جست‌وجوی استاد و پژوهشگر

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

• Data Warehousing
• E-Commerce
• IT Architecture
• +۷ مورد دیگر

Boris Jukic is a Professor and Director of Applied Data Science at the Reh School of Business , Clarkson University. With a PhD from the University of Texas at Austin, he teaches courses such as Visual Basic Programming for Business Applications and Development of Business Applications on the Internet. Dr. Jukic's research focuses on: Management and pricing of networks and telecommunication services Application of data management and presentation strategies in e-business and e-commerce IT architecture's impact on organizational success metrics His recent publications highlight trends in data warehousing , IT architecture , and business intelligence . Notable subfields include data modeling , incentive-compatible pricing , and process-data integration . Contact: Email: bjukic@clarkson.edu Phone: 315/268-3884 Office: 227 Bertrand H. Snell Hall, Clarkson University

Muhammad Ali Gulzarمشاهده پروفایل

استادیار

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +۵ مورد دیگر

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Albert M. Laiمشاهده پروفایل

استاد

• Biomedical Informatics
• Clinical Research Informatics
• Consumer Health Informatics
• +۵ مورد دیگر

Albert M. Lai, PhD, is a Professor of Medicine and Computer Science & Engineering at Washington University in St. Louis, serving as Chief Research Information Officer (CRIO) for the School of Medicine and Deputy Director of the Institute for Informatics, Data Science and Biostatistics (I²DB). He leads WashU Medicine's data warehousing and informatics services, driving innovation in clinical research infrastructure. His expertise spans biomedical informatics, natural language processing (NLP), and telemedicine. Dr. Lai is also Deputy Faculty Lead for WashU’s Digital Transformation initiative, focusing on secure AI integration with sensitive healthcare data. He holds affiliations with the Institute for Public Health, Siteman Cancer Center, and the Center for Applied Health Informatics (CAHI). Research Interests: Dr. Lai develops informatics infrastructure to support clinical trial prescreening, leveraging NLP and machine learning for phenotype extraction from EHR data. He also explores telemedicine, mobile health applications, and EHR-driven cardiovascular health interventions for cancer survivors. His recent work addresses AI ethics in healthcare, including responsible data sharing and bias mitigation in generative AI models. Key Contributions: Over 77 peer-reviewed publications across clinical informatics, AI in healthcare, and pandemic response strategies. His projects include EHR-based cardiovascular health tools, SARS-CoV-2 surveillance in schools, and machine learning models for predicting transplant outcomes. Active mentorship of PhD/MSTP students in translational informatics and data science. Labs/Teams: Leads the Informatics Services Core and collaborates with the CRITICAL consortium for intensive care analytics. Engages in multi-institutional initiatives like the Greater Plains Collaborative for cancer data integration.

Lev Sarkisovمشاهده پروفایل

استاد

• Carbon Capture
• Material Informatics
• Molecular Simulation
• +۶ مورد دیگر

Lev Sarkisov is a Professor of Chemical Engineering at the University of Manchester, leading the Sarkisov Research Group. His work focuses on advancing porous materials for carbon capture, energy storage, drug delivery, and sensing through multiscale computational workflows integrating molecular simulation, machine learning, and process modeling. He holds a Ph.D. from the University of Massachusetts Amherst (2001) and held roles at the University of Edinburgh, including Head of Chemical Engineering. Notable achievements include securing a £1M Wolfson Foundation grant for sustainable engineering (2022) and receiving the 2013 Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Education: Ph.D., Chemical Engineering, University of Massachusetts Amherst, 2001 M.Sc./B.Sc., Moscow Lomonosov Academy of Fine Chemical Technologies, 1995-1997 Research Interests: The group develops porous materials using AI-driven approaches for carbon capture, energy-efficient separations, and material informatics. Key areas include MOFs, adsorption phenomena, and open-source software for reproducible research. Grants & Awards: £1M Wolfson Foundation Grant (2022) Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2013) Edinburgh University Student Union Teaching Award (2019) Labs & Collaborations: The group collaborates globally, emphasizing open-source tools and reproducibility. Projects include CRAFTED (MOF adsorption database) and PoreBlazer v4.0.

• Business Analytics
• Supply Chain Management
• Electronic Commerce
• +۳ مورد دیگر

Joshua Ignatius is a Professor of Business Analytics at the Aston Business School, part of the College of Business and Social Sciences at Aston University. He focuses on research areas including supply chain analytics, prescriptive analytics, electronic commerce, and operations management. His work often addresses challenges such as information asymmetry in supply chains, user recommender systems, and logistics optimization. He is currently accepting PhD students in topics like Information Asymmetry in Supply Chains, User Recommender Systems, and Supply Chain Analytics. His research interests are centered on leveraging data-driven approaches to improve decision-making in supply chain and operational contexts. This includes studying disruption risk management, dynamic data modeling, and the integration of AI in cloud services. He also explores strategic decisions in e-commerce logistics, customer segmentation strategies, and environmental sustainability. Recent publications highlight his contributions to supply chain resilience, optimal security in cloud computing, and sustainable manufacturing processes. For instance, his 2025 work on supply chain network viability addresses disruption risks through dynamic data strategies. Another key area is the analysis of customer behavior in product upgrades, utilizing online review data to inform quality differentiation strategies. Dr. Ignatius has collaborated on projects involving platform information sharing, manufacturer encroachment, and logistics sourcing for e-commerce firms. His research often bridges theoretical frameworks with real-world applications, emphasizing practical solutions for operational challenges. He holds a strong record of supervising PhD students and guiding projects that combine academic rigor with industry relevance. His work frequently appears in leading journals such as the European Journal of Operational Research and Journal of Operations Management.

Bruno Bassoمشاهده پروفایل

استاد

• Food Security
• Plant Resilience
• Agricultural Science
• +۵ مورد دیگر

Bruno Basso serves as the Hannah Distinguished Professor in the Department of Earth & Environmental Sciences at Michigan State University, based in 307A Natural Science Building. He teaches GLG 446: Water and Food and maintains active research in sustainable agricultural systems, with contact via 517-353-9009 or basso@msu.edu. His work bridges academic research with practical farm applications across the US Midwest. His core research interests include: Food Security and Plant Resilience mechanisms Soil Science with emphasis on organic carbon dynamics Precision Agriculture technologies (drones, remote sensing) Climate-Smart Agriculture practices Nitrogen and phosphorus use efficiency Yield stability analysis through spatial-temporal modeling Regenerative agriculture impacts on greenhouse gas emissions Ecosystem services valuation in crop-livestock systems Analysis of his 2023-2025 publications reveals a dominant focus on quantifying climate benefits from regenerative practices using multi-model ensembles. His work consistently addresses scalability for farmer adoption, with strong emphasis on N₂O emissions mapping, soil carbon durability, and yield stability zones. Key methodological innovations include hybrid SAR-remote sensing integration and AI-driven nutrient prescription systems, primarily applied across Midwest corn-soybean systems. No scientific awards were documented in the provided materials. While specific advising details are absent, his leadership in the LTAR cropland common experiment and Soil Inventory Project indicates active mentorship of graduate researchers. His research likely attracts significant USDA and NSF funding given the scale of field experiments and modeling initiatives focused on decarbonizing agriculture. Dr. Basso co-leads the Soil Inventory Project at Kellogg Biological Station, developing integrated sampling, data repository, and modeling frameworks for regenerative agriculture. His team combines ground observations, remote sensing, and biophysical modeling to quantify soil carbon and greenhouse gas fluxes, collaborating with farmers, industry partners, and international researchers to translate science into on-farm practices.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.

Rajesh Krishna BALANمشاهده پروفایل

استاد

• Human-Machine Collaborative Systems
• Pervasive Sensing & Systems
• Learning & Work
• +۹ مورد دیگر

Rajesh Krishna BALAN is a Full-Time Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU) . His research focuses on Human-Machine Collaborative Systems , Pervasive Sensing , and Health & Wellbeing technologies. Based in Singapore, he leverages mobile computing to address urban sustainability and quality-of-life challenges. PhD from Carnegie Mellon University (2006) Specializes in WiFi sensing , VR/AR , and health monitoring Advises PhD students in areas like urban mobility , empathetic design , and cyber-physical systems Beyond academia, BALAN's work bridges ubiquitous computing and public health , with applications in ageing populations , mental health analytics , and smart city optimization . His recent publications highlight cross-disciplinary approaches to sleep analysis , group behavior modeling , and contactless physiological sensing . BALAN actively contributes to educational technology through projects like Technology-Enhanced Learning frameworks. He is also a mentor in collaborative research areas including biomedical informatics and lifestyle monitoring , with a focus on mobile GPU optimization and low-power systems .

Stephan Staudacherمشاهده پروفایل

استاد

• Aircraft Propulsion
• Gas Turbine Engineering
• Thermodynamics
• +۳ مورد دیگر

Prof. Dr.-Ing. Stephan Staudacher is the Director of the Institute of Aircraft Propulsion at the University of Stuttgart. His work focuses on aircraft propulsion systems, gas turbine performance, and turbomachinery design. He holds a professorship in the Faculty of Mechanical Engineering and Aerospace, leading research in advanced engine technologies, erosion effects, and fault detection algorithms. Research interests include engine reliability, computational fluid dynamics (CFD), and experimental validation of propulsion systems. His publications emphasize topics like neural network applications for fault detection, ice crystal icing simulations, and particle transport in additive manufacturing processes. Recent studies highlight the optimization of composite-cycle engines and assessment of mission severity caused by erosion. Key contributions include advancements in engine condition monitoring, transient performance analysis, and the development of Stuttgart University’s Altitude Test Facility (ATF). His work bridges theoretical models with industrial applications, addressing challenges in both civil and military aviation propulsion systems.

Jian Zhaoمشاهده پروفایل

دانشیار

• Information Visualization (InfoVis)
• Human-Computer Interaction (HCI)
• Data Science
• +۶ مورد دیگر

Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.

• Biomedical signal and image analysis
• Deep learning
• Computational statistics
• +۵ مورد دیگر

Chee-Ming Ting is an Associate Professor in the School of Information Technology at Monash University Malaysia. His expertise lies in machine learning, data science, and biomedical engineering, with a focus on signal processing, computational neuroimaging, and computer-aided detection. Previously, he held positions at King Abdullah University of Science and Technology (Research Scientist) and Universiti Teknologi Malaysia (Senior Lecturer). He has authored over 26 journal papers and 43 conference papers, and has secured research grants totaling RM2.5 million as PI/Co-PI. Education: PhD in Mathematics - Statistics, Master of Engineering in Electrical Engineering, and Bachelor of Engineering (Hons.) in Electrical & Electronics Engineering. Research interests include biomedical signal/image analysis, deep learning, spatio-temporal modeling, and neuroimaging applications for disease prediction and patient monitoring. He has supervised 9 graduate students (4 PhD, 5 Masters) and currently oversees 10 PhD candidates. Awards include the IEEE Signal Processing Society Malaysia's Research Excellence Award (2019, 2022) and several national/international innovation awards. His work contributes to UN Sustainable Development Goals related to health and technological advancement. Key projects include frameworks for neurological disease prediction using brain networks and generative adversarial networks for medical imaging enhancement.

Alaa Alameldeenمشاهده پروفایل

دانشیار

• Computer Architecture
• Memory Systems
• Hardware Security
• +۵ مورد دیگر

Alaa Alameldeen is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), part of the Faculty of Applied Sciences. Previously, he worked as a Research Scientist at Intel Labs (2006–2020) and held an Adjunct Faculty position at Portland State University (2008–2018). He earned a PhD in Computer Sciences from the University of Wisconsin-Madison (2006), and earlier degrees from Alexandria University, Egypt. His research focuses on computer architecture, including memory systems (processing-in-memory, cache/memory compression, security), energy-efficient architectures, and hardware-software co-design for machine learning. He advises PhD and MSc students in these areas and teaches advanced computing science courses. Key contributions include innovations in memory hierarchies, cache compression techniques, and mitigating hardware vulnerabilities. His work has been published in top conferences (e.g., ISCA, MICRO, HPCA) and patented in areas like near-memory processing and error correction. Alameldeen currently leads a research group exploring secure and high-performance memory architectures. He has supervised multiple graduate students, with many progressing to roles at leading tech companies and academic institutions.

Mahadev Satyanarayananمشاهده پروفایل

استاد

• Mobile and Pervasive Computing
• Distributed Systems
• Security and Privacy
• +۲ مورد دیگر

Mahadev Satyanarayanan is the Jaime Carbonell University Professor of Computer Science at Carnegie Mellon University. His multi-decade research focuses on performance, scalability, availability, and trust in distributed systems spanning cloud to mobile edge computing. He pioneered foundational concepts in mobile computing and Edge Computing through his seminal work on VM-based cloudlets. His current research explores cloudlet-based Edge Computing for latency-sensitive applications, wearable cognitive assistance systems integrating augmented reality, and edge-based machine learning frameworks for efficient training data discovery. He collaborates with Dan Siewiorek, Martial Hebert, and Bobby Klatzky on transformative applications. Dr. Satyanarayanan received his PhD from Carnegie Mellon University after completing Bachelor's and Master's degrees at the Indian Institute of Technology, Madras. His honors include ACM and IEEE Fellowships recognizing his contributions to distributed systems and mobile computing. ACM Fellow IEEE Fellow

Timothy H. Murphyمشاهده پروفایل

استاد

• Neuroscience
• Stroke Recovery
• Brain Circuit Mapping
• +۵ مورد دیگر

Dr. Timothy H. Murphy is a Professor in the Department of Psychiatry at the University of British Columbia's Faculty of Medicine. He is also an Associate Member of the School for Biomedical Engineering and a Member of the Djavad Mowafaghian Centre for Brain Health. Dr. Murphy leads the Dynamic Brain Circuits in Health and Disease initiative and the Division of Neuroscience and Translational Psychiatry at UBC. Dr. Murphy received his Ph.D. from Johns Hopkins University in 1989 and his B.Sc. from Saint Mary's College Maryland in 1984. His research focuses on understanding brain circuit structure-function relationships in relation to stroke recovery, psychiatric disorders, and neurological diseases. He specializes in mesoscale imaging techniques to study cortical activity patterns and develop automated approaches for brain imaging and stimulation. His laboratory develops innovative tools including open-source hardware for automated mouse brain imaging, synthetic data generation for behavioral analysis, and chronic recording systems that enable simultaneous mesoscale cortical imaging with subcortical or peripheral nerve activity monitoring. Research from the Murphy Lab has significantly advanced our understanding of how brain circuits reorganize after stroke and in models of psychiatric disorders. Dr. Murphy's recent publications reveal trends in mesoscale cortical imaging, development of synthetic data for behavioral analysis, and exploration of circuit-level changes in neurological and psychiatric disease models. His work bridges basic neuroscience with potential clinical applications for stroke recovery and mental health treatments. Dr. Murphy has mentored numerous students and postdoctoral fellows who have gone on to successful careers in neuroscience and related fields. His laboratory has received funding to support innovative approaches to understanding brain circuit function and recovery mechanisms. The Murphy Lab maintains strong collaborative ties across UBC and develops open-source tools that are widely adopted by the neuroscience community. Their work on automated home-cage imaging systems, synthetic behavioral data generation, and chronic recording technologies represents significant methodological advances in the field.