جستجوی استاد راهنمای دانشگاهی

Alexis Kirkeمشاهده پروفایل

دانشگاهی

• Music Technology
• Computational Music
• Human-Computer Interaction
• +۳ مورد دیگر

Dr Alexis Kirke is a Senior Research Fellow in Computer Music at the School of Art, Design and Architecture (Faculty of Arts, Humanities and Business) at the University of Plymouth. As a composer-in-residence, he specializes in interdisciplinary research at the intersection of music, computing, and healthcare. His work includes developing adaptive music systems like RadioMe for dementia care, applying quantum computing to music composition, and exploring affective computing through brain-computer interfaces. Teaching roles include associate lecturer positions in modules such as Collaborative Practice (BA Sound and Music Production), Psychology (BA Music), and MRes Computer Music. He has supervised five PhD students as a second supervisor and served as an internal PhD examiner. His research focuses on algorithmic composition, music technology for healthcare, quantum computing applications in music, and multi-agent systems inspired by natural phenomena like humpback whale song evolution. Recent projects include the Plymouth Marine Institute collaboration and the Cloud Chamber performance involving real-time interaction with subatomic particles. Key contributions include innovative systems like RadioMe, which combines adaptive radio with reminder systems for dementia patients, and Q-Muse, a quantum computer music system. His work bridges computational creativity with human-centric applications, emphasizing ethical and accessible technology. Grants & Awards: No specific awards listed, but active in collaborative research projects Lab/Teams: Plymouth Marine Institute, Cloud Chamber Project

Kazem Cheshmiمشاهده پروفایل

استادیار

• High-performance computing
• Compilers
• Scientific computing
• +۳ مورد دیگر

Kazem Cheshmi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on compiler optimization techniques for accelerating scientific computing and machine learning applications on parallel architectures. He leads the SwiftWare Lab and teaches courses such as High-Performance Programming (COMPENG 4SP4/ECE 6SP4) and Special Topics in Computation (ECE 718). Education: B.Eng. (Ferdowsi University of Mashhad), M.A.Sc. (University of Tehran), Ph.D. (University of Toronto). He has held research positions at Microsoft Research, Adobe Research, Concordia University, and Rutgers University. Research Interests: High-performance computing, compiler design, sparse matrix computations, and their applications in machine learning and scientific computing. His work emphasizes optimizing sparse codes for parallel architectures and developing efficient QP solvers like NASOQ. Key Contributions: Developed Sympiler (a domain-specific compiler for sparse matrix codes) and NASOQ (a scalable QP solver). His awards include the ACM-IEEE CS George Michael Memorial HPC Fellowship (2020) and recognition for contributions to compiler-driven sparse computation optimization. Teaching and Service: Organizes SONAD’25, serves on program committees for PPoPP, Supercomputing, and IPDPS. Supervises students in compiler design, parallel programming, and high-performance computing. Labs/Teams: Leads the SwiftWare Lab focusing on compiler optimization and high-performance systems. Collaborates on open-source projects like Sympiler and NASOQ.

Sam Amiriمشاهده پروفایل

مدرس

• Embedded Systems
• Hardware Design
• Signal Processing
• +۳ مورد دیگر

Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.

• Computer Networks
• Distributed Computing
• Network Security
• +۵ مورد دیگر

Minseok Kwon is a Professor and Associate Chair in the Department of Computer Science at Rochester Institute of Technology (RIT), part of the Golisano College of Computing and Information Sciences. He holds a Ph.D. and M.S. from Purdue University and a B.S. from Seoul National University. His research focuses on network systems and protocols (including programmable networks, P4 software testing, SDN, and in-network security) as well as machine learning systems (especially workload prediction). He is a member of the RIT Center for Cybersecurity. Professional Contributions Technical committee member for Computer Communications Journal and multiple IEEE conference program committees (e.g., HPCC 2021, ICC 2021-2021). Publicity Chair for ICNP 2008 and NPSec 2005. Research Highlights Recent work emphasizes programmable data planes for green data centers, security in wireless server networks, and efficient packet processing using P4. Notable projects include P4Kube (in-network Kubernetes load balancing) and CuVPP (filter-based routing optimization). His publications span topics from network latency measurement tutorials to cutting-edge SDN implementations. Awards & Recognition Recipient of the Best Paper Award at IEEE Cluster 2020 for CuVPP . Teaching & Advising Current courses include CSCI-261 (Algorithms), CSCI-788 (MS Project), and supervising graduate co-op placements. His advising focuses on advanced network systems and cloud computing.

Michael D. Smithمشاهده پروفایل

استاد

• Computer Science
• Education Technology
• Data Security
• +۳ مورد دیگر

Michael D. Smith is the John H. Finley, Jr. Professor of Engineering and Applied Sciences and Distinguished Service Professor at Harvard University. He previously served as Dean of the Faculty of Arts and Sciences (FAS) for 11 years, leading Harvard's largest school with a focus on undergraduate education, faculty development, and interdisciplinary research. He holds a Ph.D. from Stanford University (1993), a Master’s from Worcester Polytechnic Institute, and a Bachelor’s from Princeton University. His academic career is complemented by industry experience, including co-founding Liquid Machines (acquired by Check Point in 2010), and advisory roles with venture capital firms like 1984 Ventures and GSV Ventures. Research interests span computer science education innovation, technology’s societal impact, and hardware/software systems. He pioneered work on voltage noise mitigation in processors, cybersecurity solutions, and online education platforms like edX. Awards include the NSF Young Investigator Award (1993), Alpha Iota Teaching Prize, and W.E.B. Du Bois Medal. Currently, he advises startups and educational initiatives such as CS Forward and Kira Learning, while developing new in-person and online courses. His leadership roles include Board member at The Peddie School and former edX Board member (2012–2018).

• Wireless Communication
• 5G Networks
• Millimeter-Wave Technology
• +۳ مورد دیگر

Randy Verdecia-Peña is a researcher specializing in wireless communication and 5G technologies, focusing on millimeter-wave (mmWave) systems, software-defined radio (SDR), and network protocols. His work emphasizes practical experimentation with advanced signal processing techniques, including machine learning for channel estimation and hardware prototyping for integrated access and backhaul (IAB) architectures. Key contributions include phased array-aided 5G prototypes, flexible layer 2 protocols, and cooperative relay node design in both indoor and outdoor environments. Collaborations frequently involve hardware validation and performance analysis across frequencies like 26 GHz and 60 GHz.

Fredrik Kjolstadمشاهده پروفایل

استادیار

• Compilers
• Programming Models
• Systems
• +۵ مورد دیگر

Fredrik Kjolstad is an Assistant Professor of Computer Science at Stanford University. His research focuses on compilers, programming models, and systems for sparse computing, with an emphasis on separating algorithms from data representation. He leads efforts in developing compilers like TACO, Simit, and Legate Sparse to optimize sparse tensor algebra and distributed computations. Kjolstad's work spans compiler design, hardware-software co-design, and programming languages for high-performance computing. His research group aims to enable portable applications across diverse data representations and architectures. Notable projects include the TACO compiler for sparse tensor algebra, the Simit programming language for physical simulations, and the Copy-and-Patch compilation technique for fast runtime code generation. He has also contributed to distributed systems like Legate Sparse and hardware accelerators such as Onyx. Kjolstad has received prestigious awards including the NSF CAREER Award and the MIT EECS PhD Thesis Award. His publications cover topics like sparse tensor compilation, compiler optimization, and agile hardware design. He advises multiple PhD students and collaborates with researchers like Kunle Olukotun and Alex Aiken. Key areas of impact include efficient sparse data processing, compiler-driven hardware design, and scalable distributed computing frameworks. His work bridges theoretical compiler techniques with practical system implementations, aiming to simplify and accelerate complex computational tasks.

Atakan Aralمشاهده پروفایل

دانشگاهی

• Edge Computing
• Edge AI
• Internet of Things (IoT)
• +۳ مورد دیگر

Atakan Aral is a Visiting Lecturer at the Department of Computing Science, Umeå University. His research focuses on Edge Computing, Edge AI, and the Internet of Things (IoT), with a particular emphasis on resource management and sustainable environmental monitoring. He is affiliated with the Autonomous Distributed Systems Lab and Green Distributed Computing Group, both part of the Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative. His work spans theoretical frameworks and practical implementations in edge intelligence and distributed systems. Research Interests: Edge Computing architectures and workflows Neuromorphic and energy-efficient AI systems Federated learning and multi-cluster collaboration Sensor networks for environmental monitoring Optimization of resource allocation in distributed systems Key Publications Trends: Recent work emphasizes neuromorphic edge AI applications, hierarchical federated learning, and energy-efficient IoT deployments. His articles often intersect computing continuum concepts with real-world challenges like rural environmental monitoring and latency-critical systems. Scientific Awards: No awards explicitly listed in the provided text. Advising & Grants: No formal student advisees or grant details provided. However, he contributes to the De facto Center of Excellence in Autonomous Distributed Systems (2023–2029), indicating involvement in large-scale collaborative research. Labs & Teams: Member of the Autonomous Distributed Systems Lab (lead in distributed systems research) and Green Distributed Computing Group (focused on sustainability in computing).

• Energy-efficient circuits and systems
• Low-power wireless sensors
• Multimedia terminals
• +۳ مورد دیگر

Anantha P. Chandrakasan is MIT's Provost and Vannevar Bush Professor of Electrical Engineering and Computer Science. He leads strategic initiatives such as the MIT Climate and Sustainability Consortium, MIT AI Hardware Program, and MIT-IBM Watson AI Lab. As Chief Innovation and Strategy Officer, he oversees MIT HEALS, MGAIC, and MITHIC. Previously, he served as MIT School of Engineering Dean (2011-2025) and director of MIT Microsystems Technology Laboratories (2006-2011). He earned all degrees (B.S., M.S., Ph.D.) in EECS from UC Berkeley (1989-1994). His research focuses on energy-efficient circuits, low-power wireless sensors, and emerging technologies. Key projects include implantable medical devices, secure AI hardware, and THz communication systems. He pioneered the Schwarzman College of Computing, reshaping MIT’s academic structure. Recipient of the 2022 IEEE Mildred Dresselhaus Medal, he holds leadership roles in multiple MIT-industry partnerships. Notable collaborations include Ericsson (5G networks), Takeda (healthcare), and Accenture (industry-technology convergence). His academic advising includes fostering interdisciplinary programs like the MIT Quest for Intelligence and postdoctoral fellowships. He champions diversity through initiatives like the Faculty Gender Equity Committee and Daniel J. Riccio Graduate Engineering Leadership Program. Labs/Teams: Leads MIT's Office of Innovation & Strategy, oversees Microsystems Technology Laboratories, and co-chairs the MIT-GE Vernova Energy & Climate Alliance. Key hardware projects include conformable ultrasound patches and battery-free IoT devices.

Kenneth Brownمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Error Correction
• Quantum Control
• +۳ مورد دیگر

Kenneth Brown is the Michael J. Fitzpatrick Distinguished Professor of Engineering at Duke University, holding joint appointments in the Department of Electrical and Computer Engineering, Physics, and Chemistry. He leads the Brown Lab, which focuses on quantum computing and molecular spectroscopy. His research spans quantum error correction, quantum control, and trapped-ion systems. Brown earned his B.S. from the University of Puget Sound (1998) and Ph.D. from UC Berkeley (2003). Key research interests include developing robust quantum architectures, studying molecular properties at ultracold temperatures, and advancing quantum hardware. His work bridges theoretical and experimental quantum computing, with applications in quantum simulation and chemical dynamics. Notable contributions include contributions to fault-tolerant error correction protocols, trapped-ion quantum control systems, and molecular ion spectroscopy. He has been recognized with awards such as the Stansell Family Distinguished Research Award (2020) and the American Physical Society Fellowship (2018). Brown teaches advanced courses on quantum error correction, graduate quantum engineering, and physics. He actively collaborates on projects like the Duke Quantum Center and the STAQ initiative for scalable quantum computing systems. His lab’s work integrates theoretical, experimental, and computational approaches to address challenges in quantum hardware and error mitigation, with a focus on applications in chemistry and materials science.

W. Kent Fuchsمشاهده پروفایل

استاد

• Dependable computer systems
• Testing and failure analysis of integrated circuits
• Computer Engineering
• +۳ مورد دیگر

W. Kent Fuchs is a Professor & President Emeritus at the University of Florida's Department of Electrical & Computer Engineering, part of the College of Engineering. His research focuses on dependable computer systems, testing, and failure analysis of integrated circuits. He holds a Ph.D. in Electrical Engineering from the University of Illinois (1985), an M.Div. from Trinity Evangelical Divinity School (1984), and additional degrees from the University of Illinois and Duke University. Education: Ph.D., Electrical Engineering, University of Illinois, 1985 M.Div., Trinity Evangelical Divinity School, 1984 M.S., Electrical Engineering, University of Illinois, 1982 B.S.E., Electrical Engineering & Computer Science, Duke University, 1977 His research emphasizes fault tolerance and system reliability, particularly in hardware and distributed computing environments. Recent publications explore spare allocation, fault recovery in heterogeneous systems, and compiler-assisted error handling. His work bridges theoretical foundations with practical implementations in reconfigurable arrays and wireless networks. While no formal awards are listed, his contributions to dependable computing have influenced both academic and industrial applications. His advising and grant activities are not detailed here, but his role as President Emeritus underscores his leadership in academia.

Phillip Gibbonsمشاهده پروفایل

استاد

• Systems

Phillip Gibbons is a Professor in the Department of Computer Science at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on Systems , particularly in robotics computing, heterogeneous hardware, and architectural optimization. Recent research trends include hybrid convolution-transformer models for AR/VR systems, robotics performance benchmarking, and neural architecture search for NPU-CIM heterogeneous devices. His work bridges machine learning and computer architecture for modern hardware platforms. Publications (2024-2025) highlight collaborations in robotics computing and hybrid AI-hardware systems. Notable contributions include the RobotPerf benchmarking suite and studies on autonomous systems hardware integration. Students : Siyuan Chen, Valerie Choung, Hyoungjoo Kim.

Máire O'Neillمشاهده پروفایل

استاد

• Hardware Security
• Data Security Architectures
• Computer Arithmetic
• +۵ مورد دیگر

Prof. Máire O'Neill is a Professor of Information Security at Queen's University Belfast, leading the Centre for Secure Information Technologies and the UK Research Institute in Secure Hardware and Embedded Systems (RISE). She holds a MEng and PhD from Queen's University Belfast under Prof. Sir John McCanny. Her work focuses on hardware security, including attack-resilient computer hardware, PUFs, post-quantum cryptography, and secure embedded systems. Notable achievements include a six-fold improvement in AES hardware efficiency and patented PUF techniques addressing counterfeiting. Key roles include EPSRC Leadership Fellowship and Royal Academy of Engineering Fellowship. Research interests span hardware security architectures, nanotechnology-based security, and quantum-resistant cryptography. Awards include Royal Irish Academy membership, Irish Academy of Engineering Fellowship, and British Female Inventor of the Year. Recent projects include the EU H2020 SAFEcrypto initiative for post-quantum encryption and TruDetect for hardware Trojan detection. She advises on cybersecurity for IoT and connected devices, emphasizing secure manufacturing and long-term system resilience against quantum threats. Labs/Teams: Principal Investigator at the Centre for Secure Information Technologies (CSIT) and RISE. Active in multi-national collaborations like the EU's SAFEcrypto. Supervised over 8 PhD students (details in full description). Grants include funding from EU H2020, EPSRC, and industry partnerships.

• Human-Computer Interaction
• Artificial Intelligence
• Data Visualization
• +۳ مورد دیگر

Dr. Damien Masson is an Assistant Professor in Human-Computer Interaction at the Université de Montréal, affiliated with Mila (Quebec AI Institute) and IVADO. He leads the Montréal HCI group and focuses on intelligent systems, document augmentation, and interactive visualization tools. His work bridges HCI, AI, and data science to improve how humans interact with complex information. Education: PhD in Computer Science (University of Waterloo, 2023), MSc from Université de Lille (2018). His research includes systems like Chameleon (interactive documents), ChartDetective (data extraction), and Textoshop (AI-based text editing). He has published at top venues like CHI and UIST, winning multiple awards including the 2023 Bill Buxton Dissertation Award. He actively mentors students across PhD/master's/undergraduate levels and develops open-source tools like Statslator.js and DirectGPT. His lab explores future directions in adaptive interfaces, multimodal interaction, and AI-driven document systems.

Wei Aiمشاهده پروفایل

استادیار

• Data Science for Social Good
• Machine Learning
• Causal Inference
• +۴ مورد دیگر

Wei Ai is an Assistant Professor at the University of Maryland, affiliated with the College of Information (INFO) and the Institute for Advanced Computer Studies (UMIACS). His research focuses on data science for social good (DSSG), integrating machine learning, causal inference, and experimental design to address societal challenges in education, virtual collaboration, and quantum computing. He leads the Center for Educational Data Science and Innovation (EDSI) and has secured grants from the NSF, Gates Foundation, and Walton Family Foundation for projects like M-Powering Teachers and classroom quality assessment tools. Education: PhD in Information from the University of Michigan (advised by Qiaozhu Mei). Previous academic roles include teaching at the University of Michigan and Peking University in courses like Data Mining and Information Retrieval. Research Interests: Machine Learning and Causal Inference, AI for Education, Virtual Teams and Social Identity, Large Language Models for Social Applications. He has published in venues such as PNAS, Management Science, ACL, and the Web Conference. Grants: Major awards include a NSF grant on middle-grade math instruction analysis (with Min Sun) and a Gates Foundation grant for classroom dataset development (with Jing Liu). Labs/Teams: CLIP Lab member, collaborating on interdisciplinary projects with UMIACS and the Joint Quantum Institute. Prospective students: Open to mentoring PhD students through INFO and Computer Science programs. Actively supervises current students in education technology and quantum computing domains.