جست‌وجوی استاد و پژوهشگر

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +۵ مورد دیگر

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

Veronique Pepinمشاهده پروفایل

استاد

• Cardiopulmonary exercise testing
• Cardiopulmonary rehabilitation
• Exercise-training interventions for chronic disease
• +۲ مورد دیگر

Veronique Pepin, PhD, is a Professor in the Department of Health, Kinesiology & Applied Physiology at Concordia University and serves as the current interim dean of the School of Health. She holds a researcher position at the Centre de recherche du CIUSSS-NIM, based at the Centre Jean-Jacques-Gauthier, where she has established research infrastructure in cardiopulmonary exercise testing (CPET) and pulmonary rehabilitation. She is also a member of the Executive Committee of the Canadian Thoracic Society. Her research focuses on CPET, exercise interventions for chronic diseases (e.g., COPD), and physical activity monitoring. She has expertise in kinesiology, clinical exercise physiology, and translational research in respiratory health. No specific grants, advising details, or lab affiliations are explicitly listed in the provided text. Her educational background is noted but lacks specific degree details.

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +۵ مورد دیگر

João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .

Allan Davidمشاهده پروفایل

استاد

• Smart Materials
• Nanomedicine
• Cancer Research
• +۴ مورد دیگر

Allan David serves as the John W. Brown Professor of Chemical Engineering and Associate Dean for Research at Auburn University's Samuel Ginn College of Engineering. His leadership extends across academic administration and cutting-edge nanomedicine research, with a focus on translating laboratory discoveries into clinical applications. His educational foundation includes: Ph.D. in Chemical Engineering, University of Maryland B.S. in Chemical Engineering, University of Maryland Dr. David's research program pioneers nanomedicine applications through the development of smart materials for cancer diagnostics and therapy. His work spans nanoparticle-based MRI contrast agents , ocular drug delivery systems , and vaccine delivery platforms , with particular emphasis on optimizing physicochemical properties for targeted biological interactions. Current projects address critical healthcare challenges including safer contrast agents for patients with kidney impairment and precision cancer targeting mechanisms. Analysis of his 15 most recent publications reveals a cohesive research trajectory centered on magnetic nanoparticles and biomimetic delivery systems . The work demonstrates increasing translational focus, evolving from fundamental nanoparticle characterization (2020-2021) to clinically relevant applications like ocular delivery and cancer theranostics (2022-2024), culminating in commercialization efforts through NanoXort, LLC. Dr. David has secured significant research funding including an $184,773 grant from the Alabama Department of Economic and Community Affairs (ADECA) for developing cardiovascular MRI agents. He leads collaborative efforts that bridge chemical engineering with biomedical innovation, notably co-founding NanoXort, LLC to commercialize safer MRI contrast agents addressing gadolinium toxicity concerns for renal-impaired patients. His laboratory operates at the intersection of chemical engineering and medicine, focusing on nanoparticle-cell interactions and targeted delivery systems. The research group maintains strong industry partnerships through the NanoXort startup, which has secured $1 million NSF funding to advance MRI contrast agent technology toward clinical implementation.

Sadaf Salehkalaibarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Generative models
• +۵ مورد دیگر

Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.

Sean Reardonمشاهده پروفایل

استاد

• Social Inequality
• Educational Inequality
• School Segregation
• +۶ مورد دیگر

Sean Reardon is the endowed Professor of Poverty and Inequality in Education at Stanford University's Graduate School of Education and Professor (by courtesy) of Sociology. He serves as Director of the Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis and is a Senior Fellow at the Stanford Institute for Economic Policy Research. Reardon is also a member of the National Academy of Education and the American Academy of Arts and Sciences. His educational background includes an Ed.D. in Educational Administration, Planning, and Social Policy from Harvard Graduate School of Education (1997), M.Ed. from Harvard Graduate School of Education (1992), M.A. in International Peace Studies from University of Notre Dame (1991), and B.A. in Program of Liberal Studies with a minor in Honors Mathematics from University of Notre Dame (1986). Reardon's research investigates the causes, patterns, trends, and consequences of social and educational inequality, with particular focus on residential and school segregation and racial/ethnic and socioeconomic disparities in academic achievement. He develops methods for measuring social and educational inequality, including segregation and achievement gaps, and advances causal inference methods in educational research. His work has significantly shaped understanding of how poverty and school segregation impact educational outcomes across America. Analysis of Reardon's 15 most recent publications reveals a consistent focus on educational inequality, with particular emphasis on measuring achievement gaps, school segregation patterns, and the relationship between socioeconomic status and educational outcomes. His research increasingly utilizes large-scale administrative datasets, most notably the Stanford Education Data Archive (SEDA), which he developed based on 300 million standardized test scores to provide measures of educational opportunity across all U.S. public school districts. William T. Grant Foundation Scholar Award National Academy of Education Postdoctoral Fellowship Andrew Carnegie Fellow Member of the National Academy of Education Member of the American Academy of Arts and Sciences Reardon directs the Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis, mentoring the next generation of education researchers. His work has been supported by major research grants that have enabled the development of the Stanford Education Data Archive, a groundbreaking resource that provides detailed metrics on educational opportunity across all U.S. public school districts. This archive has become a critical tool for policymakers and researchers seeking to understand and address educational inequality. As developer of the Stanford Education Data Archive, Reardon leads a significant research initiative that has transformed how educational opportunity is measured and understood across the United States. His work has established new methodologies for analyzing large-scale educational data and has provided policymakers with concrete evidence about the relationship between poverty, school segregation, and academic achievement.

Christopher Piechمشاهده پروفایل

استادیار

• Computer Science Education
• Educational Data Science
• Artificial Intelligence in Education
• +۳ مورد دیگر

Christopher Piech is an Assistant Professor (Teaching) in the Department of Computer Science at Stanford University, with a courtesy appointment in the Graduate School of Education. He serves as a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and is affiliated with the Symbolic Systems Program. Current courses: AI for Social Good (CS 21SI), Introduction to Probability for Computer Scientists (CS 109), Researching Presenting and Publishing Work in AI & Education (CS 220/EDUC 481) Advises 11 Master's students and co-advises 3 Doctoral students His research focuses on computational education, leveraging artificial intelligence to enhance learning analytics, student collaboration detection, and knowledge tracing in programming education. Publications span ACM Technical Symposium on Computer Science Education (SIGCSE) and NeurIPS conferences. Key article trends include: (1) AI-driven educational tools for code analysis, (2) collaboration monitoring in large classes, and (3) probabilistic models for student learning trajectories.

Danial Chitnisمشاهده پروفایل

مدرس

• Microelectronics
• Biomedical Engineering
• Quantum Technology
• +۳ مورد دیگر

Dr. Danial Chitnis is a Chancellor's Fellow and Lecturer in Electronics at the School of Engineering, University of Edinburgh. He holds a DPhil in Engineering Science from the University of Oxford (2013) and has expertise in microelectronics, biomedical engineering, and quantum imaging. His research focuses on SPAD arrays, time-of-flight sensors, and wearable optical systems for biomedical applications. Education: BSc in Electronics Engineering, Chamran University of Ahvaz (2002–2007) MSc in Advanced Microelectronics Systems Engineering, University of Bristol (2007–2008) DPhil in Engineering Science, University of Oxford (2009–2013) Research Interests: Single-Photon Avalanche Diode (SPAD) arrays for optical communications and biomedical imaging Quantum-enhanced imaging via QuantIC Hub Wearable sensors for near-infrared spectroscopy (NIRS) AI-driven automation in test and measurement systems Articles Trends: Recent work emphasizes AI integration in electronics design, photon-counting receivers for 6G networks, and portable biomedical devices. Notable contributions include SYCL-based acceleration of circuit simulations and FPGA-driven time-to-digital converters. Grants & Collaborations: Principal Investigator of multiple grants, including EPSRC-funded projects on AI-enhanced human-machine interfaces and quantum technology applications. Collaborates with UCL, QuantIC, and industry partners like Keysight Technologies. Labs/Teams: Co-investigator at QuantIC, the UK Quantum Technology Hub in Quantum Enhanced Imaging. Leads interdisciplinary research on detector arrays and systems for quantum physics and consumer cameras.

Maithilee Kundaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computational Cognitive Systems
• Visual Reasoning
• +۳ مورد دیگر

Maithilee Kunda is an Associate Professor in the Department of Computer Science and Computer Engineering at Vanderbilt University's School of Engineering. She leads the Laboratory for Artificial Intelligence and Visual Analogical Systems and co-leads the Vanderbilt Initiative for Autism, Innovation, and the Workforce. Her research focuses on AI-driven cognitive systems, particularly visual reasoning and technology applications for neurodiverse populations, including autism spectrum disorder. Dr. Kunda holds a B.S. in Mathematics with Computer Science from MIT (2007) and a Ph.D. in Computer Science from Georgia Tech (2013). She was honored as an MIT Technology Review Innovator Under 35 in 2016 for her groundbreaking work. Her research bridges computational models of visual thinking and real-world impact, emphasizing neurodiverse cognition. Notable projects include developing AI systems for autism-related social skills interventions, designing visual analogy problem-solving frameworks, and advancing nonverbal communication understanding in AI. Her work addresses challenges in abstract reasoning tasks (e.g., Raven's Matrices), visual imagery-based learning, and ethical AI integration. She advocates for inclusive technology design, leveraging both generative AI and cognitive principles to create human-centered solutions. Key awards: MIT Technology Review Innovator Under 35 (2016) Her labs focus on practical AI applications for education, healthcare, and workforce development, emphasizing interdisciplinary collaboration between engineering, psychology, and neuroscience.

Brendan Dolan-Gavittمشاهده پروفایل

دانشیار

• Cybersecurity
• Program Analysis
• Virtualization Security
• +۵ مورد دیگر

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.

Abdulkadir C. Yucelمشاهده پروفایل

استادیار

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +۷ مورد دیگر

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

Zahra Abbasiمشاهده پروفایل

استادیار

• Applied Electromagnetics
• RF/Microwave Sensing
• Chipless Sensors
• +۵ مورد دیگر

Dr. Zahra Abbasi is an Assistant Professor at the Department of Electrical and Software Engineering, University of Calgary (since 2021), with expertise in designing passive and active RF/microwave sensors for non-invasive real-time detection and monitoring. She holds a PhD in Nanosystems and Microsystems from the University of Alberta (2020) and a postdoctoral fellowship at the Waterloo Microfluidics Lab (University of Waterloo). Her research focuses on sensor systems for environmental, biomedical, and industrial applications, including microfluidic sensing, chipless tags, and wearable devices. Dr. Abbasi’s educational background includes degrees from Iran University of Science & Technology (BSc 2014, MSc 2016) and advanced training in Canada. She chairs IEEE MTT-S/AP committees in Alberta and has received notable awards such as the ASTech Finalist (2024) and Alberta Immigrant Impact Award (2023). Her work emphasizes collaboration across disciplines to address challenges in precision agriculture, healthcare, and environmental monitoring. Her sensor research spans diverse fields: microplastic detection in water, hydrocarbon contamination monitoring, real-time medical diagnostics (e.g., fibrinogen levels), and nutrient assessment in agriculture. Recent advancements include disposable sensors for medical implants and non-invasive hydration tracking for aging populations. Awards Finalist: ASTech Early Career Academic Change Maker (2024) Early Research Excellence Award (Schulich School of Engineering, 2023/2024) Alberta Immigrant Impact Award (2023) Advising & Labs Dr. Abbasi leads the Calgary Sensor Laboratory, fostering innovation in microwave and microfluidic sensor technologies. Her team collaborates on industrial and biomedical projects, emphasizing practical applications of novel sensor designs. While no named advisees are listed, her research group actively engages in multidisciplinary projects.

Panteleimon Ekkekakisمشاهده پروفایل

استاد

• Exercise Psychology
• Affective Responses to Exercise
• Public Health
• +۵ مورد دیگر

Panteleimon Ekkekakis is a Professor and Chair of the Department of Kinesiology at Michigan State University’s College of Education. His research focuses on affective responses to exercise, including their psychobiological mechanisms and implications for exercise behavior. He holds a Ph.D. from the University of Illinois, Urbana-Champaign. Ekkekakis’ work explores the pleasure and displeasure associated with exercise, emphasizing their role in predicting long-term adherence. His research spans topics like high-intensity interval training (HIIT), obesity, mental health interventions, and physical education practices. Notably, he critiques methodological flaws in HIIT studies and advocates for evidence-based exercise guidelines. His recent articles highlight critical analyses of HIIT efficacy, the role of affective valence in exercise adherence, and the need for paradigm shifts in obesity treatment. He emphasizes the importance of personalized exercise programs that prioritize subjective enjoyment to enhance public health outcomes. While no specific awards are listed, his contributions to exercise psychology and policy advocacy are significant. His research has informed debates on fitness testing in schools and the integration of exercise into clinical depression treatment.

Lina Bernaolaمشاهده پروفایل

استادیار

• Host Plant Resistance
• Plant-Microbe-Insect Interactions
• Integrated Pest Management
• +۳ مورد دیگر

Lina Bernaola is an Assistant Professor in the Department of Entomology at Texas A&M University AgriLife Research. She holds a Ph.D. in Entomology and an M.S. in Agronomy from Louisiana State University, alongside a B.S. in Biology from Universidad Nacional Mayor de San Marcos (Peru). Her research focuses on plant-insect interactions, particularly rice pests, and explores the role of arbuscular mycorrhizal fungi (AMF) in enhancing plant defense systems. She develops sustainable pest management strategies integrating agronomy, biochemistry, and ecology. Her expertise spans Host Plant Resistance, Plant-Microbe-Insect Interactions, and Integrated Pest Management. Recent work includes studies on rice delphacid control, biocontrol mechanisms, and the ecological impacts of mycorrhizal symbiosis. She is recognized for her leadership, becoming the first Latina president of the Entomological Society of America (ESA). Education: B.S. Biology, Universidad Nacional Mayor de San Marcos M.S. Agronomy, Louisiana State University Ph.D. Entomology, Louisiana State University Awards/Honors: Incoming President, Entomological Society of America (2023). Her research bridges fundamental and applied entomology, aiming to improve crop resilience through multidisciplinary approaches. Collaborations emphasize translational science for practical agricultural solutions.

Christoph Freiمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +۴ مورد دیگر

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.