جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Natural Language Processing (NLP)
• Machine Learning
• +۶ مورد دیگر

Dr. Rejwanul Haque is a Lecturer in the Department of Computing at South East Technological University (SETU), Carlow, Ireland. He holds a PhD in Computer Science and Engineering from Dublin City University (DCU), where he was supported by an SFI PhD Fellowship (2008–2011). His career spans industry and academia: he contributed to MT systems at Lingo24 and Applied Language Solutions, developed cloud-based MT solutions like SmartMATE, and led industry-oriented research at ADAPT Centre for Huawei Noah’s Ark. He was a Research Fellow at ADAPT (2019–2021) with Marie Sklodowska-Curie Fellowship funding, and Programme Director for MSc AI at National College of Ireland (2020–2022). His research focuses on AI, NLP, and machine translation challenges, including low-resource translation, ethical MT use, and adaptive systems. He has published 70+ peer-reviewed papers and collaborates with institutions like DCU, Trinity College Dublin, and French universities. He currently supervises two PhD students at SETU and secured funding for SETU’s PhD programme. Education: PhD in Computer Science and Engineering (Dublin City University, 2011) Research Interests: Dr. Haque’s work addresses cutting-edge NLP topics such as interactive machine translation, fairness in AI, domain adaptation, and sustainable MT models. He explores applications in biomedical translation, social media analytics, and question-answering systems. His recent projects include developing adaptive MT systems using large language models and improving low-resource language translation through novel techniques. Grants & Collaborations: He secured €189,924 via a Marie Sklodowska-Curie Fellowship and attracted SETU PhD scholarships (2023–2024). Collaborators include Microsoft Ireland, ADAPT Centre, and researchers from University Saint-Etienne, La Rochhelle University, and TCD. His work on the Citizen’s Assembly for COVID-19 earned the ADAPT Recognition Award (2020). Labs/Teams: Active contributor to the ADAPT Centre’s NLP initiatives and collaborates with DCU’s MT group. Engages in cross-institutional projects with French academic partners to advance multilingual systems and ethical AI practices.

Barbara Brodskyمشاهده پروفایل

استاد پژوهشی

• biophysics
• collagen
• protein structure

Barbara Brodsky is a Research Professor in the Department of Biomedical Engineering at Tufts University, part of the School of Engineering. She previously served as a faculty member at the Department of Biochemistry at Robert Wood Johnson Medical School (Rutgers University) from 1975 to 2010, reaching the rank of Professor and Deputy Chair. Her research focuses on collagen's structural and functional roles in tissues, using model peptides and recombinant bacterial collagen to study hereditary disorders and biomaterial applications. Education: B.A. in Physics, Brandeis University (1964) Ph.D. in Biophysics, Harvard University (1970) Research Interests: Brodsky's work explores collagen's structural features and their impact on tissue mechanics, cell adhesion, and signaling. Her research clarifies how collagen mutations lead to bone disorders, kidney disease, and vascular pathologies. Current projects include developing recombinant collagen-based biomaterials to promote bone regeneration via stem cells. Awards: Helen Hay Whitney Fellowship (postdoctoral phase) Grants & Advising: Maintained continuous NIH funding during her Rutgers tenure. No student advisees listed here, though her research has likely involved mentoring graduate students. Current focus includes NIH-supported projects. Labs/Teams: Her work leverages advanced biochemical and structural analysis techniques, though specific lab names are not mentioned in the provided text.

• Medical Imaging
• Haptic Systems
• Computer Graphics
• +۶ مورد دیگر

Prof. Matthias Harders is a Professor at the Department of Computer Science, University of Innsbruck. His work focuses on medical imaging, haptic systems, virtual reality, and data-driven simulation. He leads research in interactive visualization tools, medical device development, and machine learning applications in healthcare and environmental engineering. Research areas include haptic augmented reality for surgical training, deformable medical image registration, and synthetic data generation for retinal imaging. Notable projects include SPBView for eye movement analysis and the PoRi device for post-stroke rehabilitation. His work bridges computer science with biomedical applications, emphasizing real-world impact in healthcare technology. Publications span medical simulation, machine learning for biogas prediction, and perceptual interfaces. He collaborates on EU-funded projects involving VR/AR systems and has contributed to open-source tools for point cloud analysis and surgical planning.

• Medical Education
• Cancer Research
• Health Inequalities
• +۳ مورد دیگر

Dr. Humaira Khan is an Associate Dean for EDI and Lecturer at the School of Pharmacy & Medical Sciences, Faculty of Life Sciences, University of Bradford. Her roles include academic leadership and teaching in medical education. She has expertise in cancer research, particularly prostate cancer metastasis models and sigma receptor compound analysis, alongside a focus on health inequalities and equitable access to biosimilar medicines. Her current research emphasizes Education Innovation Research and Development (EIRD), innovative outreach programs, and widening participation in higher education. Strategic partnerships include collaborations with GlaxoSmithKline (UK), Pasteur Institute (France), Biomedical Research Foundation Academy of Athens (Greece), University of Miami (US), and University of Huddersfield (UK). No specific scientific awards or grants are explicitly mentioned in the text. Advising records and lab affiliations are not detailed here.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Prof Sunil Vadera is a Professor of Computer Science at the University of Salford, affiliated with the School of Science, Engineering & Environment. He holds fellowships from the British Computer Society (FBCS) and is a Chartered Engineer (CEng) and Chartered IT Professional (CITP). His leadership roles include former Dean of the School of Computing, Science and Engineering, and Director of the Informatics Research Institute. He received the UK BDO Best Indian Scientist and Engineer award in 2014 and the Amity Award for AI contributions in 2018. His research focuses on bridging theory and practice in AI, with projects like the GM AI Foundry supporting SMEs, EU-funded energy-efficient building systems, and smart meter data analysis for British Gas. His work spans AI applications in healthcare, cybersecurity, IoT, and energy systems. He leads the Deep Learning module in the MSc in AI program and has supervised multiple PhD students, including Dr Salem Ameen. Key research interests include cost-sensitive machine learning, explainable AI, phishing detection, and medical imaging. Recent publications address AI-driven solutions in healthcare (e.g., breast arterial calcification detection), cybersecurity (Arabic phishing emails), and livestock behavior analysis using vision transformers. He has pioneered algorithms like CSNL and EECSDT, advancing decision tree and Bayesian network methodologies. Prof Vadera’s contributions extend to academic leadership, including chairing the BCS Accreditations Committee. His interdisciplinary projects often involve collaborations with industry and government, emphasizing real-world impact. The Informatics Research Institute under his direction fosters innovative AI applications across sectors.

Shirley E. Millsمشاهده پروفایل

دانشیار

• Applied Statistics
• Data Mining
• Statistical Machine Learning
• +۴ مورد دیگر

Shirley E. Mills is an Associate Professor in the Department of Mathematics & Statistics at Carleton University. She specializes in applied statistics, data mining, and statistical machine learning, with a focus on clustering and classification of high-dimensional data in security and bioinformatics. She co-leads the Carleton-Fields Centre for Quantitative Analysis and Modelling (CQAM) Lab for Anomalous Event Detection, Modelling and Prediction. Her research interests emphasize practical applications of statistical methodologies to real-world challenges, particularly in security and biomedical contexts. The CQAM Lab under her co-leadership focuses on developing predictive models and anomaly detection systems, reflecting her commitment to interdisciplinary quantitative analysis. No specific awards, grants, or advising records are detailed in the provided text, though her roles suggest active participation in academic and research leadership.

Erin Treacy Soloveyمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Human-AI Interaction
• Brain-Computer Interfaces
• +۶ مورد دیگر

Dr. Erin Solovey is an Associate Professor of Computer Science at Worcester Polytechnic Institute (WPI) and a 2024-25 Harvard-Radcliffe Institute Fellow. Her research focuses on Human-Computer Interaction (HCI) and Human-AI Interaction , with expertise in brain-computer interfaces , physiological computing , and accessibility technologies for the Deaf community. She holds affiliations with WPI’s Robotics Engineering , Artificial Intelligence , and Interactive Media & Game Development programs. Education: AB in Computer Science (Harvard, 2001), MS/PhD in Computer Science (Tufts, 2007/2012) Postdoctoral: MIT’s Humans and Automation Lab Her research designs adaptive systems using machine learning to interpret physiological signals (e.g., fNIRS, radar) for applications in education , transportation , and decision-making . Recent work includes ASL-centric survey tools and IndexPen (radar-based text input). She leads the Human-Computer Interaction Lab and directs NSF-funded Research Experiences for Teachers (RET) programs focused on the UN Sustainable Development Goals. Dr. Solovey serves as Deputy Editor of the International Journal of Human-Computer Studies (2019-2024) and Associate Editor for ACM Transactions on Computer-Human Interaction (TOCHI). She has received tenure and promotion (2022), multiple NSF grants , and best paper honors at CHI, TOCHI, and IEEE conferences.

Giridhar Reddy Bojjaمشاهده پروفایل

استادیار

• Business Analytics
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Giridhar Reddy Bojja is an Assistant Professor of Information Systems & Analytics at the College of Business, Michigan Technological University. His academic office is located in Academic Office Building G018. He holds a strong focus on interdisciplinary research and teaching in data-driven fields. Research Interests: Information Systems Capabilities and Firm Performance Social Media Analytics and Healthcare Applications Econometric Modeling in Technology Impact Design Science Research Methodologies Teaching Specializations: Business Analytics (ML, DL, Text Mining) Generative AI and Its Applications Management Science and Social Network Analysis Research Themes: His work spans AI-driven IoT security frameworks, healthcare data analytics, blockchain-enabled healthcare systems, and organizational impact of IT investments. Recent projects include predictive models for patient outcomes and frameworks for resilient IoT networks. Professional Background: His research has been published in top-tier journals and conferences, with a focus on healthcare IT, cybersecurity, and AI applications. He actively contributes to both theoretical and applied research in information systems.

Jingfeng Jiangمشاهده پروفایل

استاد

• Biomechanics
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Jingfeng Jiang is a Professor of Biomedical Engineering and Graduate Program Director at Michigan Technological University. His research focuses on translational biomedical engineering, particularly in biomechanics, medical imaging, and machine learning applications. He holds a PhD in Civil Engineering (Computational Mechanics) from the University of Kansas, along with MS degrees in Computer Science and Structural Engineering, and a BS in Civil Engineering from Zhejiang University, China. Research Interests : Dr. Jiang’s work bridges biomechanics, medical imaging, and computational intelligence. He develops tools for transforming biomedical imaging data into clinically relevant parameters, such as soft tissue elasticity and blood flow characteristics. His current projects include real-time elastography systems, AI-driven medical imaging analysis, and precision medicine tools for cardiovascular and oncological applications. He collaborates closely with clinical and industrial partners, notably contributing to Siemens’ real-time elastography system. Education : PhD, Civil Engineering (Computational Mechanics), University of Kansas MS, Computer Science, University of Kansas MS, Structural Engineering (Structural Dynamics), Zhejiang University, China BS, Civil Engineering, Zhejiang University, China Grants and Collaborations : Dr. Jiang’s work involves clinical and industrial collaborations, including contributions to commercial medical imaging systems. His lab focuses on advancing ultrasound technologies, computational fluid dynamics modeling, and AI-driven diagnostics for cardiovascular diseases and cancers. Labs and Future Work : His research lab aims to improve precision medicine through tool development in medical imaging and biomechanics. Current open positions include doctoral students and post-doctoral researchers to support projects in aneurysm rupture prediction, liver fibrosis characterization, and AI-driven diagnostic systems.

Victor DeBrunnerمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Pattern Recognition
• +۶ مورد دیگر

Victor DeBrunner is a Professor in the Department of Electrical & Computer Engineering at Florida A&M University–Florida State University. He holds a Ph.D. in Electrical Engineering from Virginia Tech (1990), an M.S. from Virginia Tech (1986), and a B.S. from Auburn University (1984). His research focuses on signal processing, image processing, and pattern recognition, with a strong emphasis on hardware-efficient implementations of convolution algorithms and transforms like the Hirschman Transform. His work often explores FPGA-based optimizations for real-time applications, low-latency systems, and efficient digital signal processing architectures. DeBrunner’s recent publications (2018–2025) highlight trends in convolution algorithm design, transform-based signal analysis, and hardware-software co-design for DSP systems. He has contributed to advancements in filter sensitivity prediction, sparse algorithms for DFT computations, and reduced-complexity convolution methods. Notable topics include FPGA-optimized architectures for 2D convolution, efficient neural network implementations using Hirschman transforms, and latency-reduction strategies in linear convolution systems. His research extends to pedagogical innovations in introductory circuits education, emphasizing student engagement through active learning methods. While no awards are explicitly listed in the provided texts, his prolific publication record indicates sustained academic impact in signal processing and hardware design. DeBrunner’s work frequently involves collaborations on structural health monitoring systems, bridge displacement measurement via accelerometers, and adaptive filtering techniques for noise cancellation. He has also explored applications in weather radar signal processing and biomedical imaging quality metrics.

Nicole McFarlaneمشاهده پروفایل

دانشیار

• Electrical Engineering
• Microelectronics
• Biosensors
• +۳ مورد دیگر

Nicole McFarlane is an Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK), where she also holds the TCE Advance Professor title. She is affiliated with the Tickle College of Engineering and leads the Micro-Bio-Electronics Group (MLAB), focused on advanced sensor technologies. Education: Nicole McFarlane earned a PhD in Electrical Engineering from the University of Maryland, College Park (2010), and dual bachelor's (BS, 2001) and master's (MS, 2003) degrees in Electrical Engineering from Howard University, Washington D.C. Research Interests: Her work centers on developing compact, efficient sensor systems using mixed-signal CMOS strategies and nanotechnology. Applications span electrochemical biosensors, point-of-care medical devices, nuclear imaging panels, and hardware security for embedded systems. The MLAB lab emphasizes cost-effective, scalable solutions for healthcare, military, and research domains. Article Trends: Recent publications (2024) highlight innovations in perimeter-gated SPAD arrays for cryptographic security, silicon photomultipliers for nuclear imaging, and asynchronous readout architectures. 2023 work includes multimodal gesture recognition systems and pH sensors with integrated encryption. These studies reflect her focus on merging cutting-edge CMOS technologies with secure, multifunctional sensor design. Scientific Awards: No awards explicitly listed in the provided texts. Advising & Grants: Nicole advises students in the Kao Department's graduate and undergraduate programs. While specific advisee names are not provided here, her research is supported by grants in microelectronics and sensor integration. She has pioneered portable biosensor systems, including wireless pH-impedance devices and encryption architectures for wearable health monitors. Labs & Teams: As founder/leader of the Micro-Bio-Electronics Group (MLAB), she oversees projects combining CMOS fabrication with nano/micro-fabrication techniques. The lab explores applications ranging from implantable medical sensors to battlefield-ready detection systems, emphasizing interdisciplinary collaboration.

Wei Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Imaging
• Clinical Research
• +۵ مورد دیگر

Wei Zhu is a Professor and Deputy Chair in the Department of Applied Mathematics and Statistics at Stony Brook University. She holds a Ph.D. in Biostatistics from UCLA (1996), an M.S. in Statistics from UIC (1992), and a B.S. in Mathematics from East China Normal University (1989). Her research focuses on biostatistics, brain image analysis, clinical trial design, and environmental modeling. Key areas include medical imaging algorithms, climate prediction systems, and statistical methodologies for biomedical studies. Her work spans interdisciplinary applications such as predictive analytics for kidney disease outcomes, synthetic data-driven climate forecasting, and cryptocurrency crash prediction frameworks. She has developed machine learning models for medical image segmentation and decision tree frameworks for risk stratification in pandemic contexts. Teaching includes courses like AMS 312 and AMS 572. Zhu’s research portfolio emphasizes translational statistics, with contributions to genomic modeling, financial market dynamics, and public health policy evaluation. Her lab addresses pressing challenges in healthcare analytics, environmental science, and computational biomedicine through rigorous statistical innovation.

Xinchen Niمشاهده پروفایل

استادیار

• Mechanics
• Programmable Materials
• Soft Robots
• +۳ مورد دیگر

Xinchen Ni is an Assistant Professor in the Department of Mechanical Engineering at The University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on mechanics, programmable materials, soft robotics, and machine learning applications in materials science. Education : PhD in Mechanical Engineering, Massachusetts Institute of Technology (2020) SM (Master's) in Mechanical Engineering, MIT (2014) BS in Mechanical Engineering, Fudan University (2012) Research Interests : Ni explores advanced composites, bioresorbable materials, and soft robotics with applications in medical devices and wearable technology. His work integrates machine learning for materials analysis and combines experimental techniques like synchrotron X-ray tomography with computational modeling to study fracture mechanics and dynamic material behavior. Key Themes in Articles : Recent publications emphasize programmable shape-morphing materials, self-powered drug delivery systems, and wireless sensor networks for biomedical applications. His work bridges materials science, robotics, and medical engineering, with a focus on adaptive systems and smart materials. Awards and Recognition : Details pending explicit mention in text. Lab and Collaborations : He leads the Ni Research Group, focusing on interdisciplinary projects in soft robotics, bio-integrated electronics, and advanced materials. Collaborations span institutions like MIT and Johns Hopkins University.

Madhu Kannanمشاهده پروفایل

استادیار

• Neuroscience
• Neurobiology
• Cellular Neuroscience
• +۳ مورد دیگر

Madhu Kannan, PhD, is an Assistant Professor in the Department of Orthopedic Surgery at the University of Minnesota. Their research focuses on neuroscience, neurobiology, and neurotechnology, with a particular emphasis on dopamine signaling, neuronal dynamics, and advanced imaging techniques. Key areas of investigation include the interplay between dopamine and memory systems, voltage imaging in behaving mammals, and the development of novel genetic tools for studying neuronal activity. Research interests span cellular neuroscience, neurophysiology, and neurotechnology, with contributions to understanding axon growth mechanisms, ubiquitin ligase pathways in neuronal survival, and the impact of environmental factors on neural development. Dr. Kannan’s work bridges molecular biology, biophysics, and behavioral studies, aiming to uncover fundamental mechanisms underlying neural function and disease. Publications highlight advancements in voltage-sensitive indicators, dual-polarity imaging systems, and insights into neurodegenerative processes. The research trajectory demonstrates a strong focus on translational neurobiology, combining cutting-edge imaging technologies with molecular and genetic approaches. No scientific awards or grants are listed in the provided information. No advisees or students are explicitly mentioned in the text.

Mikhail Balobanمشاهده پروفایل

عضو هیئت علمی

• Near-Infrared Imaging
• Optogenetics
• Fluorescent Protein Engineering
• +۳ مورد دیگر

Mikhail Baloban is a Research Assistant Professor in the Department of Genetics at the Albert Einstein College of Medicine. His work focuses on engineering fluorescent proteins and optogenetic tools for advanced imaging and gene regulation. He specializes in near-infrared technologies, bacterial phytochromes, and genetic tools for biomedical applications. His research bridges biophysics, protein engineering, and neuroscience, with a particular emphasis on developing tools for deep-tissue imaging and non-invasive optogenetic control. Key contributions include the design of red-shifted fluorescent proteins (e.g., miRFP704nano), near-infrared optogenetic systems (iLight2), and SWIR imaging platforms. His studies explore protein structure-function relationships, monomer-dimer dynamics, and photoconversion mechanisms in bacterial phytochromes. Baloban’s work also addresses challenges in nuclear localization signals for neuronal tools and antigen-dependent fluorescent nanobodies. His publications highlight advancements in multiscale imaging, genetic tool optimization, and the application of these technologies to study cellular processes in vivo. Despite no listed awards, his prolific output (over 20 publications from 2017–2025) underscores his active research role. Advising and grants are not detailed in the provided text, but his lab likely focuses on protein engineering and biomedical imaging innovations.