جستجوی استاد راهنمای دانشگاهی

Esra Ece Bayatمشاهده پروفایل

دانشیار

• Earthquake Engineering
• Soil Mechanics
• Geotechnics
• +۳ مورد دیگر

Assoc. Prof. Dr. Esra Ece Bayat is an Associate Professor in the Civil Engineering Department at Istanbul Technical University (ITU), specializing in Earthquake Engineering, Soil Mechanics, and Geotechnics. She holds a PhD from Northeastern University (USA) and has been affiliated with ITU since 2010, progressing from Lecturer to her current rank. Her research focuses on liquefaction mitigation, soil-structure interaction, and dynamic soil behavior under seismic loads. She has led multiple projects funded by TUBITAK and BAP, including studies on induced partial saturation (IPS) techniques and p-y model development for earthquake-resistant designs. Her education includes a BSc from Middle East Technical University (1997–2002) and a PhD from Northeastern University (2004–2009). She has served in administrative roles such as Erasmus Coordinator and Education Coordinator, emphasizing international collaboration and academic development. Her work contributes to UN Sustainable Development Goals related to resilient infrastructure and disaster risk reduction. Dr. Bayat has published extensively on topics like dynamic soil behavior, liquefaction assessment, and foundation systems. Her research combines experimental methods (e.g., laminar soil containers, shaking table tests) with numerical modeling (e.g., FLAC 3D, SASSI). Recent work addresses the 2023 Kahramanmaraş earthquake’s impact on foundation systems in Iskenderun. She has advised over 20 graduate students and holds the ASCE Thomas Middlebrooks Award (2014).

Marc Hodesمشاهده پروفایل

استاد

• Heat transfer
• Thermal management
• Supercritical fluids
• +۳ مورد دیگر

Marc Hodes is Professor in Mechanical Engineering and Mathematics at Tufts University. With a PhD from MIT, his research focuses on heat transfer phenomena with applications in electronics cooling, supercritical fluids, and thermoelectric systems. He directs the graduate program in Mechanical Engineering. Education: BS, University of Pittsburgh (1990) MS, University of Minnesota (1994) PhD, Massachusetts Institute of Technology (1998) Research Areas: Thermal management of electronics through microchannel cooling and liquid metal technologies; Apparent slip phenomena in microstructured surfaces; Mass transfer in supercritical CO 2 systems for aerogel processing; Thermoelectric module optimization for precision temperature control. Awards & Honors: NSF REU Fellowship (1989) E.T.S. Walton Visitorship Award Best Associate Editor, ASME Journal of Heat Transfer (2023) Research Leadership: Principal investigator on multiple NSF grants including projects on aerogel manufacturing, dropwise condensation, and analysis of convection in slip flows. Industry collaborations include Google, DARPA, and Bell Labs.

Hendrik Weberمشاهده پروفایل

استاد

• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• Regularity Structures
• +۴ مورد دیگر

Prof. Dr. Hendrik Weber is a Professor of Mathematics at the University of Münster, leading the Workgroup for Stochastic Analysis. He holds the Bridging the Gaps Professorship and is affiliated with the Faculty of Mathematics and Computer Science. His expertise lies in stochastic analysis, particularly stochastic partial differential equations (SPDEs) and their applications in mathematical physics and statistical mechanics. Weber's research focuses on regularity structures, singular SPDEs, and the interplay between stochastic processes and nonlinear dynamics. Education and Career: Weber earned his PhD from the University of Bonn (2010) and held positions at the University of Warwick (2010–2018) and the University of Bath (2018–2022) before joining Münster in 2022. He has been recognized with awards including the ERC Consolidator Grant (2022), Philip Leverhulme Prize (2017), and Rollo Davidson Prize (2016). Research Interests: Weber's work addresses theoretical challenges in SPDEs, including invariant measures, phase transitions, and scaling limits. His projects span topics like singularities in PDEs, field theory randomness, and deep learning surrogate methods. Recent studies include the dynamic Φ⁴ model, stochastic quantization in non-commutative spaces, and a priori bounds for quasilinear SPDEs. Publications: Over 60 peer-reviewed articles, including high-impact contributions to Annals of Probability , Communications in Mathematical Physics , and Archive for Rational Mechanics and Analysis . His work emphasizes rigorous mathematical analysis of stochastic systems and their physical implications. Awards and Grants: ERC Consolidator Grant (2022), Royal Society Fellowship (2016), and multiple collaborative projects funded by the EPSRC and DFG. His research also bridges theoretical developments with applications in machine learning and feature engineering using regularity structures. Labs/Teams: Leads a dynamic research group comprising PhD students (e.g., Sophie Mildenberger) and postdoctoral researchers. Collaborations with global institutions like the University of Warwick and the University of Bath drive interdisciplinary advancements in stochastic analysis.

Tianzheng Wangمشاهده پروفایل

دانشیار

• Database Systems
• Operating Systems
• Parallel Programming
• +۵ مورد دیگر

Tianzheng Wang is an Associate Professor and Director of the Dual-Degree and Partnerships Programs at the School of Computing Science, Simon Fraser University. His research focuses on database systems, transaction processing, parallel and distributed computing, and embedded systems. He holds a PhD in Computer Science from the University of Toronto (2017) and a BSc in Computing from Hong Kong Polytechnic University (2012). Research Interests: Database systems optimized for modern hardware, parallel programming, synchronization, and distributed architectures. His work emphasizes high-performance transaction processing and efficient indexing techniques, with applications in cloud and embedded systems. Awards: ACM SIGMOD Best Paper Award (2025), IEEE TCSC Early Career Award (2019), and multiple distinguished reviewing recognitions (SIGMOD/VLDB 2021-2024). His research has been integrated into systems like Amazon Redshift and DragonflyDB. Teaching: Leads courses such as CMPT 454 (Database Systems II), CMPT 300 (Operating Systems), and special topics in databases. Actively mentors graduate and undergraduate students in research projects. Labs & Collaborations: Heads the Data-Intensive Systems Lab, part of SFU's Data Science and Systems groups. Collaborates on tools like PiBench for persistent memory benchmarking and contributes to open-source projects like CoroBase and Tabular.

Muhammad Rashedمشاهده پروفایل

استادیار

• Electronic Design Automation (EDA)
• In-Memory Computing
• Artificial Intelligence Acceleration
• +۵ مورد دیگر

Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015). Ph.D. : Computer Engineering, University of Central Florida, 2024 B.S. : Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015 His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing. The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals. Scientific Awards: UTA CARES Grant for OER Creation Research Experiences for Undergraduates (REU) Grant Alireza Seyedi Doctoral Research Innovation Endowed Scholarship David T. & Jane M. Donaldson Memorial Scholarship IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination Best Research Video Award, Design Automation Conference (DAC) Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college. His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.

• Climate Policy
• Long-Term Policy
• International Institutions
• +۳ مورد دیگر

Detlef F. Sprinz is a Senior Scientist at the Potsdam Institute for Climate Impact Research (PIK) and a Professor at the Faculty of Economics and Social Sciences of the University of Potsdam, Germany. During 2024-2025, he leads the project "AMaReNa - A Market for the Restoration of Nature" , focusing on carbon removals. His academic background includes a Ph.D. and M.A. in Political Science from the University of Michigan and an M.A. in Economics from the University of the Saarland. Research Interests: Climate policy at multiple levels, long-term policy design, evaluation of international/national institutions, European/international environmental policy, and modeling political decisions for sustainability. Notable Projects: Tandem Fellowship (2020-2022) for interdisciplinary course development, Visiting Researcher at University of Lund (2022-2023), and coordination of research at the Käte Hamburger Kolleg (2017). Publications emphasize climate cooperation under the Paris Agreement, effectiveness of environmental regimes, and predictive modeling for climate negotiations. His work spans empirical analyses of acid rain regulation, Kyoto Protocol implementation challenges, and bioenergy accounting errors. Scientific Awards & Memberships: Tandem Fellowship (German Stifterverband) Member, European Academy Co-founder, Ecologic Institute Teaching includes courses on global climate governance, political decision modeling, and environmental policy at the University of Michigan, Yale, University of Oslo, and University of Potsdam. He has contributed to IPCC’s Sixth Assessment Report as a Lead Author. Advisory Roles: Chairmanship of the European Environment Agency’s Scientific Committee (2009-2012), Senior Research Fellow at CICERO (2011-2013), and consultancy for governments, corporations, and think tanks.

• Medical Image Understanding
• Radiology Report Generation
• Deep Learning
• +۳ مورد دیگر

Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.

Merry Maniمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Signal Processing
• Image Reconstruction
• +۴ مورد دیگر

Dr. Merry Mani is an Associate Professor in Radiology and Imaging Sciences and Biomedical Engineering, specializing in biomedical imaging and signal processing. Her work focuses on advancing MRI-based imaging technologies to study neurological disorders such as Alzheimer's, Autism, and Epilepsy. She holds a Ph.D. in Electrical and Computer Engineering from the University of Rochester (2014) and completed a postdoctoral fellowship at the University of Iowa School of Medicine (2018). Her research combines biophysical modeling with machine learning to explore brain microstructures. Key achievements include the NNARSAD Young Investigator Grant and NIH-funded projects like 'Fast Multi-dimensional Diffusion MRI with Sparse Sampling'. Her lab develops cutting-edge reconstruction methods like qModeL and MUSSELS, prioritizing high spatio-temporal resolution imaging. Major contributions span diffusion MRI acquisition, model-based deep learning, and clinical applications in neurodegenerative diseases. Notable grants include NIH R01EB031169 for Alzheimer’s neurodegeneration studies and projects on rTMS for depression. Her work bridges imaging innovation with clinical impact, aiming to improve diagnosis and treatment through advanced imaging biomarkers.

• Human Resource Management
• Organizational Behavior
• HR Analytics
• +۷ مورد دیگر

Dr. Hanbo Shim is an Assistant Professor in the Department of Management at The University of Texas at Arlington, College of Business. He holds a PhD in Industrial Relations and Human Resources from Rutgers University and a BA in Mathematics and Economics from the University of Illinois at Urbana-Champaign. His research and teaching focus on Human Resource Management, Organizational Behavior, and HR Analytics. PhD, Industrial Relations and Human Resources, Rutgers University (2022) MS, Industrial Relations and Human Resources, Rutgers University (2019) MA, Human Resource Management, Rutgers University (2016) BA, Mathematics and Economics, University of Illinois (2012) Dr. Shim's research explores the temporal dynamics of employee performance, compensation systems, emotional intelligence, and social networks in organizations. He uses longitudinal analysis, multilevel modeling, meta-analysis, and computer simulation to examine how individual and interpersonal factors influence organizational outcomes. His work bridges HR analytics with behavioral science. His recent publications span topics such as green HRM, pay policy dynamics, emotional intelligence, and HR analytics education. These works reflect a strong interdisciplinary approach integrating psychology, sustainability, data science, and strategic management. His research has been presented at leading conferences including the Academy of Management and European Reward Management Conference. Award highlights include: Ralph Alexander Best Dissertation Award (2024) Innovative Teaching Award, Academy of Management HR Division (2022) Best Student Convention Paper Award (2020) Best Doctoral Conference Paper Award, Samsung Economic Research Institute (2020) Dr. Shim actively advises students through capstone and honors projects and serves on PhD and graduate studies committees. He has collaborated with SL Corporation on HR analytics and developed open-source educational materials. His service includes editorial board membership for Compensation & Benefits Review and ad hoc reviewing for top journals. He is also engaged in professional development workshops and public speaking, including for the Society for Human Resource Management at UTA. His lab and research activities emphasize simulation-based methods and real-world HR analytics applications.

Anna Bagnoliمشاهده پروفایل

مربی

• Methodological Innovation in Social Sciences
• Qualitative Research Methods
• Creative Methods
• +۷ مورد دیگر

Dr. Anna Bagnoli is a Tutor and Director of Studies for Human Social and Political Sciences (HSPS) at Wolfson College, University of Cambridge. She is affiliated with the Department of Sociology and contributes to methodological training in qualitative and visual research methods. Her research bridges social sciences and arts, focusing on identity processes, migration, and gender. Alma Mater: PhD from University of Cambridge (2001) Research: Methodological innovation, narrative analysis, migration of Southern Europeans Her methodological expertise includes creative arts-based methods , visual research , and autobiographical narratives . She has led projects like Picturing Identities and Wolfson’s Worlds , exploring migration, belonging, and cultural capital reconstruction. Anna’s recent publications emphasize gendered migration experiences , arts-based research , and identity construction through visual methods . Key topics include the interplay of migration with science career aspirations, narrative ethics, and longitudinal youth studies. Scientific Awards EC Marie Curie TMR Fellowship (1998-2000) ESRC Postdoctoral Fellowship (2002-2003) Nuffield Foundation Grant (2010) She supervises doctoral students in migration, gender, and narrative research, with a focus on participatory and arts-driven approaches. Anna is also involved in the Cambridge Migration Research Network (Cammigres).

Jean K Chalabyمشاهده پروفایل

استاد

• International Communication
• Transnational Media
• Global Media Systems
• +۵ مورد دیگر

Professor Jean K. Chalaby is a faculty member in the Department of Sociology at City, University of London . He obtained his PhD in Sociology from the London School of Economics in 1994 and joined City University London as a lecturer in 2000, serving as Head of the Sociology Department from 2012 to 2014. He has also been a visiting lecturer at the University of Geneva for 15 years. His research focuses on international communication, media globalization, transnational television networks , and the global value chain dynamics of the streaming era. He has authored influential works such as The Format Age (2017) and Transnational Television in Europe (2009), while editing collections like Transnational Television Worldwide (2005). His work critically examines the decline of European media groups against U.S. streaming giants, exploring digital policy, cultural dependency , and the uneven power dynamics in global entertainment. Recent publications analyze the streaming market , media value chains , and TV content production , emphasizing network effects , platform economies , and EU policy challenges . Despite his extensive output (over 70 single-authored publications), no scientific awards or student supervision data are explicitly mentioned in the provided text.

Heiner Litzمشاهده پروفایل

دانشیار

• Computer Architecture
• Computer Systems
• Hardware-Software Interface
• +۳ مورد دیگر

Heiner Litz is an Associate Professor in the Computer Science & Engineering Department at UC Santa Cruz's Baskin School of Engineering. He holds the Kumar Malavalli Endowed Chair of Storage Systems Research and serves as Director of the Center for Research in Storage Systems (CRSS). He is also a member of UCSC's Hardware Systems Collective (HSC). Litz earned his PhD from Mannheim University and previously held positions at MIT, Google, and Stanford University. His research focuses on computer architecture and systems optimization , specifically improving hardware-software interfaces for data center workloads. Key areas include microarchitectural mechanisms (branch prediction, prefetching, cache design), profile-guided optimizations, and storage/disaggregated memory systems. His work bridges compiler techniques and hardware efficiency for emerging cloud applications. Recent publications emphasize profile-guided optimization across microarchitecture layers, storage scalability, and resource allocation in distributed systems. Trends include hardware-software co-design for data centers, RDMA-based protocols, and real-time control systems. Awards & Honors: Kumar Malavalli Endowed Chair of Storage Systems Research Best Paper Award at MICRO (2022) IEEE Micro Top Picks (2019, 2023) Best Paper Awards at ICPP (2008) and ARC (2009) Advising & Grants: He currently advises 11 PhD students and has graduated 8 MS/PhD students. Research is supported by NSF, Intel, Samsung, Google, Meta, Nutanix, HPE, ARM, Marvell, Cerabyte, Western Digital, and Broadcom. Labs & Teams: Directs CRSS, focusing on storage systems innovation, and collaborates with HSC on hardware-software integration projects.

Per Christian Hansenمشاهده پروفایل

استاد

• Numerical Analysis
• Numerical Linear Algebra
• Iterative Reconstruction Methods
• +۸ مورد دیگر

Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.

Il Memming Parkمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Dynamics
• Machine Learning
• +۳ مورد دیگر

Il Memming Park is a Professor and Group Leader at the Centre for Restorative Neurotechnology within the Champalimaud Research division of the Champalimaud Foundation in Lisbon, Portugal. His work bridges computational neuroscience, machine learning, and statistical modeling to understand neural dynamics and computation. Dr. Park's research focuses on developing statistical and machine learning methods for analyzing neural time series data. His lab investigates the appropriate language for neural dynamics that can explain and generate specific predictions on neural data and behavior. He builds on foundations of dynamical systems and stochastic processes to create models of neural computation tightly tied to biology. His publications reveal a strong emphasis on developing methods like variational latent Gaussian processes and exponential family dynamical systems to extract meaningful patterns from complex neural recordings. His work spans both theoretical developments in computational methods and their application to real neural data from areas like visual cortex, parietal cortex, and other brain regions involved in perception and decision making. Dr. Park has previously held positions at Stony Brook University and the University of Texas at Austin, where he was affiliated with departments of Neurobiology and Behavior, Applied Mathematics and Statistics, Psychology, and Neuroscience. His lab at Champalimaud includes multiple PhD students, postdoctoral researchers, and research staff working collaboratively on various aspects of neural data analysis and modeling. The team employs an interdisciplinary approach combining neuroscience, statistics, machine learning, and dynamical systems theory.

Peihan Miaoمشاهده پروفایل

استادیار

• Cryptography
• Secure Multi-Party Computation
• Theoretical Computer Science
• +۲ مورد دیگر

Peihan Miao is an Assistant Professor in the Department of Computer Science at Brown University, where she is a member of the Theory Group. Her academic journey began with a BS from the ACM Honors Class at Shanghai Jiao Tong University, followed by a PhD from UC Berkeley in 2019 under the supervision of Sanjam Garg. BS: ACM Honors Class at Shanghai Jiao Tong University PhD: University of California, Berkeley (2019) Dr. Miao's research focuses on cryptography and security, with particular emphasis on bridging the gap between theoretical cryptography and practical applications, especially in the realm of secure multi-party computation. Her work spans both foundational theoretical aspects and applied systems, demonstrating a strong commitment to developing cryptographic techniques that can be implemented in real-world scenarios. She has made significant contributions to private set intersection protocols, secure computation frameworks, and cryptographic primitives that enable privacy-preserving data analysis across various domains including genomics and machine learning. Her publication record reveals a consistent focus on advancing secure computation techniques, with recent work exploring structure-aware private set intersection, updatable cryptographic protocols, and applications of cryptography to emerging domains like federated genomics. Her research demonstrates a progression from theoretical foundations toward practical implementations that address real-world privacy challenges. NSF CAREER Award Meta Research Award Google Research Scholar Award Amazon Research Award Dr. Miao actively mentors PhD students including Xinyi Shi, Phuoc Van Long Pham, and Jifeng Wang, as well as postdoc Gayathri Garimella. She serves on program committees for major cryptography conferences including Crypto, TCC, and Asiacrypt. Her teaching portfolio includes courses on applied cryptography, introduction to cryptography and computer security, and special topics in secure computation, demonstrating her commitment to educating the next generation of cryptographers. She also runs a crypto reading group at Brown University for students interested in cryptography research. As part of Brown's Theory Group, Dr. Miao collaborates with colleagues on advancing the theoretical foundations of computer science while maintaining a strong focus on practical applications of cryptographic techniques.