جستجوی استاد راهنمای دانشگاهی

Swarun Kumar is the Sathaye Family Foundation Professor at Carnegie Mellon University (CMU) in the Electrical and Computer Engineering (ECE) department, with affiliate appointments in the Computer Science (CS) department, Human-Computer Interaction (HCI) Institute, and CyLab. He leads the Emerging Wireless Technologies (WiTech) lab, focusing on next-generation wireless network protocols and services. Ph.D., Computer Science, MIT (George Sprowls Award winner) B.Tech., IIT Madras (President of India Gold Medal) His research spans Low-Power WANs , Battery-Free IoT , mmWave Sensing , and Soft Robotics . Key projects include MetaMorph (robotic signal enhancers), WASER (RF-powered soft robots), and Hydra (multi-bounce mmWave sensing). He pioneered wireless actuation, body-frame tracking, and whale rendezvous frameworks using autonomous robots. His recent work explores wireless systems for high-temperature materials (Viton), microwave energy optimization (MicroSurf), and dental health sensing (ToMoBrush). Articles reflect trends in Robotics , Material Science , and Wireless Systems . 2024 ACM SIGMOBILE Rockstar Award 2021 ACM SIGBED Early Career Researcher Award NSF CAREER Award George Sprowls Award (MIT Best Ph.D. Thesis) President of India Gold Medal (IIT Madras) He has advised numerous Ph.D. students, including alumni joining universities like University of Washington, UC San Diego, and NUS. His teaching includes courses on Computer Networks , Wireless Systems , and Telecommunication Networks .

• Low-Power Wide-Area Networks (LP-WANs)
• Battery-Free IoT
• Future Wi-Fi
• +۷ مورد دیگر

Patrícia Amaral is a Professor at the Department of Spanish and Portuguese within the College of Arts & Sciences at Indiana University Bloomington. She holds an adjunct position in Linguistics and has been actively researching the syntax-semantics interface, historical linguistics, and experimental pragmatics. Education: Ph.D. in Hispanic Linguistics from Ohio State University (2007) M.A. in Linguistics from University of Coimbra (1999) B.A. in Classical Languages from University of Coimbra (1996) Her research systematically explores how lexical, contextual, and pragmatic factors influence interpretation, with a focus on inferential mechanisms in semantic change. She has contributed extensively to understanding syntactic and semantic evolution in Ibero-Romance languages, particularly Portuguese and Spanish, and has conducted in-depth studies on Barranquenho, a mixed language from the Portuguese-Spanish border region. Methodologically, she combines corpus analysis with experimental techniques. The most recent articles reveal a concentration on semantic change dynamics, distributional methods in historical linguistics, adversative connectives, and pragmatic markers. Her work spans from theoretical frameworks to computational approaches, often bridging diachronic and synchronic analysis. Affiliations: Cognitive Science Program Institute for European Studies

• Syntax-Semantics Interface
• Historical Linguistics
• Lexical Semantics
• +۷ مورد دیگر

Stephen P. Boyd is the Samsung Professor in the School of Engineering and Professor in the Department of Electrical Engineering at Stanford University. He is also a Member of the Institute for Computational and Mathematical Engineering. His office is located in Packard 254 at 350 Jane Stanford Way, Stanford, CA 94305. Professor Boyd's research spans multiple domains where mathematical optimization serves as the unifying framework. His work focuses on convex optimization theory, algorithms, and applications across diverse fields including control systems, signal processing, machine learning, finance, and circuit design. He has pioneered methods for real-time embedded convex optimization and developed software frameworks that have made optimization accessible to researchers and practitioners across disciplines. His recent publications demonstrate a consistent trend toward practical applications of convex optimization, with significant emphasis on financial engineering (portfolio optimization, tax-efficient strategies), machine learning integration (differentiable optimization, parametric models), and computational efficiency (GPU acceleration, code generation). The breadth of co-authors across different papers highlights his collaborative approach spanning electrical engineering, computer science, finance, and applied mathematics. Professor Boyd teaches several optimization-related courses including ENGR108 (Introduction to Applied Linear Algebra), EE364a (Convex Optimization I), and EE364b (Convex Optimization II), maintaining an active teaching schedule with courses planned through the 2025-26 academic year. His office hours are held on Tuesdays from 1:15pm-2:30pm in Packard 254.

• Convex Optimization
• Control Theory
• Mathematical Optimization
• +۵ مورد دیگر

Zhi-Yun Li is a Professor in the Department of Astronomy at the University of Virginia, specializing in theoretical astrophysics. His research focuses on star and planet formation through magnetohydrodynamic (MHD) simulations , examining molecular clouds, protostellar disks, jets, and outflows. He actively contributes to ALMA and JWST observational projects , bridging theoretical models with empirical data. Key Research Areas : Magnetic field dynamics, circumstellar disk evolution, relativistic jets in AGNs, and exoplanetary magnetospheres. Observational Work : Involvement in studies of HL Tau, Orion Nebula Cluster, and Galactic Center regions. Simulation Expertise : MHD modeling of accretion flows, vortex formation, and magnetic field alignment in protoplanetary disks. Recent publications highlight his work on dust polarization, kinematic structures in Perseus, and insights into early planet formation mechanisms.

• Theoretical Astrophysics
• Star Formation
• Planet Formation
• +۷ مورد دیگر

Dhireesha Kudithipudi is a Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA) and Director of the MATRIX AI Consortium. Her work focuses on advancing neuromorphic computing, energy-efficient machine learning architectures, and lifelong learning systems. She holds a Ph.D. in Electrical and Computer Engineering from UTSA and an M.S. in Computer Engineering from Wright State University. Her research interests include AI algorithms, neuromorphic hardware design, spiking neural networks, and memristor-based systems. She leads initiatives in neuromorphic benchmarking (NeuroBench), energy-efficient computing roadmaps (EES2), and collaborative frameworks like the Neuromorphic Commons (THOR). Her lab develops neuromorphic chips with on-device learning capabilities, such as the Genesis chip, and explores applications in edge computing. Dr. Kudithipudi has pioneered techniques for continual learning in spiking networks, probabilistic metaplasticity, and low-precision numerical formats (e.g., PositCL). Her work bridges theoretical neuroscience principles with practical hardware implementations, emphasizing sustainability and scalability. She also contributes to NSF-funded projects like EFRI BRAID and NAIAD, advancing interdisciplinary AI research. Her lab’s collaborations include developing the NeuroBench framework for fair benchmarking and exploring neuromorphic systems for tasks like video/activity recognition and time-series forecasting. She advises on hardware-software co-design strategies for efficient neural network deployment on constrained devices.

• Artificial Intelligence
• Neuromorphic Computing
• Energy-Efficient Machine Learning
• +۵ مورد دیگر

Erik Golen is a Senior Lecturer in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology. He holds both a BS and Ph.D. from RIT. His research focuses on data science education, information systems curriculum design, and interdisciplinary applications of data analytics. Current teaching responsibilities include courses such as Foundations of Data Science and Analytics, Principles of Data Science, and Visual Analytics. Dr. Golen's scholarly work emphasizes educational technology innovations, including web-based learning platforms for non-technical audiences and curriculum modernization to address emerging industry needs. His publications span data science pedagogy, social media analysis, disaster response informatics, and networking architecture. He actively contributes to conferences like DataEd, IEEE Frontiers in Education, and SIGITE. His research trends show strong focus on bridging technical and non-technical domains through data-driven solutions, with recent emphasis on scalable educational frameworks and real-world data application scenarios. Teaching and research synergize in exploring how advanced analytics can be effectively integrated into diverse academic and professional contexts. No scientific awards are explicitly listed in the provided materials. Advising records and lab affiliations are not detailed here. Office location is GOL-2671.

• Data Science
• Data Analytics
• Informatics
• +۴ مورد دیگر

Murray Patterson is an Assistant Professor in the Department of Computer Science at Georgia State University. His academic journey includes a Ph.D. from the University of British Columbia (2012), followed by postdoctoral research at Inria (France), CWI (Netherlands), Lyon's Evolutionary Biology Lab, and Milan's Experimental Algorithmics Lab. He previously held a visiting position at Fairfield University before joining Georgia State in 2020. Education: B.C.S., Acadia University, 2003 M.S., Computing Science, Simon Fraser University, 2006 Ph.D., Computer Science, University of British Columbia, 2012 Research Interests: Focuses on algorithmic approaches to bioinformatics challenges, particularly evolutionary biology and cancer genomics. Key areas include: - Evolutionary tree construction and dynamics analysis - Haplotype assembly and cancer cell progression modeling - Machine learning for molecular sequence classification - Privacy-preserving federated learning methods - Interdisciplinary applications of computational geometry and kernel methods. Research Trends: Recent work emphasizes scalable tools for analyzing long-read sequencing data, developing novel embeddings for biological sequences (e.g., Hist2Vec, Spike2Vec), and integrating geometric approaches (Bezier curves, Poincaré distances) into bioinformatics workflows. The 2025 publications highlight advancements in privacy-enhanced federated learning frameworks and kernel-based sequence analysis techniques. Awards & Grants: No explicitly listed awards. His work has been supported by grants for tumor phylogeny pipelines and pandemic response initiatives. Labs & Collaborations: Active in the Experimental Algorithmics Lab (algolab.eu) and affiliated with Georgia State's bioinformatics research community. Develops open-source tools like Plastic for tumor phylogeny benchmarking and PDB2Vec for 3D protein structure analysis.

• Bioinformatics
• Computational Biology
• Theoretical Algorithmics
• +۳ مورد دیگر

Zijie Zhang is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on federated learning, graph-based machine learning, and trustworthy AI systems. He holds a Ph.D. in Computer Science from Auburn University and an M.S. from Stevens Institute of Technology. Education: Ph.D. in Computer Science, Auburn University M.S. in Computer Science, Stevens Institute of Technology His work emphasizes secure and robust machine learning techniques, including adversarial defense mechanisms, federated learning optimizations, and graph alignment algorithms. Recent publications explore topics like machine unlearning, resilient graph matching, and cross-lingual knowledge graph alignment. His research bridges theoretical advancements with practical applications in distributed systems and cybersecurity. Dr. Zhang’s contributions address challenges in privacy, model efficiency, and adversarial robustness, reflecting a commitment to advancing trustworthy AI methodologies.

• Federated learning
• Graphs
• Trustworthy machine learning
• +۳ مورد دیگر

Jeff Frolik is a Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont. He holds a Ph.D. in Electrical Engineering from the University of Michigan, an M.S. from the University of Southern California, and a B.S. from the University of South Alabama. His research focuses on wireless communications, sensor networks, and distributed control algorithms, with applications in environmental monitoring, energy systems, and infrastructure health. Frolik has authored over 150 peer-reviewed publications, secured 12 patents, and co-founded Packetized Energy, a cleantech startup acquired by EnergyHub in 2021. Education: Ph.D. in Electrical Engineering (Systems), University of Michigan, 1995 M.S. in Electrical Engineering, University of Southern California, 1988 B.S. in Electrical Engineering, University of South Alabama, 1986 Research Interests: Wireless communication systems in complex environments Harmonic transponders for passive sensing and channel characterization Packetized energy management for smart grids IoT networks and antenna diversity techniques Awards: IEEE Outstanding Teaching Award (2015) Green Mountain Section Faculty of the Year (2011) TTU Innovation and Creativity Awards (2002, 2000) Hughes Aircraft Fellowship (1991–1994) Professional Experience: Co-founder of Packetized Energy (cleantech startup) Research sabbaticals at UC Santa Barbara's SNARL and University of Oxford Fulbright Distinguished Chair at Czech Technical University (2017, 2022) Labs/Teams: Active in developing harmonic transponder technology, wireless sensor networks, and grid coordination systems.

• Wireless Communications
• Sensor Networks
• Distributed Control Algorithms
• +۵ مورد دیگر

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

• Deep learning
• Bayesian data analysis
• Learning theory
• +۳ مورد دیگر

Paul Ampadu is a Professor of Electrical and Computer Engineering at Virginia Tech and serves as the diversity lead at the Virginia Tech Innovation Campus. He leads the Embedded Integrated Systems-on-chip (EdISon) research group and is the associate director of the Advanced Research Institute (ARI). His work focuses on reliable, secure, and energy-efficient systems-on-chip (SoC), low-power VLSI design, and IoT security. He holds a Ph.D. from Cornell University (2004), an M.S. from the University of Washington (1999), and a B.S. from Tuskegee University (1996). Research interests span secure embedded systems design, low-power circuit architectures, and hardware security countermeasures against side-channel attacks. He is affiliated with the Center for Embedded Systems for Critical Applications and the Power and Energy Center. Recent contributions include scalable DC-DC converters, fault-resistant NoC designs, and neural network-based security analysis tools. Publications emphasize interdisciplinary approaches to hardware-software co-design for energy efficiency and security. His work addresses challenges in multicore systems, FPGA security, and IoT reliability through innovative circuit and system-level solutions.

• Reliable Systems-on-Chip (SoC)
• Secure Embedded Systems
• Low-Power VLSI Design
• +۴ مورد دیگر

Majid Manteghi is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). He holds a Ph.D. in Electrical Engineering from UCLA (2005), and M.S. and B.S. degrees from the University of Tehran (1997 and 1994). His research focuses on advanced antenna systems, electromagnetic theory, and wireless communication technologies. Manteghi’s work emphasizes antenna array design, mutual coupling mitigation, RFID systems, and applications in biomedical engineering such as transcranial magnetic stimulation. Key research areas include: Electromagnetic field manipulation for high-efficiency antennas Low-power ultra-low-frequency (ULF) transmitter design Calibration-free antenna characterization techniques Parametric amplifier-based impedance matching RFID systems using distributed exciter networks His recent publications (2022-2025) highlight innovations in antenna array mutual coupling reduction, transcranial magnetic stimulation efficiency, and broadband parametric impedance matching. Manteghi has contributed to both theoretical advancements (e.g., Array Coupling Index) and practical implementations (e.g., self-sustaining maritime mesh networks). No formal scientific awards are explicitly listed in the provided materials. His research group actively explores electromagnetic challenges in underwater communication, medical devices, and energy-efficient wireless systems.

• Antenna Engineering
• Electromagnetic Fields
• Wireless Communication Systems
• +۴ مورد دیگر

Zhijie Jerry Shi is an Associate Professor at the School of Computing, University of Connecticut. His research focuses on cybersecurity, underwater acoustic networks, hardware security, and blockchain technology. He leads projects such as Ocean-TUNE, a community testbed for underwater communication, and has contributed to secure firmware updates, cryptographic algorithms, and IoT security architectures. His work integrates hardware and software solutions for resilient systems. Key research areas include cryptographic side-channel attacks, underwater acoustic communication protocols, and blockchain scalability. He has developed innovative solutions for underwater sensor networks, including Aqua-Net and Aqua-Sim simulators, and explored secure metering systems using blockchain (SMChain). His hardware security research addresses vulnerabilities in embedded systems and trustable computing education. His publications span over 15 years, with recent emphasis on hardware trojan detection, diffractive optical neural networks, and secure IoT protocols. Collaborations involve experimental deployments in underwater environments and joint work on FPGA-based acoustic modems. He advises on secure wireless protocols, cryptographic implementations, and underwater network resilience.

• Cybersecurity
• Underwater Acoustic Networks
• Hardware Security
• +۳ مورد دیگر

Dr. Yazan Alqudah is a Professor in the Department of Electrical and Computer Engineering at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He holds a Ph.D. in Electrical Engineering from Pennsylvania State University (2003) and has held roles at Intel Corporation and multiple universities, including Princess Sumaya University for Technology and Western Carolina University. His research focuses on optical wireless communication, mobile networks, and embedded systems, with contributions to machine learning applications in transportation and healthcare. Education: Ph.D., Electrical Engineering, Pennsylvania State University, 2003 M.S., Electrical Engineering, Pennsylvania State University, 1997 B.S., Electrical Engineering and Computer Science, University of Jordan, 1993 Research Interests: His work spans broadband optical wireless communication, next-generation mobile networks, embedded systems design, and machine learning applications in healthcare and transportation. Notable areas include autonomous vehicle sensing, wearable health monitoring, and fault-tolerant automotive systems. Publications Trends: Recent articles emphasize machine learning for driving behavior analysis, sensor fusion in autonomous systems, and optimization of wireless communication protocols. Earlier work includes contributions to WiMAX technology, optical wireless MIMO systems, and energy storage solutions for solar PV arrays. Awards: Intel Recognition Awards (2005, 2007) Best Researcher Award at PSUT (2014) Labs & Projects: Developed hands-on labs for broadband wireless technology and embedded systems education. Current projects include a mobile application for breathing abnormality detection and tools for facial thermography-based lie detection.

• Optical Wireless Communication
• Mobile Networks
• Embedded Systems
• +۳ مورد دیگر

Bo Ji is an Associate Professor in the Department of Computer Science and a Faculty Fellow in the College of Engineering at Virginia Tech. He holds a Ph.D. in Electrical and Computer Engineering from The Ohio State University (2012) and M.E. and B.E. degrees in Information Science from Zhejiang University (China). Prior to Virginia Tech, he was an Associate Professor at Temple University and a Senior Member at AT&T Labs. Research Interests: His work focuses on the synergy between networking and artificial intelligence/machine learning, addressing challenges in wireless networks, edge computing, data centers, and IoT systems. Key areas include optimizing data freshness (Age of Information), security/privacy in AR/XR, and efficient resource allocation. His research combines theoretical insights with empirical studies, supported by NSF awards and industry collaborations. Awards and Recognition: Bo Ji has received the NSF CAREER Award (2017), IEEE INFOCOM Best Paper Award (2019), IEEE TNSE Excellent Editor Award (2021-2024), and Dean's Faculty Fellow Award (2023). He is a Senior Member of IEEE/ACM and serves on editorial boards for journals like IEEE/ACM Transactions on Networking and ACM SIGMETRICS. Grants and Labs: Leads the Synergistic Networking and Augmented Intelligence Lab (SNAIL). Active in securing grants from NSF, Amazon, and ICAT/CHCI for projects on AR privacy, AI-augmented decision-making, and federated learning systems. Collaborates on interdisciplinary projects with experts in cybersecurity and human-computer interaction. Teaching: Teaches courses in algorithms (CS 4104/6104) and online learning at Virginia Tech, with prior experience at Temple University.

• Networking
• Machine Learning
• Security and Privacy
• +۵ مورد دیگر