Alan Briones Delgado is a researcher at the La Salle School of Engineering , Universitat Ramon Llull , with a focus on Internet of Things , Cybersecurity , and Transport Protocols . His work spans projects funded by the European Commission and national grants, including EXCEL4HOUSING4.0 , WeB-Nimbus , and NG-SOC , addressing challenges in cloud computing education, ecological monitoring, and security operations. His research integrates Artificial Intelligence and Wireless Sensor Networks for sustainable solutions. Key research areas include Quality of Service in heterogeneous networks, Environmental Conservation via IoT, and Teaching and Learning strategies for Big Data. Projects like EcoSentinel and BTL-COP highlight his commitment to Environmental Monitoring and Community Policing applications. His collaborations extend to institutions in the UK , Albania , and Western Balkans . Contact: alan.briones@salle.url.edu
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۷۳۶-۷۵۰ از ۱۶٬۹۹۳ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.
Anna Stuhlmacherمشاهده پروفایل
استادیار
Anna Stuhlmacher is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. Her research focuses on the optimization of uncertain distributed energy resources (DERs) and the coordination of the power grid with other critical infrastructure systems including water and agricultural networks. Dr. Stuhlmacher received her academic credentials from prestigious institutions: PhD in Electrical Engineering, University of Michigan MS in Electrical Engineering, University of Michigan BS in Electrical Engineering, Boston University Her research program addresses a critical challenge in modern energy infrastructure: how distributed generation, storage, and flexible loads can be optimized to provide grid flexibility while coordinating with other essential infrastructure systems. Dr. Stuhlmacher specializes in modeling and optimizing the inherent flexibility and uncertainty propagation between power systems and other infrastructure systems such as drinking water, wastewater treatment, and agricultural systems. This interdisciplinary approach is vital for improving grid reliability, particularly during periods of network stress, by increasing demand flexibility through coordinated management of multiple infrastructure systems. Dr. Stuhlmacher's publication record reveals a consistent progression from fundamental optimization techniques for water distribution networks to more complex systems involving wastewater treatment biogas and agrivoltaics. Her research demonstrates sophisticated application of advanced optimization methods including chance-constrained programming, robust optimization, and machine learning techniques like input convex neural networks to address uncertainty in coupled infrastructure systems. The majority of her work focuses on the water-power nexus, with recent expansion into agrivoltaics as renewable energy and food production compete for land resources. Her notable achievements include: Best paper award for the Electric Energy Systems Track, HICSS 2025 Dr. Stuhlmacher actively secures research funding as Principal Investigator on multiple significant grants including an NSF award focused on biogas from wastewater treatment, a PSERC award on flexible load dispatch (as Co-PI with Georgia Tech researchers), and a Michigan Tech Research Excellence Fund grant on agrivoltaics. While she indicates she is not actively seeking graduate students for the 2025-26 academic year, she remains open to working with exceptional students with strong foundations in power systems and mathematics. Her undergraduate teaching includes courses on Distributed Energy Resources, Electrical Energy Systems, and Power System Optimization, building on her previous teaching experience at the University of Michigan. Her research leverages Michigan Tech's DOE-designated Regional Test Center for Emerging Solar Technologies, particularly for her agrivoltaics research. She has established connections with national laboratories including NREL, where she interned during her PhD studies, and maintains active collaborations with researchers at institutions like Georgia Tech. Her work bridges theoretical optimization techniques with practical applications that have immediate relevance to utility companies and infrastructure operators.
Erin Bell is a Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire . She holds a Ph.D. in Structural Engineering from Tufts University and has extensive experience in structural health monitoring, finite element modeling, and infrastructure sustainability. B.C.E., Georgia Institute of Technology M.S., Civil Engineering, Tufts University Ph.D., Structural Engineering, Tufts University Her research focuses on structural health monitoring, bridge condition assessment, and integrating AI techniques like artificial neural networks and deep reinforcement learning for infrastructure asset management. Recent work includes equitable maintenance strategies for aging bridges in flood-prone zones and tidal energy conversion for sustainable bridge monitoring systems. Key trends in her publications include the application of machine learning to structural analysis, finite element model calibration, and climate change adaptation in transportation infrastructure. She has led projects on deep reinforcement learning for bridge scour maintenance, modal-based uncertainty quantification, and multi-scale modeling of steel bridges. Grants and Collaborations : Erin Bell has secured funding from the National Science Foundation (NSF) , US Department of Energy (DOE) , and New Hampshire Department of Transportation . Notable projects include the Living Bridge initiative for tidal energy-powered smart infrastructure and statewide data exchange systems for bridge condition assessment.
Kamal Al Haddad is a Lecturer in the Department of Electrical Engineering at École de technologie supérieure (ÉTS). He holds a Doctorate from INTP, Toulouse, and advanced degrees from UQTR. His research focuses on power electronics, renewable energy integration, and smart grid technologies. He leads the GREPCI research group, specializing in Power Electronics and Industrial Control. Education: B.Eng., M.Sc.A. (UQTR), Doctorate (INTP, Toulouse). Research interests span energy conversion, industrial electronics, power quality, and electromagnetic interference. He emphasizes sustainable energy solutions, electric traction systems, and high-efficiency power sources. His work includes developing advanced power electronic converters and grid stability solutions. Recent articles highlight advancements in modular converters for STATCOM, AI-driven fault detection in hydrogenerators, and renewable energy policy frameworks. He has received notable awards, including the 2014 IEEE Eugene Mittelmann Prize and Fellowships from IEEE and other institutions. Supervised over 60 students, including doctoral theses on topics like hydrogenerator diagnostics, EV charging systems, and renewable energy integration. His research also involves real-time simulation of power systems and FPGA-based implementations. Labs/Teams: GREPCI – Power Electronics and Industrial Control Research Group, leading projects on smart grids and energy efficiency.
Tamara Drucksمشاهده پروفایل
پژوهشگر
Tamara Drucks is a PreDoc Researcher at the Department of Machine Learning, Technische Universität Wien. She specializes in machine learning, with a focus on graph neural networks, bioinformatics, and optimization algorithms. Drucks teaches courses including 'Introduction to Machine Learning' and 'Theoretical Foundations and Research Topics in Machine Learning.' Her research explores expressive power of graph networks and applications in phylogenetic modeling. Key projects include the StruDL initiative (2023–2027) focusing on maximally expressive GNNs for outerplanar graphs. She has advised one PhD student, Martin Plattner, on optimization techniques in machine learning. Publications span theoretical advancements in GNNs and practical applications in computational biology. Drucks holds a Diploma in Technical Mathematics from TU Wien (2021) and is involved in interdisciplinary research at the intersection of AI and biological data analysis.
Joel Rosenthal is Professor and Chair of the Department of Chemistry and Biochemistry at the University of Delaware, where he also serves as Associate Dean for Research and Graduate Affairs in the College of Arts and Sciences. His group integrates inorganic synthesis, electrochemistry, and photochemistry to create functional materials and catalysts for energy, environmental, and biomedical challenges. Education & Training B.S. with Honors, New York University (2001) Ph.D., Massachusetts Institute of Technology (2007) NIH Postdoctoral Fellow, MIT (2007-2010) Research Directions The Rosenthal Research Lab pursues four intertwined themes: Environmental & energy sustainability via CO₂ reduction and solar-to-fuel conversion. Design of catalytic platforms for small-molecule up-conversion. Light-activated therapeutics targeting cancer and other diseases. Electrosynthetic routes to advanced inorganic materials and coordination complexes. To tackle these goals, the group synthesizes non-traditional tetrapyrroles, porous inorganic frameworks, and metal alloys, then interrogates them with electrochemical, spectroscopic, and ultrafast methods in collaboration with colleagues across UD, other universities, and National Laboratories. Recent Publication Trends Between 2021-2025 the group has published extensively on (i) selective electrochemical CO₂ reduction using bismuth, tin, and alloy catalysts, (ii) structure–function relationships in palladium and ruthenium tetrapyrrole complexes for singlet-oxygen generation, and (iii) new metal–organic framework (MOF) electrosyntheses. The work bridges fundamental mechanistic insights with practical device demonstrations, including 3-D-printed flow cells and solar-powered reactors. Scientific Awards & Honors While specific awards are not enumerated in the provided text, Prof. Rosenthal has garnered recognition through sustained federal funding, invited colloquia, and extensive peer-reviewed publication records. Students, Collaborators & Infrastructure The group actively recruits graduate students, post-docs, and undergraduates interested in interdisciplinary research. Trainees gain expertise spanning chemical synthesis, electrochemical cell design, ultrafast spectroscopy, computational modeling, and biological assays through partnerships both on campus and at national user facilities. The lab maintains state-of-the-art instrumentation for electrochemistry, photochemistry, and materials characterization, and communicates its latest findings via Twitter @rosenthal_lab .
Trevor E. Carlsonمشاهده پروفایل
استادیار
Trevor E. Carlson is an Assistant Professor at the School of Computing, National University of Singapore (NUS), focusing on high-efficiency microarchitectures, hardware/software co-design, and secure chip design for IoT and server applications. He earned his Ph.D. in Computer Science from Ghent University (2014) and B.Sc./M.Sc. in Electrical & Computer Engineering from Carnegie Mellon University (2002/2003). Research Interests include energy-efficient processors, secure computing platforms, neuromorphic accelerators, and fast simulation methodologies. He co-developed the Sniper Multi-Core Simulator used globally for performance/power evaluation. Scientific Awards : Best Paper Award, International Conference on Embedded Computer Systems (2016) Best Paper Award, International Symposium on Performance Analysis of Systems and Software (2013) Heidelberg Laureate Forum participation (2015) HiPEAC Technology Transfer Award for Sniper Simulator (2013) Current Research involves secure Systems-on-Chip (SOCure project), hardware security for IoT, and simulation methodologies. He leads a lab with researchers working on topics like Capstone for trustless secure memory access and LABS for laser fault injection benchmarks.
Charles Rizzoمشاهده پروفایل
استادیار
Charles Rizzo is a Research Assistant Professor in the TENNLab neuromorphic computing group at the University of Tennessee, Knoxville, within the Department of Electrical Engineering and Computer Science. He earned his PhD in Computer Science (2024), MS (2021), and BS (2019) from the same institution. PhD in Computer Science, University of Tennessee, Knoxville (2024) MS in Computer Science, University of Tennessee, Knoxville (2021) BS in Computer Science, University of Tennessee, Knoxville (2019) His research focuses on neuromorphic computing, particularly for embedded applications involving event-based vision processing and machine learning with spiking neural networks. He has contributed to neuromorphic control systems, event camera data processing, and spiking network architectures. Recent publications emphasize neuromorphic hardware design (e.g., memristor-based synapses, RISP neuroprocessor), algorithm adaptation (DBSCAN clustering), and real-time applications in vision processing and control. Key subfields include event-based sensors, recurrent spiking networks, and low-power embedded systems. Charles is affiliated with the TENNLab neuromorphic computing group and supports course website development for EECS programs. His work bridges neuromorphic theory with practical implementations in embedded environments.
- Wearable Sensors
- Biosensors
- Bioinstrumentation
- +۵ مورد دیگر
Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.
Andrea Fumagalli is a Professor in the Department of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science , The University of Texas at Dallas. He earned his Ph.D. (1992) and Laurea (1987) in Electrical Engineering from Politecnico di Torino, Italy. Research Interests: All-Optical Network Architectures, Photonic Slot Routing, Wavelength Routing and Protection, Sensor Networks, Cooperative Wireless Networks, Network Optimization, Next Generation Internet (NGI), and Multi-hop Optical Networks. Education: Ph.D., Electrical Engineering, Politecnico di Torino (1992) Laurea, Electrical Engineering, Politecnico di Torino (1987) Key Research Trends: His recent publications focus on 5G networking, optical network automation, elastic optical networks, network reliability, and cross-layer optimization. He explores FPGA acceleration in 5G Low-PHY functions, live migration of containerized network components, and spectral fragmentation mitigation in EONs. Scientific Awards: Best Teaching Award, Electrical Engineering, UTD (2002) Best Thesis Award for Ph.D. Advisee Isabella Cerutti (2002) IEEE ComSoc Distinguished Lecturer Tour (2000) Best Paper Award (1999): 'An Optimal Design Algorithm for Photonic Slot Routing Networks Migrating to Optical Packet Switching' Advising and Grants: He advised Ph.D. student Isabella Cerutti. In 2001, he secured a $300,000 grant from FUNDACAO CPqD for optical network reliability research. He leads the Optical Networking Advanced Research (OpNeAR) Lab at UTD, collaborating on international projects like the Italian government-funded grid computing initiative (2002) and the OMEGA Test-bed for differentiated reliability. Laboratories and Teams: He directs the OpNeAR Lab , which develops tools for optical network emulation and reliability testing. His projects involve partnerships with institutions in Brazil (Unicamp), Sweden (KTH), Italy (Politecnico di Torino, Scuola Superiore Sant'Anna), and CNR/CNIT.
Kimia Zamiri Azarمشاهده پروفایل
استادیار
Kimia Zamiri Azar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, focusing on hardware security and verification methodologies. Her research bridges theoretical formal methods with practical security implementations in semiconductor design and testing. Her educational background includes: Ph.D. in Electrical and Computer Engineering, George Mason University (2021) Postdoctoral Research, University of Florida Dr. Azar's research spans hardware security with emphasis on system-level verification, VLSI design-for-trust, and advanced IC testing. She pioneers techniques in logic locking, secure heterogeneous integration, and IC supply chain security, developing frameworks for authenticated encryption in Systems-in-Package and runtime security monitoring. Her work integrates formal verification with innovative testing methodologies to address hardware trust challenges across the semiconductor lifecycle. Analysis of her recent publications reveals two dominant trends: (1) Application of large language models (LLMs) to hardware design tasks including high-level synthesis code generation and RTL optimization, and (2) Advancement of secure heterogeneous integration techniques for System-in-Package architectures with focus on counterfeit prevention and split-test security protocols. These directions address critical gaps in hardware trustworthiness amid increasingly complex semiconductor supply chains. Her scientific contributions have earned significant recognition: Best Paper Award at ICCAD 2019 Best Paper Award at ISVLSI 2020 Best Paper Award at ICCAD 2020 Best Paper Award at IEEE DCAS 2020 Best Paper Award at HOST 2022 Best Paper Award at DATE 2023 Dr. Azar secures substantial research funding from premier agencies including NSF, SRC, DARPA, AFRL, DoD (NG), and Microsemi. Her grants support projects spanning hardware security validation frameworks, secure heterogeneous integration, and AI-augmented verification methodologies. She actively mentors students in her research group, guiding publications in top venues like IEEE D&T, IEEE TC, and DAC while fostering industry-academic collaborations. Her work directly impacts semiconductor security standards through patented innovations and open-source verification tools. As an active IEEE and ACM member, she contributes to community advancement through conference organization (HOST, DATE), journal editorial roles, and workshop leadership on hardware security standards. Her research group collaborates with semiconductor industry leaders to translate theoretical security frameworks into practical design-for-trust methodologies for next-generation integrated circuits.
Dr. Pinon Hermida Victor is a Researcher at the Institute of Electronic Structure and Laser (IESL) under the Foundation for Research and Technology – Hellas (FORTH). He holds a PhD in Physics from the University of A Coruña (2011) and has conducted extensive research on Laser-Induced Breakdown Spectroscopy (LIBS), focusing on femtosecond lasers, double-pulse configurations, and applications in material analysis, archaeology, and environmental science. His career includes roles at Applied Photonics Ltd (UK) as Senior Applications Scientist (2014-2020) and postdoctoral fellowships at FORTH-IESL through the Marie Curie ATLAS program (2006-2008). Research interests span LIBS methodology development, optical fiber systems for high-power lasers, and software for spectral analysis. Notable contributions include portable LIBS instrument design and radiation-resistant optical components for nuclear facilities. Awards include the 2008 LIBS Contest and the 2011 Premio Extraordinario de Doctorado. Recent work focuses on applying LIBS to archaeological mollusc shell analysis for climate and environmental studies. He collaborates internationally on LIBS quantification challenges and instrument durability in harsh environments. Education: PhD in Physics (2011), University of A Coruña; Diploma in Physics (2001), University of Santiago de Compostela Key Roles: Senior Applications Scientist (Applied Photonics), Marie Curie Fellow (FORTH-IESL), Researcher (Laboratory of Industrial Applications of Lasers) Lab Affiliations: IESL-FORTH and University of A Coruña laser labs
Jian Liuمشاهده پروفایل
استادیار
Jian Liu is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He leads the Mobile Sensing and Intelligence Security (MoSIS) Lab, focusing on robust AI, mobile security, computational sensing, and smart healthcare. His research has been published in top-tier venues like S&P/Oakland, CVPR, and IEEE journals, with over 6,400 citations. He holds seven U.S. patents, including two licensed to industry. Education: PhD in Electrical and Computer Engineering from Rutgers University (2019), ME and BE in Communication Engineering from Wuhan University of Technology (China). Research interests include trustworthy AI, federated learning, privacy-preserving technologies, and adversarial machine learning. His recent work includes HarmonyCloak (music copyright protection against generative AI), 3D facial authentication systems, and robust backdoor attack defenses. Notable awards include the University of Tennessee’s Professional Promise in Research Award (2025), Stanford’s World’s Top 2% Cited Scientists, and multiple best paper awards. He teaches Mobile and Embedded System Security (ECE 469/569) and has supervised projects funded by UT Grand Challenges grants. Labs/Teams: MoSIS Lab focuses on AI-driven security solutions, wearable sensing, and healthcare applications. Collaborations involve interdisciplinary projects with UT’s engineering and medical schools.
Karan Ahujaمشاهده پروفایل
استادیار
Karan Ahuja is the Lisa Wissner-Slivka & Benjamin Slivka Assistant Professor of Computer Science at Northwestern University, directing the Sensing, Perception, Interactive Computing & Experiences (SPICE) Lab. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University (2023) and a B.Tech. in Computer Science (2017). His research focuses on creating technologies that sense and understand human behavior, with applications in mobile health, extended reality, and natural user interfaces. Key projects include LemurDx for ADHD diagnosis, EITPose for wearable hand pose tracking, and MobilePoser for full-body pose estimation via consumer IMUs. Awards include Forbes 30 Under 30 (2024), MIT 35 Innovators Under 35 Asia Pacific, and ACM SIGCHI's Outstanding Dissertation Award. He has worked at Google, Apple, Microsoft Research, Meta Reality Labs, and IBM Research. His lab emphasizes real-world deployments, with technologies licensed and integrated into products used by millions. Prospective students are invited to join his lab at Northwestern via a dedicated application form. Research spans embedded systems, computer vision, and on-device ML, with a focus on impactful applications in healthcare and XR.
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