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Aishwarya AgrawalView profile

Assistant Professor

• Computer Vision
• Natural Language Processing
• Deep Learning
• +7 more

Aishwarya Agrawal is an Assistant Professor in the Department of Computer Science and Operations Research at the University of Montreal and a core academic member of Mila – Quebec AI Institute, where she holds the Canada CIFAR AI Chair. She also works as a Research Scientist at Google DeepMind, spending one day per week there. Her research sits at the intersection of computer vision, natural language processing, and deep learning, aiming to build AI systems that can both 'see' and 'talk'. Her research interests include vision-language reasoning, data-efficient pretraining, robust evaluation, improving model reliability and explainability, and aligning AI systems with diverse cultural values. She has made significant contributions to visual question answering, particularly in addressing dataset bias through work like the VQA-CP dataset. The recent publications show a strong focus on evaluating and improving vision-language models, especially in areas like cultural alignment, consistency, reliability, and robustness. There is a clear trend toward developing better evaluation methodologies and enhancing model behavior in complex, real-world scenarios. Her group actively publishes in top-tier conferences including CVPR, NeurIPS, ICML, and ACL. Canada CIFAR AI Chair Award Georgia Tech 2020 Sigma Xi Best Ph.D. Thesis Award Georgia Tech 2020 College of Computing Dissertation Award Runner-up, 2019 AAAI/ACM SIGAI Dissertation Award NVIDIA Graduate Fellowship 2018-2019 Rising Stars in EECS 2018 Aishwarya advises several PhD and MSc students, including Rabiul Awal, Qian Yang, Le Zhang, and Oscar Mañas. She has received research grants, such as one from Samsung for compositional reasoning in multimodal transformers. She is actively recruiting graduate students and postdocs through Mila’s supervision request process. She co-organized the annual VQA Challenge and Workshop from 2016 to 2021 and is involved in broader AI community initiatives, including panels and talks on leadership and AI research trends. Her lab, part of Mila, fosters a collaborative environment focused on advancing multimodal AI.

• Data Management
• Data Analytics
• Spatio-temporal Databases
• +9 more

Mohammed Eunus Ali is a Senior Lecturer in the Department of Software Systems & Cybersecurity within the Faculty of Information Technology at Monash University, Australia. He holds a PhD in Computer Science and Software Engineering from the University of Melbourne and has previously served as a Professor at the Bangladesh University of Engineering and Technology (BUET), where he led a research group in Data Science and Engineering for over a decade. He has also held research positions at Monash University, Swinburne University, the University of Melbourne, and RMIT University. PhD : Computer Science and Software Engineering, University of Melbourne (2010) M.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (2002) B.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (1999) Dr. Ali’s research spans data management, analytics, and learning , with a strong focus on spatio-temporal data, geo-social networks, and multimodal high-dimensional data . His work enables applications in urban computing, intelligent transportation systems, and smart, sustainable cities . In recent years, he has expanded into Generative AI and large language models (LLMs) , exploring their role in enhancing geo-spatial query processing, SQL generation, and data engineering tasks. His publications appear in top-tier venues such as ACL, TKDE, VLDB, ICDE, SIGSPATIAL, and IEEE Access . His recent publications reflect a strong trend toward AI-driven solutions for real-world spatial and health problems , including blood glucose prediction for diabetics, seismic intensity forecasting, eco-friendly route planning, and LLM-based code generation. These works demonstrate a convergence of deep learning, spatio-temporal analytics, and real-world system design . Scientific Awards: Bangladesh University Grants Commission Award (2012) ADC Best Poster Award (2016) SSTD Best Demo Award (2017) ADC Best Paper Award (2022) Dr. Ali actively contributes to the research community as a Program Committee Member for premier conferences including SIGMOD, VLDB, ICDE, and SIGSPATIAL . He is a Senior Member of the ACM and currently supervises PhD students, focusing on cutting-edge topics in data science and AI. His collaborative research network spans institutions in Australia and Bangladesh, contributing to advancements in both academic and applied domains. His work aligns with the UN Sustainable Development Goals , particularly in the areas of sustainable cities, innovation, and quality education.

• Terahertz Spectroscopy
• Terahertz Imaging
• Nanoscale Sensing
• +7 more

Dr. John Bowen is a prominent researcher in terahertz science and technology, affiliated with the Department of Physics at the University of Reading, under the School of Mathematical, Physical and Computational Sciences. His work spans terahertz spectroscopy, imaging, and sensing, with applications in cultural heritage, biomedical systems, and nanotechnology. Research Interests: Terahertz Spectroscopy and Imaging Nanoscale Sensing and Light-Matter Interactions Signal and Wavelet Processing for THz Transients Non-Destructive Testing in Cultural Heritage Biomedical Applications of Terahertz Radiation His recent publications emphasize advancements in super-resolved microscopy, deconvolution techniques, and nanoscale interactions, reflecting a strong focus on enhancing spatial resolution and system performance in THz systems. Collaborative work with institutions in Europe highlights interdisciplinary impact. Scientific Contributions: Over 90 peer-reviewed publications in journals and conferences Contributions to NATO and Springer book series on biophotonics and nano-optics Development of novel THz imaging and signal processing methods Dr. Bowen has advised or collaborated with numerous researchers in physics and engineering, though formal advisees are not listed. His work has contributed to understanding material properties, biological systems, and ancient artworks using THz radiation. He has also participated in major international conferences such as IRMMW-THz and SPIE events. Research Teams and Labs: He has been part of a long-standing research group at the University of Reading focused on terahertz systems, collaborating with Prof. G. C. Walker and Prof. S. Hadjiloucas. The group has developed instrumentation for THz time-domain spectroscopy and imaging, with applications in both scientific and heritage domains.

Mark NiedreView profile

Professor

• Biomedical Optics
• Non-invasive Imaging
• Circulating Tumor Cell Detection
• +4 more

Dr. Mark Niedre is an Associate Dean of PhD Education and COE Distinguished Professor of Bioengineering at Northeastern University's College of Engineering. His research focuses on biomedical optics, non-invasive imaging, and the detection of rare circulating cells (e.g., circulating tumor cells) using diffuse optical techniques. He leads the Biomedical Optics Research Group, which integrates optical engineering, biology, and signal processing to develop innovative diagnostic tools. Dr. Niedre earned his PhD in Medical Biophysics from the University of Toronto (2005) and completed a postdoctoral fellowship at Harvard Medical School/Massachusetts General Hospital. His work has been supported by grants from the National Cancer Institute, NIH, and other agencies, including a $2.7M NIH grant for studying metastasis mechanisms. His research interests emphasize ultrafast time-domain imaging, image reconstruction, and biomedical signal processing. Notable projects include developing instruments for real-time detection of fluorescently labeled cells and non-invasive monitoring of cancer progression. His lab’s advancements aim to translate these technologies into clinical applications for early cancer detection and personalized medicine. Awards: COE Distinguished Professor (2015), Søren Buus Outstanding Research Award, Massachusetts Life Sciences Center New Investigator Award Grants: NIH/NCI grants for in vivo flow cytometry and tumor cell monitoring Lab Team: Includes students like Jane Lee, Amber Williams, and Fernando Ivich, focusing on sensor design and signal processing Dr. Niedre’s work bridges engineering and medicine, with a vision to create low-cost, accessible medical devices for global health challenges, as highlighted in student-driven projects and co-op experiences.

Gooitzen Michell van DamView profile

Professor

• Oncology
• Surgical Imaging
• Optical Molecular Imaging
• +3 more

Prof. Gooitzen Michell van Dam is a Professor at the University of Groningen's Faculty of Medical Sciences, specializing in surgical oncology and molecular imaging. He holds leadership roles including CEO of TRACER Europe BV (a clinical research organization) and Founding Member of the European Society of Molecular Imaging. His research focuses on optical and nuclear molecular imaging, tracer development, micro-dosing, and clinical translation in oncology, infectious diseases, and cardiovascular imaging. Education: Not explicitly detailed in provided texts Key research areas include EGFR-targeted fluorescence imaging, laser speckle contrast imaging for perfusion assessment, and SARS-CoV-2 vaccine development. His work bridges surgical innovation with molecular imaging technologies, aiming to improve diagnostic accuracy and treatment outcomes. Prof. van Dam has led multinational clinical trials and contributed to over 218 publications, with recent focus on vaccine efficacy, surgical guidance systems, and perfusion monitoring. He collaborates with industry through TRACER Europe BV, advancing translational research and imaging solutions. Notably, his team developed dye-free perfusion imaging tools and explored novel vaccine strategies for global health equity.

Yapei ZhangView profile

Researcher

• Nanomedicine
• Drug Delivery Systems
• Cancer Therapy
• +3 more

Yapei Zhang is a Researcher at the Institute for Quantitative Health Science & Engineering within the College of Engineering at Michigan State University. Her work focuses on advancing nanomedicine, drug delivery systems, and biomaterials for applications in cancer therapy, cardiovascular disease, and biomedical imaging. She has contributed to the development of targeted nanotherapies, stimuli-responsive materials, and CRISPR delivery technologies, integrating computational modeling with experimental approaches. Her research interests span nanomedicine, drug delivery optimization, and therapeutic genome editing. Key areas include nanoparticle design for targeted drug delivery, photoactive nanocarriers for theranostics, and magnetic hyperthermia therapies. Zhang’s studies also address atherosclerosis treatment via pro-efferocytic nanotherapies and explore the use of carbon nanotubes in imaging inflamed plaques. Notable contributions include advances in pH-sensitive drug delivery systems, electrospun wound-healing membranes, and multimodal imaging techniques. Her work emphasizes translational applications, such as improving hypertension therapies and enhancing tumor diagnosis through nanotechnology. While no formal awards are listed, her publications reflect a strong focus on interdisciplinary innovation at the intersection of engineering and medicine. She is affiliated with the Institute for Quantitative Health Science & Engineering, leveraging its resources for collaborative research in health science and engineering.

Adam AlessioView profile

Professor

• Medical Imaging Algorithms
• Quantitative Diagnostics
• Machine Learning in Healthcare
• +5 more

Adam Alessio is a Professor at Michigan State University (MSU), holding joint appointments in the Department of Computational Mathematics, Science, and Engineering, the Department of Biomedical Engineering, and the Department of Radiology. He currently serves as Interim Chair of the Department of Biomedical Engineering. Alessio specializes in non-invasive cancer quantification and advanced imaging algorithms, focusing on translational research at the intersection of medical imaging and decision-making. His work spans machine learning for diagnostics, cardiac perfusion estimation, and radiation dose optimization. He earned a Ph.D. in Electrical Engineering from the University of Notre Dame and completed post-doctoral training in nuclear medicine physics at the University of Washington. Prior to MSU, he was a Professor of Radiology at the University of Washington. His research group emphasizes clinically motivated projects, including AI-driven diagnostics, quantitative PET/CT imaging, and system modeling for improved patient outcomes. Key research themes include automated pathology detection (e.g., rib fractures, pneumoconiosis), biomarker discovery in cancer, and multimodal imaging fusion. His contributions to imaging technology include innovations in PET/MRI quantification, nanoparticle-enhanced tracking of transplanted cells, and motion correction algorithms for PET/CT.

• Neuroradiology
• Magnetic Resonance Imaging
• Contrast Media Safety
• +3 more

Carlo Cosimo Quattrocchi serves as Full Professor at the University of Trento with joint appointments in the Department of Physics and Center for Mind/Brain Sciences (CIMeC), concurrently holding the position of Deputy Dean at the Centre for Medical Sciences (CISMED). His academic roles bridge fundamental physics and clinical neuroscience applications. Research interests focus on neuroradiology innovation, particularly: Safety optimization of gadolinium-based contrast agents in MRI Environmental sustainability in radiology practice AI-driven diagnostic imaging techniques Brain structural changes in neurological/metabolic disorders He actively develops clinical guidelines through European radiology societies. Publication analysis reveals strong trends: Leadership in contrast media safety research (7/10 recent papers) Integration of AI methodologies in neuroimaging diagnostics Focus on practical clinical applications in neuroradiology Interdisciplinary approaches combining physics, medicine, and environmental science Teaching responsibilities include core medical courses: Diagnostic Imaging : Covers principles of ultrasound, CT, MRI, and nuclear medicine with emphasis on clinical integration AI for Diagnostic Imaging : Focuses on deep learning applications in medical image analysis Professional education courses on healthcare technology and socio-medical needs No information about research grants, supervised students, laboratory facilities, or scientific awards was found in provided materials.

Zhenpeng QinView profile

Associate Professor

• Nanomaterials
• Photonics
• Neuroscience
• +13 more

Zhenpeng Qin is an Associate Professor and Fellow of the Eugene McDermott Professorship in the Department of Mechanical Engineering at the University of Texas at Dallas, within the Erik Jonsson School of Engineering and Computer Science. He leads the Nano-Thermal Bioengineering Laboratory, where his interdisciplinary research bridges nanomaterials, photonics, neuroscience, and rapid diagnostics. PhD in Mechanical Engineering, University of Minnesota, Twin Cities (2014) MS in Mechanical Engineering, University of Akron (2009) BS in Energy and Power Engineering, Xi'an Jiaotong University (2007) Dr. Qin's research focuses on biotransport and nanotechnology , particularly the fundamental interactions between nanomaterials and biological systems. His work explores thermo-plasmonics, bio heat and mass transfer, point-of-care diagnostics, nano biosensors, immunoassay enhancement, and cancer nanotechnology . A major thrust of his research is modulating the blood-brain and blood-brain-tumor barriers using optical and nanomaterial-based approaches to enable drug delivery for brain tumors and neurological disorders. His recent publications reveal a strong trend in optical control of biological systems , including photoswitchable nanovesicles for neuromodulation, plasmonic amplification in diagnostics, and nanoparticle-mediated barrier modulation . These works span disciplines such as neuroscience, oncology, bioengineering, and analytical chemistry, with emphasis on translational applications in diagnostics and therapy. Dr. Qin has received numerous scientific honors, including: Y.C. Fung Early Career Award, ASME (2021) NIH MIRA R35 Award (2019) Discovery Award, Department of Defense (2019) Faculty Research Award, Jonsson School (2020) Inaugural Pitch Competition Winner, MassChallenge (2022) Member, Sigma Xi (2020) He has been principal investigator on multiple major grants from NIH, NSF, CPRIT, and DoD , including an R01 on rapid RSV diagnostics and an NSF NCS-FR grant on neuropeptide modulation. He advises students in an interdisciplinary lab environment and has co-founded Avsana Labs while serving as a consultant for Acthera Therapeutics . His lab, the Nano-Thermal Bioengineering Laboratory , fosters collaboration across engineering, biology, and medicine to develop new tools for fundamental research and clinical translation.

Carmel MoranView profile

Professor

• Medical Imaging
• Ultrasound
• Preclinical Research
• +3 more

Carmel Moran is a Personal Chair of Preclinical Ultrasound at the Centre for Cardiovascular Science, University of Edinburgh, leading her research group with expertise in advanced imaging techniques. Her primary research focuses on high-frequency ultrasound and ultrasonic contrast agents, enabling real-time structural and blood flow analysis in adult, neonatal, and embryonic hearts, livers, and kidneys. She established the university's preclinical ultrasound imaging facility in 2008 through a £516K Wellcome Trust grant and recently contributed to securing additional Wellcome Trust funding for a preclinical large animal imaging facility. Current collaborations with NHS colleagues involve developing high-resolution test phantoms for preclinical imaging validation. Professor Moran's research group includes Preclinical Imaging Research Associate Adrian Thomson and advises the following students: Marion Bacou (PhD Student, IGMM) Harry Carstairs (MPhys Student, School of Physics) Adeel Shafi (PhD Student, School of Engineering) Her major funding achievements include the 2008 Wellcome Trust award establishing the preclinical ultrasound facility and the recent successful application for the preclinical large animal imaging facility. The group operates from the preclinical ultrasound imaging facility at the Queen's Medical Research Institute and is expanding capabilities through the new large animal imaging facility development.

• Neuroscience
• Medical Imaging
• Regenerative Medicine
• +3 more

Øystein Olsen is an Associate Professor at the Radiography BSc program, UiT The Arctic University of Norway, with research expertise in Magnetic Resonance Imaging (MRI) and neuroregenerative applications using manganese-based contrast agents . His work spans Central nervous system injury visualization Optic nerve regeneration tracking Alginate hydrogel contrast delivery systems Academic program evaluation Research trends include neuroimaging innovation , axon regeneration monitoring , and multimodal MRI techniques for neural repair assessment. Recent publications focus on 2019 education policy analysis 2015 radiography curriculum evaluation 2013 glial cell therapy for nerve repair

Pavlos AnastasiadisView profile

Assistant Professor

• Focused Ultrasound
• Brain Tumors
• Glioblastoma
• +5 more

Pavlos Anastasiadis, PhD, serves as an Assistant Professor at the University of Maryland, Baltimore School of Medicine with primary appointment in the Department of Neurosurgery and secondary appointments spanning Diagnostic Radiology and Nuclear Medicine. Primary Department: Neurosurgery Secondary Departments: Diagnostic Radiology Nuclear Medicine His research program pioneers ultrasound-based neurological interventions, focusing on tumor ablation (particularly glioblastoma), blood-brain barrier permeabilization, and seizure management. Key methodologies include microbubble-enhanced focused ultrasound, contrast agent development, and computational modeling of acoustic-biological interactions to optimize therapeutic outcomes. Scientific Awards: No awards documented in source material Advising and Funding: Source text provides no details regarding graduate student mentorship, research grants, or sponsored projects. Contact information indicates active institutional engagement via email (panast@som.umaryland.edu) and phone (410-706-4453). Laboratory infrastructure and collaborative research teams are not described in the available profile data.

• Molecular simulation
• Computational chemistry
• Rare earth elements
• +3 more

David Cantu is an Associate Professor in the Department of Chemical and Materials Engineering at the University of Nevada, Reno, College of Engineering. Based in the Scrugham Engineering and Mines Building (room 208), he teaches core chemical engineering courses including Computer Applications in Chemical Engineering, Process Control, and Separation Processes. His research spans molecular simulation, computational chemistry, rare earth elements, separations, enzymes, and bioinformatics. He employs advanced computational methods to investigate lanthanide complex structures, protein family evolution, and sustainable separation techniques for critical materials, with particular emphasis on gadolinium and europium systems for medical and environmental applications. Recent publications (2023-2025) demonstrate a dominant focus on lanthanide chemistry through molecular dynamics and X-ray absorption spectroscopy, revealing structural insights into complex stability, magnetic properties, and separation mechanisms. Concurrent work applies submodular optimization to protein sequence analysis and develops sustainable waste management strategies for radioiodine capture. Dr. Cantu's research excellence is recognized through prestigious awards: CAREER Award, National Science Foundation (2021) New Investigator Award, American Chemical Society Petroleum Research Fund (2019) While specific advising details and grant portfolios beyond the cited awards are not documented in source materials, his work integrates computational chemistry with practical separation engineering challenges. Laboratory facilities and collaborative teams are not explicitly described in available documentation.

Nitesh ChawlaView profile

Professor

• Artificial Intelligence
• Data Science
• Network Science
• +3 more

Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame, where he serves as the founding Director of the Lucy Family Institute for Data and Society. His research focuses on artificial intelligence, data science, and network science, with a mission to advance technology for the common good through interdisciplinary collaboration. Education: Ph.D. in Computer Science and Engineering, University of South Florida, 2002 M.S. in Computer Science, University of South Florida, 2000 B.E. in Computer Science and Engineering, University of Poona, 1997 Research Focus: Chawla pioneers work at the intersection of AI and societal impact, specializing in machine learning fairness, graph neural networks, federated learning, and large language model applications. His lab addresses real-world challenges in healthcare, education, and social equity through innovative data-driven methodologies. Publication Trends: Recent work (2023-2025) demonstrates a strong emphasis on foundation models for graphs, ethical AI deployment, and human-centered applications. Key themes include LLM robustness, federated learning architectures, bias mitigation, and educational AI, reflecting a consistent focus on scalability and societal relevance. Awards & Honors: Fellow, American Association for the Advancement of Science (AAAS) AAAI Fellow (2024) ACM Fellow IEEE Fellow (2022) Leadership: Directs the Lucy Family Institute for Data and Society, an interdisciplinary hub developing data science solutions for global challenges. The institute fosters collaborations across academia, industry, and communities to translate research into measurable social impact.

• Radiation Oncology
• Medical Physics
• Dosimetry
• +3 more

Svetlana Kuznetsova is an Assistant Professor in the Department of Therapeutic Radiology at Yale School of Medicine. She works at Yale New Haven Hospital as a Medical Physicist, focusing on advancing radiation oncology methodologies through clinical research and standardization of tools. Ph.D. in Medical Physics, University of Calgary (2021) B.Sc. (Honors) in Physics and Astronomy, University of Calgary (2015) Medical Physics Residency, University of California, San Diego (2023) Her research spans radiation therapy optimization, including: SBRT (Stereotactic Body Radiation Therapy) for liver and abdominal cancers Deformable image registration for multi-modal treatment planning Dosimetric analysis of critical organ interactions Development of auto-delineation frameworks for post-treatment MRI scans Characterization of dosimeters for low-energy clinical applications Her recent publications focus on prostate cancer contouring variability (2024), SBRT liver treatment accuracy (2021), and abdominal compression dosimetry (2019), reflecting a consistent emphasis on precision radiation delivery across tumor sites. Scientific recognitions include: NSERC Postgraduate Doctoral Scholarship IOMP Best Oral Presentation Award (2018) Ph.D. thesis nominated for Governor General Gold Medal Kuznetsova's work bridges clinical physics challenges with innovative technical solutions, particularly in geometric accuracy verification and cross-modality imaging integration for stereotactic radiotherapy.