جست‌وجوی استاد و پژوهشگر

Marin Litoiuمشاهده پروفایل

استاد

• Cloud Computing
• Self-Adaptive Systems
• DevOps
• +۶ مورد دیگر

About Marin Litoiu is a Professor at York University, holding dual affiliations in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering and the School of Information Technology in the Faculty of Liberal Arts and Professional Studies. He is a Fellow of the Canadian Academy of Engineering and a recipient of the 2020 IBM Faculty of the Year Award. His research focuses on cloud computing, self-adaptive systems, DevOps, IoT, and machine learning-driven performance engineering. Research & Awards Litoiu leads the Dependable Internet-of-Things Applications (DITA) program, funded by NSERC, and co-founded Bitnobi Inc., acquired by Myant. His notable awards include the CASCON 2019 Most Influential Paper Award and Best Paper Awards at multiple conferences. His work emphasizes practical applications of adaptive systems, cybersecurity, and smart infrastructure integration. Grants & Projects NSERC CREATE Program: $1.65M for the DITA program (2018) York Innovation, TIAP, NSERC, and OCI-funded Bitnobi incubation Leadership in multiple CASCON workshops on cloud computing and AIOps Labs & Teams Litoiu’s lab has produced impactful startups like Bitnobi and pioneered research in self-driving systems, edge computing, and AI-driven operations. His team collaborates with industry partners like IBM and explores cutting-edge topics such as LLMs in performance optimization and fault detection.

Amin Hammadمشاهده پروفایل

استاد

• BIM and Digital Twins
• Construction Automation & Robotics
• Sustainable Infrastructure Management
• +۴ مورد دیگر

Amin Hammad is a Professor at the Concordia Institute for Information Systems Engineering, with an additional appointment as Affiliate Professor in Building, Civil, and Environmental Engineering at Concordia University. His research focuses on advancing construction technology through digital transformation, automation, and AI integration. He leads work in BIM applications, 4D simulation, robotic systems, and sustainable infrastructure management. His interdisciplinary approach bridges civil engineering with computer science and data analytics. Key research areas include: Automation and robotics in construction (Construction 4.0) BIM and digital twin lifecycle management AI-driven defect detection and inspection systems Occupational safety through exoskeleton performance evaluation Multi-purpose utility tunnel optimization Energy-efficient building systems Recent work emphasizes applying machine learning to construction equipment activity recognition, UAV path optimization for infrastructure inspection, and ontology development for integrated systems. His research addresses industry challenges in productivity, safety, and sustainability through data-driven solutions.

Tracy Hechtمشاهده پروفایل

دانشیار

• Work-Life Balance
• Organizational Behavior
• Industrial and Organizational Psychology
• +۵ مورد دیگر

Dr. Tracy Hecht is an Associate Professor of Management and Graduate Program Director of the PhD Program in Business Administration at the John Molson School of Business, Concordia University . She earned her PhD in Industrial and Organizational Psychology from the University of Western Ontario. Her research focuses on the interplay between work, family, and other life roles, particularly how these interactions influence job search behaviors, career trajectories, and well-being. Key themes include work-family boundary management, career procrastination, and the impact of multi-role dynamics on professional outcomes. As an Associate Editor at the Journal of Organizational Behavior , Dr. Hecht contributes to advancing scholarly discourse in her field. Her work has been published in top-tier journals such as Journal of Applied Psychology and Journal of Occupational and Organizational Psychology . Recent research highlights include studies on gender differences in job search intensity and the psychological mechanisms behind career decision delays. Her teaching and supervision span the Management (MSc) and Business Administration (PhD) programs. While no specific grants or labs are explicitly noted, her research consistently addresses contemporary challenges at the intersection of work and personal life, offering actionable insights for organizations and policymakers.

• Information Systems
• Business Systems
• Strategy and Organizational Behavior
• +۷ مورد دیگر

Thompson S.H. Teo is a Professor in the Department of Analytics and Operations (DAO) at the National University of Singapore (NUS) Business School. He holds editorial roles in top journals including European Journal of Information Systems, International Journal of Information Management, and Communications of the AIS. His research spans information systems strategy, business-IT alignment, e-commerce, sustainability, and AI's societal impacts. With over 200 publications, he ranked #143 globally in 2023 among business scientists (research.com) and was recognized in Stanford's top 2% global scientists (2021-2023). Awards include the Best Associate Editor Award (2017) and AIS Distinguished Membership. Thompson's research interests include IT adoption, cyberloafing, supply chain digitalization, and green innovation. He teaches courses on innovation, Industry 4.0, and strategic IT at undergraduate, masters, and executive levels. Notable contributions include frameworks for commute experience analysis and AI adoption market signals. His work bridges theory and practice, addressing challenges in sustainability, organizational behavior, and digital governance. Affiliations: NUS Business School, Distinguished Editorial Advisory Board (IJoIM), Senior Member (INFORMS) Grants & Leadership: Extensive editorial leadership, supervised China Scholarship Council PhD students, and executive programs on innovation design thinking. Key Contributions: Over 270 publications, four co-edited books on IT/e-commerce, and policy-relevant studies on environmental regulations and green tech adoption. His research often employs mixed methods (e.g., QCA for corruption analysis, PLS-SEM for cyberloafing models) and addresses global issues like fake news mitigation via ChatGPT and pandemic-era customer engagement in short videos.

Ali Maniمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Turbulence Modeling
• Numerical Simulations
• +۳ مورد دیگر

Ali Mani is an Associate Professor of Mechanical Engineering at Stanford University and a faculty affiliate at the Institute for Computational and Mathematical Engineering. He earned his PhD in Mechanical Engineering from Stanford in 2009, following an M.S. (2004) and B.S. (2002) from Stanford and Sharif University of Technology, respectively. His research focuses on fluid mechanics, turbulence, and numerical simulations, with applications in multiphase flows, electrokinetic systems, and applied mathematics. His group develops high-fidelity simulation tools and reduced-order models to understand transport processes in turbulent and chaotic systems. Research interests include turbulence modeling, two-phase flow dynamics, and electrochemical transport. Recent work explores eddy viscosity operators, nonlocal transport phenomena, and computational methods for multiphase systems. The group's studies often bridge experimental validation and numerical analysis to improve predictive engineering models. Key contributions span electrokinetic transport in porous media, superhydrophobic surface slip effects, and phase field modeling. His lab’s work is supported by grants focusing on fluid dynamics, renewable energy systems, and advanced simulation frameworks.

• Human-Computer Interaction
• Human-AI Interaction
• Accessibility
• +۶ مورد دیگر

Jeffrey P. Bigham is an Associate Professor at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University . His research spans human-computer interaction , human-AI interaction , accessibility , dialog systems , NLP , and crowdsourcing . Current PhD Students: Hamza El Alaoui, Jessica Yin Huynh, Sara Kingsley, Peya Mowar, Yi-Hao Peng, Atieh Taheri PhD Graduates: Erin Brady, Yu Zhong, Ting-Hao Huang, Anhong Guo, Cole Gleason, Prakhar Gupta, Stephanie Valencia, Kundan Krishna, Jason Wu His work is funded by Apple , Bosch , DARPA , Google , Microsoft , the National Institute of Disability Rehabilitation Research , the National Science Foundation , and Yahoo! He also holds a CMU HCII Career Development Fellowship . Selected Awards: NSF CAREER Award 2019 Best Paper at ASSETS 2021 Best Paper Nomination at CHI 2024 Best Paper Nomination at CHI 2021 Best Paper Nomination at DIS 2021

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +۳ مورد دیگر

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Jun Hwa Cheahمشاهده پروفایل

دانشیار

• Digital Marketing
• Technology Marketing
• Consumer Behavior
• +۳ مورد دیگر

Jun Hwa (Jacky) Cheah is an Associate Professor in Marketing at the Norwich Business School, University of East Anglia (UEA), a position he has held since February 2023. Before joining UEA, he served as a Senior Lecturer at Universiti Putra Malaysia and Universiti Teknologi Malaysia in Malaysia. He holds a PhD in Business Economics and an MBA in International Business from Universiti Putra Malaysia, along with a Bachelor of Management in Marketing from Universiti Malaysia Terengganu. His research focuses on digital marketing, technology marketing, consumer behavior, data analytics, and statistical modeling. He has published extensively in top journals such as the Journal of Business Research , European Journal of Marketing , and Psychology & Marketing . He currently serves as a Guest Editor, Associate Editor, and Editorial Board member for multiple academic journals. Dr. Cheah has received several accolades, including the Emerald Young Researcher Award (2021) and a Young Researcher Award from his former university. He has supervised PhD students, one of whom successfully completed their doctoral program. He is actively engaged in PhD supervision and collaborative research, with a strong emphasis on advancing quantitative methodologies and addressing contemporary marketing challenges.

Thomas Zwickمشاهده پروفایل

استاد

• Radio Wave Propagation
• Radio Channel Modeling
• Measurement Techniques
• +۵ مورد دیگر

Prof. Dr.-Ing. Thomas Zwick is a full professor and director of the Institute of High Frequency Engineering and Electronics (IHE) at the Karlsruhe Institute of Technology (KIT). He holds a Dipl.-Ing. (M.S.E.E.) and Dr.-Ing. (Ph.D.E.E.) from the University of Karlsruhe. His career includes roles at IBM Research (2001–2004), Siemens AG (2004–2007 managing automotive radar teams), and KIT since 2007. He leads research in high-frequency technologies, antennas, radar systems, and wireless communications. Research interests include radio wave propagation, antenna design, automotive radar architectures, and millimeter-wave systems. He has authored/co-authored over 400 papers, 20 patents, and received IEEE Fellow status (2018), honorary doctorate from Budapest University (2022), and membership in the Heidelberg Academy and acatech. His work emphasizes integrating sensing and communication systems, 3D-printed RF components, and high-frequency measurement techniques. Teaching focuses on high-frequency engineering, electronic circuits, and radar systems. He oversees the IHE’s laboratories, including the Microwave Engineering Lab and Student Innovation Lab. Recent work explores sub-THz communication, RIS-aided ISAC systems, and beamforming for reduced EMF exposure in urban scenarios.

José del R. Millánمشاهده پروفایل

استاد

• Brain-machine Interfaces (BMI)
• Neuroengineering
• Neuroprosthetics
• +۶ مورد دیگر

Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).

Hima Lakkarajuمشاهده پروفایل

استادیار

• Trustworthy AI
• Machine Learning
• Fairness
• +۸ مورد دیگر

Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.

• 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.

• 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.

• Edge Computing
• Fog Computing
• Distributed Systems
• +۵ مورد دیگر

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

Rachel M. Wernerمشاهده پروفایل

استاد

• Health Economics
• Health Care Disparities
• Quality Improvement Incentives
• +۵ مورد دیگر

Rachel M. Werner, MD, PhD, is a Professor of Medicine at the Perelman School of Medicine and holds the Robert D. Eilers Memorial – William Maul Measey Professorship in Health Care Management and Economics at the Wharton School , both at the University of Pennsylvania . She serves as Executive Director of the Leonard Davis Institute of Health Economics and is an Attending Physician at the Philadelphia Veterans Affairs Medical Center . Dr. Werner is a Core Investigator with the VA Center for Health Equity Research and Promotion and a Senior Fellow at the Leonard Davis Institute . Education: B.A. in Political Science, Macalester College (1992) M.D. in Medicine, University of Pennsylvania School of Medicine (1998) Ph.D. in Health Economics, Wharton School, University of Pennsylvania (2004) Dr. Werner's research program, spanning over two decades, explores the effects of health care payment policies and quality improvement incentives on delivery systems, with a focus on unintended consequences such as worsening racial disparities through public quality reporting. She employs causal inference methods from observational data and leads R01 grants from the Agency for Healthcare Research and Quality (AHRQ) and the National Institute on Aging (NIA) . Scientific Awards: Alice Hersh New Investigator Award Presidential Early Career Award for Scientists and Engineers American Federation of Medical Research (AFMR) Outstanding Investigator Award Elected member of the National Academy of Medicine Her work influences federal and state advisory committees and evaluates Medicaid policies , Medicare systems, and post-acute care models . She directs a VA national center assessing the medical home effectiveness and collaborates with institutions like the Center for Health Equity Research and Promotion and the Center for Health Incentives and Behavioral Economics .