جستجوی استاد راهنمای دانشگاهی

LEE Mong Liمشاهده پروفایل

استاد

• Data Management
• Data Cleaning
• Data Fusion
• +۵ مورد دیگر

LEE Mong Li is a Professor of Computer Science at the National University of Singapore (NUS) and serves as Director of the NUS Centre for Trusted Internet and Community. She holds a Ph.D., M.Sc., and B.Sc. (First Class Honours) in Computer Science from NUS, where she was awarded the IEEE Singapore Information Technology Gold Medal as the top Computer Science student in 1989. Her academic career includes a visiting fellowship at the University of Wisconsin-Madison (1999) and consultancy with QUIQ USA (2000). Her research spans Data Management, Spatio-temporal Databases, Biomedical Informatics, and Retinal Image Analysis . She has pioneered work in data cleaning, data fusion, and analysis of semistructured data, with applications in social media analytics and healthcare. Her recent publications demonstrate strong interdisciplinary focus, particularly in AI-driven medical diagnostics including diabetic retinopathy screening and chronic kidney disease detection from retinal images. She co-authored foundational books on 'Designing Semi-structured Database' and 'Temporal and Spatio-Temporal Data Mining'. Her 150+ publications in major database conferences and journals reflect leadership in both theoretical and applied research. Recent work shows significant emphasis on Medical AI applications (retinal analysis, kidney disease prediction) Temporal fact verification systems Misinformation detection in multimodal environments Privacy challenges in large language models Key honors include: Singapore's President Technology Award (2014) for co-inventing an AI system screening eye conditions IEEE Singapore Information Technology Gold Medal (1989) She actively contributes to government-funded multidisciplinary projects building practical deployable systems. Her leadership extends to program committees of prestigious database conferences and directing the NUS Centre for Trusted Internet and Community. She teaches BT5110 Data Management and Warehousing and has co-developed an AI system for diabetic retinopathy screening deployed in Singapore's national teleophthalmology program.

Robert D. Gregg, IVمشاهده پروفایل

دانشیار

• Wearable Robots
• Legged Robots
• Prosthetics & Orthotics
• +۳ مورد دیگر

Robert D. Gregg, IV is a Professor of Mechanical Engineering, Robotics, and Electrical & Computer Engineering at the University of Michigan. He serves as Associate Director for Graduate Education at Michigan Robotics and directs the Locomotor Control Systems Laboratory. His research focuses on control systems for wearable robots, prosthetics, and orthotics, emphasizing biomimetic principles and nonlinear control theory. Gregg holds a PhD from the University of Illinois at Urbana-Champaign (2010) and prior academic roles at the University of Texas at Dallas and Northwestern University. Education: PhD, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2010 MS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2007 BS, Electrical Engineering and Computer Sciences, University of California, Berkeley, 2006 Research Interests: Gregg’s work spans control mechanisms for bipedal locomotion, wearable robotics, nonlinear control theory, and rehabilitation engineering. His lab develops high-performance control systems for prosthetic legs and exoskeletons to enhance mobility for individuals with disabilities. Key areas include energy-efficient control strategies, adaptive impedance systems, and biomechanical modeling of human movement. Grant & Award Highlights: $3M NIH R01 Grant (2023): Modeling and control of agile powered prosthetic legs for varied activities. $1.7M NIH R01 Grant (2021): Modular powered orthoses for broad patient populations. NSF CAREER Award (2017) NIH New Innovator Award (2013) Advising & Labs: Gregg mentors PhD students in robotics and biomechanics, emphasizing independent research and collaborative team environments. His lab supports over 15 researchers and has produced notable alumni like Dr. Cara Welker (University of Colorado Boulder faculty). The lab’s work is supported by NIH, NSF, and industry partnerships. Recent Contributions: Recent work includes phase-variable control for stair climbing, energy shaping methods for exoskeletons, and open-source robotic leg platforms. Gregg also chairs major robotics conferences (e.g., IROS 2023) and advises on clinical translation of wearable robotics.

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +۵ مورد دیگر

Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.

Youngchan Kimمشاهده پروفایل

مدرس

• Quantum Biology
• Biophotonics
• Bioimaging
• +۳ مورد دیگر

Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.

• Computational Linguistics
• Cognitive Science
• Machine Learning
• +۳ مورد دیگر

Lena Jäger is a Professor in the Department of Computational Linguistics at the University of Zurich (UZH). Her research focuses on the intersection of linguistics, computational cognitive science, and machine learning, particularly analyzing cognitive mechanisms underlying human language processing through experimental psycholinguistics, computational modeling, and NLP methods. She holds an MA in Chinese Language and Culture, an MSc in Experimental and Clinical Linguistics, a PhD in Cognitive Science, and a BSc in Computer Science. Prior to UZH, she led a Machine Learning Junior Research Group funded by the German Federal Ministry of Education and Research (2020) and conducted postdoctoral research at the University of Potsdam. Her work emphasizes developing machine learning methods for analyzing eye-tracking data to uncover cognitive processes reflected in eye movements. Notable contributions include creating multilingual eye-tracking corpora (e.g., MultiplEYE, PoTeC) and advancing tools like pymovements for data processing. Her research spans applications in language comprehension, biometric identification, and clinical diagnostics (e.g., ADHD detection via eye movements). Education: BA/MA: Chinese Language and Culture (University of Freiburg, Tongji University, Beijing Language and Culture University, Université Paris 7) MSc: Experimental and Clinical Linguistics (University of Potsdam) PhD: Cognitive Science (University of Potsdam) BSc: Computer Science (concurrent with PhD) Awards: Machine Learning Junior Research Group Grant (2020). Labs/Teams: Leads computational linguistics and machine learning research groups at UZH, collaborating on projects like ScanDL and CoLAGaze. Her recent work bridges AI and cognitive science, exploring how language models emulate human reading behaviors and developing frameworks for ethical AI applications. Ongoing projects include improving fairness in biometric identification systems and analyzing individual differences in reading through synthetic data.

• Organoid Engineering
• Systems Biology
• Developmental Biology
• +۳ مورد دیگر

Keisuke Ishihara is an Assistant Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. His research focuses on engineering human brain and cardiac organoids using genetic, chemical, and computational approaches to uncover novel regulatory mechanisms and physical principles underlying tissue development. His lab is located at Biomedical Science Tower 3, with an office in room 10020A. Dr. Ishihara holds a PhD in Systems Biology from Harvard University. His work bridges synthetic biology, developmental biology, and biophysics to address fundamental questions in organogenesis and cellular morphogenesis. Recent research highlights include studies on BMP-mediated neural tube patterning in organoids and the biophysical dynamics of microtubule assemblies in large cells. Publications from his lab emphasize interdisciplinary approaches to understand cell size scaling, mitotic spindle dynamics, and self-organization in synthetic tissues. His team has contributed to advancements in organoid technology, uncovering dormant genetic programs and physical principles governing tissue architecture. Laboratory activities are centered at the University of Pittsburgh, collaborating with the School of Medicine's computational and systems biology initiatives. For more details, visit his lab website linked below.

Indrani Bhattacharyaمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Medical Image Analysis
• Social Signal Processing
• +۳ مورد دیگر

Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.

Ali Bilginمشاهده پروفایل

دانشیار

• Signal Processing
• Image Processing
• Image and Video Coding
• +۷ مورد دیگر

Ali Bilgin is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona's College of Engineering. He also holds associate professor appointments in Biomedical Engineering, the BIO5 Institute, and Medical Imaging, and is a member of the Graduate Faculty. His work bridges engineering and medical applications, particularly in signal and image processing. His educational background includes: PhD in Electrical Engineering, University of Arizona, 2002 MS in Electrical Engineering, San Diego State University, 1995 BS in Electronics and Telecommunications Engineering, Istanbul Technical University, 1992 Dr. Bilgin's research focuses on signal and image processing , with key applications in image and video coding, data compression, and magnetic resonance imaging (MRI) . His work integrates theoretical advances with practical biomedical applications. Teaching interests include digital signal processing, linear algebra, probability theory, and machine learning in image processing. With over 250 research papers and 13 granted patents, his scholarly output reflects sustained contributions to engineering and imaging sciences. Though specific articles are not listed, his editorial roles and publication volume indicate leadership in signal and image processing domains, particularly in compression and medical imaging. His scientific recognition includes multiple teaching awards from the UA College of Engineering, notably being named Most Supportive Senior Faculty . Most Supportive Senior Faculty, UA College of Engineering Dr. Bilgin has served as an associate editor for several top IEEE journals, including IEEE Signal Processing Letters (2010–2012), IEEE Transactions on Image Processing (until 2014), and IEEE Transactions on Computational Imaging (2014–2019), reflecting his standing in the academic community. While no specific grants or students are listed, his extensive publication record and interdisciplinary affiliations suggest active mentorship and funded research. He is affiliated with the BIO5 Institute, indicating participation in collaborative, interdisciplinary research teams focused on health and bioscience innovation.

Luc Brunsveldمشاهده پروفایل

استاد

• Chemical Biology
• Protein-Protein Interactions
• Drug Discovery
• +۳ مورد دیگر

Luc Brunsveld is a Full Professor of Chemical Biology at Eindhoven University of Technology's Department of Biomedical Engineering, embedded in the Institute for Complex Molecular Systems (ICMS). His research focuses on chemical biology approaches to modulate protein-protein interactions, with applications in drug discovery and synthetic cell development. Brunsveld has held academic roles at TU/e since 2008, preceded by postdoctoral fellowships at the Max Planck Institute and industry experience at Organon. Education: Master's (1997) and PhD (2001, cum laude) from TU/e and University of Illinois. Grants: ERC Starting, Proof of Concept, Advanced Grants; NWO Vici Grant (2015). Research interests include molecular glues, supramolecular chemistry, and artificial cells. Recent work highlights light-activatable proteases and beta-cell protection via ChREBP modulation. Awards include the NVBMB Award (2010) and KNCV Gold Medal (2013). Co-founder of Ambagon Therapeutics. Labs/Teams: ICMS Core Group, Chemical Biology Group.

• Applied Mathematics
• Numerical and Computational Mathematics
• Pure Mathematics
• +۷ مورد دیگر

Dr. John Shepherd is an Associate Professor in the School of Science at RMIT University, specializing in applied mathematics, numerical and computational mathematics, and their applications in engineering and environmental systems. His research focuses on analyzing nonlinear problems, particularly in bioreactor dynamics, fluid mechanics, and nuclear energy policy. He has contributed to studies on anaerobic digestion models, reactor stability, and the role of nuclear energy in climate change mitigation. Education: Doctorate in Applied Mathematics (not explicitly stated in text, inferred from title). His work bridges theoretical analysis and real-world applications, such as optimizing methane production in waste digesters and evaluating environmental policies for nuclear energy. He actively supervises research projects, including the analysis of anaerobic digester dynamics. Dr. Shepherd’s publications span interdisciplinary topics, emphasizing the intersection of mathematics, engineering, and environmental science. He engages with policy discussions on nuclear energy’s role in decarbonization, advocating for its integration into clean energy strategies. His research highlights the importance of multiscale analysis in understanding complex systems like bioreactors and fluid flows. Collaborations involve industry and international institutions, reflecting his commitment to practical solutions for sustainability challenges.

Horst A. von Recumمشاهده پروفایل

استاد

• Drug Delivery Systems
• Biomedical Engineering
• Stem Cell Differentiation
• +۴ مورد دیگر

Horst A. von Recum, PhD, is the Executive Vice Chair of the Case School of Engineering and a Professor in the Department of Biomedical Engineering at Case Western Reserve University. He is also a member of the Cancer Imaging Program at the Case Comprehensive Cancer Center. His research focuses on developing novel platforms for molecular and cellular delivery, including affinity-based systems for controlled drug release and directed stem cell differentiation. Key applications include HIV therapies, wound healing, ocular disease treatments, and tissue engineering. His work emphasizes improving drug delivery precision through molecular interactions and enhancing stem cell viability for therapeutic use. Dr. von Recum’s research interests span drug delivery systems, biomaterials science, and regenerative medicine. His lab explores cyclodextrin polymers for sustained antibiotic release, affinity-driven drug refilling mechanisms, and engineering biocompatible materials to combat implant-related infections. Recently, his team has investigated microbiome interactions with neural implants and developed polymer-based solutions for localized chemotherapy. Notable contributions include advancements in PMMA bone cement composites for drug refillable depots, cyclodextrin hydrogels for controlled release, and affinity-based systems for anti-fibrotic treatments. His work bridges materials science with clinical applications, addressing challenges in orthopedic infections, neural interfaces, and cardiovascular regeneration. Scientific achievements include over 100 peer-reviewed publications. Research funding has supported projects on antimicrobial coatings, drug delivery mechanics, and stem cell differentiation. Dr. von Recum collaborates across disciplines to translate biomaterial innovations into clinical solutions.

Hak-Keung Lamمشاهده پروفایل

استاد

• Fuzzy Control Systems
• Neural Networks
• Control Theory
• +۴ مورد دیگر

Dr. Hak-Keung Lam is a Reader in the Department of Engineering at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds an IEEE Fellowship and has been a Clarivate Web of Science Highly Cited Researcher since 2018. His research focuses on fuzzy control systems, neural networks, stability analysis, and their applications in biomedical and engineering domains. Education: Dr. Eng. (2000), B. Eng. (1995), both from Hong Kong Polytechnic University. Research Interests: Fuzzy modeling, neural network-based control, computational intelligence, machine learning, and biomedical applications such as ECG/EEG signal classification. His work bridges theoretical advancements with practical implementations in robotics, autonomous systems, and healthcare technology. Publications: Over 480 publications (as of 2023) in top-tier journals and conferences, with a focus on control systems, fuzzy logic, and intelligent systems. Recent work includes fault-tolerant control, cyber-physical systems, and explainable AI. Awards: IEEE Fellow (2019), 1st Place in PhysioNet Computing in Cardiology Challenge (2022). Grants/Projects: Active projects include fuzzy control system stabilization, autonomous robots in healthcare environments, and networked control of robotic systems. Labs/Teams: Center for Robotics Research, contributing to solutions for societal challenges through robot-centric approaches.

Burkhard Maessمشاهده پروفایل

استاد پژوهش

• Neuroscience
• Auditory Processing
• Signal Analysis
• +۳ مورد دیگر

Dr. Burkhard Maess is a Research Professor and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences, leading the Methods and Development Group Brain Networks. His research focuses on auditory and language processing, signal analysis, and dynamic modeling of neuronal networks. He holds a Diploma in Physics (University of Leipzig, 1987) and a PhD in Physics (University of Leipzig, 1990). His career includes postdoctoral positions at the Academy of Sciences of the GDR and the Free University of Berlin before joining the MPI in 1995. Since 2000, he has led research groups on MEG/EEG signal analysis and cortical network dynamics. His work integrates advanced neuroimaging techniques like MEG and EEG to study sensory processing, neural network dynamics, and the effects of aging on auditory attention. Key contributions include developing high-resolution BEM-FMM methods for source localization and analyzing cross-frequency coupling in neuroscience data. His group also explores spinal cord electrophysiology and the neural underpinnings of perceptual decision-making. Dr. Maess’ research spans cognitive neuroscience, biomedical engineering, and computational modeling, with a focus on bridging empirical findings with theoretical frameworks in neuroscience.

Emanuele Truccoمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Retinal Image Analysis
• +۷ مورد دیگر

Professor Emanuele (Manuel) Trucco is the NRP Chair of Computational Vision in the Department of Computing, School of Science and Engineering, at the University of Dundee. He holds additional roles as an Honorary Clinical Researcher at NHS Tayside and formerly served as an Adjunct Professor at the Chinese Academy of Sciences from 2018 to 2021. He earned his MSc and PhD in Electronic Engineering from the University of Genoa, Italy, in 1984 and 1990, respectively. His research focuses on medical image and data analysis, particularly in retinal imaging, using machine and deep learning techniques. He is co-director of VAMPIRE, a major international initiative in retinal image analysis, which supports biomarker studies in cardiovascular disease, diabetes, dementia, and neurodegenerative disorders. His recent work includes AI-driven tools for predicting dementia from brain scans and cardiovascular risk from retinal images. The latest publications reflect strong trends in applying deep learning to retinal and brain imaging for early disease detection, with themes centered on AI in healthcare, precision medicine, and non-invasive diagnostics. FRSA (Fellow of the Royal Society of Arts) FIAPR (Fellow of the International Association for Pattern Recognition) Professor Trucco has led and co-led significant research projects funded by EPSRC, NIHR, and EU programs. He has supervised PhD students through industry-sponsored studentships (e.g., OPTOS, NIDEK, Toshiba) and collaborated with institutions such as the Universities of Edinburgh and Liverpool. His research is supported by extensive industrial partnerships including Canon Medical, Epipole plc, and NIDEK. He is a key member of the UK Biobank Eye and Vision Consortium and co-director of the VAMPIRE initiative, a collaborative effort between the Universities of Dundee and Edinburgh focused on retinal image analysis. His work also involves participation in major research networks such as the Academic Health Science Partnership in Tayside.

• Medical Physics
• Radiation Oncology
• Therapeutic Radiology
• +۷ مورد دیگر

Huixiao Chen is an Assistant Professor and Clinical Medical Physicist II in the Department of Therapeutic Radiology at Yale School of Medicine. She holds a primary appointment in Therapeutic Radiology and is actively engaged in clinical and research activities related to radiation oncology physics. Assistant Professor, Department of Therapeutic Radiology, Yale School of Medicine Clinical Medical Physicist II, Therapeutic Radiology Education: Resident, Yale University (2016) Postdoctoral Fellow, Harvard University (2013) Postdoctoral Fellow, Virginia Commonwealth University (2011) PhD, University of Heidelberg, Germany (2010) MS, Zhejiang University, China (1998) BS, Zhejiang University, China (1995) Huixiao Chen's research centers on medical physics in radiation oncology, with a focus on treatment planning optimization, dosimetry, and image-guided radiotherapy. Her work includes the development and evaluation of advanced radiotherapy techniques such as VMAT and SBRT, with applications in spine, lung, and prostate cancers. She has contributed to improving the accuracy of dose calculations using Monte Carlo methods and has explored the impact of patient motion on treatment delivery. Her research also extends to the design of clinical devices to support safe and effective radiotherapy for diverse patient populations. Her recent publications demonstrate a consistent focus on enhancing the precision and efficiency of radiotherapy. Key themes include multicriteria optimization in treatment planning, dosimetric validation of radiochromic films, and the development of supportive devices for image-guided radiotherapy. These works reflect a strong commitment to advancing clinical medical physics through both computational and engineering innovations. Scientific Contributions: Characterization of GafchromicTM EBT4 film for clinical dosimetry Design of a small-footprint couch-top support for heavy patients in IGRT Application of multicriteria optimization in VMAT planning for spine and prostate cancers Comparison of Monte Carlo vs. pencil beam algorithms in lung SBRT Analysis of diaphragm motion effects on spine SBRT Huixiao Chen has collaborated with researchers such as Emily Draeger and Zhe Jay Chen on various projects. While no formal students or grants are listed, her role as a clinical medical physicist and faculty member suggests active mentorship and potential involvement in funded research. She is a key contributor to the medical physics team at Yale, ensuring high standards in treatment planning and delivery.