جستجوی استاد راهنمای دانشگاهی

Marzyeh Ghassemiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Healthcare
• +۶ مورد دیگر

Marzyeh Ghassemi is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering & Science (IMES). She also holds a Canadian CIFAR AI Chair and is a faculty member at the Vector Institute. Her affiliations include the Jameel Clinic and CSAIL. Dr. Ghassemi's work focuses on ethical AI, algorithmic fairness in healthcare, and mitigating bias in medical datasets. She has pioneered projects analyzing racial disparities in end-of-life care, vocal disorder diagnosis using accelerometers, and improving subgroup robustness in machine learning. Education: PhD in Computer Science, MIT MSc in Biomedical Engineering, University of Oxford (Marshall Scholar) B.S. in Computer Science and Electrical Engineering, New Mexico State University (Goldwater Scholar) Research Interests: Her work bridges AI and healthcare, emphasizing fairness, reproducibility, and ethical deployment. Key areas include: - Algorithmic Fairness: Addressing biases in medical imaging and clinical decision systems. - Bias Mitigation: Developing methods to decorrelate sensitive information from predictions. - Health Equity: Studying disparities in end-of-life care and organ procurement. - Generative AI: Applications in mental health and medical imaging. Publications: Her recent work highlights challenges in vision-language models (e.g., handling negation), regulatory gaps in health AI, and bias reduction techniques. These efforts aim to ensure AI systems are both accurate and equitable in clinical settings. Awards: Herman L. F. von Helmholtz Career Development Professorship CIFAR Azrieli Global Scholar MIT Tech Review’s 35 Innovators Under 35 Canada Research Chair Leadership & Impact: She co-chaired the NeurIPS Workshop and founded the Association for Health Learning and Inference. Her projects like Quantifying Racial Disparities in End-of-Life Care reveal systemic biases in ICU treatments. She advocates for transparent AI deployment through frameworks like FUTURE-AI and TRIPOD+. Labs & Teams: Leads MIT’s HealthyML group, collaborating on projects such as voice misuse detection, federated learning for medical imaging, and AI-driven mental health prediction.

Misa Faezipourمشاهده پروفایل

دانشیار

• System Dynamics Modeling
• Healthcare Systems Engineering
• Complex Systems Modeling
• +۳ مورد دیگر

Dr. Misa Faezipour is an Associate Professor in the Department of Engineering Technology at Middle Tennessee State University (MTSU) and director of the Systems Engineering Lab (SEL). She holds a Ph.D. and M.Sc. in Industrial Engineering and Engineering Management from the University of Texas at Arlington and a B.Sc. in Software Engineering from Islamic Azad University North Tehran Branch. Her research focuses on system dynamics modeling, healthcare systems engineering, complex systems, and sustainability optimization. Dr. Faezipour has advised numerous graduate students in M.S. and Ph.D. programs, focusing on topics such as healthcare disparities, AI robustness, and policy impact analysis. She teaches courses including Engineering Economy, Operations Management, and Engineering Management Systems. Her work spans collaborations with institutions like NASA, the Systems Engineering Research Center, and healthcare organizations. Her recent publications emphasize AI-driven healthcare solutions, system dynamics applications in policy analysis, and optimizing healthcare delivery through data-driven models. She actively contributes to professional organizations such as INCOSE, IEEE, and the System Dynamics Society. Dr. Faezipour’s lab, SEL, develops innovative tools for healthcare informatics, including diagnostic recommenders and policy simulation frameworks. Her research bridges systems engineering with healthcare challenges, addressing issues like racial health inequities and pandemic response strategies.

Brian Y. Chenمشاهده پروفایل

دانشیار

• Bioinformatics
• Structural Biology
• Computational Biology
• +۵ مورد دیگر

Dr. Brian Y. Chen is an Associate Professor and Doctoral Program Director in the Department of Computer Science & Engineering at Lehigh University. His research focuses on bioinformatics, structural biology, and machine learning applications in computational biology. He holds a Ph.D. in Computer Science from Rice University and B.A. degrees in Mathematics and Computer Science from Rutgers University. Dr. Chen's work emphasizes developing algorithms to analyze protein structures, protein-protein interactions, and ligand binding mechanisms. He has contributed to tools like DeepVASP-S and MechPPI, which explain molecular interactions and predict binding specificity. His recent projects include Alzheimer’s disease diagnosis using multimodal data and containerization frameworks for bioinformatics software. He previously served as a postdoctoral researcher in Barry Honig's Lab at Columbia University, where he contributed to the Center for Computational Biology and Bioinformatics. His research spans structural bioinformatics, computational methods for protein function prediction, and interdisciplinary applications in medicine and materials science. Key achievements include a nomination for Outstanding Mentorship (2017) and collaborative projects funded by the Army Research Lab and Lehigh University. His lab explores cutting-edge AI techniques for biomedical problems, including interpretable machine learning models and scalable bioinformatics pipelines.

Jeffrey Brockمشاهده پروفایل

استاد

• Low-dimensional geometry and topology
• Hyperbolic geometry
• Teichmüller theory
• +۳ مورد دیگر

Jeffrey F. Brock is the Zhao and Ji Professor of Mathematics and Dean of the School of Engineering & Applied Science at Yale University. He also serves as the inaugural Dean of Science in Yale's Faculty of Arts and Sciences. His research focuses on low-dimensional geometry and topology, particularly hyperbolic geometry and its applications to data science. He has held leadership roles including Chair of Brown University's Mathematics Department (2013–2017) and founding Director of Brown’s Data Science Initiative (2016). Roles: Dean of Engineering, Mathematics Professor Key Affiliations: Yale University, Brown University Education: B.S. from Yale University, Ph.D. in Mathematics from U.C. Berkeley (under Curtis McMullen). Postdoctoral positions at Stanford and University of Chicago. Extensive academic leadership experience, including administrative roles at both Brown and Yale. Research Interests: Hyperbolic 3-manifolds, Teichmüller theory, geometric structures in data science. Notable contributions include geometric classification of hyperbolic 3-manifolds (with R. Canary and Y. Minsky) and advancing topological methods for analyzing complex data sets. Publications: Over 40 peer-reviewed articles, including foundational work on the ending lamination conjecture and Weil-Petersson geometry. Recent work applies geometric methods to machine learning and medical imaging. Awards: John Simon Guggenheim Fellowship (2008), Fellow of the American Mathematical Society (2017). Labs/Initiatives: Brown’s Data Science Initiative, geometric and topological data analysis projects.

Nan Marie Jokerstمشاهده پروفایل

استاد

• Photonics
• Optoelectronic Devices
• Integrated Optoelectronics
• +۷ مورد دیگر

Nan Marie Jokerst is the J. A. Jones Distinguished Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering and Executive Director of the Duke Shared Materials Instrumentation Facility. She previously served as Chair of the Duke Academic Council (2014-2015) and Associate Dean for six years. B.S. in Physics, Creighton University (1982) M.S.E.E., University of Southern California (1984) Ph.D. in Electrical Engineering, University of Southern California (1989) Her research spans chip-scale photonic sensing systems , III-V thin-film lasers on silicon , metamaterials , and heterogeneous integration . She develops optical systems for medical diagnostics, environmental monitoring, and security applications through the Jokerst Laboratory, which combines optical system design, optoelectronic device development, and semiconductor fabrication expertise. Her recent publications show strong focus on terahertz strain mapping using metamaterials, multi-pixel tissue characterization for cancer margin detection, and microfluidic sensing platforms with embedded photodetectors. Key trends include advancing non-destructive structural monitoring and miniaturized biomedical diagnostics through novel photonic integration techniques. IEEE Fellow (2003) Optica Fellow (2001) NSF Presidential Young Investigator Award IEEE Third Millennium Medal IEEE/HP Harriet B. Rigas Medal USC Viterbi School Alumni Award She has advised numerous graduate students in photonic device research and secured major grants including the NSF National Nanotechnology Coordinated Infrastructure ($6M, 2015-2021) and NNCI: North Carolina Research Triangle Nanotechnology Network ($20M, 2020-2026). Her leadership extends to co-founding Triangle Women in STEM and serving on the National Academies Board on Global Science and Technology. The Duke Shared Materials Instrumentation Facility under her direction provides critical cleanroom and characterization resources for interdisciplinary research.

• Digitisation
• Document Analysis
• Digital Transformation
• +۷ مورد دیگر

Prof Apostolos Antonacopoulos is a Professor of Pattern Recognition at the University of Salford, leading the PRImA research Lab (Pattern Recognition and Image Analysis). He holds a PhD from UMIST (1995) and has held academic roles at the University of Liverpool and Salford. His expertise spans Document Analysis, Computer Vision, and AI applications in Cultural Heritage. Education: PhD in Computer Science, University of Manchester Institute of Science and Technology (UMIST), UK (1995) Research Interests: Digitisation of historical documents and large-scale data Image Analysis and Pattern Recognition AI-driven solutions for cultural heritage preservation Performance evaluation frameworks for OCR systems Recent Projects: Leading a £750K ONS-funded project (2020–2025) digitising UK census reports Europeana Newspapers (€4M EU project, 2012–2015) for European Digital Library SUCCEED (€1.8M EU project, 2013–2015) for digitisation competencies Awards and Roles: IAPR/ICDAR Young Investigator Award (2005) Former President of International Association for Pattern Recognition (IAPR) Editorial roles in IJDAR and IEEE Transactions on Multimedia Labs/Teams: Director of PRImA Lab, collaborating with institutions like British Library and Wellcome Library. Active in industry partnerships for digitisation solutions.

Fahim Khanمشاهده پروفایل

استادیار

• Computer Graphics
• Data Visualization
• Computer Vision
• +۵ مورد دیگر

Fahim Khan is an Assistant Professor in the Department of Computer Science and Software Engineering at California Polytechnic State University’s College of Engineering. He specializes in computer graphics, data visualization, computer vision, and machine learning, with a focus on applying these technologies to education, environmental monitoring, and public safety. His research emphasizes making complex data accessible through advanced tools, bridging the gap between raw data and actionable insights. He is deeply committed to inclusive education and fostering interdisciplinary collaboration. His work often integrates citizen science initiatives, empowering communities through mobile applications and machine learning. Notable projects include real-time rip current detection systems and platforms for high school students to engage in research. Khan advocates for equity in technology, designing inclusive learning environments and promoting diversity in STEM. He actively supports the university’s Learn by Doing philosophy, blending practical education with theoretical rigor. Professionally, he contributes to coastal observation networks and autonomous vehicle datasets while maintaining a balance through outdoor activities like exploring Pismo Beach. His research trends reflect a strong focus on mobile computing, environmental applications, and education technology, with recent efforts emphasizing citizen science and data-driven solutions. While no formal grants or advising records are detailed, his projects implicitly involve collaborative efforts. He is affiliated with labs focused on environmental monitoring and mobile technology development, though specific lab names are not mentioned.

Wei Pangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۷ مورد دیگر

Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.

• Entrepreneurship
• 3D Scanning
• Additive Manufacturing
• +۴ مورد دیگر

Roger Uceda Molera is an Associate Professor at the Universitat Politècnica de Catalunya (UPC) in the Department of Mechanical Engineering, affiliated with the Barcelona Higher Technical School of Industrial Engineering (ETSEIB). He is a member of the DigiFACT research group, focusing on advanced manufacturing technologies. His expertise spans entrepreneurship, 3D scanning, additive manufacturing, 3D printing, Industry 4.0, IoT, and mechanization. He has contributed to over 25 research activities, including articles, patents, and awards. Roger’s research emphasizes innovative additive manufacturing techniques, such as hybrid multi-material 3D printing and applications in medical prototyping. His work includes developing materials for concrete additive manufacturing and optimizing ceramic sintering processes. He has collaborated with companies like BCN3D Technologies and led projects funded by public-private partnerships. Key awards include the 2019 Premi Nacional al Partenariat Publicoprivat en R+I and the World to NYC Global Industry Challenge (W2NYC). He holds a patent for extrusion systems in cementitious material manufacturing. His research group, DigiFACT, drives advancements in digital factory technologies and industrial innovation.

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Reinforcement Learning
• Bayesian Inference
• +۳ مورد دیگر

Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.

Paul Hendersonمشاهده پروفایل

مدرس

• Generative AI
• Probabilistic Machine Learning
• 3D Computer Vision
• +۳ مورد دیگر

Dr. Paul Henderson is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. He holds a BA in Mathematics (University of Cambridge, 2009), an MSc in Informatics (University of Edinburgh, 2010), and a PhD in Computer Vision (University of Edinburgh, 2018). His research focuses on generative AI, probabilistic machine learning, and minimally-supervised approaches to 3D computer vision, with applications in healthcare, computer graphics, and physical sciences. Education: PhD in Computer Vision (University of Edinburgh, 2018) MSc in Informatics (University of Edinburgh, 2010) BA in Mathematics (University of Cambridge, 2009) His work spans generative models, medical imaging, and robotics. Notable contributions include datasets like Flat’n’Fold and techniques in diffusion models for text-to-image retrieval. He has received grants including the Royal Society Research Grant (2022-2023) and the Vesuvius Challenge Autosegmentation Prize (2025). He supervises PhD students in topics such as medical image segmentation and generative AI. Teaching: CS5002 Advanced Programming, CS4061/CS5014 Machine Learning.

Ghassan Hamarnehمشاهده پروفایل

استاد

• Medical Image Analysis
• Scientific computing
• Signal and image processing
• +۵ مورد دیگر

Ghassan Hamarneh is a Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on medical image analysis, with expertise in super-resolution microscopy, explainable AI, and biomedical computing. He teaches courses in biomedical computing and scientific computing, emphasizing practical applications like signal processing and health informatics. Education: Ph.D. in Signal and Systems (Chalmers University, 2001), M.Sc. in Digital Communications (Chalmers, 1997), B.Sc. in Electrical Engineering (Jordan University, 1995). Research Interests include developing AI-driven tools for medical imaging, analyzing cellular structures using super-resolution techniques, and addressing ethical challenges in AI deployment. His work bridges computational methods with clinical applications, such as lesion segmentation, PET image analysis, and bias mitigation in medical algorithms. Recent publications highlight advancements in network analysis of molecular structures, debiasing AI models, and improving diagnostic accuracy through deep learning. His lab contributes to open-source software like SuperResNET and MCS-DETECT for super-resolution microscopy analysis. No scientific awards explicitly listed, but his extensive publication record reflects recognition in the field. Advising and grants information is not detailed in the provided texts. Active in teaching, including CMPT 340 (Biomedical Computing) and special research projects.

Rongxing Luمشاهده پروفایل

استاد مدعو

• Applied Cryptography
• Privacy Enhancing Technologies
• IoT Security
• +۴ مورد دیگر

Rongxing Lu is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick (UNB), Canada, since August 2016. Previously, he held positions at Nanyang Technological University (NTU), Singapore (2012–2016) and the University of Waterloo, Canada (PhD in 2012). His research focuses on applied cryptography, privacy enhancing technologies, and IoT-big data security. He has over 7,500 citations and received prestigious awards like the Governor General’s Gold Medal (2012) and the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award (2013). He is an IEEE senior member and serves on editorial boards of journals like IEEE Network. **Education**: PhD in Electrical & Computer Engineering, University of Waterloo (2012), awarded Governor General’s Gold Medal Postdoctoral Fellow at University of Waterloo (2012–2013) **Research Interests**: Developing cryptographic protocols for IoT and big data systems Privacy-preserving techniques for distributed systems Secure communication in 5G/6G networks and vehicular systems **Awards and Recognition**: Recipient of multiple best paper awards in IEEE conferences 2016–2017 Excellence in Teaching Award at UNB **Editorial and Leadership Roles**: Symposium co-chair at IEEE Globecom’16 Secretary of IEEE ComSoc CIS-TC Organized special issues on fog computing security (Elsevier) and big data security (IEEE IoT Journal) **Key Contributions**: Pioneered privacy-aware data reporting schemes for vehicular networks Designed lightweight IoT authentication protocols Advanced secure machine learning frameworks with privacy guarantees

Joseph Alejandro Gallego Mejiaمشاهده پروفایل

دانشگاهی

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۱۲ مورد دیگر

Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.

Roman Kucمشاهده پروفایل

استاد

• Robotics
• Biomimetic Sensors
• Digital Signal Processing
• +۴ مورد دیگر

Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.