جست‌وجوی استاد و پژوهشگر

• Materials Science
• Mechanical Engineering
• Energy Systems
• +۵ مورد دیگر

Vikas Tomar is a Professor in the School of Aeronautics and Astronautics at Purdue University, where he has served since 2006. He holds a Ph.D. from Georgia Institute of Technology (2005), an MBA from UC Berkeley (2023), and Indian degrees from IIT Madras and NIT Kurukshetra. His research focuses on materials in extreme environments, including non-equilibrium phenomena (e.g., shock dynamics) and autonomy energy intelligence (e.g., battery management systems). He leads the Interfacial Multiphysics Lab, which has produced over 400 publications, 11 patents, and 16 PhD graduates. Key achievements include ASME Fellow status (2016), the AFoSR Young Investigator Award (2009), and multiple teaching/mentorship awards. His lab’s work spans experimental methods like Mechanical Raman Spectroscopy (patented) and computational models for battery safety and energetic materials. Education: MBA, UC Berkeley, 2023 Ph.D., Georgia Tech, 2005 M.Tech., IIT Madras, 2001 B.Tech., NIT Kurukshetra, 1998 Awards: University Faculty Scholar (2016–2021) W A Gustafson Best Teacher Award (2019–20) VAJRA Faculty Award (2019) Over 30+ patents, grants, and editorial roles in journals. Research interests include: Shock physics and nanosecond Raman spectroscopy Battery safety and AI-driven energy systems Interface mechanics in materials and biomaterials Lab contributions include pioneering work on battery thermal runaway prediction and smart management systems, as well as advanced imaging techniques for material characterization under extreme conditions.

Molly Brightمشاهده پروفایل

استادیار

• Neurovascular Imaging
• MRI Techniques
• Cerebrovascular Reactivity
• +۵ مورد دیگر

Molly Bright is an Assistant Professor in Biomedical Engineering and Physical Therapy and Human Movement Sciences at Northwestern University's McCormick School of Engineering. Her research focuses on developing advanced MRI techniques to study neurovascular interactions in healthy and diseased populations, with applications in stroke, spinal cord injury, and multiple sclerosis. Education: D.Phil. in Clinical Neurosciences from the University of Oxford and B.S. in Physics from MIT. She leads the Applied Neuro-Vascular Imaging Lab (ANVIL), collaborating with departments including Radiology, Neurology, and the Shirley Ryan AbilityLab. Key research areas include cerebrovascular reactivity, spinal cord fMRI, and imaging tools for rehabilitation. Her lab has received NIH and NSF funding for projects like Acute Intermittent Hypoxia trials and spinal cord imaging standardization. Notable awards include the IREK12 Career Development Award and NUCATS Pilot Grant. Publications highlight advancements in BOLD fMRI protocols, cerebrovascular dynamics, and neurovascular network analysis. Collaborations span institutions like Stanford and the Basque Center on Cognition.

• Robust Statistics
• Econometrics
• Time Series Analysis
• +۳ مورد دیگر

Christophe Croux is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven and serves as the coordinator of the Operations Research and Statistics Research Group (ORSTAT). His research focuses on robust statistical methods, econometric modeling, and high-dimensional data analysis with applications in finance, marketing, and chemometrics. He has contributed extensively to the development of robust estimation techniques, sparse regression models, and volatility forecasting frameworks, often leveraging computational tools like R packages such as 'robustbase.' Key areas of research include robust correlation analysis, time series modeling (e.g., state space models, volatility spillovers), and machine learning applications for classification and prediction. His work emphasizes handling outliers and large datasets, with practical implications for financial risk management, credit scoring, and commodity market dynamics. Croux has published over 150 articles since 2015, spanning topics from robust canonical correlation to sparse vector autoregressive models. Notable contributions include the development of the robustbase R package, which provides essential tools for robust statistical analysis. His interdisciplinary approach bridges theoretical statistics with practical applications in economics and business analytics. He holds leadership roles in academic networks and committees, furthering the impact of his research in both academia and industry.

• Cryptography
• Cybersecurity
• Theoretical Computer Science
• +۳ مورد دیگر

Dana Dachman-Soled is an Affiliate Associate Professor in the Department of Computer Science at the University of Maryland, with a joint appointment in the Department of Electrical and Computer Engineering. Her research focuses on cryptography, algorithmic fairness, and theoretical computer science, with applications to post-quantum security, privacy-preserving algorithms, and secure multiparty computation. She has advised at least one PhD student, Yvonne Zhou. Her work combines foundational cryptographic theory with practical implementations, addressing challenges such as tamper-resistant data encoding, fair machine learning, and secure communication protocols. In 2023, she received a $1M NSF award for research on post-quantum cryptography. She has contributed to advancements in non-malleable codes, leakage-resilient cryptography, and the security of lattice-based schemes like LWE and NTRU. Her research also explores ethical AI, including fairness in data classification and privacy-preserving synthetic data. She has published extensively in top venues like ITC, CRYPTO, and IEEE conferences, with a focus on cryptographic primitives, side-channel vulnerabilities, and algorithmic bias mitigation. Dr. Dachman-Soled collaborates widely, with grants supporting her work on non-malleable codes and the theoretical foundations of secure computation. Her lab engages in interdisciplinary projects at the intersection of computer science, mathematics, and cybersecurity.

Anton Feenstraمشاهده پروفایل

دانشیار

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Biology
• +۳ مورد دیگر

Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.

• Supply Chain Management
• Operations Research
• Engineering Education
• +۷ مورد دیگر

Dr. Vincent Hargaden is an Associate Professor and Head of School at the School of Mechanical and Materials Engineering, University College Dublin. He holds a PhD in Decision Science from Rensselaer Polytechnic Institute and has over two decades of academic and industry experience. His research focuses on three core areas: Workforce Planning in professional services, Design and Optimization of resilient supply chains, and innovations in Engineering Education. He leads the UCD Master of Engineering Management program and has received multiple teaching awards including the 2017 University Teaching Excellence Award. Education: BE (Mechanical Engineering), University College Dublin MEngSc (Computer Integrated Manufacturing), NUI Galway MBA, UCD Michael Smurfit Graduate Business School PhD (Decision Science), Rensselaer Polytechnic Institute Professional Diploma in University Teaching & Learning, UCD Research Interests: Supply Chain Resilience & Risk Assessment Blockchain Applications in Construction & Engineering Digital Twin Technologies for Manufacturing Industry 4.0 Integration in Agri-Food Sectors Active Learning Pedagogies in Engineering Education Notable Achievements: Co-Principal Investigator in Enterprise Ireland Dairy Processing Technology Centre H2020 VALUMICS Project (Food Value Chain Dynamics) IBM Global PhD Fellowship (2010) UCD Learning Factory Initiative Participation

• Machine Learning
• Generative Artificial Intelligence
• Deep Learning
• +۹ مورد دیگر

Dr. Aera Kim LeBoulluec serves as an Associate Professor of Instruction in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington's College of Engineering. She additionally holds the position of IMSE academic advisor and is a core faculty member of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) Lab at UTA, while collaborating with the Center for Depression Research and Clinical Care at UTSW Medical Center. Dr. LeBoulluec completed her entire academic training at UTA: PhD in Industrial Engineering (2013) MS in Industrial Engineering, Manufacturing, & Systems Engineering (2009) BS in Industrial Engineering (1990) Certificate in Applied Statistics (2013) Her research portfolio spans multiple domains of data science with significant healthcare applications. Dr. LeBoulluec specializes in machine learning, deep learning, generative AI, and statistical analysis for healthcare systems optimization. Her recent work focuses on handling time-varying confounding in state transition models for dynamic optimization of adaptive interdisciplinary pain management and depression treatment. She also explores innovative applications of AI in areas such as recycling plastics and financial fraud detection, demonstrating the versatility of her analytical approaches. Analysis of Dr. LeBoulluec's publication history reveals a clear research trajectory evolving from foundational work in pain management modeling to broader applications of machine learning and AI across healthcare, transportation, and business domains. Her work consistently features interdisciplinary collaboration, particularly with medical researchers at UTSW, and demonstrates increasing sophistication in handling complex longitudinal data and developing adaptive decision support systems. Her notable professional achievements include: Certificate in ACUE (2024) Certificate in Lean Six Sigma Green Belt (2015) Certificate in Applied Statistics (2013) Graduate Research Fellow, GAANN (2013) Tau Beta Pi, Engineering Honor Society membership (2010) Alpha Phi Mu, Industrial Engineering Honor Society membership (2007) Dr. LeBoulluec has advised numerous graduate students across various research projects, particularly in machine learning applications for healthcare analytics, transportation systems, and business operations. She has secured substantial research funding, including an NSF grant for "Statistics-based Optimization Methods for Adaptive Interdisciplinary Pain Management" and industry-sponsored research with Foxconn Technology on AI applications in manufacturing process optimization. She maintains active involvement with the COSMOS Lab at UTA and the Center for Depression Research and Clinical Care at UTSW Medical Center, focusing on interdisciplinary approaches to healthcare optimization, pain management, and depression treatment through advanced data analytics and modeling techniques.

• Computational Intelligence
• Soft Computing Methods
• Decision Support Systems
• +۵ مورد دیگر

Jozef M. Zurada is a Professor in the Department of Computer Information Systems at the University of Louisville's College of Business. He has held visiting roles at Edith Cowan University (Australia) and the University of Alberta (Canada). His research focuses on computational intelligence, soft computing, and data analytics applications in business and manufacturing systems. He teaches Data Mining, Machine Learning, and Infrastructure Technologies. Education: DSc (Technical Sciences, Informatics) - Polish Academy of Sciences, Poland PhD (Computer Science Engineering) - University of Louisville MS (Electrical Engineering) - Technical University of Gdansk, Poland Research Interests: Dr. Zurada explores advanced computational methods to enhance decision-making in business systems, including neural networks, data stream analysis, and predictive modeling. His work bridges theoretical computer science with practical applications in real estate analytics, manufacturing optimization, and sustainable business strategies. Awards: 2011 Distinguished Research and Development Award (International Association of Assessing Officers) 1998 Faculty Excellence Award (University of Louisville) 1997 Achievement Award Nominee (Conference of Southern Graduate Schools) 1996 Outstanding Scholarship Award (University of Louisville) 1981 President's Award (University of Gdansk) Teaching & Advising: Teaches core data science courses. No student advisees explicitly listed in texts. Recent Publications Trends: Recent works emphasize big data's role in business innovation, text classification improvements using feature selection, and real-time adverse event detection in streaming data environments.

Tariq Alwada'nمشاهده پروفایل

مدرس ارشد

• Cloud Computing
• Fog Computing
• Grid Computing
• +۹ مورد دیگر

Tariq Alwada'n is a Senior Lecturer in Cyber Security at Teesside University's School of Computing, Engineering, and Digital Technologies. He serves as Course/Programme Leader for B.Sc IT and Business Technology and Cisco Academy Coordinator. With expertise in Cloud Computing, Fog Computing, IoT, and Cybersecurity, he leads research projects focusing on AI-enhanced cybersecurity training, industrial digitalization, and data-driven solutions. Education includes a PhD in Computer Science (Security for Mobile Grid Systems) from De Montfort University, an MSc in Computer and Information Networks from the University of Essex, and a BSc in Computer Engineering from Al-Balqa Applied University. Research interests span Cloud/Grid Computing, Big Data, and Security. Notable projects include CyberPathway (2025–2025) for diversity-focused cybersecurity training, Dynamic CCTV GAP (2022–2023) for business intelligence systems, and Industrial Digitalisation Technology Centre (2021–2023) supporting SMEs. He has contributed to over 40 research outputs and serves as a reviewer for journals like the International Journal of Environmental Research and Public Health. Teaching certifications include Cisco-accredited courses in Cybersecurity Essentials, IoT, and Networking. He actively mentors PhD students and seeks collaborations on Cloud Security and Distributed Systems.

Rodney X. Sturdivantمشاهده پروفایل

استاد

• Biostatistics
• Applied Statistics
• Logistic Regression
• +۵ مورد دیگر

Dr. Rodney X. Sturdivant is a Professor of Statistical Science and Director of the Statistical Consulting Center at Baylor University. He has held roles at institutions including The Ohio State University and Azusa Pacific University, and previously served as a U.S. Army Colonel for 27 years. He founded the West Point Center of Data Analysis and Statistics (CDAS) and holds the academic rank of Professor of Applied Statistics. Education: Ph.D. in Biostatistics (University of Massachusetts Amherst, 2005); M.S. in Statistics and Operations Research (Stanford University, 1995); B.S. from U.S. Military Academy (West Point, 1986). His research focuses on applied statistics and biostatistics, with specialties in categorical data analysis, logistic regression, survival analysis, and innovative educational methods using games. He collaborates across disciplines including public health, sports medicine, and ecology. Notable awards include the Army Wilks Memorial Award (2021) and recognition as a top 2% cited researcher (2023). He has authored influential textbooks like Applied Logistic Regression and pioneered game-based learning approaches for statistics education. His advisory and grant activities include leading statistical consulting initiatives and developing academic programs like the M.S. in Applied Statistics and Analytics at Azusa Pacific. He is affiliated with Baylor’s Marrs McLean Science facility and actively contributes to both academic and military research networks.

Harry Foxwellمشاهده پروفایل

دانشیار

• Cloud Computing
• Data Analytics
• Artificial Intelligence
• +۲ مورد دیگر

Harry Foxwell is an Associate Professor in Information Sciences and Technology at George Mason University (Research Hall, Fairfax campus). He teaches cloud computing, data analytics, and AI courses, drawing from prior industry experience at Oracle and Sun Microsystems. Military service includes Vietnam deployment (1968-1969) with 1st Infantry Division, earning Air Medal and Bronze Star. Education includes a PhD in Information Technology (GMU, 2003), MS in Applied Statistics (Villanova), and BA in Mathematics (Franklin and Marshall College). Research interests span: Cloud computing infrastructure Data science pedagogy AI/ML applications Cybersecurity frameworks Recent publications focus on metacognition in STEM education, data quality management, and IoT security. Authored technical books including Creating Good Data (2020) and Oracle Solaris Administration Handbook . No funded research projects, student advising, or labs are detailed in sources.

• Machine Learning
• Multi-Agent Systems
• Data Mining
• +۶ مورد دیگر

Riyaz Sikora is an Associate Professor in the Department of Information Systems and Operations Management at the University of Texas at Arlington (UTA), College of Business. He holds a Ph.D. in Information Systems from the University of Illinois at Urbana-Champaign and a B.E. in Electronics and Communication Engineering from Osmania University, India. His research focuses on machine learning, multi-agent systems, data mining, and evolutionary computation with applications in supply chain management and business analytics. Education: PhD, University of Illinois at Urbana-Champaign, 1994 (Business Administration & Information Systems) MS, Washington University, 1989 (Systems Science & Mathematics) BE, Osmania University, 1987 (Electronics & Communications Engineering) Research Interests: Machine learning algorithms, multi-agent frameworks, data mining techniques, evolutionary computation, reputation mechanisms in e-marketplaces, and adaptive supply chain strategies. Key contributions include work on class imbalance problems, reinforcement learning applications, and agent-based modeling. Teaching: Courses include Information Systems Analysis & Design, Web and Social Analytics, Business Data Mining, and graduate seminars. He has advised doctoral students such as YoonSang Lee and Sahil Raina. Service & Leadership: Chair of multiple search committees, member of the College’s Executive Committee, and active in IEEE Computational Intelligence Society. Served on editorial boards of journals like Journal of Information Systems and e-Business Management . Grants: Secured federal funding for research on information system strategies and industry performance (2005-2006).

Hui Yangمشاهده پروفایل

دانشیار

• Data Mining
• Text Analytics
• Bioinformatics
• +۵ مورد دیگر

Hui Yang is an Associate Professor in the Department of Computer Science at San Francisco State University (SFSU), with a secondary affiliation in the Center for Computing for Life Sciences (CCLS). He holds a PhD in Computer Science from The Ohio State University (2006) under Dr. Srinivasan Parthasarathy. His research focuses on Data Mining and Text Analytics, particularly in biomedical applications such as food-disease-gene networks, clinical note analysis, and microarray data visualization (e.g., XMAS system). He also explores structural analysis of protein folding data. Education: PhD in Computer Science, The Ohio State University, 2006 Research Interests: Biomedical text mining for health networks and clinical informatics Development of data visualization tools for biological datasets Application of machine learning in education and public health Spatio-temporal analysis for social and urban data Notable Contributions: Received Best Poster Award at IEEE Computing Conferences 2017 Co-developed the XMAS system for microarray data exploration Active in curriculum design and student retention research Advising & Grants: Advises undergraduate and graduate students in CS curricula (e.g., CSC869 Data Mining) Maintains a research lab with RA positions in data mining and bioinformatics Collaborates with institutions like Genentech and IBM Almaden Labs & Teams: Leads the CCLS at SFSU, focusing on interdisciplinary computing solutions in life sciences.

Maria Tariqمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Machine Learning
• Image Pre-processing
• +۴ مورد دیگر

Maria Tariq is a Research Fellow and Doctorate Student at Coventry University, UK, affiliated with the Centre for Computational Sciences and Mathematical Modelling. She holds a BS and MS in Computer Science from NUCES-FAST, Lahore and Islamabad, respectively. Her research focuses on developing AI-driven solutions for medical diagnostics, particularly in diabetic retinopathy detection using deep learning, transfer learning, and reinforcement learning techniques. Education: PhD: Coventry University (2021–present) MS: National University of Computer and Emerging Sciences (2017–2019) BS: National University of Computer and Emerging Sciences (2012–2016) Research Interests: Maria specializes in Deep Learning , Image Pre-processing , and Transfer Learning , with applications in biomedical imaging. Her work addresses challenges like imbalanced datasets and synthetic image generation using GANs. Publications: Her recent work includes optimizing classification models for small medical datasets and improving diabetic retinopathy detection through hybrid learning frameworks. These studies contribute to UN SDG 3 (Good Health and Well-being). Collaborations: Supervised by Prof. Vasile Palade and Dr. YingLiang Ma, her research integrates interdisciplinary approaches to advance healthcare technology. Labs/Teams: Active in Coventry University’s biomedical research labs, focusing on computational science and mathematical modeling for healthcare solutions.

Kary Främlingمشاهده پروفایل

استاد

• Explainable Artificial Intelligence
• Machine Learning
• Data Analysis
• +۵ مورد دیگر

Kary Främling is a Professor in Data Science with emphasis on data analysis and machine learning at the Department of Computing Science, Umeå University. He serves as the Head of the Explainable AI (XAI) team and holds the prestigious position of WASP Professor within the Wallenberg AI, Autonomous Systems and Software Program, focusing on making AI systems transparent and understandable to end-users in real-world contexts. Professor Främling's research centers on Explainable Artificial Intelligence (XAI), particularly "outcome explanation" - explaining and justifying AI system results. His foundational work includes the Contextual Importance and Utility (CIU) method developed during his 1991-1996 PhD. His research spans Intelligent products, Internet of Things (IoT), Digital Twin technology, and Systems of Systems, emphasizing that explainability is crucial for ensuring AI remains "humane" - able to communicate its reasoning appropriately for different users and situations. Analysis of his recent publications reveals a strong focus on practical XAI implementations across healthcare, smart buildings, IoT systems, and image classification. His work bridges theoretical AI research with practical tool development (py-ciu, ciu R packages) and application-specific implementations, consistently emphasizing contextually appropriate explanations tailored to specific users and scenarios. His scientific recognition includes: WASP Professor (Wallenberg AI, Autonomous Systems and Software Program) As Head of the XAI team, Professor Främling leads research developing and applying explainability methods to real-world AI systems. He teaches "Data preprocessing and visualisation (5DV217)" and has been featured in ACM Computing Surveys, Springer LNCS, and various IEEE/MDPI publications. His work involves international collaborations across Europe, focusing on making black-box AI models transparent and trustworthy through context-aware explanations. Professor Främling leads the XAI research group at Umeå University, developing and applying the Contextual Importance and Utility method across domains including healthcare diagnostics, smart building energy management, agricultural technology, and affect detection systems. The group collaborates with industry partners to implement explainable AI in practical systems while advancing theoretical foundations of explanation generation.