spatial omics, and transgenic mouse models. His laboratory at ETH Zürich’s Institut für Biomechanik is equipped for advanced imaging, mechanical testing, and molecular biology.

Varsha SureshView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Multimodal Language Models
• +3 more

Dr. Varsha Suresh serves as a Postdoctoral Researcher at Saarland University's Computer Science and Computational Linguistics department, affiliated with Prof. Vera Demberg's research group since May 1, 2024. She also contributes to the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding" as part of project B2 focused on "Cognitive modelling of information density for discourse relations". Her research expertise spans multiple dimensions of language technology: Development of knowledge-augmented language models to enhance language understanding capabilities Creation of multimodal language models incorporating non-verbal communication cues Cognitive modeling approaches to information density in discourse relations Integration of gestures, body language, and speech features with linguistic processing Dr. Suresh's work represents an interdisciplinary fusion of computational linguistics, cognitive science, and artificial intelligence, aiming to create language models that more accurately reflect human communication patterns through multimodal data integration. Her research has significant implications for advancing human-computer interaction systems and natural language understanding technologies.

Sina SarehView profile

Professor

• Soft robotics
• Multi-modal mobility
• Manipulation and attachment
• +1 more

Sina Sareh is a robotics researcher at the Royal College of Art (RCA), where he leads the RCA Robotics Laboratory within the School of Design. He has established himself as an expert in soft robotics and multimodal sensing, developing innovative solutions for human safety and access problems in industrial operations. Dr. Sareh's educational background includes: PhD in Robotics from the University of Bristol, where he worked on monolithic design of flexible actuators for operation in confined liquid environments MSc in Control Systems from the University of Sheffield BSc in Electrical Engineering from Amirkabir University of Technology, Tehran Dr. Sareh's research focuses on soft robotics, multi-modal mobility, manipulation and attachment, and multimodal sensing. His work bridges the gap between robotics engineering and practical applications, particularly in medical and industrial settings. He has developed novel approaches to robotic attachment inspired by octopus biology, created haptic interfaces that mimic the feeling of touching human internal organs, and designed soft robotic technologies to help articulate pain symptoms. His research consistently demonstrates innovation in creating adaptable robotic systems that can operate effectively in complex, unstructured environments where traditional rigid robots face limitations. His publication record demonstrates a strong trajectory in robotics research, with emphasis on soft robotics, medical applications, and novel sensing techniques. The research shows progression from fundamental soft actuator design to practical applications in surgery, industrial operations, and human-robot interaction, with a consistent focus on solving real-world problems through biologically inspired approaches. Dr. Sareh has successfully secured multiple research grants, including EPSRC funding for 'Getting a Grip' and 'Multi-vendor Interoperability in Robotics,' as well as InnoHK funding for 'Intelligent Medicine Warehousing.' He has also served as an impact assessor for the Research Excellence Framework (REF) 2021 in Engineering and is a member of the editorial board at IET Cyber-physical Systems and Robotics Journal. Currently, Dr. Sareh advises research students including Filippo Sanzeni, and maintains active collaborations with industry and academic partners through the RCA Robotics Laboratory, which serves as a hub for interdisciplinary robotics research at the intersection of design, engineering, and human-centered applications. His work on projects like 'Topographies of Pain' and 'Reminisys' demonstrates a commitment to applying robotics technology to improve healthcare outcomes and quality of life.

Nabil BassimView profile

Associate Professor

• Materials Science
• Electron Microscopy
• Nanomaterials
• +3 more

Nabil Bassim is an Associate Professor in the Department of Materials Science and Engineering at McMaster University and serves as Scientific Director of the Canadian Centre for Electron Microscopy (CCEM). His research focuses on advanced electron microscopy techniques, ion microscopy, nanofabrication, and beam-sample interactions, applied to nanomaterials, 2D materials, and structural materials like concrete and alloys. He holds a B.S. in Mechanical Engineering from the University of South Florida, and M.Sc. and Ph.D. degrees from the University of Florida. Research interests include: Development of novel electron/ion microscopy techniques Nanomaterial synthesis and characterization Beam-induced damage and doping mechanisms Structural materials analysis Machine learning optimization for microscale processes Recent publications demonstrate strong focus on semiconductor characterization, nanomaterials synthesis, and advanced microscopy techniques. Article trends highlight innovative approaches to nanoscale analysis, materials for energy applications, and correlative microscopy methods. As Faculty Lead for McMaster Engineering's Aerospace and Defense Initiative, Dr. Bassim coordinates interdisciplinary research. He co-founded the FIB-SEM User Meeting and teaches graduate courses in electron/ion microscopy characterization techniques.

Chung-Hsing YehView profile

Associate Professor

• Multicriteria Decision Analysis
• Applied Artificial Intelligence
• Intelligent Decision Support Systems
• +6 more

Chung-Hsing Yeh is an Associate Professor at Monash University's Faculty of Information Technology, Department of Data Science & AI. He holds a visiting professorship at National Cheng Kung University, Taiwan, and has extensive experience in academic roles including Chief Examiner and Lecturer for numerous IT and business-related courses. His research focuses on multicriteria decision analysis, applied artificial intelligence, fuzzy logic, neural networks, and sustainable operations management. He has led collaborative projects on e-waste recycling, supply chain optimization, and public health policy, funded by organizations like the Ministry of Science and Technology (Taiwan) and the Australian Research Council. Education: PhD in Operations Research/Information Systems, Monash University (1988) MSc in Management Science, National Cheng Kung University (1982) BSc (Engineering) in Industrial Design, National Cheng Kung University (1977) Research Interests: His work spans decision support systems, optimization modeling, transport research, and recycling operations. Notable contributions include algorithms for production scheduling, AI-driven solutions for healthcare, and sustainable e-waste management strategies. Awards: Listed in Marquis Who's Who in the World Listed in Who's Who in Finance and Industry Listed in Who's Who in Science and Engineering Grants & Projects: Led 6 major projects, including 'Maximizing E-waste Recycling Profitability' (2019–2020) and 'Smoke-Free Policy Effectiveness' (2007–2010). Active in grant review roles for ARC and the Netherlands Organisation for Scientific Research. Teaching: Overseeing courses such as Fundamentals of Artificial Intelligence, Business Intelligence Modelling, and Management Information Systems.

Tao HuanView profile

Associate Professor

• Metabolomics
• Exposomics
• Analytical Chemistry
• +5 more

Tao Huan is an Associate Professor in the Department of Chemistry , University of British Columbia , and holds the Canada Research Chair in Metabolomics and Exposomics . His research focuses on advancing mass spectrometry (MS) for metabolomics , integrating bioinformatics to address challenges in cancer metabolism , disease biomarker discovery , and exposome characterization . Education: Ph.D. in Analytical Chemistry (University of Alberta, 2015), Postdoctoral Research Associate (The Scripps Research Institute, 2015-2018). Dr. Huan’s work emphasizes systems biology , combining metabolomics with genomics and proteomics to decode complex biological mechanisms. He has pioneered methods for chemical isotope labeling and multimodal data integration , enhancing metabolite identification and pathway analysis. His recent publications (2020-2019) highlight innovations in LC-MS/MS workflows , freeze-thaw sample stability , and applications in colorectal cancer and Alzheimer’s disease . Dr. Huan’s lab actively recruits students and postdocs in analytical chemistry, metabolomics, and bioinformatics. Awards: Fred Beamish Award (2025), President’s Award, Metabolomics Society (2025), UBC Killam Faculty Research Award (2024), Michael Smith Health Research BC Scholar Award (2023). He serves as a faculty member in UBC’s Graduate Program in Bioinformatics , Genome Science and Technology , and the Cluster for Microplastics, Health and Environment . Lab alumni include Ph.D. and M.Sc. students now in academia and industry.

Véronique HosteView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +7 more

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

Jiang HongboView profile

Professor

• Distributed systems
• Mobile computing
• Smart sensing
• +5 more

Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.

Patrick WuView profile

Professor

• AI/ML
• Natural Language Processing
• Computational Social Science
• +3 more

Patrick Wu is a Professor in the Department of Computer Science at American University, with additional affiliations as Faculty Fellow at the Center for Data Science and Faculty Affiliate at the Center for Security, Innovation, and New Technology. He holds a PhD in Political Science and Scientific Computing from the University of Michigan, an MA in Statistics from Michigan, and a BA in Political Science and Statistics from the University of Chicago. His research develops AI/ML and natural language processing approaches for computational social science, focusing on: Political elite and non-elite ideology measurement Affective polarization on social media platforms Detection of hateful/abusive speech and memes Application of large language models to political science research Recent work explores innovative methods for political attitude measurement using LLMs, in-context learning techniques for social media analysis, and frameworks for multimodal representation learning. His publications demonstrate consistent innovation in applying NLP and machine learning to political discourse analysis, with emerging focus on generative AI's impact on political science education and methodology. Wu teaches courses including Object-Oriented Programming and topics in Natural Language Processing/Text as Data.

Ali AhrariView profile

Lecturer

• Evolutionary Algorithms
• Multimodal Optimization
• Multi-objective Optimization
• +5 more

Dr. Ali Ahrari is a Lecturer at the School of Systems and Computing, University of New South Wales, Canberra. He holds a Ph.D. in Mechanical Engineering from Michigan State University (2016) and has extensive experience in research and academia, including roles as a Research Fellow and Associate at UNSW-Canberra and the University of Sydney. His research focuses on evolutionary algorithms, multimodal and multi-objective optimization, and surrogate-assisted optimization. Ahrari is a recipient of prestigious awards, including the ARC-DECRA 2023 and multiple international competition wins in optimization (e.g., CEC/GECCO competitions). He leads research groups like the Canberra Evolutionary Optimization (EvOpt) and serves on editorial boards, including Applied Soft Computing. Education: Ph.D. (2016, Michigan State University), M.Sc. and B.Sc. (University of Tehran). Awards: ARC-DECRA, ISCSO, and GECCO/CEC competition wins. Grants: ARC DECRA (2023), NCI Adapter Schemes, UNSW HPC allocations. Supervision: Currently advising 1 PhD student at SEIT, UNSW-Canberra. Engagements: Chair of IEEE Task Force on Multi-modal Optimization, organizer of optimization competitions (GECCO'2024, CEC'2022). His research emphasizes computational optimization, evolutionary computation, and swarm intelligence, with applications in engineering design and dynamic environments. He actively contributes to academic communities through editorial roles and conference organization.

• Medical Imaging
• Machine Learning
• Radiology
• +3 more

Sumit Chopra is an Associate Professor at the Grossman School of Medicine , affiliated with the Department of Radiology at New York University. His work focuses on integrating machine learning and artificial intelligence with medical imaging to enhance diagnostic accuracy and clinical decision-making. Research interests include: Deep learning applications in prostate cancer imaging and MRI reconstruction Development of open-access medical imaging datasets (e.g., FastMRI Prostate) Improving biopsy decision strategies via representation learning AI-driven Alzheimer's disease risk prediction using electronic health records Advancements in radiologic assessment for pancreatic cystic lesions Email: Sumit.Chopra@nyulangone.org

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +6 more

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology