جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Parallel Algorithms
• Optimization
• +۴ مورد دیگر

Betül Boz is an Assistant Professor at the Department of Computer Hardware, Faculty of Engineering, Marmara University. She holds a B.Sc. and M.Sc. in Computer Engineering from Marmara University, and a Ph.D. in Computer Engineering from Boğaziçi University. Her research focuses on computer architecture, optimization, and evolutionary computing. B.Sc., M.Sc., and Ph.D. in Computer Engineering Her research interests include computer architecture, parallel algorithms, optimization techniques, and evolutionary algorithms applied to graph coloring and scheduling. Recent work explores cloud computing scheduling, register allocation, and bioinformatics applications like circRNA-disease prediction. She has published extensively in these areas, utilizing evolutionary computing and machine learning. Key trends in her publications include evolutionary algorithms for graph coloring (2015–2025), register allocation (2004–2024), and cloud computing optimization (2023). She also investigates biomedical applications such as circRNA-disease association prediction. She has advised one thesis, managed one project, and her work aligns with UN Sustainable Development Goals. Her research outputs include 14 WoS-indexed publications, 11 WoS citations, and an h-index of 25 on WoS.

Sreenath Chalil Madathilمشاهده پروفایل

استادیار

• Operations Research
• Data Science
• Healthcare Systems
• +۳ مورد دیگر

Sreenath Chalil Madathil is an Assistant Professor in the Department of Systems Science and Industrial Engineering at Binghamton University. His academic journey includes previous roles as Assistant Professor at the University of Texas at El Paso (2019–2021) and Research Scientist at the Watson Institute of Systems Excellence (WISE) at Binghamton University's Research Foundation (2017–2019). He holds a PhD and MS in Industrial Engineering from Clemson University, an MS in Data Science from the University of Texas at Austin, and a Bachelor of Technology in Electrical and Electronics Engineering from Mahatma Gandhi University, India. Dr. Madathil's research focuses on applying operations research, simulation modeling, and data science to address healthcare challenges such as patient-centered care and healthcare disparities. His work bridges theoretical methodologies with practical applications, including maternal healthcare equity, healthcare facility design, and supply chain resilience. He has secured funding from the National Science Foundation and the U.S. Department of Commerce. Prior to academia, he gained industry experience as a software consultant and client-side analyst at Cognizant Technology Solutions (2006–2011), alongside collaborations with Los Alamos National Lab and BMW Manufacturing. His research has been published in high-impact journals like Healthcare Management Science and Computers and Industrial Engineering . His professional expertise spans interdisciplinary projects, including optimizing healthcare workflows, analyzing social media data for public health interventions, and developing resilient microgrid systems. Current research trends include leveraging AI/ML techniques for healthcare analytics and improving operational efficiency in healthcare systems.

Srikanth Rangarajanمشاهده پروفایل

استادیار

• Energy Storage Management
• Thermal Management Systems
• Electronics Packaging
• +۷ مورد دیگر

Srikanth Rangarajan is an Assistant Professor at Binghamton University's School of Systems Science and Industrial Engineering. He holds a PhD and MS from the Indian Institute of Technology Madras (2017) and a BE from Anna University Chennai (2011). His research focuses on energy storage systems, thermal management of electronics, battery optimization, and digital twinning. He previously served as an Associate Research Professor in Mechanical Engineering at Binghamton under Bahgat Sammakia. Rangarajan authored the book Phase Change Material Heat Sinks: A multi-objective Perspective and holds a patent for a rotatable heat sink design. His teaching includes optimization techniques, thermal modeling, and neural networks. Recent work explores virus spread modeling via genetic algorithms, with a preprint under review in Journal of Healthcare Informatics . He has received multiple awards including an Institute Post-Doctoral Fellowship and Research Assistantships from the Indian government. His research bridges thermal engineering with advanced manufacturing and sustainability, addressing challenges in high-power electronics and data center cooling. Education: BE in Mechanical Engineering, Anna University (2011) MS in Thermal Engineering, IIT Madras (2017) PhD in Heat Transfer, IIT Madras (2017) Research Interests: Digital twin systems for battery optimization Thermal energy storage design Advanced electronics packaging Data center cooling innovations Phase change material composites His recent articles highlight cooling solutions for high-density electronics, battery recycling challenges, and predictive models for epidemiological patterns using computational methods. Ongoing work includes embedded cooling technologies for heterogeneous integrated circuits and sustainable thermal management strategies. Awards: Patent: Rotatable Heat Sink (Government of India) Institute Post-Doctoral Fellowship (IIT Madras, 2017) Research Associate, Divecha Centre (IISc, 2017) Half-Time Research Assistantship (MHRD, 2012-2013) Advising & Grants: While no formal advisees are listed, his prior roles indicate involvement in mentorship. His research has been supported by institutional grants including those from the Indian Ministry of Human Resource Development. Labs/Teams: Active in Binghamton's Systems Science and Industrial Engineering lab, collaborating on thermal management and additive manufacturing projects.

Jason E. Ybarraمشاهده پروفایل

استادیار

• Galactic Star Formation
• Protostellar Outflows
• Physics Education Research
• +۲ مورد دیگر

Jason E. Ybarra serves as a Teaching Assistant Professor and Director of the WVU Planetarium and Observatory at West Virginia University. His academic home resides within the Astronomy and Astrophysics department, where he integrates observational astronomy with innovative educational practices. As coordinator for the Sloan Digital Sky Survey (SDSS-V) Faculty and Student Team (FAST) program, he bridges research infrastructure with undergraduate development. Dr. Ybarra's educational background includes: Ph.D. in Astronomy from University of Florida (NASA GSRP Fellow) M.S. in Physics from San Francisco State University (co-discoverer of precessing jet evidence) His research spans galactic star formation in regions like the Rosette Molecular Cloud, protostellar outflow dynamics , and physics education with special focus on neurodiversity inclusion . Historical astronomy investigations feature prominently, particularly in rediscovering early variable star observations. His educational philosophy emphasizes neurodivergent accessibility, reflected in publications on inclusive STEM pedagogy. Recent publications reveal interdisciplinary trends merging astronomical research with computational methods (CNN analysis of historical records) and social sciences (neurodiversity studies). The Sloan Digital Sky Survey serves as a unifying thread across observational, educational, and historical investigations. Scientific recognition includes: NASA Graduate Student Researchers Program (GSRP) fellowship NASA Florida Space Grant Consortium fellowship As an educator, Dr. Ybarra has taught across diverse settings from Davidson College to Drepung Loseling Monastery in India through the Emory-Tibet Science Initiative. His FAST program coordination creates sustained undergraduate research pathways within SDSS-V. Current projects integrate planetarium outreach with neurodiversity-aware instructional design. The WVU Planetarium and Observatory serves as his primary research and educational hub, while SDSS-V provides large-scale collaborative infrastructure. His work uniquely connects historical astronomical practices with modern neuroinclusive education frameworks.

Jonatan Lassaمشاهده پروفایل

مدرس

• Disaster Governance
• Climate Change Adaptation
• Political Will in Policy Reform
• +۶ مورد دیگر

Dr Jonatan Lassa is a researcher and lecturer specializing in disaster governance and climate resilience at the University of Sydney. His work focuses on political will for disaster risk reduction (DRR), climate change adaptation, and systemic risk management in Southeast Asia and the Pacific. He developed the concept of 'utopiability' to explore societal visions of resilience and has conducted global assessments of DRR policy reforms. Dr Lassa emphasizes interdisciplinary approaches, including object-oriented ontology in critical disaster studies. Research interests include crisis leadership, environmental migration, and community-based disaster management. He has pioneered studies on multi-hazard interactions in volcanic island communities and evaluates policy frameworks for food security and agrarian reform. His recent work addresses the impact of cascading crises (e.g., pandemics, climate disasters) on vulnerable populations. Publications analyze DRR integration in education curricula, emergency animal management in Indigenous communities, and pandemic responses in Indonesia. He advocates for child-centric disaster education and participatory approaches to resilience measurement. Dr Lassa supervises students interested in topics like humanitarian ecosystems, fire governance in Indonesia, and long-term climate adaptation practices. His research bridges academic inquiry with practical policy recommendations for governments and NGOs.

R. Manmathaمشاهده پروفایل

استاد مدعو

• Computer Vision
• Information Retrieval
• Image and Video Retrieval
• +۸ مورد دیگر

R. Manmatha is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst and a Principal Scientist at Amazon A9 since 2013. His academic journey includes a Ph.D. in Computer Science from University of Massachusetts Amherst (1997), an M.S. in Electrical Engineering from University of Hawaii (1986), and a B.Tech in Electrical Engineering from Indian Institute of Technology Kanpur (1983). Research Interests Manmatha's work spans Computer Vision , Information Retrieval , and Document Analysis . Key projects include: Developing Vision-Language Models for GUI grounding and OCR-free document understanding Creating Word Spotting techniques for historical manuscripts like George Washington's papers Advancing Image Retrieval through statistical and relevance models Building Meta Search systems using score distribution analysis Optimizing Diffusion Transformers for text-to-image generation Scientific Contributions His research has led to numerous publications in conferences like SIGIR , CVPR , and ICDAR , focusing on: Automatic Image Annotation using cross-media relevance models Scale Space Techniques for handwritten manuscript segmentation Alignment Methods for document-groundtruth generation Indian Language Document Search via locality-sensitive hashing Transformer-based architectures for multimodal and document tasks Advising & Collaborations Manmatha has mentored students including Jiwoon Jeon , Shaolei Feng , Toni Rath , Jamie Rothfeder , and Nitin Srimal . He co-founded Snaptell (acquired by Amazon) and contributed to Amazon's mobile search technology. Labs & Teams He leads the Multi-media Indexing and Retrieval (MIR) group at the Center for Intelligent Information Retrieval (CIIR) , focusing on non-textual information indexing through ASCII conversion and direct content analysis.

Zhongguo Liمشاهده پروفایل

مدرس

• Distributed Control
• Optimization
• Robotics
• +۵ مورد دیگر

Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.

Bettina Kemmeمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Database Systems
• +۵ مورد دیگر

Bettina Kemme is a Professor in the School of Computer Science at McGill University, Montreal, Canada. She leads the Distributed Information Systems Lab (DISL) and specializes in large-scale data management, distributed systems, and cloud computing. Her academic roles include teaching COMP 512 (Distributed Systems) and COMP 421 (Database Systems). Education: Diplom (M.Sc. equivalent) in Computer Science, Friedrich-Alexander University, Erlangen, Germany (1996) PhD in Computer Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2000) Research Interests: Distributed systems, cloud-native data management, in-database analytics (AIDA project), monitoring-as-a-service frameworks, and scalable pub/sub systems for online games. Current projects focus on integrating machine learning with databases, cloud performance monitoring using SDN, and sustainable data systems for data science. Lab & Collaborations: Leads the Distributed Information Systems Lab (DISL) with active projects in distributed databases, cloud computing, and game systems. Collaborates on EU-Canada initiatives like the SustainSys program for sustainable data infrastructure. Advising: Supervises PhD and M.Sc. students in topics like monitoring frameworks (Mona ElSaadawy), in-database ML (Winnie He), and distributed systems (Maximilian Schiedermeier). Alumni include over 50 researchers from PhD candidates to undergraduate researchers.

Mina Konakovic Lukovicمشاهده پروفایل

استادیار

• Computer Graphics
• Computational Fabrication
• 3D Geometry Processing
• +۴ مورد دیگر

Mina Konaković Luković is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) , affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) . She leads the Algorithmic Design Group , focusing on computational design and fabrication, geometry processing, and robotics. Education: PhD in Computer Science (EPFL), MS & BS in Mathematics (University of Belgrade) Research Interests: Spanning computer graphics, computational fabrication, and 3D geometry processing with applications in smart materials, architectural geometry, and physics-based simulations. Her work integrates machine learning and differential geometry to optimize design algorithms for novel materials and deployable structures. Articles Trends: Recent publications emphasize multi-objective optimization for 3D printing materials, graph grammar in robot design, and computational methods for auxetic structures. Themes revolve around data-driven design, deployable shells, and terrain-adaptive robotics. Scientific Awards: Schmidt Science Fellows Additional Study Grant (2020) ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention (2020) Eurographics PhD Award (2020) Patrick Denantes Memorial Prize (2019) Doctoral Program Thesis Distinction from EDIC EPFL (2019) SIAM Early Career Prize (2020) Eurographics Junior Fellows (2021) Advising & Grants: Mentored by Prof. Dr. Wojciech Matusik and Prof. Dr. Mark Pauly, with funding from Swiss National Centre of Competence in Research (NCCR) Digital Fabrication. She actively participates in program committees and summer schools.

• Cybersecurity
• Mobile Systems Security
• AI in Security
• +۳ مورد دیگر

Dr. Suranga Seneviratne is a Senior Lecturer in Security at the School of Computer Science, University of Sydney. He holds a PhD from the University of New South Wales (2015) and a Bachelor's degree from the University of Moratuwa, Sri Lanka (2005). Before academia, he worked in telecommunications for six years. His research focuses on cybersecurity, particularly privacy and security in mobile systems, AI applications in security, and behavioral biometrics. He has developed tools like an app security rating system and intrusion-free authentication methods. Key awards include the ACM Mobicom 2015 Gold Prize, NASSCOM Technical Innovation Award, and IESL NSW Engineering Excellence Award (all 2015). Current research students include Pasindu Marasinghe (Multi-Objective Optimization in Flat Glass Cutting Production), Braylon SHU (Efficient Parameter Tuning for Large Language Models), and Gaurav VERMA (Threats and Defenses in IoT Wireless Protocols). Grants include funding from the Australian Research Council, NSW Network for Cyber Security, and Google Research. His work spans collaborations with the NSW Smart Sensing Network and the University of Technology Sydney. Labs/Teams: Collaborates with the Centre for Distributed and High-Performance Computing and the NSW Smart Sensing Network (NSSN).

• Machine Learning
• Deep Learning
• Video Classification
• +۳ مورد دیگر

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Min Shinمشاهده پروفایل

استاد

• Computer Vision
• Multiple Object Tracking
• Automated Visual Inspection
• +۴ مورد دیگر

Min Shin is a Professor and Chair of the Computer Science Department at the University of North Carolina at Charlotte (UNC Charlotte), where he also serves as Assistant Dean for Research in the College of Computing and Informatics. He earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2001. His research focuses on Computer Vision, particularly multiple object tracking and automated visual inspection of nuclear power plants. He leads the Video and Image Analysis Lab, developing user-friendly tracking software funded by NSF, NIH, DARPA, and industry grants. Notable projects include the ABCTracker.org platform, which deploys algorithms refined over 10+ years of research. Dr. Shin’s work spans tracking applications in diverse domains such as ants, bees, cells, termites, robots, and vehicles. He has mentored Ph.D. and undergraduate students extensively, emphasizing computational methods in biology and engineering. His service includes roles as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics-B, and program committees for top conferences (ICCV, CVPR, ECCV). His grants and collaborations reflect interdisciplinary strengths in vision algorithms, usability, and real-world deployment.

Antonio Plazaمشاهده پروفایل

استاد

• Hyperspectral Image Analysis
• Remote Sensing
• High-Performance Computing
• +۴ مورد دیگر

Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .

Vedhus Hoskereمشاهده پروفایل

استادیار

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۵ مورد دیگر

Nisar Ahmed is an Associate Professor at the University of Colorado within the Aerospace Engineering Sciences department. His research focuses on the intersection of Artificial Intelligence , Robotics , and Autonomous Systems , emphasizing decision-making under uncertainty, sensor fusion, and human-machine collaboration. Key research interests include: Active Inference for autonomous planning Decentralized Data Fusion in multi-robot systems Machine Self-Confidence and competency assessment Reinforcement Learning for spacecraft and robotic guidance Uncertainty Quantification in dynamic environments Recent publications highlight trends in Pareto-optimal decision-making , Bayesian optimization , contextual bandits , and trust calibration for UAS and planetary rovers. His work integrates probabilistic modeling with real-time autonomy , ensuring robustness in applications like search-and-rescue missions and lunar exploration. Contact: Nisar.Ahmed@Colorado.EDU