جست‌وجوی استاد و پژوهشگر

Minjia Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۹ مورد دیگر

Minjia Zhang is a tenure-track Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering (ECE) and the Center for Artificial Intelligence Innovation (CAII) at the National Center for Supercomputing Applications (NCSA). He leads the Supercomputing System and AI Lab (SSAIL) and previously served as a Principal Researcher at Microsoft Research Redmond from 2016 to 2023, contributing to projects like DeepSpeed and DeepCPU. Education: Ph.D. in Computer Science and Engineering from Ohio State University (2016), advised by Prof. Michael D. Bond. Prior to UIUC, he held visiting positions at Microsoft Research during his doctoral studies. His research focuses on efficient machine learning systems, including training/inference optimization on parallel/distributed/heterogeneous hardware, model compression, and large-scale AI applications such as agentic AI and vision-language models. Research Interests: Large-scale DL/AI applications (e.g., agentic AI, multimodal systems), effective efficiency algorithms (model compression, data efficiency), and high-performance ML systems. He emphasizes cross-disciplinary collaboration, integrating system design with AI advancements to tackle scalability challenges in scientific discovery and industrial deployment. He has been recognized with the NSF CAREER Award (2024), Honorable Mention of the ICLR 2024 Outstanding Paper Award, and OOPSLA 2015 Distinguished Paper and Artifact Awards. His work has been applied to Microsoft products like Bing and Azure, enhancing latency and cost efficiency. Advising and Grants: Actively mentoring students in PhD/Master's/Undergraduate programs, emphasizing systems and AI. Recipient of NSF grants and the Amazon PhD Fellowship (Xinyu Lian). His lab's VecFlow and MiniKV projects highlight contributions to vector databases and efficient LLM inference. Labs/Teams: SSAIL Lab, collaboration with Microsoft Research (DeepSpeed), BigScience consortium (BLOOM 176B model), and NCSA's CAII. His work bridges systems research and AI, targeting high-impact applications in science and industry.

Nishanth Sastryمشاهده پروفایل

استاد

• Internet Data Science
• Social Networks
• Computational Social Science
• +۹ مورد دیگر

Professor Nishanth Sastry is a distinguished academic in the Department of Computer Science at the University of Surrey , serving as Associate Head of Research and Innovation. He holds a PhD from the University of Cambridge and has extensive industry experience at Cisco Systems, IBM, and MIT. His research focuses on social networks, computer networks, online harms, edge computing, and privacy , with notable projects analyzing hate speech on Twitter, optimizing LEO satellite networks, and developing privacy-preserving technologies. He has received multiple awards including a SIGCOMM Best Paper Award and nine US patents. Education: Bachelor’s (Distinction) – RV College of Engineering Master’s – University of Texas at Austin PhD – University of Cambridge Research Interests: Nishanth’s work bridges computer networks and social dynamics , applying Internet Data Science to improve system efficiency. Key areas include: Peer-assisted content delivery and edge caching Hate speech detection and mitigation Blockchain-based pervasive systems Satellite network optimization Privacy in social platforms Articles & Impact: His recent work explores conversation kernels for context analysis , LEO satellite gateway placement , and emotion-aware speech synthesis . These studies address real-world challenges like reducing video traffic load and enhancing decentralized web accessibility. Awards & Recognition: Best Paper Award (SIGCOMM 2017) Yunus Innovation Challenge Award (MIT 2016) Multiple IBM and industry accolades Advising & Grants: Supervises PhD students in distributed systems and cybersecurity. Active in securing grants for projects like Transition Guardian (vulnerable user privacy) and EdgeMart (edge network economies). Labs & Collaborations: Leads the Distributed and Networked Systems Group and co-leads the Surrey Security Network . Collaborates with organizations like the Alan Turing Institute, BBC R&D, and Wikimedia Foundation.

Ju Hyoung Munمشاهده پروفایل

استادیار

• Data Systems
• Networking
• Bloom Filters
• +۵ مورد دیگر

Ju Hyoung Mun is an Assistant Professor in the Computer Science Department at Brandeis University. Previously, he served as a Postdoctoral Associate at Boston University (2019–2024) and a Postdoctoral Researcher at Ewha Womans University (2018–2019). He holds a Ph.D. in Electronic and Electrical Engineering from Ewha Womans University (2018), focusing on Bloom filter applications in Information Centric Networking (ICN). His research interests include data systems, networking, and algorithm optimization, with a focus on enhancing lookup performance in storage and network architectures. Education: Ph.D., Electronic and Electrical Engineering, Ewha Womans University (2018) M.S., Information Electronics Engineering, Ewha Womans University (2007) B.S., Information Electronics Engineering, Ewha Womans University (2005) Research interests span Bloom filters, data storage optimization (e.g., LSM trees), and network efficiency. His work bridges theoretical foundations with practical implementations, addressing challenges in distributed systems and high-performance computing. Recent projects include Mnemosyne (dynamic BF tuning in LSM trees) and Relational Fabric (data transformation frameworks). Publications reflect contributions to databases, networking, and storage systems, with a focus on reducing computational overhead and improving system performance. Collaborations include work with Prof. Manos Athanassoulis at Boston University and Prof. Hyesook Lim at Ewha Womans University. Teaching engagements include undergraduate computer networking courses at Ewha Womans University (2015). Professional experience spans roles at Samsung Electronics (2007–2013) and academic research institutions.

Ata Turkمشاهده پروفایل

مدرس

• Cloud Computing
• High-Performance Computing (HPC)
• Data Center Management
• +۳ مورد دیگر

Ata Turk is a Lecturer in the Department of Electrical and Computer Engineering at Boston University, with an affiliation as Adjunct Faculty. He holds a Ph.D. from Bilkent University, awarded in 2012. His research focuses on cloud architecture, high-performance computing (HPC), and interdisciplinary applications such as medical informatics. Key interests include optimizing cloud resource management, diagnosing performance variations in HPC systems via machine learning, and developing scalable distributed systems. His research outputs span cloud software discovery, distributed caching strategies, and medical imaging analysis using MRI. He has contributed to frameworks like Praxi for cloud software detection and D3N for multi-layer caching. Recent work integrates machine learning for real-time diagnostics in HPC environments and explores edge-based sampling techniques for large-scale graph analysis. Dr. Turk’s publications emphasize practical solutions for cloud security, workload management in data centers, and parallel processing techniques. Notable areas include optimizing MapReduce task scheduling and improving load balancing in space plasma simulations. His work intersects system architecture, distributed computing, and healthcare informatics, reflecting a commitment to bridging theoretical research with applied technology.

Li Xiaoمشاهده پروفایل

استاد

• Distributed and Networking Systems
• Wireless Networks
• Cognitive Radio Networks
• +۶ مورد دیگر

Roles & Affiliations: Professor at Michigan State University's Department of Computer Science and Engineering (CSE), College of Engineering. Member of the Experimental Laboratory for Advanced Networking and Systems (eLANS). Education: Ph.D. in Computer Science, College of William and Mary (2002). Research Interests: Distributed systems, wireless networks (including sensor/mesh networks), cognitive radio networks, system resource management, experimental algorithms, and security. Current projects focus on wireless systems design, AI-enhanced networks, and security vulnerabilities in wireless systems. Publications: Over 100 peer-reviewed papers in top venues like INFOCOM, MobiCom, and IEEE Transactions. Recent work focuses on adversarial attacks in audio systems, 5G/4G vulnerabilities, and novel wireless protocols. Key contributions include security frameworks for cellular emergency services and neural network-based demodulation techniques. Awards: Best Paper Awards at Sensys 2021 and CCS 2022, AT&T Security Award (2022). Recognized for innovative work in network security and system design. Grants & Labs: Funded by NSF, DARPA, NIH, and industry partners (Microsoft, IBM). Leads eLANS lab focused on both theoretical advancements and practical deployments in wireless systems.

Christopher Battenمشاهده پروفایل

استاد

• Computer Architecture
• Electronic Design Automation (EDA)
• VLSI Design
• +۷ مورد دیگر

Christopher Batten is a Professor of Electrical and Computer Engineering at Cornell University, affiliated with the Computer Science department. He leads the Batten Research Group within the Computer Systems Laboratory (CSL), focusing on computer architecture, electronic design automation, and VLSI systems. His work spans programmable accelerators, interconnection networks, and agile chip design methodologies. Batten holds a PhD from MIT, an M.Phil. from the University of Cambridge, and a B.S. from the University of Virginia. He has held visiting roles at UC Berkeley and NVIDIA. Research Interests: Computer Architecture: Accelerators, interconnection networks, and emerging technologies VLSI Design: Agile methodologies, chip prototyping, and physical design Hardware-Software Co-Design: Parallel programming frameworks and productivity tools Recent Work: Focuses on optical interconnects, 3D integration, and AI-driven hardware design. Led projects like Cornell Custom Silicon Systems (C2S2), which taped out multiple chips in SkyWater 130nm. Collaborates with industry partners like NVIDIA and Meta. Awards: Recognized with the ACM/IEEE MICRO Hall of Fame, NSF CAREER Award, and multiple teaching accolades. His group emphasizes student-led chip projects and open-source frameworks like PyMTL3. Grants & Sponsors: Supported by NSF, DARPA, AFOSR, and industrial partners including Intel, NVIDIA, and Xilinx. Current efforts include NSF CSSI projects improving gem5 and NSF Panorama for pangenomics.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Compiler Verification
• +۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at MIT, with appointments in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. His research integrates programming languages and formal methods to develop verified computer systems. Education: PhD in Computer Science, University of California, Berkeley MS in Computer Science, University of California, Berkeley BS in Computer Science, Carnegie Mellon University Research Focus: Dr. Chlipala's work spans: Verified compilers and programming tools Cryptographic protocol implementation Hardware description languages Proof automation in theorem provers High-performance parallel computing His current projects focus on rethinking abstractions for parallel systems with end-to-end formal verification. Teaching: He has created and teaches courses on verified software engineering (6.S057), programming fundamentals (6.1010), and formal reasoning about programs (6.5120). His educational materials include textbooks on Certified Programming with Dependent Types and Formal Reasoning About Programs. Awards and Honors: His research has been recognized with the German IT Security Award, Humies Gold Award for evolutionary computation, and multiple teaching prizes including the Jamieson Prize. He is an ACM Distinguished Member and NSF CAREER awardee. Research Teams: Dr. Chlipala leads the Programming Languages & Verification Group at MIT CSAIL. His startup Nectry develops no-code enterprise software based on the Ur/Web language and UPO proof system.

Rahul Parhiمشاهده پروفایل

استادیار

• Machine Learning
• Neural Networks
• Signal Processing
• +۹ مورد دیگر

Rahul Parhi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), where he has been since 2024. Previously, he was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) from 2022 to 2024, and completed his Ph.D. in Electrical Engineering at the University of Wisconsin–Madison in 2022. His research focuses on the mathematics of data science, particularly the theoretical foundations of neural networks and deep learning, with a strong emphasis on functional/harmonic analysis, signal processing, and nonparametric statistics. Key research themes include the mathematics of computed tomography, sparsity, compressed sensing, and the geometry of Banach spaces. Education: B.S. in Mathematics, University of Minnesota, Twin Cities (2018) B.S. in Computer Science, University of Minnesota, Twin Cities (2018) Ph.D. in Electrical Engineering, University of Wisconsin–Madison (2022) His research interests span the interplay between functional and harmonic analysis with data science, including: Foundations of neural networks and deep learning Approximation properties of neural networks Nonparametric statistical estimation Inverse problems and compressed sensing Wavelet-based signal processing Banach space theory His recent publications explore topics such as neural network optimization, attention mechanisms, and the theoretical limits of learning in high-dimensional spaces. He has contributed to understanding the mathematical principles underlying deep learning's success in breaking the curse of dimensionality. His work bridges abstract mathematical concepts with practical applications in signal processing and machine learning. Parhi's research also extends to collaborative projects with experts in optimization and applied mathematics, aiming to develop novel algorithms with rigorous theoretical guarantees. His lab at UCSD focuses on advancing interdisciplinary research at the intersection of mathematics and data science.

• High Performance Computing
• Computer Architecture
• Hardware-Software Co-design
• +۳ مورد دیگر

Shuaiwen Song is a SOAR Associate Professor (tenured) at the School of Computer Science , University of Sydney, and directs the Future System Architecture (FSA) Lab . He holds affiliated professor positions at the University of Washington's Electrical Engineering department and serves as a Visiting Professor at Microsoft. Key research areas: High Performance Computing (HPC), Hardware-Software Co-design, Emerging Architectures (heterogeneous, quantum), and System ML Current projects: Large-Scale Sparse Model Design (Google), Tiered Memory Systems (Google), Compiler Optimizations for Heterogeneous Computing (Microsoft/Alibaba), Planet-Scale XR Systems (Meta), Quantum System Architecture (Australian Research Council) His work bridges system software and hardware, focusing on holistic design for complex many-accelerator systems and futuristic architectures like VR/AR and quantum accelerators. Recent publications highlight advancements in temporal graph processing, VR rendering, and ReRAM-based CNN training. He has received prestigious awards including IEEE Mid-Career Award for Scalable Computing , Alibaba AIR Faculty Award , and Australia's Most Innovative Engineers recognition.

Kefei Wangمشاهده پروفایل

استادیار

• Computer Systems Architecture
• Storage and Caching Systems
• Internet of Things
• +۱ مورد دیگر

Kefei Wang is an Assistant Professor of Computer & Information Science at Gannon University. He holds a Ph.D. from Louisiana State University (2023) where his dissertation focused on optimizing large-capacity key-value systems. His teaching portfolio includes Problem Solving, Object-Oriented Programming, Data Structures, and Database Management courses. Research expertise spans storage system optimization, in-memory database management, and edge computing efficiency. Publications demonstrate consistent contributions to cache management algorithms, flash-based storage innovations, and energy-efficient database designs for IoT environments. He serves on the University Review Council and Faculty Search Committees while reviewing for academic conferences including ICNS and AFIN.

Sourav Duttaمشاهده پروفایل

دانشیار

• High-performance Computing
• Hardware/Software Co-design
• Machine Learning
• +۳ مورد دیگر

Sourav Dutta is an Associate Professor in the Department of Computer Science at Ramapo College of New Jersey, part of the School of Theoretical and Applied Science (TAS). He joined the institution in 2017. His research focuses on high-performance computing, hardware/software co-design, and machine learning. Dutta holds a PhD in Computer Engineering from Southern Illinois University Carbondale. His educational background includes advanced studies in computer engineering, with a specialization in parallel computing systems. His work bridges theoretical computer science with practical applications in distributed systems, resource optimization, and intelligent algorithms. Key research areas include multithreaded systems performance analysis, collaborative recommendation systems, and bioinformatics applications using big data techniques. His publications span venues like IEEE Transactions on Parallel and Distributed Systems, IEEE Big Data, and ACM Transactions on Parallel Computing. Dutta teaches courses such as Computer Science I and II, Data Structures and Algorithms, Operating Systems, and Big Data Computing. His contributions to the field include innovations in concurrent system design, IoT resource allocation, and energy-efficient computing architectures.

Ali Manzakمشاهده پروفایل

عضو هیئت علمی

• Low Power VLSI Design
• Embedded Systems
• Mixed-Signal IC Design
• +۳ مورد دیگر

Ali Manzak is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at Florida State University (FSU). He holds a M.Sc. in Control Systems from the University of Colorado Boulder and a Ph.D. in VLSI Systems from Arizona State University. Prior to academia, he worked as a digital IC design engineer at Lattice Semiconductor in California. His research focuses on low-power VLSI design, embedded systems, mixed-signal IC design, and hardware implementation of machine learning algorithms. He actively contributes to academia through teaching and lab supervision, designing ICs for both educational and research purposes. Education: Ph.D., Electrical Engineering (VLSI Systems), Arizona State University M.Sc., Electrical Engineering (Control Systems), University of Colorado Boulder Research Interests: Dr. Manzak’s work emphasizes energy-efficient VLSI circuits, emerging nanotechnologies like carbon nanotube FETs, and applying machine learning to hardware systems. His publications span IEEE journals and conferences, addressing topics such as power-aware cache design, low-power latch optimization, and AI-driven solutions for electromagnetic problems. Teaching & Labs: He instructs courses like EEL 3705/L, EEL 3927, and EEL 4742/L, and maintains hands-on IC design experience across multiple platforms. His labs integrate industry practices with academic research, fostering practical engineering skills.

• Hardware Design for Network Devices
• Fault Tolerance
• Error Correction Codes
• +۳ مورد دیگر

Salvatore Pontarelli is a Researcher at CNIT (Italian National Inter-University Consortium for Telecommunications), affiliated with the University of Rome Tor Vergata. He holds a Master's in Electronic Engineering from the University of Bologna (2000) and a PhD in Microelectronics and Telecommunications from the University of Rome Tor Vergata (2003). His work focuses on hardware design for network devices, fault tolerance, error correction codes, and high-speed packet processing. He contributed to EU-funded projects and received a CISCO Research Award (2011) for work on Bloom filters and Ternary CAM. Research interests include hash-based structures (e.g., Bloom filters, hash tables), stateful programmable data planes, and FPGA-based network intrusion detection systems. He led the Defect and Fault Tolerance Group (DFTGroup) under Prof. Adelio Salsano and has extensive experience in collaborative projects with academic and industry partners. Publications highlight innovations in flow monitoring, data plane optimization, and efficient memory management. His work often bridges theoretical algorithms (e.g., cuckoo hashing) with practical hardware implementations, emphasizing real-world applications in networking and telecommunications.

• Parallel Computing
• Distributed Systems
• High-Performance Computing
• +۳ مورد دیگر

Andrew Sohn is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). His research focuses on parallel computing, distributed systems, and high-performance computing, with emphasis on optimizing memory management, virtual machine migration, and cloud infrastructure efficiency. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on distributed-memory multiprocessors and scalable cloud systems. Education & Background : While specific academic credentials are not detailed, his extensive research output and grant history indicate a strong background in computer science and computational engineering. Research Interests : Dr. Sohn’s work spans distributed memory architectures, multi-processor systems, and cloud resource optimization. His recent studies address challenges in live migration efficiency, energy-efficient VM relocation, and scalable web caching solutions. Grants & Projects : MRI: A PC-based Multithreading Environment (NSF, 1999–2003) U.S.-Korea Cooperative Research on Communication Behavior (NSF, 1997–2000) Performance Studies of the EM-X Multiprocessor (NSF, 1997–2000) Parallelization of Production Systems on EM-4 (NSF, 1993–1993)

Douglas Downمشاهده پروفایل

استاد

• Performance Evaluation
• Stochastic Models
• Data Centres
• +۷ مورد دیگر

Dr. Douglas Down is a Professor in the Department of Computing and Software at McMaster University. His research focuses on performance evaluation, stochastic models, data centers, and scheduling algorithms. He is actively involved in the Digital & Smart Systems research cluster, addressing challenges in healthcare logistics, energy-efficient computing, and resource allocation. Dr. Down teaches CAS 736: Analysis of Stochastic Networks, exploring statistical modeling techniques and current research in stochastic networks. His work integrates machine learning and optimization to solve real-world problems, such as blood product inventory management and thermal-aware data center management. He has published extensively on topics including demand forecasting, inventory policies, and energy-aware workload assignment. While no specific awards are listed, his contributions to operations research and computing systems are evident through his prolific publication record. Dr. Down advises graduate students and collaborates on projects involving healthcare systems, cloud computing, and distributed systems. His research addresses both theoretical and applied challenges, with a focus on decision-making under uncertainty and system optimization.