Find Academic Supervisors

• Experimental astroparticle physics
• Dark matter detection
• WIMP detection
• +4 more

Matthew Szydagis is an Associate Professor in the Department of Physics at the University at Albany, State University of New York, where he conducts cutting-edge research in experimental astroparticle physics with a focus on dark matter detection. Education: PhD, University of Chicago, 2010 Postdoctoral Associate, University of California Davis, 2010-2014 Dr. Szydagis leads research efforts centered around the LZ (LUX-ZEPLIN) Dark Matter Experiment, the world's largest direct dark matter search project operating at the Sanford Underground Research Facility. His expertise lies in the physics of two-phase Xenon time-projection chambers and the development of sophisticated Monte Carlo simulation techniques to understand detector responses. In 2011, he created the NEST (Noble Element Simulation Technique) software package, which has become an essential tool for the broader scientific community working with noble element detectors. His research spans multiple disciplines including particle physics, astrophysics, and computational physics, with applications extending beyond dark matter research into neutrino physics and medical physics. Dr. Szydagis is an active member of the international LZ collaboration and leads the Dark Matter Research Group at the University at Albany. His work contributes significantly to establishing the world's most sensitive limits on dark matter interactions across a wide range of particle masses.

Prasad TadepalliView profile

Professor

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

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

Mujdat CetinView profile

Professor

• Computational Imaging
• Signal Processing
• Machine Learning
• +5 more

Mujdat Cetin is a Professor of Electrical and Computer Engineering at the University of Rochester, serving as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science. Previously, he held faculty positions at Sabanci University, Istanbul, Turkey, and MIT. He earned his BS from Bogazici University (1993), MS from the University of Salford (1995), and PhD from Boston University (2001). His research focuses on computational imaging, signal processing, and machine learning applications in biomedical imaging, radar systems, and neuroscience. Key areas include uncertainty quantification in imaging, probabilistic methods for image analysis, and brain-computer interfaces. His work bridges electrical engineering, computer science, and biomedical applications. Notable awards include the IEEE Signal Processing Society Best Paper Award and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is an IEEE Fellow and has held editorial roles for journals like IEEE Transactions on Computational Imaging and SIAM Journal on Imaging Sciences. His contributions span conference leadership, including Technical Program Co-chair roles for major signal processing and fusion events. Cetin’s research group has advanced computational sensing, medical imaging algorithms, and EEG-based emotion/motor imagery classification. Ongoing work emphasizes integrating physics-based models with deep learning for robust imaging and signal analysis.

• Microwave Imaging
• Dielectric Properties
• Antenna Engineering
• +5 more

Mehmet Çayören is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. His research focuses on applying microwave imaging techniques to medical diagnostics, particularly for early detection of breast cancer. He leads the development of the SAFE (Screening and Early Detection) microwave imaging system, which has shown promising results in clinical investigations. His work bridges electrical engineering, biomedical applications, and machine learning for improved cancer screening. Dr. Çayören's research interests span multiple domains within electromagnetic applications for medical imaging: Microwave imaging systems for breast cancer detection and screening Determination of dielectric properties of biological tissues Development of open-ended coaxial probe techniques for material characterization Application of machine learning algorithms (XGBoost, SVM) to enhance medical imaging Monitoring of intracerebral hemorrhage using microwave imaging Development of tissue-mimicking phantoms for medical device validation His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with microwave imaging systems to improve diagnostic accuracy. The SAFE platform represents a significant advancement in non-invasive breast cancer screening, particularly for dense breast tissue where traditional mammography has limitations. His work also extends to neurological applications, with research on microwave-based monitoring of brain hemorrhages. Scientific awards received by Dr. Çayören include: Teknoloji Ödülü (Technology Award) in 2014 Dr. Çayören has supervised 24 students and leads multiple research projects funded by various sources including TUBITAK. His current projects focus on microwave imaging systems for breast cancer screening, monitoring of intracerebral hemorrhage, and hardware design for microwave imaging systems. He collaborates extensively with medical professionals to validate his imaging systems in clinical settings. Dr. Çayören leads a research group focused on microwave imaging applications in medicine. His team develops both hardware systems (like the SAFE platform) and advanced signal processing algorithms to improve medical diagnostics. The group maintains close collaborations with hospitals for clinical validation of their imaging systems.

• Cryo-electron microscopy/tomography
• Cell motility
• Cellular energy metabolism
• +3 more

Jack Zhang is an Assistant Professor in the Department of Molecular Biophysics and Biochemistry at Yale School of Medicine. His research focuses on developing advanced cryo-electron microscopy/tomography (cryo-EM/ET) methods to investigate dynamic molecular machines in cellular contexts, particularly mechanisms of cell motility and energy metabolism. PhD in Biophysics from Institute of Biophysics (CAS) Postdoctoral work at MRC Laboratory of Molecular Biology Joined Yale faculty in 2019 Research interests include: Mechanistic analysis of dynein motor proteins Structural studies of mitochondrial respiratory supercomplexes Cytoskeletal repair mechanisms via Abl2-tubulin interactions Environmental signal response in mastigoneme assembly Allostery in microtubule transport activation Recent publications demonstrate expertise in: High-resolution in situ structural biology Tomographic analysis of cellular machines Mechanochemical cycle mapping Male infertility structural pathology Contact: jack.zhang@yale.edu

• Haptics
• Human-Computer Interaction
• Acoustic Engineering
• +3 more

Yasutoshi Makino is a researcher specializing in ultrasound-based haptics, tactile feedback systems, and human-computer interaction. He has collaborated extensively with Hiroyuki Shinoda and other colleagues, focusing on mid-air haptic displays, noncontact object manipulation, and sensory reproduction. Research Interests Makino's work explores the intersection of acoustics, neuroscience, and engineering to create immersive tactile experiences without physical contact. His innovations include ultrasound-driven actuation mechanisms, thermal sensation rendering, and real-time human motion prediction for robotic systems. Recent Publications The 15 most recent articles highlight advancements in airborne ultrasound tactile displays, texture synthesis via GANs, and applications in guide dog training analysis, virtual reality, and interactive robotics. Key themes include dynamic pressure control , multi-stimulus integration , and low-latency systems . Collaborations Co-authored with Masahiro Fujiwara (43 papers) Collaborated with Hiroyuki Shinoda (145 papers) Worked with Shun Suzuki, Takaaki Kamigaki, and Ryoya Onishi on thermal and mechanical haptic feedback systems.

Davide GuccioneView profile

Researcher

• Rock Mechanics
• Geomechanics
• Photogrammetry
• +2 more

Davide Guccione is an ARC Industry Fellow at the University of Newcastle within the College of Engineering, Science and Environment . His research focuses on rock mechanics , rockfall analysis , and photogrammetry , with significant contributions to understanding rock fragmentation dynamics and improving monitoring systems. Education : PhD in Civil Engineering (University of Newcastle, 2021), Master of Civil Engineering (University of Parma), Bachelor of Civil Engineering (University of Parma), and Diploma in Mining and Geoenvironmental Qualified Industrial Technician. Research Interests span experimental rock mechanics, rockfall hazard assessment, and photogrammetry. His work includes developing a novel VoxFall algorithm for volumetric rockfall detection and a stochastic fragmentation model for rockfall simulations. Current projects involve coastal cliff monitoring and deep learning-based photogrammetric calibration. Scientific Awards include: D.H. Trollope Medal (2023) Early Career Researcher Excellence Award - Highly Commended (2023) NSW Young Geomechanical Professionals Award 1st Runner-Up (2024) Australian Geomechanics Society Postgraduate Research Prize (2019) Grants and Funding highlight a $496,614 ARC Early Career Industry Fellowship and a $300,000 Newcastle City Council grant for coastal cliff monitoring. He supervises three PhD students and collaborates with institutions like Rocscience and the European Commission.

Manuel Sierra CastañerView profile

Professor

• Planar Antennas
• Antenna Measurement Systems
• Telecommunications Engineering
• +3 more

Prof. Manuel Sierra Castañer is a full professor at the Technical University of Madrid (UPM), serving as the Dean of the School of Telecommunications Engineering since May 2021. He holds a Telecommunication Engineering degree (1994) and a PhD (2000), both from UPM. His career includes roles as assistant and associate professor, research positions at Tokyo Tech and EPFL, and leadership in antenna measurement systems and international projects. He is a Senior Member of IEEE and Fellow of AMTA, with research focused on planar antennas and measurement systems. Notable achievements include the IEEE APS 2007 Schelkunoff Prize and leadership roles in EurAAP (Vice-Chair, General Chair for EuCAP 2022). He directed 7 PhD theses and led projects like FUTURE-RADIO and TERASENSE. His administrative roles include Director of International Cooperation at UPM (2010–2020) and leadership in the European School of Antennas. Key contributions span antenna design for 5G, satellite communications, and energy harvesting. He has authored 40+ journal papers and pioneered phaseless near-field techniques, robotic antenna measurement systems, and 3D-printed antenna components. His work bridges academic research with industry applications, emphasizing sustainable engineering education and global collaboration.

Shuvra S. BhattacharyyaView profile

Professor

• Signal Processing
• Embedded Systems
• Biomedical Engineering
• +3 more

Shuvra S. Bhattacharyya is a Professor at the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering, with a joint appointment in UMIACS. He is also a part-time visiting professor at Tampere University (Finland) and INSA Rennes (France). His research focuses on signal processing systems, embedded systems, biomedical engineering, and machine learning. He has authored 7 books and over 300 papers, and directs the Maryland DSPCAD Research Group. He holds an NSF Career Award and is an IEEE Fellow. His honors include the Fulbright Senior Specialist and FiDiPro Professorship. He advises graduate students and leads projects in neural decoding, cyber-physical systems, and criminal justice AI. Education: B.S., University of Wisconsin-Madison Ph.D., University of California, Berkeley Research Interests: Signal processing architectures, biomedical circuits, embedded software, hardware/software co-design, and AI for healthcare and criminal justice. His work emphasizes real-time systems, energy efficiency, and interdisciplinary applications. Grants & Awards: NIH Grant for real-time neural decoding (2023) IEEE Fellow (2011) Jimmy Lin Entrepreneurship Award (2012) Labs & Teams: Maryland DSPCAD Research Group, collaborating on dynamic data-driven systems, neural interface technologies, and AI ethics in criminal justice. His work integrates academic and industrial partnerships, including roles at Hitachi America and Kuck & Associates.

Manuel Gonzalez RiveroView profile

Research Fellow

• Marine Ecology
• Climate Change
• Coral Reef Monitoring
• +6 more

Dr. Manuel Gonzalez Rivero is a Research Fellow and Team Leader of Reef Process Dynamics at the Australian Institute of Marine Science (AIMS). His work focuses on multidisciplinary approaches to understand coral reef ecosystem dynamics, combining ecological theory, mathematical modeling, and advanced technologies like AI and 3D mapping. He leads initiatives integrating global coral reef monitoring with climate change impact assessments. Education: PhD in Biological Sciences (University of Exeter, UK, 2012), BSc in Biological Sciences (Universidad Central de Venezuela, 2005). Research Interests: Spatial and disturbance ecology of coral reefs Climate change effects on reef resilience Automated coral reef monitoring using AI and autonomous systems Long-term trajectories of reef communities Technological advancements in habitat mapping and 3D modeling Publications highlight contributions to coral reef recovery dynamics, AI-driven monitoring, and global bleaching impacts. His work emphasizes scalable solutions for reef conservation amid climate pressures. Grants and Labs: Active in AIMS-led projects, including XL Catlin Seaview Survey and Reef 2050 monitoring. Collaborates with institutions like the University of Queensland and UNESCO.

Bradley J. EricksonView profile

Professor

• Quantitative Imaging
• Computer-Aided Diagnosis
• Deep Learning
• +3 more

Bradley J. Erickson, M.D., Ph.D. Bradley J. Erickson is a Professor of Radiology and Consultant at the Mayo Clinic in Rochester, Minnesota. He holds a joint appointment in the Division of Biomedical Statistics and Informatics. His research focuses on quantitative imaging, computer-aided diagnosis, and deep learning applications in medical imaging. He has pioneered systems for team science integration across imaging, genomics, and clinical data. Education MD/PhD in Biophysics/Biomedical Engineering, Mayo Graduate School Residency in Diagnostic Radiology, Mayo Clinic Research Interests Erickson's work emphasizes extracting diagnostic and prognostic information from medical images using machine learning and AI. Key areas include: Polycystic Kidney Disease (PKD) progression analysis via imaging Development of explainable AI models for cancer diagnosis (e.g., hepatocellular carcinoma) Privacy-preserving LLMs for echocardiography reports Awards & Recognition Team Science Award, Mayo Clinic (2020) Samuel J. Dwyer III, Ph.D., FSIIM Memorial Lectureship (2013) Chair, American Board of Imaging Informatics (2013–2018) Grants & Projects Principal Investigator for NIH-funded projects on synthetic medical images in AI fairness (2024–2025) Mayo Translational PKD Center (2010–2020) Objective decision support for clinical trials (2012–2015) Labs & Affiliations He leads the Imaging and Analysis Core within the Mayo Clinic Pirnie Translational PKD Center. Collaborates with teams in computational biology, radiology informatics, and clinical trials.

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

Morten BrunView profile

Associate Professor

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +3 more

Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.

Sharib AliView profile

Assistant Professor

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +12 more

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

Christophe GrovaView profile

Professor

• Biomedical Engineering
• Neuroscience
• Epilepsy Research
• +5 more

Christophe Grova , Professor in the Department of Physics at Concordia University and scientific lead of the PERFORM physiology platform , is a multidisciplinary researcher at the intersection of Biomedical Engineering , Neuroscience , and Medical Physics . He directs the Multimodal Functional Imaging Laboratory , focusing on advanced neuroimaging techniques like EEG/MEG , fMRI , and fNIRS to study brain mechanisms in epilepsy and sleep . His work emphasizes multimodal data fusion to uncover neurovascular coupling and functional connectivity patterns, with software developments such as MEM and NIRSTORM enhancing analysis precision. Key Research Areas : Epilepsy, Sleep Studies, Neuroimaging, Computational Neuroscience, Multimodal Data Fusion Affiliations : Full Professor at Concordia University (Physics), Adjunct Professor at McGill University (Biomedical Engineering, Neurology and Neurosurgery). Scientific Awards : Not explicitly listed in the provided text. Software Contributions : Developed MEM (EEG/MEG source localization) and NIRSTORM (fNIRS analysis) for open-source platforms like Brainstorm .