جست‌وجوی استاد و پژوهشگر

Xin Fangمشاهده پروفایل

استادیار

• Power System Optimization
• Cyber-Physical Systems
• Renewable Energy Integration
• +۳ مورد دیگر

Xin Fang is an Assistant Professor in the Electrical & Computer Engineering Department at Mississippi State University, affiliated with the Bagley College of Engineering. His research focuses on power system optimization with uncertainty, cyber-physical dynamic modeling, and renewable energy integration. He holds a Ph.D. from the University of Tennessee, Knoxville, an M.S. from China Electric Power Research Institute, and a B.S. from Huazhong University of Science and Technology. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee, Knoxville, 2016 M.S., Power System Automation, China Electric Power Research Institute, 2012 B.S., Power System Automation, Huazhong University of Science and Technology, 2009 His research interests emphasize cyber-physical systems integration, grid resiliency through multi-timescale modeling, and equitable energy distribution. Recent work addresses challenges in high-renewable grid operations, including dynamic co-simulation frameworks for electricity-transportation networks. He explores innovative solutions for frequency stability, voltage control, and market mechanisms for emerging energy systems. Publications highlight advancements in hybrid power plant optimization, distributed energy resource coordination, and equitable load-shedding methodologies. His work bridges theoretical modeling with practical applications in grid security and renewable integration.

W. Raghupathiمشاهده پروفایل

استاد

• Analytics
• Artificial Intelligence
• Deep Learning
• +۱۰ مورد دیگر

W. Raghupathi is a Professor at Fordham University’s Gabelli School of Business, Department of Information, Technology, and Operations, where he has been since 1998. He directs the Design and AI labs within the Center for Digital Transformation and co-edits the International Journal of Healthcare Information Systems and Informatics for North America. His work bridges analytics, artificial intelligence, and deep learning with applications in climate change, healthcare, crowdfunding, cybersecurity, and ESG. Education : L.L.M. in Intellectual Property in Information Technology (Fordham Law School, magna cum laude ), Ph.D. (University of Texas at Arlington), M.B.A. (University of Texas at Arlington), M.Com (Sri Venkateswara University, India), B.Com and B.L. (Osmania University, India). His research spans health informatics , climate change analytics , ESG and sustainability , cybersecurity , and AI ethics . Articles highlight trends in visual analytics for public health and climate impact, machine learning in legal and cybersecurity contexts, and cross-country sustainability studies . Awards include grants from IBM and Verizon, while his labs focus on digital transformation and AI-driven research . Scientific Awards : Faculty awards from IBM Corporation Grants from IBM Corporation Faculty awards from Verizon Grants from Verizon Dr. Raghupathi’s prior roles include positions at California State University, The University of Texas at Arlington, Intel Corporation, and industry experience in India and the U.S. before his academic career.

Bingzhe Liمشاهده پروفایل

استادیار

• DNA Storage Systems
• Machine Learning Systems
• Low-Power Computing
• +۳ مورد دیگر

Dr. Bingzhe Li is an Assistant Professor in Computer Science at UT Dallas' Erik Jonsson School of Engineering. His research at the Lab for Intelligent Storage and Computing (Lab4ISC) focuses on DNA storage systems, machine learning infrastructure, and energy-efficient computing architectures. Awarded the NSF CAREER Award (2025) and recognized for Best Paper nominations at leading conferences. Research spans DNA storage capacity optimization, reinforcement learning for hybrid SSDs, Kubernetes storage optimization, and stochastic computing architectures. Recent publications demonstrate innovations in out-of-core graph processing and blockchain storage systems. Leads multiple NSF/NASA-funded projects on DNA storage and convertible SSDs. Teaches Digital Logic and Computer Architecture courses. Supervises 8 PhD students and 3 master's candidates in storage systems and low-power computing research.

Pirathayini Srikanthaمشاهده پروفایل

دانشیار

• Game Theory
• Large-scale Optimization
• Distributed Control
• +۵ مورد دیگر

Pirathayini Srikantha serves as Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, holding a Tier 2 Canada Research Chair in Reliable and Secure Power Grid Systems with critical national security implications for Canada's power infrastructure. Her academic foundation includes: B.A.Sc. in Systems Design Engineering from University of Waterloo (2009) M.A.Sc. in Electrical and Computer Engineering from University of Waterloo (2013) Ph.D. from University of Toronto's Edward S. Rogers Sr. Department of Electrical and Computer Engineering (2017) Professor Srikantha's research centers on game theory, large-scale optimization, and distributed control to enable adaptive power grid operations. Her work addresses cybersecurity vulnerabilities in smart grids through distributed coordination frameworks and develops transactive energy market mechanisms that incentivize grid support during congestion. By integrating AI breakthroughs, she focuses on creating systems that automatically infer, predict, and respond to power disturbances while enhancing infrastructure resilience against evolving threats. Her publication trajectory reveals consistent innovation in grid security and optimization, with increasing emphasis on AI-driven solutions for grid resilience since 2015. Key thematic evolution shows progression from foundational distributed control (2015-2017) to sophisticated cybersecurity applications (2018-2021), consistently appearing in premier venues like IEEE Transactions on Smart Grid. Her scholarly impact is recognized through: Best paper award at IEEE Smart Grid Communications Runner-up best poster award at ACM Women in Computing As Canada Research Chair holder, she directs a federally funded research program addressing critical grid vulnerabilities, while serving as Associate Editor for IEEE Transactions on Smart Grid. Her work bridges theoretical advances in game-theoretic security with practical grid applications, though specific student mentorship details aren't publicly enumerated in source materials. Her research group operates at the intersection of electrical engineering and computer science, developing next-generation tools for grid operators through collaborations with Canadian energy infrastructure stakeholders.

Yanzhao Wuمشاهده پروفایل

استادیار

• Machine Learning
• Computing Systems
• Deep Learning
• +۳ مورد دیگر

Dr. Yanzhao Wu is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU) since December 2022. He previously worked as a Research Scientist at Meta Platforms (Facebook) from May to December 2022, and has completed internships at both Facebook and IBM Research. He earned a Ph.D. in Computer Science from Georgia Institute of Technology in 2022 and a B.E. in Computer Science from University of Science and Technology of China in 2017. Ph.D. in Computer Science – Georgia Institute of Technology (2022) B.E. in Computer Science – University of Science and Technology of China (2017) Dr. Wu's research focuses on the intersection of machine learning and computing systems, with particular attention to: Systems for Machine Learning Machine Learning for Systems Ensemble Learning Big Data Systems & Analytics Edge AI Systems His work has been published in top venues such as CVPR, ICSE, IEEE ICDCS, IEEE ICDM, IEEE TSC, and ACM TOIS, demonstrating expertise in both theoretical and applied aspects of machine learning optimization and system design. He has received multiple awards including the IEEE CIC Best Paper Award and ICDM 2021 Student Attendance Award. Professional recognition includes: IEEE CIC Best Paper Award (2021) ICDM 2021 Student Attendance Award (2021) College of Computing Student Travel Award (2020) Outstanding Graduate Award (USTC) (2017) ISC Student Cluster Competition 4th Place (2016) Dr. Wu serves as a reviewer for prestigious conferences and journals including ICDE, WWW, CVPR, ECCV, AAAI, IEEE ICDCS, IEEE TKDE, and ACM TOIT. He maintains active involvement in professional activities such as program committee membership for AAAI 2023 and ICDCS 2023.

Isaac Kofi Ntiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +۴ مورد دیگر

Dr. Isaac Kofi Nti is an Assistant Professor in the School of Information Technology at the University of Cincinnati, with over 16 years of interdisciplinary research experience in applied machine learning, health informatics, cybersecurity, and data governance. His work emphasizes responsible AI deployment in high-impact domains including healthcare, finance, and education. He concurrently holds affiliations with Ghanaian institutions (University of Energy and Natural Resources and Sunyani Technical University) and serves as a Visiting Assistant Professor at Cincinnati until 2024. Education PhD in Computer Science, University of Energy and Natural Resources, Ghana (2021) MSc in Information Technology, Kwame Nkrumah University of Science and Technology, Ghana (2016) BSc Computer Science, Catholic University of Ghana (2011) Higher National Diploma in Electrical & Electronic Engineering, Sunyani Technical University, Ghana (2007) Research Focus Dr. Nti develops lightweight, explainable AI systems for cybersecurity, healthcare diagnostics, agricultural optimization, and educational technology. His research integrates secure data analytics, ethical AI frameworks, and cloud-based learning infrastructures. Current projects explore generative AI in education, predictive modeling for disease outbreaks, and blockchain applications for health data verification. Publication Trends His 60+ publications (2022-2024) demonstrate strong emphasis on ensemble learning methods, healthcare AI applications, and cybersecurity innovations. Recent works frequently utilize hybrid models (LSTM/XGBoost/StackNet) for fraud detection, disease prediction, and renewable energy forecasting. Awards & Grants Provost Office Strategic Collaborative Faculty Team Award (2024-2025): $19,200 for AI integration in higher education Faculty Development Grant (2023-2024): $10,038 for emerging technology training Faculty Incentive Awards for Research (CECH, UC 2022-2024) Teaching & Service Teaches graduate/undergraduate courses in machine learning, cybersecurity, and data technologies. Leads multiple university committees including PhD Admissions Review and Applied Research teams. Professional memberships include ACM, IEEE, and AAAI.

Dharmendra Sharmaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Constraint Processing
• +۳ مورد دیگر

Professor Dharmendra Sharma is a Professor of Computer Science at the University of Canberra, holding leadership roles such as Chair of the Faculty Board (Science and Technology) and former Dean of the Faculty of Information Sciences and Engineering. He specializes in AI, robotics, and distributed systems. With over 40 years of academic experience, he has published 320+ papers, supervised 40+ students, and led numerous research projects. His expertise spans constraint processing, machine learning, and applications in healthcare, security, and education. Education: PhD in Artificial Intelligence (Australian National University, 1988–1992) MCompSci, PGradDipMath, BSc (Maths/Chemistry) from the University of the South Pacific Research Interests: Distributed AI and multi-agent systems Machine learning/deep learning Constraint satisfaction models Applications in health, education, and security His work aligns with UN Sustainable Development Goals, particularly in education and technology access. Key Projects: Future Jobs Fund - Open Source Institute IAIM: Optimized Smart Data Centers eLiving Lab Collaboration Awards: Order of Australia (AM, 2019) Fellowships: ACS, South Pacific Computer Society Companion of Engineers Australia Advising and Grants: Supervised over 40 PhD/Master’s students Secured competitive grants for AI, cybersecurity, and health tech Collaborations with industry and international universities Labs/Teams: Leadership in AI and Robotics research groups Industry partnerships for innovation and training

Damien Ernstمشاهده پروفایل

استاد

• Energy Systems
• Operations Research
• Remote Renewable Energy Hubs
• +۵ مورد دیگر

Prof. Damien Ernst is a faculty member at the Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liège (Belgium). His research focuses on energy systems, remote renewable energy hubs, reinforcement learning, and optimization algorithms. He actively contributes to decarbonization strategies for hard-to-abate sectors like shipping and aviation. Academic Rank: Professor University: University of Liège School: Montefiore Institute Department: Electrical Engineering and Computer Science Research interests include: Design and techno-economic analysis of remote renewable energy systems Reinforcement learning applications in energy and optimization Energy transition policy and sustainability modeling Machine learning techniques for energy system transparency Integration of nuclear, hydrogen, and battery technologies His recent publications demonstrate expertise in: Remote Renewable Energy Hub (RREH) design Small Modular Reactor (SMR) integration Asymmetric Actor-Critic algorithms Energy system optimization and modeling Grid stability and hosting capacity analysis Explainable AI for closed-domain question answering Prof. Ernst leads PhD projects at the intersection of Operations Research and Energy Systems, focusing on energy systems models' transparency and optimization techniques.

• Biomedical Informatics
• Nursing Informatics
• Clinical Research Informatics
• +۸ مورد دیگر

Mollie R. Cummins is a Professor of Nursing and Adjunct Professor of Biomedical Informatics at the University of Utah, holding the Jon M. Huntsman Presidential Chair. With a PhD in nursing science and information science from Indiana University, she has pioneered the application of complexity science, AI, and data science in healthcare since joining the University of Utah in 2004. Her work spans biomedical informatics , exposure health informatics , and telemedicine , with a focus on health information exchange between emergency departments and poison control centers. Education : PhD (Indiana University), MSN (Northern Kentucky University), BSN (University of Illinois at Chicago) Her research develops models for bidirectional data exchange, sensor metadata integration in pediatric asthma studies (NIEHS R24), and digital phenotyping of PASC (NHLBI RECOVER). She leads national telemedicine trend analyses and co-directs the University of Utah's Exposure Health Informatics Ecosystem . Key contributions include: Scientific Awards Fellow of the American Academy of Nursing (FAAN) Fellow of the American College of Medical Informatics (FACMI) Jon M. Huntsman Presidential Chair She has mentored junior researchers in studies of poisoning communication, telehealth VR applications, and decentralized clinical research, with over 200 peer-reviewed publications. Her work aligns with the Institute of Medicine's learning health system vision, integrating population health, clinical care, and discovery through informatics innovation.

Dong Chenمشاهده پروفایل

دانشیار

• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• Embedded AI
• +۵ مورد دیگر

Dong Chen is an Associate Professor in the Department of Computer Science at the Colorado School of Mines. His research focuses on building data-driven experimental systems in Cyber-Physical Systems (CPS), IoT, Embedded AI, and Embodied AI, with applications in smart devices, homes, cities, and renewable energy systems. He leads the Next Generation Cyber-Physical Systems Laboratory (CPSLab), emphasizing open-source systems and datasets. Dr. Chen holds PhDs in Electrical and Computer Engineering (2018, University of Massachusetts Amherst) and Computer Science (2014, Northeastern University). His work addresses security, privacy, sustainability, and efficiency in smart environments. Notable contributions include SolarFinder, SolarTrader, PrivacyGuard, and VoiceAttack, which tackle challenges in IoT privacy, energy trading, and adversarial attacks. He received the NSF CAREER Award (2023) and is a member of Sigma Xi, ACM, AAAI, and IEEE. His research spans system design, AI applications, and cross-cutting domains like solar energy modeling and edge computing. Current projects include AgileDART (edge stream processing) and SolarDetector (satellite-based PV array identification). Advising and collaborations: Dr. Chen seeks PhD and undergraduate students with strong CS/EE backgrounds. His lab focuses on CPS/IoT security, energy systems, and AI-driven solutions. He has published extensively on topics ranging from smart grid optimization to adversarial machine learning.

Daniel Huttenlocherمشاهده پروفایل

استاد

• Artificial Intelligence + Machine Learning
• AI and Society
• Graphics and Vision
• +۳ مورد دیگر

Daniel Huttenlocher is the Dean of the MIT Stephen A. Schwarzman College of Computing and holds the Henry Ellis Warren (1894) Professorship in Computer Science and Artificial Intelligence + Decision-making (AI+D). He leads the interdisciplinary College of Computing while maintaining academic ties to the Electrical Engineering & Computer Science Department. His research focuses on AI ethics, societal impact of technology, machine learning applications, and vision systems. Key research areas include AI governance frameworks, adaptive public health strategies, and large-scale computer vision algorithms. He has pioneered work on decentralized collaboration systems, social media dynamics, and generative AI's societal implications. Huttenlocher’s recent publications address challenges in epidemic testing optimization and the evolving role of AI in knowledge ecosystems like Wikipedia. His leadership roles include steering MIT’s computing initiatives and fostering industry partnerships, such as the MIT Generative AI Impact Consortium. Despite no explicitly listed awards, his contributions to AI ethics and technical systems reflect significant academic influence. Advising and grant activities remain unspecified in available records. His work bridges technical innovation with societal challenges, emphasizing the ethical deployment of AI across healthcare, education, and digital economies.

Kim Nayyerمشاهده پروفایل

استاد آموزشی

• Copyright Law
• Artificial Intelligence Ethics
• Indigenous Knowledge Protection
• +۵ مورد دیگر

Kim Nayyer is the Edward Cornell Law Librarian, Associate Dean for Library Services, and Teaching Professor at Cornell University ’s Cornell Law School . She specializes in Copyright Law , Artificial Intelligence Ethics , and Indigenous Knowledge Protection , focusing on critical information literacy, digital citizenship, and decolonization in legal education. Her work examines the ethical implications of AI bias in library systems and the intersection of copyright with Indigenous customary law. Extensive experience in legal research and education Advocate for equitable access to legal information 2020 Fastcase 50 Award recipient Her recent publications analyze AI’s impact on copyright, controlled digital lending, and Indigenous knowledge protocols. She has represented library interests before the Supreme Court of Canada and parliamentary copyright committees, emphasizing social responsibility and data ethics in academic libraries.

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Eddie Ballمشاهده پروفایل

استاد

• RF Engineering
• Wireless Systems Design
• Millimeter-Wave Communications
• +۵ مورد دیگر

Professor Eddie Ball is a Professor of Radio Frequency Engineering at the University of Sheffield's School of Electrical and Electronic Engineering, and a UKRI Future Leaders Fellow (2021-2028). He leads the Electromagnetics, Wireless Hardware & RF Devices research theme and directs the EPSRC Millimetre Wave Measurement Laboratory. His expertise spans RF circuit/system design, SDR, and millimeter-wave technologies, with a focus on IoT applications and hardware manufacturing. Qualifications: Ph.D., University of Sheffield (2024) M.Eng (1st class), University of York (1996) Chartered Engineer Research Interests: Novel RF circuit/system design Millimeter-wave transceivers and antennas RF-MMIC and SiGe design IoT radio systems and blockchain integration Low-cost, high-performance wireless protocols Teaching: Creator and instructor for EEE6239: Radio Transceiver System & Circuit Design 2nd-year course leader for VHF Synthesiser for Wireless Communications Labs/Teams: EPSRC Millimetre Wave Measurement Laboratory Future Millimetre Wave RF Transceiver Architectures Project

Annette Veeمشاهده پروفایل

دانشیار

• Computation and Writing
• Digital Humanities
• Coding Literacy
• +۶ مورد دیگر

Annette Vee is an Associate Professor of English and Director of the Composition Program at the University of Pittsburgh, housed in the Kenneth P. Dietrich School of Arts & Sciences. She specializes in the intersection of computation and writing, with affiliations to the DSAM Certificate, Forbes Corridor Digital Initiative, Humanities Engage, Pitt Cyber, and STEM Coding Lab. Her research explores coding literacy, blockchain, AI-based text generation, and automated writing systems, with a focus on their implications for education, law, and rhetoric. Key publications include Coding Literacy: How Computer Programming is Changing Writing (MIT Press, 2017) and co-edited TextGenEd: Teaching with Text Generation Technologies (2023). Current projects include a monograph on the historical pursuit of automating writing and a co-curated digital exhibit on the 1966 Dartmouth Seminar. She teaches courses like Digital Humanity and Computational Media , emphasizing digital composition and critical engagement with technology. Vee’s work bridges disciplines, examining how computational systems reshape literacy, pedagogy, and societal trust. She has led a Mellon-funded Sawyer Seminar on information ecosystems and collaborates across computer science and humanities departments to design interdisciplinary programs like the Digital Narrative and Interactive Design major. Her research also addresses ethical concerns in AI, automated writing’s impact on political discourse, and the legal frameworks governing code. Vee advocates for open-access publishing and has published widely in journals like Computational Culture , Interfaces , and Literacy in Composition Studies .