جست‌وجوی استاد و پژوهشگر

• Memory Systems Design
• High-Performance Computing
• Exascale Systems Architecture
• +۷ مورد دیگر

Bruce Jacob is a Keystone Professor and Full Professor in the Department of Electrical & Computer Engineering at the University of Maryland College Park's College of Engineering. His research primarily focuses on memory systems design and exascale computing architectures, with significant contributions to DRAM simulation and high-performance computing systems. Dr. Jacob received his A.B. in Mathematics from Harvard University (1988), followed by his M.S. and Ph.D. in Computer Science & Engineering from the University of Michigan (1995 and 1997 respectively). His research interests include memory systems design, exascale computing architectures, embedded systems, circuit integrity, and algorithmic composition. His recent publications demonstrate a strong focus on next-generation memory technologies, particularly ReRAM and advanced DRAM architectures. His work bridges the gap between theoretical modeling and practical implementation, with significant contributions to memory system simulation through projects like DRAMsim. His research shows a consistent trajectory toward solving the memory bottleneck problem in high-performance computing systems. Named Fellow, IEEE (2021) Multiple University of Maryland Research Leader awards (2006, 2010, 2012, 2016, 2017) Clark School of Engineering Keystone Professor (2006) National Science Foundation CAREER Award (2000) University of Maryland Award for Teaching Excellence (2004) Dr. Jacob has led significant research initiatives including the University of Maryland Exascale Systems Research and Memory-Systems Research groups. He has developed important computational artifacts such as DRAMsim (a public-domain DRAM-system simulator) and BioBench (a set of bioinformatics workloads). His work has influenced both academic research and industry practices in memory system design.

• Manufacturing Management
• Production Planning
• Production Control
• +۱۴ مورد دیگر

Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).

• Cybersecurity
• Blockchain
• Machine Learning
• +۱۲ مورد دیگر

Edgar Weippl is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice Dean and Head of the Research Group Security and Privacy. His work spans cybersecurity, blockchain, and machine learning, with teaching roles in information security and software security courses. Current Positions: Vice Dean (Faculty of Computer Science), Head (Security and Privacy Research Group), Deputy Head (Neuroinformatics & Knowledge Engineering Groups) Research Interests: Cybersecurity, blockchain, IoT security, code obfuscation, privacy technologies, reinforcement learning, and socio-technical systems security Selected Publications: Focus on blockchain privacy, VoWiFi security, code obfuscation, and reinforcement learning applications

Ivan Canayمشاهده پروفایل

استاد

• Econometric Theory
• Partially Identified Models
• Moment Inequalities
• +۵ مورد دیگر

Ivan Canay is a Professor of Economics and Director of the Mathematical Methods in the Social Sciences Program at Northwestern University’s Weinberg College of Arts & Sciences. He holds a PhD from the University of Wisconsin, Madison (2008). His research focuses on econometric theory, particularly developing statistical methods for assessing partially identified models, including tests for moment inequalities and randomization-based inference techniques. Recent work addresses challenges in clustered data analysis, covariate-adaptive randomization, and regression discontinuity designs. Canay’s academic contributions include advancing methodologies for handling non-ignorable cluster sizes and improving the robustness of inference in settings with limited data. He serves as an associate editor for the Journal of Econometrics , Journal of Business and Economic Statistics , and Econometrics Journal . His work bridges theoretical econometrics with practical applications in policy evaluation and causal inference. Key research themes include: Partially identified models and moment inequality frameworks Bootstrap methods for clustered data Covariate-adaptive randomization in clinical trials Statistical software development (e.g., Stata modules) His publications emphasize methodological rigor while addressing real-world complexities in economic data. Current projects likely expand his work on inference under structural constraints and improving accessibility of econometric tools for applied researchers.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +۳ مورد دیگر

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

Ray Baiمشاهده پروفایل

استادیار

• Bayesian Statistics
• High-Dimensional Modeling
• Scalable Machine Learning
• +۷ مورد دیگر

Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.

Wooram Parkمشاهده پروفایل

دانشیار

• Robotics
• Biomedical Robotics
• Computational Structural Biology
• +۳ مورد دیگر

Wooram Park is an Associate 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. He leads the Robotics and Intelligent Systems Laboratory (ROBINS Lab) and holds a PhD from Johns Hopkins University (2008), along with MS and BS degrees from Seoul National University (2003 and 1999). His research focuses on robotics, biomedical robotics, computational structural biology, and image processing. Key projects include flexible needle steering for medical applications, haptic feedback systems, and advanced algorithms for motion planning and image reconstruction. He has received notable awards such as the Creel Fellowship (2007) and Critics’ Choice Award in ArtBot Design (2004). His work spans theoretical contributions in stochastic systems and practical innovations like vibratory magnetic robots (Vimbot) and wearable haptic devices. The ROBINS Lab emphasizes interdisciplinary research at the intersection of mechanical engineering, computer science, and biomedical applications.

Alistair Sterlingمشاهده پروفایل

استادیار

• Physical organic chemistry
• Computational chemistry
• Chemical bonding
• +۳ مورد دیگر

Alistair Sterling is an Assistant Professor in the Department of Chemistry & Biochemistry at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. His research integrates computational modeling, theoretical physical organic chemistry, and electronic structure theory to study chemical reactivity, particularly in catalysis, molecular editing, and polymer upcycling. Sterling leads the Sterling Lab, emphasizing interdisciplinary collaboration with synthetic chemists and fostering skills in reaction mechanism analysis, coding, and scientific communication. He actively engages in outreach to promote public understanding of chemistry. Education includes a DPhil in Organic Chemistry (University of Oxford, 2021), an MChem in Chemistry (University of Oxford, 2017), and postdoctoral fellowships at Lawrence Berkeley National Lab and UC Berkeley. He holds the EPSRC Doctoral Prize Fellowship (2021). Research focuses on understanding chemical bonding and reactivity through computational tools, with applications in drug discovery and sustainable materials. Key themes include delocalization-enabled reactions, node-induced electron confinement, and strain-release mechanisms. The lab develops novel synthetic methods for creating complex molecules with pharmaceutical relevance, such as meta-substituted arene bioisosteres. Notable awards include the Global Young Scientists Summit (2021), Young Modellers' Forum Best Talk Prize (2019), and multiple University of Oxford scholarships. His work bridges theory and experiment, aiming to advance both fundamental understanding and practical innovations in chemistry.

Sudhir R. Paulمشاهده پروفایل

استاد

• Biostatistics
• Statistical Inference
• Generalized Linear Models
• +۳ مورد دیگر

Dr. Sudhir R. Paul is a Professor in the Department of Mathematics and Statistics at the University of Windsor, Faculty of Science. He holds a Ph.D. from Wales and has received prestigious awards including Fellowships from the American Statistical Association (2006) and the Royal Statistical Society (1982). His research focuses on Biostatistics and Statistical Inference, with expertise in areas such as Generalized Linear Models, Clustered/Longitudinal Data Analysis, and Categorical Data Analysis. He has supervised numerous graduate students and maintains an active research program addressing topics like risk difference estimation, bias correction in statistical models, and applications in environmental and medical contexts. Education: Ph.D. (Wales). Research interests span advanced statistical methodologies, including zero-inflated models, measurement error correction, and dose-response modeling. His work bridges theoretical development and practical applications in epidemiology, clinical trials, and environmental studies. His publications reflect contributions to clustered data analysis, interval estimation, and generalized estimating equations. Awards highlight his impact in advancing statistical science through teaching, research, and service. Advising: Over 30 M.Sc. and Ph.D. students have been supervised, with current students engaged in doctoral and master’s research. Postdoctoral fellows include experts in statistical theory and applications.

Tingkai Wangمشاهده پروفایل

مدرس ارشد

• Mobile Robots
• Intelligent Systems
• Artificial Intelligence (AI)
• +۵ مورد دیگر

Dr. Tingkai Wang is a Senior Lecturer in the School of Computing and Digital Media at London Metropolitan University. His research focuses on mobile robots, intelligent systems, artificial intelligence, control systems, image/signal processing, and virtual reality. He teaches the Programming for Computer Science module and has led projects like the Virtual Environment and Simulation System (2000-2002) and Navigation and Control of Mobile Robots (1995-1998). His work emphasizes interdisciplinary approaches, combining expert systems, neural networks, and fuzzy logic to address challenges in autonomous systems. Notable contributions include AGV navigation algorithms, hybrid control systems, and predictive modeling. Over 30 publications span robotics, control engineering, and AI applications. He collaborates internationally and has presented at venues like the International Conference on Intelligent Systems Engineering and the IEEE Conference on Engineering in Medicine and Biology. Dr. Wang’s expertise bridges theoretical modeling and practical implementation, with applications in manufacturing automation, environmental monitoring, and industrial management systems. His current research continues exploring adaptive control mechanisms and AI-driven robotics solutions.

• Fluid Power (Hydraulics)
• Acoustic Sensing
• Dynamic Modelling
• +۴ مورد دیگر

Travis Wiens is an Associate Professor in the Department of Mechanical Engineering at the University of Saskatchewan. His research focuses on fluid power systems, acoustic sensing, dynamic modeling, mining mechatronics, and artificial intelligence applications in engineering. He holds a B.Sc., M.Sc., and Ph.D. in Mechanical Engineering from the same institution, completed in 2002, 2004, and 2008 respectively. His research interests emphasize innovative solutions for hydraulic control systems, pipeline resonance reduction, and mining safety through acoustic technologies. He has developed low-cost electrohydrostatic actuators and explored additive manufacturing in valve design. Notable contributions include vibrational data communication tools for extractive industries and machine learning models for mine roof stability assessment. Dr. Wiens teaches ENGC 01A - Introduction to Fluid Power Components . His work bridges theoretical fluid dynamics with practical applications in energy efficiency, structural health monitoring, and biomimetic systems. Ongoing research trends include integrating AI with hydraulic systems and advancing non-destructive testing methods for infrastructure integrity. His publications highlight interdisciplinary approaches, combining mechanical engineering principles with computational modeling, neural networks, and mechatronics. Collaborative efforts focus on mining safety, oil and gas infrastructure, and sustainable hydraulic technologies.

Jordan Kidneyمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Jordan Kidney is a Lecturer in the Department of Mathematics & Computing at Mount Royal University (MRU), where he has taught since 2011. His academic background includes a B.Sc. and M.Sc. in Computer Science from the University of Calgary (2003 and 2006, respectively). Kidney’s teaching focuses on introductory and advanced programming, systems architecture, operating systems, and web development. His research interests span Artificial Intelligence, particularly machine learning and multi-agent systems. He is recognized for contributions to educational methodologies in computer science and the development of tools like the ARES Simulator for multi-agent systems research. Education: M.Sc. Computer Science, University of Calgary (2006) B.Sc. Computer Science, University of Calgary (2003) Research Interests: Kidney’s work emphasizes AI applications, including machine learning algorithms and multi-agent systems. He explores emergent behavior testing, cooperative behavior learning, and educational frameworks for complex topics like MAS. His research often intersects with practical tools, such as the ARES Simulator, which he has refined to enhance teaching and testing methodologies. Articles Trends: His publications highlight two key areas: computer science education for non-majors and multi-agent systems research. Recent work (2018) addresses course design challenges for interdisciplinary students, while earlier contributions (2002–2006) focus on ARES-based testing and cooperative behavior analysis in MAS. His earlier studies (2003) also explored genetic algorithms and diversity-driven optimization. Grants & Advising: No advising roles or grants are explicitly listed in the provided information.

Eric Bettingerمشاهده پروفایل

استاد

• Economics of Education
• Higher Education
• Student Success and Completion
• +۵ مورد دیگر

Eric Bettinger is the Conley DeAngelis Family Professor in the Stanford University School of Education and a Senior Fellow at the Hoover Institution. He also holds appointments as a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) and Professor (by courtesy) of Economics. Bettinger directs the Center for Educational Policy Analysis and the Lemann Center for Brazilian Education at Stanford, and serves as a research associate with the National Bureau of Economic Research. His educational background includes a Ph.D. in Economics from the Massachusetts Institute of Technology (2000) and a B.A. in Economics, Magna cum Laude with Honors, from Brigham Young University (1996). Bettinger's research focuses on the economics of education, with particular emphasis on student success and completion in college, the impacts of online education, financial aid effects, teacher characteristics, and voucher programs. His work employs rigorous statistical methods to identify cause-and-effect relationships in higher education. Notably, his research on simplifying financial aid applications has directly influenced White House efforts to streamline financial aid processes, demonstrating the real-world policy impact of his scholarship. His recent publications reveal consistent focus on educational interventions, technology in education, and policy evaluation. Bettinger's work spans multiple methodologies including randomized controlled trials, natural experiments, and large-scale data analysis. His research often addresses practical educational challenges while maintaining strong theoretical foundations in economics. Bettinger has served as a consultant to the White House and various state governments on financial aid policies, translating academic research into actionable policy recommendations. His work bridges the gap between academic scholarship and practical educational policy implementation. As an academic leader, Bettinger directs multiple research centers and has advised numerous doctoral students. His current advisees include Joseph Moore, Sergio Arango, Michelle Blair, Dallas Gilmore, Gabriel Koraicho, and postdoctoral researcher Saloni Gupta.

Caitlin C. Rosenthalمشاهده پروفایل

دانشیار

• Economic History
• Business History
• Labor History
• +۵ مورد دیگر

Caitlin C. Rosenthal is an Associate Professor in the Department of History at the University of California, Berkeley's College of Letters and Science. A historian specializing in 18th and 19th century U.S. history, she is renowned for her interdisciplinary approach that blends qualitative and quantitative methods across business history, economic history, and labor history. Her research focuses on the development of management practices, particularly those based on data analysis, with special attention to the historical relationship between violence and innovation. Her groundbreaking work examines how accounting and management techniques evolved, including their disturbing applications on slave plantations. Rosenthal's scholarship bridges historical analysis with contemporary concerns about data practices and ethical business conduct. Her influential book Accounting for Slavery: Masters and Management (Harvard University Press, 2018) won the Simkins Award from the Southern Historical Association and the first book prize from the Economic History Society. It was featured as one of 'Five Books' best economics books of 2018 and was incorporated into the New York Times' 1619 Project. The book explores how plantation management contributed to modern business practices while examining the troubling intersection of violence and innovation. Simkins Award of the Southern Historical Association First book prize of the Economic History Society Carol D. Soc Graduate mentoring award (2019) Hellman Faculty Fellowship (2018-2020) American Cultures Innovation in Teaching Prize (2016) Rosenthal teaches undergraduate courses on the history of capitalism and data practices, including 'The History of American Capitalism' (which won the American Cultures Innovation in Teaching Prize in 2016) and 'Calculating Americans,' which examines the evolution of data practices from the U.S. Census to medical records. She actively mentors graduate students on topics related to data and economy, having received the Carol D. Soc graduate mentoring award in 2019. Her educational background includes a PhD in History of American Civilization from Harvard University, an AM in History from Harvard, and a BA in Political Science summa cum laude from Rice University.

• Synthetic Biology
• Biomedical Engineering
• Systems Biology
• +۴ مورد دیگر

Harrison Steel is an Associate Professor of Engineering Science at the University of Oxford and Tutorial Fellow at Harris Manchester College. He holds a BEng in Mechanical Engineering and BSc in Physics and Mathematics from the University of Sydney, followed by a DPhil at Oxford as a Monash Scholar. His research focuses on synthetic biology, control engineering, and bioprocess optimization, with a particular emphasis on microbial systems and genetic circuit design. Dr. Steel’s work integrates computational modeling, experimental biology, and control theory to engineer robust biological systems. His contributions include advancements in genome editing via SIBR-Cas systems, cybernetic control of microbial co-cultures, and the development of open-source platforms like Chi. Bio for automated biological experimentation. His recent publications highlight innovations in directed evolution strategies, modular biomolecular control architectures, and the application of machine learning to fitness landscape analysis. He has pioneered approaches for stabilizing genetically engineered cell populations and enhancing bioprocess efficiency through adaptive control systems. Dr. Steel’s research is supported by collaborations across engineering, biology, and computational disciplines. He actively contributes to academic leadership through his role at Harris Manchester College and maintains an experimental focus on bridging theoretical models with practical biological implementations.