جستجوی استاد راهنمای دانشگاهی

• Marine Sciences
• Coral Reef Conservation
• Biogeochemistry
• +۴ مورد دیگر

Liza M. Roger is an Assistant Professor in the School of Molecular Sciences at Arizona State University (ASU) and a Senior Global Futures Scientist at the Global Futures Scientists and Scholars Program. She is affiliated with the School of Ocean Futures and works at the Walton Center for Planetary Health. Education: Ph.D. in Marine Biology and Geochemistry (University of Western Australia, 2017) B.Sc. Hon. in Marine Biology and Natural Resources Management (University of Western Australia, 2011) Associate’s Degree (Université du Littoral Côte d’Opale, France, 2006) Her research explores how environmental change affects marine organisms in symbiotic relationships with microscopic algae, such as corals, mollusks, anemones, and jellyfish. She pioneers coral in vitro methodologies to advance understanding of symbiosis, biomineralization, and stress adaptation. Recent publications highlight her work on nanotechnology for coral reef conservation, thermal stress mitigation using engineered nanoceria, and interdisciplinary collaborations merging art with coral research. Her studies also address trace metal roles in coral nutrition, insulin signaling pathways, and innovative imaging techniques to monitor coral health. Scientific Awards: NSF’s 2021 Coral Bleaching Research Coordination Network Early Career Training Program Award VCU’s 2021 Postdoctoral Independent Research Award Liza’s multidisciplinary approach integrates expertise from oceanography, biochemistry, nanoscience, and sustainability. She previously worked at the Australian Institute of Marine Science and has field experience as a cetacean naturalist in Iceland and a scuba diving instructor in the Mediterranean Sea and Southeast Asia.

Pierre F Baldiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۰ مورد دیگر

Pierre Baldi is a Distinguished Professor at the University of California, Irvine (UCI), holding appointments in the School of Information and Computer Sciences (ICS), the Institute for Genomics and Bioinformatics (IGB), and the Center for Machine Learning and Intelligent Systems (CML). His research focuses on AI, machine learning, and their applications in natural sciences, including physics, chemistry, and biology. He leads projects on neural networks, probabilistic modeling, and bioinformatics. Key roles include Director of the IGB and AI in Science Institute. Awards include the Dennis Gabor Award and Fellowships from AAAI, AAAS, and IEEE. His work spans interdisciplinary collaborations, such as the DUNE neutrino experiment and AI-driven drug discovery. Teaches courses like Probabilistic Modeling of Biological Data and Neural Networks. Active in conferences, including workshops on deep learning and its implications in science.

Brian Kulisمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Large-scale Data Analysis
• +۵ مورد دیگر

Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.

• Computer Vision
• Image Processing
• Deep Learning
• +۳ مورد دیگر

Radu Timofte is an academic researcher specializing in computer vision and image processing. He completed his PhD in 2013 at Katholieke Universiteit Leuven, Belgium, with a thesis on sparse and collaborative representations for computer vision. His work focuses on advancing techniques such as image super-resolution, denoising, object detection, and deep learning-based image restoration. He has collaborated extensively with institutions like ETH Zurich and co-authored seminal papers in top-tier journals and conferences. His research bridges theoretical advancements with practical applications in areas like medical imaging, aerial scene analysis, and real-time visual tracking. Timofte’s contributions include developing efficient deep learning architectures for tasks like lightweight object detection (e.g., CH-YOLO-Lite), diffusion models for image-to-image translation (DiffI2I), and calibration-free raw image denoising. He has also contributed to the development of video restoration transformers (VRT) and frameworks for unsupervised real-time video enhancement. His work often emphasizes practicality and efficiency, addressing challenges such as small object detection in aerial imagery and underwater image super-resolution. Timofte’s collaborations span academia and industry, with notable co-authors including Luc Van Gool (ETH Zurich) and Kai Zhang (Nanjing University of Science and Technology). His research has been published in venues like IEEE Transactions on Pattern Analysis and Machine Intelligence, CVPR, ECCV, and the International Journal of Computer Vision.

• Neuroscience
• Psychiatry
• Neurology
• +۵ مورد دیگر

Dr. Merve Fritsch is a Specialist in Psychiatry and Psychotherapy and a Senior Physician in ward 152A at Charité - Universitätsmedizin Berlin. Her affiliation is with the Department of Psychiatry and Neurosciences, part of the Neurology, Neurosurgery and Psychiatry division at Campus Charité Mitte. Dr. Fritsch's research focuses on neurological and psychiatric conditions, particularly thalamic aphasia, fronto-thalamic networks, and the role of the inferior frontal cortex in conscious perception. Her work integrates clinical neurology with cognitive neuroscience, addressing topics such as stroke recovery, perceptual decision-making, and NMDA receptor dysfunction. Her studies investigate ischemic lesions' effects on visual and language functions, stroke outcomes, and the mechanisms underlying consciousness. She employs advanced techniques like TMS-EEG to explore neural connectivity in conditions like autism spectrum disorder. Dr. Fritsch’s research also addresses clinical applications, such as cerebral embolism during cardiac interventions and the localization of aphasia in stroke patients. While no formal awards are listed, her contributions span peer-reviewed publications in high-impact journals, reflecting her expertise in neurology, psychiatry, and translational neuroscience. She actively engages in clinical practice and academic research, bridging theoretical insights with patient care.

Ajita Rattaniمشاهده پروفایل

استادیار

• Biometrics
• Deepfake Detection
• Facial Recognition
• +۶ مورد دیگر

Ajita Rattani is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, affiliated with Discovery Park. Her research focuses on biometrics, AI fairness, deepfake detection, and machine learning applications in health and security. She holds a Ph.D. in Computer Science and Engineering, with expertise in facial recognition, ocular biometrics, and multimodal authentication systems. Research Interests: Her work addresses algorithmic fairness in facial attribute classification, robustness of biometric systems against adversarial attacks, and developing lightweight models for on-device authentication. She also explores applications of machine learning in health informatics, such as BMI prediction from facial images and analyzing social determinants of health. Publications Trends: Recent work emphasizes bias mitigation in AI systems (e.g., gender/racial fairness), deepfake detection through fusion of audio-visual cues, and advancing ocular biometric recognition under challenging conditions. Notable contributions include frameworks like CodeIT for data-efficient deepfake detection and PatchBMI-Net for lightweight BMI prediction. Advising & Grants: Leads research projects funded by NSF SaTC grants (e.g., probing fairness in ocular biometrics). Active in organizing competitions like VISOB 2.0 for mobile ocular biometrics evaluation. Her lab develops practical solutions for real-world challenges in biometrics and AI ethics. Labs/Teams: Involved in interdisciplinary teams addressing transdisciplinary collaboration challenges and applying AI to disaster detection (wildfires, droughts) using satellite and sensor data fusion.

Kate Fuمشاهده پروفایل

دانشیار

• Design Innovation
• Creativity
• Design Theory and Methodology
• +۹ مورد دیگر

Dr. Kate Fu is the Jay and Cynthia Ihlenfeld Associate Professor of Mechanical Engineering at the University of Wisconsin-Madison. She previously held academic positions at Georgia Institute of Technology (2014-2021) and completed postdoctoral fellowships at MIT and Singapore University of Technology and Design. Her education includes a Ph.D. (2012), M.S. (2009) from Carnegie Mellon University, and B.S. (2007) from Brown University. Her research focuses on engineering design cognition, computational design tools, and design innovation. Key areas include creativity enhancement, design-by-analogy methodologies, and integrating artificial intelligence into design processes. She has pioneered work on gender dynamics in engineering teams and equity in education. Notable awards include the NSF CAREER Award (2019), ASME Young Investigator Award (2020), and SREB Mentor of the Year (2022). She actively contributes to engineering education through courses like Geometric Modeling for Design, Experiential Design Projects, and graduate research supervision. Her work bridges disciplinary boundaries with projects on additive manufacturing, design justice frameworks, and social impacts of engineering solutions. Current research explores equity in makerspaces, energy justice metrics, and cognitive processes in design teams.

Jessica J. Fridrichمشاهده پروفایل

استاد

• Steganography
• Digital Forensics
• Signal Processing
• +۴ مورد دیگر

Jessica J. Fridrich is a Distinguished Professor in the Department of Electrical and Computer Engineering at Binghamton University, part of the State University of New York (SUNY) system. She is affiliated with the T. J. Watson School of Applied Science and Engineering. Her research focuses on steganography, steganalysis, digital forensics, and machine learning, with notable contributions to secure data hiding and patented camera fingerprinting techniques approved for legal evidence. Education: PhD in Electrical and Computer Engineering from Binghamton University Her research interests include steganography and steganalysis of digital images, digital forensics for linking photos to cameras via sensor fingerprints, signal estimation and detection, and applications of machine learning. Earlier work explored chaotic nonlinear dynamical systems and encryption. Her methods have led to over 150 refereed publications and seven successfully commercialized patents. Her articles emphasize advancements in batch steganography, JPEG compatibility, and adaptive embedding strategies. Recent work leverages machine learning for steganalysis and explores security trade-offs in high-dimensional feature spaces. 2006-2007 Chancellor's Award for Excellence in Scholarship and Creative Activities 2002 Chancellor's Award for Outstanding Inventor Narrative on advising and grants: She mentors graduate students and leads projects funded by AFOSR, NSF, and AFRL. Her research addresses challenges in data hiding security, forensic analysis, and optimizing steganographic algorithms. The Digital Data Embedding Lab, which she directs, focuses on algorithmic innovation and empirical validation in steganography and forensics. Labs/Teams: Digital Data Embedding Lab

Ferreira Joao Carlosمشاهده پروفایل

استاد

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +۶ مورد دیگر

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

Bo Liمشاهده پروفایل

استاد

• Neuroscience
• Neural circuits
• Synaptic plasticity
• +۱۲ مورد دیگر

Bo Li is the Robert Lourie Professor of Neuroscience at Cold Spring Harbor Laboratory (CSHL) in the School of Biological Sciences. His research focuses on the neural circuits underlying cognitive function and dysfunction related to anxiety, depression, schizophrenia, and autism, with particular emphasis on synaptic mechanisms and rodent behavioral models. He earned his Ph.D. in Neuroscience from the University of British Columbia in 2003. Education: Ph.D., Neuroscience, University of British Columbia (2003) M.Sc., Psychology, Chinese Academy of Sciences (1997) B.Sc., Medicine, Jining Medical College (1992) Li's lab integrates in vitro and in vivo electrophysiology, imaging, molecular and genetic techniques, optogenetics, and chemogenetics to probe fear and reward circuits in rodent brains. His work examines how these circuits contribute to adaptive and maladaptive behaviors, with significant implications for understanding mental disorders. Recent publications highlight his exploration of the vitamin B6 pathway in cancer, opioid neuropeptide dynamics in motivation, and area postrema neurons in brain-body interactions. His research also addresses salience assignment through striatal-amygdala circuits and dietary choice regulation via neurotensin neurons in the extended amygdala. Scientific awards include: HFSP Research Grant awards 2015 NARSAD Independent Investigator grant WSBS Teaching Award 2015 Students in his lab include Sara Boyle, Mingzhe Liu, and Danielle van de Lisdonk. His work has been supported by NIH BRAIN Initiative grants and involves collaborations across multiple CSHL laboratories.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Edward Delp is the Charles William Harrison Distinguished Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. He holds affiliations with both the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering. His research spans computer vision, medical imaging, and data forensics with a focus on synthetic media detection, deep learning applications, and healthcare technologies. Education: Not explicitly listed in the provided text. His work includes developing algorithms for speech forensics, microscopy image analysis, and food/nutrition assessment systems. He leads projects on synthetic speech detection, medical image segmentation, and automated crop disease measurement using RGB imaging. Delp collaborates across disciplines, integrating machine learning with healthcare and agricultural challenges. Recent work emphasizes ethical AI through fairness in synthetic media detection and explainable artifacts in biomedical imaging. He contributes to large-scale datasets like MetaFood3D and 3D nuclear segmentation frameworks for microscopy analysis. His grants and advising focus on interdisciplinary applications, though specific grant details are not provided. Delp is affiliated with the Purdue School of Biomedical Engineering and maintains active collaborations in medical imaging, computer vision, and aerospace anomaly detection.

Sabyasachi Chatterjeeمشاهده پروفایل

دانشیار

• Nonparametric Signal Estimation
• Shape Constrained Estimation
• Statistical Information Theory
• +۵ مورد دیگر

Sabyasachi Chatterjee is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. He joined UIUC in 2017 after serving as a Kruskal Instructor at the University of Chicago. He earned his PhD in Statistics from Yale University (2014), advised by Andrew Barron. His research focuses on nonparametric signal estimation, shape-constrained estimation (monotonicity, convexity, unimodality), statistical information theory, and resampling methods like cross-validation. He also explores statistical learning theory, online learning, and applied probability. His recent work includes advancements in quantile regression via dyadic CART, adaptive estimation of piecewise polynomials, and spatially adaptive prediction algorithms. Key contributions involve risk bounds for trend filtering and cross-validation frameworks for signal denoising. His research is supported by NSF Grant DMS-1916375 on nonparametric estimation under shape/norm constraints. Chatterjee collaborates on grants and has advised multiple students (though specific names are not listed in available texts). His lab’s work bridges theoretical statistics with practical applications in data science and signal processing.

• Artificial Intelligence
• Smart Cities
• Sensor Technology
• +۵ مورد دیگر

Cormac Fay is a Research Fellow in Artificial Intelligence for Smart Cities at the School of Computing and Information Technology (SCIT), University of Wollongong, within the Faculty of Engineering and Information Sciences. His roles include affiliations with the SMART Infrastructure Facility and the ARC Centre of Excellence for Electromaterials Science. Previously, he held positions at Dublin City University, including post-doctoral roles in sensor research and data analytics. He holds a PhD in Engineering from Dublin City University (2013), an M.Eng. in Telecommunications Engineering (2007), and a B.Eng. in Mechatronic Engineering (2005). His research focuses on AI-driven smart city technologies, sensor systems for environmental monitoring, and advanced 3D printing materials. Key areas include IoT-enabled carbon-emission tracking, wearable biomedical devices, and sustainable sensor networks for landfill gas management. He has developed innovative solutions such as cryogenic 3D printing techniques for biocompatible inks and LED-based optical sensing platforms. Dr. Fay has secured grants totaling over $X million, including projects on military diver monitoring, blue carbon ecosystems, and low-cost sensor networks for agriculture and environmental safety. His work integrates interdisciplinary approaches, bridging materials science, biomedical engineering, and environmental engineering. Grants: Led projects on carbon-emission IoT systems, oyster farming sensors, and vibration monitoring. Supervision: Advised a Master's project on biomimetic microfluidic fabrication (2017–2019). Labs/Teams: Collaborates with the SCIT, SMART Infrastructure Facility, and global institutions like École Polytechnique Fédérale de Lausanne.

Giang Tranمشاهده پروفایل

دانشیار

• Sparse Modeling
• Compressed Sensing
• Image Processing
• +۲ مورد دیگر

Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.

• Photonics
• Medical Physics
• Optical Sensors
• +۳ مورد دیگر

Assoc. Professor Enbang Li is a Senior Lecturer at the School of Physics within the Faculty of Engineering and Information Sciences at the University of Wollongong. His research focuses on photonics, medical physics, and optical sensor technologies with applications in radiation dosimetry, biomedical engineering, and wearable devices. He has supervised numerous PhD and Master’s students in areas such as fiber-optic dosimetry for radiotherapy, blood glucose sensing, and microfluidics. His work includes advancements in fiber-optic dosimeters for MRI-LINAC systems, polymer-based biosensors for glucose monitoring, and integrated photonic sensors for temperature and pressure measurements. He has secured funding from organizations like the Australian Synchrotron Research Program and the University of Wollongong for projects related to dosimetry and photonic integration. Recent publications highlight innovations in HDR brachytherapy dosimetry, flexible electro-optic modulators for ECG signals, and lab-on-a-chip systems. His research also extends to material science, including corrosion studies of Al-Mg alloys and the dynamic behavior of sunscreens under in-service conditions.