جست‌وجوی استاد و پژوهشگر

• Operations Management
• Business Analytics
• Project Management
• +۳ مورد دیگر

Bert DE REYCK is the Dean of the Lee Kong Chian School of Business at Singapore Management University (SMU) and holds the rank of Professor of Operations Management. He holds a PhD in Business Economics from the University of Leuven, Belgium, along with an MSc in Business Information Systems and a BSc in Business Engineering from the same institution. His career includes roles as Founding Director and Professor at UCL School of Management (2015–2021), Professor and Head of Department at University College London (2009–2015), and various visiting professorships at institutions like London Business School and the University of Ghent. He has held academic positions since 1998, including Assistant and Associate Professor roles at prestigious institutions such as the London Business School and Rotterdam School of Management. DE REYCK’s research focuses on Operations Management, Business Analytics, and Project Management, with notable contributions to AI applications, transportation systems, and strategic sourcing. His work bridges theoretical rigor and practical impact, addressing challenges in project valuation, contract design, and operations optimization. He has received numerous accolades, including INFORMS’ AAS Best Paper Award (2022), multiple Daniel H. Wagner Prize recognitions, and teaching awards from London Business School and UCL. His research has been supported by grants from organizations like Eurocontrol, NHS, and private sector firms such as Vungle and Noble Group. DE REYCK has led significant industry collaborations, including projects on airport operations optimization, pharmaceutical R&D valuation, and AI-driven advertising strategies. His advisory work spans sectors like aviation, healthcare, and transportation, emphasizing real-world problem-solving through analytics and decision science.

Esteban G. Tabakمشاهده پروفایل

استاد

• Fluid Dynamics
• Data Science
• Optimal Transport
• +۴ مورد دیگر

Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.

• Nonlinear Control
• Robust Control
• Adaptive Control
• +۷ مورد دیگر

Mike Grimble is a Research Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. His work is centered on advanced control systems with applications across automotive, aerospace, marine, and industrial domains. He is actively involved in theoretical and applied research, particularly in nonlinear and robust control methodologies. Research Interests: Theory and application of nonlinear and robust control for multivariable systems Adaptive control and estimation methods Benchmarking and performance assessment of control systems Condition monitoring and industrial applications Real-time control and embedded systems His recent publications highlight a strong trend in applying predictive and adaptive control techniques to electric vehicles, battery systems, underwater robotics, and industrial machinery. These works emphasize real-time implementation, energy optimization, and robustness—critical for modern sustainable and autonomous systems. Scientific Recognition and Activities: Invited speaker on the benefits and challenges of advanced control in industrial applications (2013) Contributor to UN Sustainable Development Goals, particularly in sustainable industry and innovation Active research output with over 158 publications, including journals, conferences, and book chapters Research Leadership and Funding: Principal Investigator on multiple EPSRC and RSE-funded projects Co-investigator in interdisciplinary initiatives such as the Medical Devices Doctoral Training Centre Organizer of international workshops on hybrid and predictive control Labs and Research Teams: He is associated with the Industrial Control Centre at the University of Strathclyde, a leading hub for control engineering research. His collaborations span departments and institutions, involving real-time LabVIEW implementations, hardware demonstrations, and partnerships with industry players like National Instruments and Quanser Inc.

Dan Jiaoمشاهده پروفایل

استاد

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +۸ مورد دیگر

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team

Henrike K. Blumenfeldمشاهده پروفایل

دانشیار

• Bilingualism
• Cognitive Psychology
• Language Processing
• +۴ مورد دیگر

Henrike K. Blumenfeld is an Associate Professor at San Diego State University (SDSU), affiliated with the School of Speech, Language and Hearing Sciences within the College of Health and Human Services. She serves as Director of the Bilingualism and Cognition Laboratory and holds joint faculty status in the SDSU/UCSD Joint Doctoral Program in Language and Communicative Disorders. Her primary research focuses on bilingualism, cognitive control, and language processing across the lifespan. Education: Ph.D., Communication Sciences and Disorders, Northwestern University (2001–2008) B.A., Linguistics, Bryn Mawr College (1997–2001) Research Interests: Bilingualism and cognitive aging Cross-linguistic effects in language comprehension Cognitive control mechanisms in bilingual speakers Neurobiological bases of bilingual language processing Assessment of multilingual populations Grants & Awards: She has secured funding from NIH/NIDCD, NSF, and SDSU grants totaling over $200,000. Notable awards include the 2022 CHHS YES Award for service, 2013 Undergraduate Professor of the Year, and multiple research travel grants. Labs/Teams: Directs the Bilingualism and Cognition Lab, collaborating with institutions like the Gary and Mary West Senior Wellness Center and Bangor University’s Centre for Research on Bilingualism.

Humberto Terrones Maldonadoمشاهده پروفایل

استاد

• 2-Dimensional Materials
• Complex 3-D Atomic Structures
• Solid State and Condensed Matter Physics
• +۲ مورد دیگر

Humberto Terrones Maldonado holds the Rayleigh Endowed Chair Professor position in the Department of Physics, Applied Physics and Astronomy at Rensselaer Polytechnic Institute (RPI). An internationally recognized scholar, he has served as an invited professor at numerous institutions including the University of Louvain (UCL, Belgium), Federal University of Ceará (UFC, Brazil), Shinshu University (Japan), Oak Ridge National Laboratory (ORNL, USA), Penn State University (USA), and the University of Sussex (UK). He is a member of the World Academy of Sciences (TWAS) and the Mexican Academy of Sciences. His educational background includes: PhD, University of London (Birkbeck), UK BSc, Iberoamericana University, Mexico Professor Terrones pioneered the concept of curvature in graphite and graphene in 1991, introducing Schwarzites—graphitic structures with negative Gaussian curvature. His research focuses on electronic, optical, mechanical, and chemical properties of few-layered 2D materials and their application in novel 3D nanostructures. Key areas include: 2-Dimensional Materials Complex 3-D Atomic Structures Solid State and Condensed Matter Physics Nanoscience and Nanotechnology Nonlinear Optics His recent publications (2022-2025) reveal a strong emphasis on transition metal dichalcogenides, defect engineering, machine learning for materials design, and energy applications. Work spans experimental characterization of heterostructures, computational simulations of lattice mechanics, and innovative synthesis techniques like liquid metal exfoliation. His notable scientific awards and honors include: Rayleigh Endowed Chair Member of the World Academy of Sciences (TWAS) Member of the Mexican Academy of Sciences

• IoT Security
• AI Security
• Wireless Networks
• +۲ مورد دیگر

Xiaojiang Du is the Anson Wood Burchard Endowed Professor at Stevens Institute of Technology, directing research in IoT security, AI security, and wireless networks. An IEEE Fellow and ACM Distinguished Member, he leads NSF-funded projects on secure IoT systems and cross-platform security vulnerabilities. Education PhD in Electrical Engineering, University of Maryland MS in Electrical Engineering, Tsinghua University BE in Electrical Engineering, Tsinghua University Research Focus: Develops security frameworks for IoT ecosystems and adversarial machine learning, with recent breakthroughs in smart home security anomaly detection. Honors: IEEE Fellow, ACM Distinguished Member, multiple best paper awards at IEEE conferences. Graduated PhD students hold faculty positions at UNC Charlotte, UL Lafayette, and ShanXi University. Professional Service: IEEE ComSoc Distinguished Lecturer, Associate Editor for IEEE Transactions, and General Co-Chair for IEEE/ACM IWQoS 2023. Secured $9M+ in research funding from NSF, NSA, and DOD.

Mehmet Esat Belviranliمشاهده پروفایل

استادیار

• Heterogeneous Architectures
• Runtime Systems
• Performance Modeling
• +۵ مورد دیگر

Mehmet Esat Belviranli is an Assistant Professor in the Computer Science Department at the Colorado School of Mines, where he directs the High Performance Systems and Software Lab (HyperSys). His research focuses on increasing resource utilization in heterogeneous architectures through runtime systems, scheduling algorithms, and performance modeling, with publications in top venues including MICRO, PPoPP, and SC. Education: Ph.D. in Computer Science, University of California, Riverside (2016) M.S. in Computer Science, Bilkent University (2009) B.S. in Computer Science, Bilkent University (2006) Belviranli's research spans heterogeneous architectures, runtime systems, performance modeling, parallel programming, autonomous computing, deep learning acceleration, cyber-physical systems, and edge-cloud platforms. His work develops analytical models and programming abstractions to address resource management, scheduling, and security challenges in diversely heterogeneous systems, with applications in edge computing, autonomous systems, and machine learning acceleration. Recent projects emphasize real-world constraints and security implications. His publication trends reveal increasing focus on edge-cloud resource management (e.g., HARNESS), security vulnerabilities in heterogeneous systems (e.g., MC3), and deep learning acceleration under resource constraints. Key themes include memory contention modeling, scheduling for cyber-physical systems, and concurrent DNN execution, reflecting a shift toward practical deployment in security-sensitive edge environments. Scientific Awards: U.S. Air Force Research Lab Summer Faculty Fellowship Award (2022) U.S. Air Force Research Lab Summer Faculty Fellowship Award (2021) Oak Ridge National Laboratory Significant Event Award (2019) Best Paper Finalist, IEEE HPEC 2018 Outstanding Paper Award, DATE 2024 Belviranli mentors Ph.D. students Ismet Dagli (MLCommons Rising Star 2024, CGO'24 SRC finalist) and Justin Davis (DATE'24 Outstanding Paper Award winner). He has secured $2M+ in funding from NSF, DoE, and SRC, including an NSF-SaTC grant on mobile security (2024), a DoE grant on superconductive systems (2023), and an NSF FuSe grant on graphene nanoribbons (2023), often leading multi-institutional teams from Rochester, Virginia, Arizona, and Minnesota. The HyperSys Lab develops ecosystems for high-performance heterogeneous systems, with recent projects including HARNESS for edge-cloud resource management and MC3 for mobile SoC security. The lab has received equipment donations from Google Coral.ai and Xilinx, and collaborates with national labs on security challenges and next-generation semiconductor technologies.

• Energy Efficiency
• Waste Heat Recovery
• Organic Rankine Cycle (ORC)
• +۷ مورد دیگر

Dr. Muhammad Imran is a Reader and Lecturer in Mechanical, Biomedical & Design Engineering at Aston University, UK. He is affiliated with the Energy and Bioproducts Research Institute (EBRI) and the College of Engineering and Physical Sciences. His research focuses on energy efficiency, waste heat recovery, and low-temperature power cycles such as Organic Rankine Cycle (ORC) and Supercritical CO₂ systems. He has contributed to the commercialization of ORC systems and collaborates internationally on hybrid energy systems, solar-thermal integration, and district heating networks. Dr. Imran holds a PhD in Energy System Engineering (2016), MSc in Thermal Power Engineering (2012), and BEng in Mechanical Engineering (2009). He has held academic roles at institutions in Pakistan, South Korea, and Denmark, including a Marie Curie Fellowship at the Technical University of Denmark. His awards include the Marie Curie Fellowship (EU), Innovation Award (South Asia Triple Helix), and multiple Research Excellence Awards from South Korea. He leads funded projects on hybrid energy systems for agriculture, waste heat recovery in industries, and sustainable energy solutions in developing countries. His editorial roles include associate editorships in Frontiers in Thermal Engineering and Resources, Environment and Sustainability . He supervises PhD students in renewable energy and low-temperature thermodynamic systems, with ongoing projects on solid-state heat pumps and advanced ORC control strategies. Dr. Imran’s work bridges engineering, data science, and environmental science to address energy challenges. Notable collaborations include projects in Ethiopia, Kenya, Nigeria, and Sudan, focusing on off-grid cold storage, smart irrigation, and biomass energy systems. His research outputs include over 130 peer-reviewed articles, patents, and contributions to international conferences.

• Wearable Sensors
• Smart Garments
• Sustainable Textiles
• +۵ مورد دیگر

Dr. Shirley Coyle is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU) and Programme Chair for the BSc Global Challenges. She holds a BEng in Electronic Engineering from DCU (2000) and a PhD in Biomedical Engineering from NUI Maynooth (2005). Her career includes roles as a Telecoms Engineer at Siemens, Research Fellow at the National Centre for Sensor Research, and Team Leader of Wearable Sensors in the INSIGHT Centre for Data Analytics. She also studied part-time at the Grafton Academy for Fashion Design and later founded a consultancy in wearable technologies. Her research focuses on smart garments, wearable sensors, and sustainable textiles, with applications in healthcare, sports performance, and S.T.E.A.M. integration. Key interests include developing wearable chemical sensors, energy-autonomous sensing systems, and IoT-enabled rehabilitation devices. She has pioneered work on wearable sensors for monitoring chronic diseases, athlete training, and home rehabilitation using VR. Dr. Coyle’s work spans interdisciplinary collaboration, combining biomedical engineering with textile design. Her contributions include innovations in electrospun textiles, self-powered sensors, and sensor integration with microfluidics. She has held leadership roles in DCU’s Governing Authority and promotes STEM education through design-focused initiatives.

• Machine Learning
• Probabilistic Modeling
• Deep Learning
• +۴ مورد دیگر

Geoff Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with CAIDA's AIM-SI cluster. He is also a Canada CIFAR AI Chair and faculty member at the Vector Institute. His research bridges deep learning and probabilistic modeling, focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss earned his PhD in Computer Science from Cornell University (2020), followed by a postdoc at Columbia University. He holds multiple awards, including the AISTATS Top Reviewer and NeurIPS recognitions. His work emphasizes scalable algorithms and open-source contributions, such as the GPyTorch library. Pleiss advises students in Computer Science and Statistics, including Donney Fan (PhD), Tim G. Zhou (MSc), and others. He teaches advanced courses like STAT 547U (Deep Learning Theory) and STAT 520P (Bayesian Optimization). Grants include NSERC Discovery and New Frontiers in Research funding. Pleiss collaborates on interdisciplinary projects, such as astrophysical discovery via machine learning, and actively participates in academic service and outreach. Education: PhD in Computer Science, Cornell University (2020) MSc in Computer Science, Cornell University (2018) BSc in Engineering (Computing with Applied Mathematics), Olin College (2013) Key Research Themes: Uncertainty-aware decision-making with neural networks Scalable Gaussian processes and Bayesian optimization Ensemble methods and their theoretical limitations Recent Grants: NSERC Discovery Grant (2024) New Frontiers in Research Fund (2025, co-PI) His publications span foundational theory to applied machine learning, with over 14,500 citations. He actively mentors students through research internships and advises on open-source software development. Pleiss frequently presents at top conferences and collaborates with industry partners like Microsoft and ASAPP.

Mark Bockoمشاهده پروفایل

استاد

• Audio and Acoustic Signal Processing
• Computer Audition
• Sensors and Transducers
• +۳ مورد دیگر

Mark Bocko is a Distinguished Professor of Electrical and Computer Engineering at the University of Rochester, affiliated with the Hajim School of Engineering & Applied Sciences. He holds roles as Director of the Center for Emerging and Innovative Sciences (CEIS) and Director of Audio & Music Engineering. He earned his PhD in Physics from the University of Rochester in 1984, focusing on gravitational wave detectors. His research spans audio signal processing, sensors, superconductivity, and quantum computing. Notable contributions include flat-panel loudspeaker development, non-contact ECG sensors, and quantum coherence studies in Josephson junctions. Research interests include audio and acoustic signal processing, computer audition, and sensor technologies. His work integrates interdisciplinary approaches, combining electrical engineering, physics, and computer science. Awards include the 2012 Goergen Award for Teaching and Mercer Brugler Distinguished Teaching Professor (2008–2011). Recent publications address modal crossover networks for loudspeakers, vibrational touch sensing, and room impulse response modeling. He has advised PhD students on topics like spatial audio rendering and musical vibrato analysis. His labs focus on advancing audio engineering and smart sensor systems through collaborative industry partnerships.

Fabio Milaniمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Economics
• International Macroeconomics
• +۳ مورد دیگر

Professor Fabio Milani is a faculty member in the Department of Economics at the University of California, Irvine (UCI), where he also serves as Director of Graduate Studies for the Economics Ph.D. Program. He holds a Laurea in Economics from Bocconi University (2000) and a Ph.D. in Economics from Princeton University (2006). His research focuses on Macroeconomics, Monetary Economics, International Macroeconomics, and Time Series Econometrics, with an emphasis on deviations from rational expectations and the impact of learning behavior on macroeconomic fluctuations. Professor Milani’s work explores how psychological factors and heterogeneous expectations drive economic outcomes, including business cycles and inflation dynamics. He has extensively analyzed the role of sentiment, news shocks, and adaptive learning in macroeconomic models. His teaching spans Ph.D. courses like Macroeconomic Theory and Advanced Macroeconomics, as well as undergraduate courses on Financial Markets and the Macroeconomy. He advises numerous Ph.D. students, many of whom hold academic and policy roles globally. His publications span over 30 articles in top journals, including the Economic Journal , Journal of Economic Dynamics and Control , and Journal of Monetary Economics . Recent research includes studies on inflation tolerance, heterogeneous expectations, and the implications of learning models for monetary policy design. While no scientific awards are explicitly listed, his contributions reflect significant scholarly impact in macroeconomic theory and policy analysis.

Thijs Broekhuizenمشاهده پروفایل

دانشیار

• Innovation Adoption
• Value Appropriation
• Digital Business Models
• +۹ مورد دیگر

Dr. Thijs Broekhuizen is an Associate Professor at the University of Groningen's Faculty of Economics and Business, specializing in Innovation Management & Strategy. He currently serves as Scientific Director of the University of Groningen Business School, Coordinator of the Northern-Netherlands Innovation Monitor, and Programme Director of the Executive MBA. His research focuses on digital transformation, value creation in innovation, and digital business models, with interdisciplinary insights bridging strategy, innovation, and digitalization. Education: PhD in Marketing (University of Groningen, 2006) MSc in Marketing (University of Groningen, 2001) Research Interests: Broekhuizen explores value appropriation in technology-driven contexts, digital business models, and strategic responses to disruptive technologies. His work emphasizes SMEs' digital transformation challenges, organizational identity during technological change, and AI-driven innovation management. Key areas include digital platforms, motion picture industries, and social media dynamics. Grants & Projects: TALENT4S3 (€165K, Interreg 2024-2028) SIRM (€189K, Interreg 2023-2027) NWO-funded studies on construction industry profitability and online customer journeys Awards: Best Paper Award at ISoF 2021 Best Short Paper Award Nomination 2021 Teaching & Leadership: Broekhuizen teaches strategy and digitalization in executive programs and has led the MScBA and EMBA initiatives. His educational roles include Programme Director of the Executive MBA (Energy Transition, Health, Sustainable Business Models tracks) and member of the Groningen Digital Business Centre. Labs/Teams: He coordinates the Northern-Netherlands Innovation Monitor (surveying 10,000+ SMEs) and collaborates with the Groningen Digital Business Centre to advance digital strategy research.

Hau-Tieng Wuمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Applied Mathematics
• Machine Learning
• +۵ مورد دیگر

Hau-Tieng Wu is a Professor in the Department of Mathematics at the Courant Institute of Mathematical Sciences, New York University. Originally from Kaohsiung, Taiwan, he holds an MD from National Yang-Ming University (2003) and a PhD in Mathematics from Princeton University (2011). His research focuses on developing mathematical foundations for biomedical signal analysis, particularly in high-frequency and heterogeneous physiological signals such as ECG, EEG, and PPG. He leads the MISTA Lab, which bridges theoretical advancements with clinical applications in areas like sleep dynamics, surgical monitoring, and wearable device data analysis. Key academic roles include tenured positions at Duke University (2017–2023) and the University of Toronto (2014–2017). Notable awards include the Sloan Research Fellowship (2015) and PIMS Early Career Award (2017). His lab actively collaborates with physicians and engineers to advance interpretable medical AI systems. Research interests span nonlinear time-frequency analysis, manifold learning, and spatiotemporal data processing. Over 100+ journal publications and 10 conference proceedings highlight contributions to signal processing theory and clinical applications. The lab is recruiting PhD students/postdocs with backgrounds in applied math, statistics, or biomedical engineering.