جست‌وجوی استاد و پژوهشگر

Rajeev Balasubramonianمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• DRAM/NVM Technologies
• +۷ مورد دیگر

Rajeev Balasubramonian is a Professor and Associate Director at the School of Computing, University of Utah. He specializes in computer architecture, with a focus on memory systems, emerging technologies, and energy-efficient computing. His research addresses challenges in DRAM/NVM architectures, security, and acceleration for big data and machine learning workloads. Education: PhD in Computer Science (University of Rochester, 2003), M.S. (University of Rochester, 2000), B.Tech in Computer Science (IIT Bombay, 1998). Research Interests: Memory reliability, near-data processing, cache hierarchies, transactional memory, and hardware-software co-design for emerging technologies. He has led projects on crossbar accelerators, secure memory systems, and resistive memory architectures. Recent Trends in Publications: Focus on encrypted inference (Hyena), data prefetching (PATHFINDER), and neuromorphic computing (SpinalFlow). His work bridges hardware and software, emphasizing practical acceleration and security solutions. Awards: IEEE Fellow (2021), Google Faculty Awards (2019/2020), Intel Research Award (2017), and multiple best paper awards (ISCA, ISPASS, PACT). Grants & Students: Over $4M in NSF/industry funding. Advised 15+ PhD students (e.g., Ali Shafiee, Karl Taht) and currently mentors researchers in resistive memory and security accelerators. His lab includes teams like Utah Arch Research Group. Labs & Teams: Leads the Utah Arch Research Group , organizing workshops on near-data processing and memory systems (e.g., ISCA, HPCA).

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +۳ مورد دیگر

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

Christos Miasمشاهده پروفایل

دانشیار

• Electromagnetic Shielding
• Computational Electromagnetics
• Microwave Engineering
• +۳ مورد دیگر

Dr Christos Mias is a Reader in Electrical and Electronic Engineering at the School of Engineering, University of Warwick, UK. He serves as Director of Studies for the Electrical and Electronic Engineering stream and has held roles such as UK URSI Commission B (Fields and Waves) representative from 2009-2014. His research focuses on electromagnetic shielding, computational electromagnetics, microwave engineering, antennas, periodic structures, and radiowave propagation. Dr Mias' work bridges theoretical advancements with practical applications in antenna design, frequency selective surfaces, and electromagnetic modeling. His research interests emphasize innovative solutions in antenna array analysis, periodic structure optimization, and advanced simulation techniques such as finite element time-domain (FETD) modeling. He has contributed to understanding scattering phenomena, non-reflecting boundary conditions, and molecular vs electromagnetic wave propagation comparisons. Recent studies include railway infrastructure monitoring and critiques of superlensing concepts in plasmonics. Publications highlight advancements in MoM formulations, FSS analysis, and sparse matrix methodologies for boundary condition simulations. His work often integrates computational methods with real-world engineering challenges, such as optimizing antenna performance and improving electromagnetic compatibility in complex environments. Dr Mias advises through his role as Director of Studies, fostering academic excellence in electrical engineering education. His office is located in A325, with advice hours on Fridays 10 AM-12 PM during term time.

• Emerging memory and storage technologies
• Embedded systems
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.

Karunanayake Esselleمشاهده پروفایل

استاد

• Electromagnetic Engineering
• Antenna Design
• Telecommunications
• +۶ مورد دیگر

Karunanayake Esselle (Karu P. Esselle) is a Distinguished Professor in Electromagnetic & Antenna Engineering at the University of Technology Sydney (UTS), School of Electrical and Data Engineering. He is a world-renowned expert in telecommunications, defence, and space technologies, leading the MetaSteerers research team. His affiliations include roles as Senior Editor for IEEE Access, Visiting Professor at Macquarie University, and Fellow of the Royal Society of New South Wales, IEEE, and Engineers Australia. Education includes a BSc (First Class Hons) in Electronic Engineering from the University of Moratuwa (Sri Lanka), and MASc/PhD in Electrical Engineering from the University of Ottawa (Canada). Research focuses on electromagnetic engineering, antenna design (including resonant cavity, metasurface, and millimeter-wave antennas), wireless communication systems, and applications in defence/space. His work emphasizes high-gain beam-steering antennas, wearable/implantable devices, satellite communications, and electromagnetic shielding. Recent trends include meta-steering techniques, CubeSat-compatible arrays, solar-cell-integrated antennas, and 3D-printed metasurfaces. Awards include the Premier’s Prize for Innovation Leadership (2024), Australian Eureka Prize (2023), Professional Engineer of the Year (2022), and 21 major prizes in 5 years. Key recognitions span innovation, mentorship, and research excellence across defence, space, and engineering sectors. Advises undergraduate/postgraduate students in engineering capstones and internships. Secured research grants exceeding $35 million, including ARC grants and the $245M SmartSat CRC. Leads labs like the MetaSteerers team and collaborates with defence/space industries.

• Wireless Communications
• Machine Learning
• Digital Twins
• +۵ مورد دیگر

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Lutz Warnkeمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Lutz Warnke is a Professor of Mathematics at the University of California, San Diego, with prior affiliations at Georgia Institute of Technology (where he received tenure in 2021) and Peterhouse, Cambridge University (Junior Research Fellow until 2016). His research focuses on probabilistic combinatorics, random graphs, phase transitions, and combinatorial probability, with applications to extremal combinatorics and Ramsey theory. Education : Ph.D. in Mathematics from the University of Oxford (2012), supervised by Oliver Riordan. Dr. Warnke's research explores the structure and evolution of random graphs and processes, including Achlioptas processes, Ramsey numbers, and extremal problems. His work often bridges probabilistic methods with algorithmic applications and theoretical computer science. His publications from 2022–2025 reveal trends in random graph isomorphisms, clique coloring thresholds, extremal subgraph counts, and hardness of online algorithms. Key subfields include percolation, phase transitions, and probabilistic methods applied to combinatorial structures. Scientific Awards : Dénes König Prize (2016), Alfred P. Sloan Research Fellowship (2018), NSF CAREER Award (2020), Richard Rado Prize (2014). Dr. Warnke actively supervises PhD students and postdocs, including Matthew Cho (PhD ongoing), Erlang Surya (PhD 2025), Emily Zhu (PhD 2025), and He Guo (PhD 2021). He has received teaching accolades at Georgia Tech and contributes to graduate courses in probabilistic combinatorics, random graph theory, and stochastic processes.

Suresh Venkateshمشاهده پروفایل

استادیار

• Metamaterials
• Millimeter-Wave and Terahertz Communication
• Antenna Design
• +۴ مورد دیگر

Suresh Venkatesh is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He previously served as a postdoctoral research associate at Princeton University (2018–2022) and earned his Ph.D. in Electrical Engineering from the University of Utah in 2017. His academic journey also includes a Master's degree in Electrical Engineering from NC State in 2010. Education: Ph.D. in Electrical Engineering, University of Utah, 2017 Master's in Electrical Engineering, North Carolina State University, 2010 Research Interests: His work focuses on metamaterials and surfaces at GHz-THz frequencies, millimeter-wave phased arrays, and innovative applications in 5G/6G communication systems. Key areas include antenna design, physical layer security, advanced electromagnetic simulations, and reconfigurable systems using CMOS integration. He explores origami-based platforms for adaptive RF imaging and employs spatio-temporal modulation techniques to enhance wireless security and efficiency. Publications Trends: Recent work emphasizes mmWave and THz communication, with a focus on reconfigurable intelligent surfaces, secure low-latency links, and CMOS-based designs for direct detection receivers and cytometers. His research bridges theoretical concepts with experimental validation, particularly in wavefront manipulation and sparse imaging techniques. Awards and Honors: Mistletoe Research Fellowship (2021) Advising and Grants: Venkatesh contributes to research teams like the NC State 6GNC initiative, which spans 6G technologies. He actively organizes conferences such as the World Microwave Congress 2024 as TPC Co-Chair and participates in IEEE technical committees. His grants and collaborations drive advancements in metamaterials, secure wireless systems, and CMOS-integrated solutions. Labs and Teams: He leads efforts in the NC State ECE Department, focusing on labs involving reconfigurable antennas, computational imaging, and integrated systems. His work intersects with the 6GNC team to develop future communication technologies and secure mmWave/THz systems.

Kenneth Zickمشاهده پروفایل

استاد پژوهشی

• Unconventional computing
• Bio-inspired compute systems
• Ising machines
• +۷ مورد دیگر

Dr. Kenneth Zick is a Research Professor at the University of Southern California's Information Sciences Institute (USC ISI), where he serves as Research Director of Transformational Computing. His work focuses on game-changing computer architectures, hardware, and systems for solving critical government problems, with expertise in unconventional computing, quantum computing, and bio-inspired systems. Ph.D. in Computer Science & Engineering, University of Michigan-Ann Arbor M.S. in Electrical Engineering, University of Texas at Dallas Bachelor's in Electrical Engineering, University of Michigan-Ann Arbor Dr. Zick's research interests span unconventional computing , bio-inspired systems , Ising machines , quantum annealing , FPGA-based solutions , and neuromorphic computing . His group develops hardware-centric algorithm discovery and Cosm, a heuristic algorithm for sparse Ising optimization. Current projects include superconducting digital architectures, analog-digital hybrid computing, and human-AI co-design for breakthrough hardware. His team leverages advanced facilities such as USC ISI's MOSIS 2.0 and the California DREAMS hub in the DoD Microelectronics Commons, with expertise in high-speed I/O, FPGA prototyping, and radiation-hardened systems. He has received a NASA Fellowship for his Ph.D. work and mentored students like Aditi, who won the USC ECE Outstanding Academic Achievement Award.

• Ocean Renewable Energy
• Robotics
• Biomechanics
• +۲ مورد دیگر

Dr. Boyin Ding is an Associate Professor at the University of Adelaide , serving as Academic Director at Haide College and researcher in the Mechanical Engineering department within the Faculty of Sciences, Engineering and Technology. He leads the Wave Energy Research initiative established in 2014, while also contributing to Robotics and Biomechanics through his work with the Flinders Medical Device Research Institute. Research Areas: Ocean Wave Energy Harvesting Control Systems for Renewable Energy 6DOF Robotic Testing Spine Biomechanics Transnational Education Programs Key Collaborations: Australia-China Joint Research Centre for Offshore Wind & Wave Energy Acoustics, Vibration and Control Research Group Scientific Awards: Australian Endeavour Fellowship Malcolm Kinnaird Engineering Excellence Award (2012) His recent publications focus on hybrid offshore energy systems, nonlinear hydrodynamics in wave energy converters, and biomechanical testing technologies. He has developed control algorithms for floating offshore wind-wave systems and pioneered 6DOF robotic platforms for medical applications. As an eligible PhD supervisor, he actively collaborates with global industries and academic institutions.

• Antennas
• Applied Electromagnetics
• Computational Electromagnetics
• +۲ مورد دیگر

Giuseppe Vecchi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , Italy. He leads the Applied Electromagnetics research group and contributes to projects in computational electromagnetics, metamaterials, and biomedical applications of electromagnetic fields. He has been a IEEE Fellow since 2010 and serves on PhD college committees for Electrical, Electronic, and Communications Engineering. Research Interests : Antennas, Applied and Computational Electromagnetics, Metamaterials, Microwave Imaging for medical applications, Nuclear Fusion Reactor Physics. Scientific Leadership : Principal Investigator for projects like METEOR, MTSA, and RESOLVED-K, focusing on terahertz generation, metasurface antennas, and real-time temperature mapping in hyperthermia. Awards : IEEE Fellow (2010), recognizing his contributions to electromagnetic simulations and antenna design. Students : Supervises PhD candidates in advanced antenna engineering, computational electromagnetics, and biomedical applications, including Owais Khan, Francesco Lattanzio, and Sara Paknezhad Panahi. Patents : Holds multiple patents in antenna diagnostics, encrypted metasurface antennas, and microwave soil disinfection systems.

Figen S. Oktemمشاهده پروفایل

دانشیار

• Computational Imaging
• Physics-informed Machine Learning
• Inverse Problems
• +۵ مورد دیگر

Figen S. Oktem is an Associate Professor in the Department of Electrical and Electronics Engineering at Middle East Technical University (METU), Ankara, Turkey. Her research focuses on advanced imaging systems and algorithms, including computational imaging, inverse problems, and machine learning applications in signal processing. Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (UIUC) (2014) M.S., Electrical and Electronics Engineering, Bilkent University (2009) B.S., Electrical and Electronics Engineering, Bilkent University (2007) Her work bridges physics-informed machine learning and computational optics, with applications in spectral imaging, radar, and microwave systems. She has also explored Fourier phase retrieval, denoising diffusion models, and real-time MIMO radar imaging. Recent publications highlight trends in phase retrieval using deep learning ( prNet , I2I-PR , DDRM-PR ), 3D MIMO imaging, and compressive spectral imaging. Techniques often integrate plug-and-play regularization, stochastic refinement, and physics-based priors. She can be contacted via email at figeno@metu.edu.tr . Curriculum vitae and publications are accessible through her Google Scholar profile.

Sean Andersonمشاهده پروفایل

مدرس ارشد

• Bioinspired Robotics
• Adaptive and Optimal Control
• System Identification
• +۳ مورد دیگر

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

Alan Kaptanogluمشاهده پروفایل

استاد

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +۳ مورد دیگر

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Meng Heمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Geometry
• +۷ مورد دیگر

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.