جستجوی استاد راهنمای دانشگاهی

Krishna Krishnanمشاهده پروفایل

استاد

• Facilities Planning
• Supply Chain Optimization
• Manufacturing Systems
• +۱ مورد دیگر

Krishna Krishnan is a Professor in the College of Engineering at Wichita State University, specializing in Industrial, Systems, and Manufacturing Engineering. His research spans facilities planning, supply chain optimization, and manufacturing systems, with recent projects addressing Industry 4.0 applications in logistics. Research areas include: Facility layout optimization and digital process twinning Supply chain logistics under uncertainty Machine learning applications in manufacturing Robotic system reliability Recent publications (2022-2025) focus on AI-driven solutions for industrial challenges, including machine vision for quality control (2025), soft robotics design (2024-2025), deep learning for vehicle routing (2023), and cyber-physical systems for manufacturing (2022). Research demonstrates strong integration of operations research, machine learning, and smart manufacturing principles. Current projects include WCM Productivity & Quality Improvement for CNHi, facility layout optimization for aerospace manufacturers, and Region 7 University Transportation Center initiatives. Laboratory capabilities support advanced manufacturing simulation and logistics optimization.

Saman Amarasingheمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Architecture
• +۵ مورد دیگر

Professor Saman Amarasinghe is a leading academic in the MIT Electrical Engineering and Computer Science Department, specializing in compiler design, high-performance computing, and programming languages. His research bridges theoretical computer science with practical applications, focusing on optimizing compilers for modern hardware architectures. He holds a professorship within the School of Engineering at MIT. His research interests span artificial intelligence, machine learning integration into compilers, sparse tensor algebra optimizations, and domain-specific languages (DSLs) for high-performance computing. He has pioneered frameworks like TACO (Tensor Algebra Compiler) and GraphIt, which enable efficient handling of complex data structures across diverse computational domains. Recent work emphasizes compiler-driven optimizations for sparse data formats, machine learning models for code optimization, and unified interfaces for large language models. His projects often address scalability challenges in exascale computing and bioinformatics applications. Publications highlight advancements in compiler techniques for sparse tensor operations, graph analytics, and memory-efficient algorithms. His contributions to Halide, Simit, and Weld further demonstrate his impact on DSLs for image processing and physical simulation. Awards and recognitions are integral to his career, though specific personal accolades are not detailed here. His work is characterized by a strong focus on practical compiler solutions for emerging computational challenges.

• High Performance Computing
• Numerical Algorithms and Software
• Parallel and Distributed Computing
• +۳ مورد دیگر

Panruo Wu is an Associate Professor in the Department of Computer Science at the University of Houston (UH). He joined UH in 2018 as an Assistant Professor and holds a Ph.D. in Computer Science from the University of California, Riverside, and a B.S. in Mathematics from the University of Science and Technology of China (USTC). His primary research interests include high performance computing, numerical algorithms, parallel and distributed computing, fault-tolerant systems, and big data analytics. He leads projects such as LATER (Linear Algebra on Tensor Cores) and LibKernel, focusing on accelerating numerical computations and kernel machines. His recent work emphasizes optimizing linear algebra operations on emerging hardware accelerators like GPUs, with a focus on tensor cores and energy-efficient solvers. Key contributions include fault-tolerant matrix decompositions, scalable frameworks for serverless parallel computing (Wukong), and accelerating kernel machines (TensorSVM). Awards include a Best Paper Nomination at HPDC'20. His teaching spans algorithms, parallel computing, and numerical analysis, with courses like COSC 3320 (Algorithms and Data Structures) and COSC 6374 (Parallel Computations). His research is supported by NSF Grant No. 2146509.

Liu Hanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Machine Learning
• +۷ مورد دیگر

Liu Han is the Orrington Lunt Professor of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI (CFMG). Previously, he served as a professor at Princeton University and Johns Hopkins University, and as Director of the Deep Reinforcement Learning Center at Tencent AI Lab. His research focuses on integrating AI with computer systems, leveraging large foundation models and probabilistic graphical models to advance science, engineering, and business. He holds a joint PhD in Machine Learning and Statistics from Carnegie Mellon University, advised by John Lafferty and Larry Wasserman. Key research interests include transformer architectures, edge AI deployment, reinforcement learning, and genomic modeling . He actively contributes to academic governance as an associate editor for journals like the Journal of American Statistical Association and Technometrics , and serves as area chair for NeurIPS, ICML, and ICLR. His work emphasizes scalable machine learning solutions for heterogeneous data, with applications in genomics, traffic prediction, and proton beam control systems. Awards: Presidential Early Career Award (2025?), Alfred P Sloan Fellowship (Mathematics), IMS Tweedie New Researcher Award, ASA Noether Young Scholar Award, NSF CAREER Award, Howard B Wentz Award, and Umesh Gavaskar Memorial Dissertation Award. Advising & Grants: Advised students at top-tier institutions (specific names omitted due to lack of explicit listing). His grants focus on edge AI, foundation models, and ethical AI frameworks. Collaborates with Fermilab and CERN on real-time control systems using ML. Labs/Teams: MAGICS Lab (Northwestern) and CFMG Center, previously led Tencent's Deep Reinforcement Learning Center. Involved in interdisciplinary teams like Hetegraph-Mamba and the KGML-xDTD project.

Yin Sunمشاهده پروفایل

دانشیار

• Wireless Networks
• Wireless Communications
• Cloud Computing
• +۵ مورد دیگر

Yin Sun is the Godbold Associate Professor in the Department of Electrical and Computer Engineering at Auburn University. His research focuses on timely information updates, wireless networks, and IoT, with emphasis on data freshness metrics and remote estimation systems. He earned his B.S. and Ph.D. in Electronic Engineering from Tsinghua University. Education: B.S. Electronic Engineering, Tsinghua University Ph.D. Electronic Engineering, Tsinghua University His research interests include wireless communications, cloud computing, and machine learning applications in safety-critical systems. Notable achievements include co-authoring the seminal book on Age of Information and receiving the NSF CAREER Award for his work on goal-oriented status updating. He also serves as Technical Co-Chair for the ACM MobiHoc Symposium and leads Auburn's Wireless Engineering Research and Education Center, addressing real-world communications challenges through interdisciplinary collaboration. Awards include the 2021 Best Paper Award (Journal of Communications and Networks) and contributions to food pantry inventory optimization via machine learning. His work bridges theoretical foundations with practical systems, emphasizing real-time data relevance and efficient resource allocation strategies. Advisees/Grants: Leads NSF-funded projects on semantic status updating and collaborates on applications like disease detection in agriculture. Active in advising graduate students through his lab's focus on edge computing and network optimization. Labs/Teams: Core member of Auburn's Wireless Engineering Research and Education Center, fostering multi-university collaborations in communications research.

Suman Banerjeeمشاهده پروفایل

استاد

• Networking
• Wireless Networking
• Mobile Computing
• +۳ مورد دیگر

Suman Banerjee is the David J. DeWitt Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. He leads the WIsconsin WIreless and NetworkinG Systems (WiNGS) Laboratory, focusing on wireless and mobile networking, distributed systems, and network security. Education: Ph.D. (Computer Science, 2003) from University of Maryland; M.S. (1999) and B.Tech. (1996) from University of Maryland and IIT Kanpur, respectively. Research Interests: Mobile and wireless networking systems, overlay systems, traffic classification, and network security. His WiNGS lab explores topics like interference detection (Airshark), enterprise WLAN optimization (FLUID), and vehicular networking (WiRover). Awards: ACM SIGMOBILE Rockstar Award (2013), NSF Career Award (2008), ACM MobiCom Best Paper (2009), and Wisconsin Governor’s Business Plan Competition Grand Prize (2011). Grants & Activities: Over $4M in NSF and industry grants since 2005. Served as Program Chair for ACM MobiCom, IEEE SECON, and ACM CellNet. Editor-in-Chief of Mobile Computing and Communications Review. Labs & Teams: WiNGS Lab develops systems like WiRover for vehicular connectivity and Airshark for non-WiFi interference detection. Collaborates with industry partners like Cisco, Microsoft, and Intel.

Hairong Zhaoمشاهده پروفایل

استاد

• Combinatorial Optimization
• Sequence and Scheduling
• Operations Research
• +۶ مورد دیگر

Hairong (Helen) Zhao is a full-time Professor of Computer Science and Graduate Advisor at Purdue University Northwest's Department of Computer Science. Her research focuses on using interdisciplinary methods from computer science, mathematics, and statistics to solve complex decision-making problems in areas such as resource allocation, manufacturing, and production scheduling. She holds a Ph.D. in Computer Science from New Jersey Institute of Technology, an M.S. from Beijing University of Posts and Telecommunications, and a B.S. from Taiyuan University of Technology. Research Interests: Dr. Zhao specializes in Combinatorial Optimization, Sequence and Scheduling, Operations Research, and Algorithm Design. Her work bridges theoretical foundations with practical applications, addressing challenges in scheduling algorithms, approximation methods, and computational complexity. Publications Overview: Her recent publications span journals like Omega, European Journal of Operational Research, and IEEE Transactions on Computers, reflecting her expertise in scheduling optimization, machine availability constraints, and fault-tolerant systems. Her work also includes contributions to real-time scheduling and industrial process optimization in sectors like steel manufacturing. Contact: hairong@pnw.edu | Hammond Campus, CLO 366

Henry Yuenمشاهده پروفایل

استادیار

• Quantum Computing
• Computational Complexity Theory
• Cryptography
• +۳ مورد دیگر

Henry Yuen is an Assistant Professor of Computer Science at Columbia Engineering, Columbia University. He is affiliated with the Cybersecurity and Foundations of Data Science centers. His research focuses on quantum computing, complexity theory, cryptography, and information theory. Yuen holds a BA in Mathematics from the University of Southern California (2010) and a PhD in Computer Science from MIT (2016). He has received prestigious awards including the NSF CAREER Award and a Sloan Fellowship. His work bridges quantum mechanics and computational complexity, with contributions to quantum non-locality, pseudorandomness, and cryptographic protocols. His recent research explores quantum advantage in algorithms, quantum circuit design, and quantum cryptographic primitives. Notable achievements include advancing the theoretical foundations of quantum pseudorandomness and demonstrating hardness results in quantum learning and state tomography. Key contributions span quantum interactive proofs, error correction, and quantum money schemes. His grants include funding for exploring theoretical foundations of quantum pseudorandom primitives. He collaborates on projects like unitary complexity analysis and superdense coding rigidity, contributing to both algorithmic and foundational aspects of quantum computing.

• Theoretical Computer Science
• Complexity Theory
• Machine Learning Theory
• +۲ مورد دیگر

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University. His research focuses on theoretical computer science, particularly complexity theory and its intersections with machine learning and algorithm design. He holds a Ph.D. from Columbia University, advised by Rocco Servedio, and has been recognized with prestigious awards including the Sloan Fellowship and best paper awards at FOCS, CCC, and SAT conferences. His work bridges foundational theoretical contributions with practical algorithmic innovations, emphasizing topics like decision tree learning, computational hardness, and pseudorandomness. Notable achievements include polynomial-time decision tree learning algorithms, hardness results for learning problems, and contributions to coding theory and property testing. He collaborates extensively with researchers such as Guy Blanc, Caleb Koch, and Carmen Strassle on projects addressing core challenges in computational complexity. Tan's publications span top venues like FOCS, STOC, and JACM, reflecting his impact on areas such as query complexity, certification algorithms, and parallel computation lower bounds. His research also extends to cryptographic primitives and information-theoretic methods, showcasing a broad yet deeply technical approach to theoretical computer science.

Mark Horowitzمشاهده پروفایل

استاد

• High-Performance Digital Systems
• VLSI Circuit Design
• Microprocessor Architecture
• +۷ مورد دیگر

Mark Horowitz holds the Fortinet Founders Chair in Electrical Engineering and is the Yahoo! Founders Professor in Stanford University's School of Engineering. He also serves as Professor of Computer Science. His career spans over four decades, with foundational contributions to microprocessor design, high-speed link architectures, and computational photography innovations like the Lytro camera. Education: PhD in Electrical Engineering, Stanford University (1984) MS in Electrical Engineering, MIT (1978) BS in Electrical Engineering, MIT (1978) Research Interests: His work bridges EE and CS with interdisciplinary focus on: Agile hardware design methodologies for VLSI systems High-speed signaling and memory interfaces (e.g., Rambus technology) Reconfigurable computing architectures (e.g., CGRA-based accelerators) Applications in computational biology and molecular systems Recent Directions: Recent publications emphasize energy-efficient accelerators for machine learning, memory subsystem innovations, and compiler-driven hardware design frameworks like AHA! His work on Onyx and Opal architectures demonstrates leadership in sparse tensor computation hardware. Labs & Teams: Leads the Stanford VLSI Research Group and AHA! initiative. Collaborations include work with Prof. Marc Levoy on computational imaging and partnerships with industry on memory interface standards.

• Geographic Information Systems (GIS)
• Parallel Computing
• GPU Acceleration
• +۳ مورد دیگر

Jianting Zhang is an Assistant Professor of Computer Science at the City College of New York (CUNY). His research focuses on geospatial data management, parallel computing, and GPU acceleration techniques for handling large-scale spatial datasets. He specializes in optimizing spatial query processing, indexing methods for moving objects, and high-performance computing frameworks for web-based GIS applications. His work bridges theoretical algorithm design with practical implementations in distributed and cloud environments. Key research interests include parallel indexing of geospatial rasters and trajectories, efficient spatial join algorithms, and GPU-based acceleration of data compression and visualization. He has explored applications in biodiversity analysis, urban mobility (e.g., NYC taxi data), and query-driven web-GIS systems. His contributions emphasize scalable solutions for big data challenges in geographic information systems. His publications highlight advancements in GPU-accelerated spatial processing, including novel approaches to quadtree construction, sparse matrix operations, and top-K trajectory similarity queries. While no scientific awards are explicitly mentioned, his active publication record reflects sustained contributions to the field of geospatial computing.

Stratos Idreosمشاهده پروفایل

استاد

• Data Systems
• Database Systems
• Machine Learning
• +۴ مورد دیگر

Stratos Idreos is a Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data Systems Laboratory (DASlab), focusing on self-designing systems, adaptive data structures, and high-performance key-value stores. His research bridges theoretical foundations with practical implementations to enhance data processing efficiency. Education: Ph.D. in Computer Science, University of Amsterdam (Netherlands) Research Interests: Idreos explores systems that automate design decisions for optimal performance. His work includes the Periodic Table of Data Structures , self-tuning storage engines like Cosine , and machine learning integration into database systems. Recent efforts focus on cloud-optimized systems, ensemble learning techniques, and image-AI acceleration. Key Contributions: Developed the Data Calculator for automated system design Pioneered self-designing key-value stores (e.g., Cosine) Advanced adaptive indexing and LSM-tree optimizations Awards & Recognition: ACM SIGMOD Jim Gray Dissertation Award (2011) IEEE TCDE Rising Star Award (2015) Sloan Research Fellowship (2023) McDonald Mentoring Award (2023) Grants & Leadership: Holds NSF and DOE grants, chairs the ACM Symposium on Cloud Computing steering committee, and co-directs the Harvard Data Science Initiative. His lab collaborates with Microsoft, IBM, and academic institutions globally. Labs & Teams: DASlab fosters interdisciplinary projects, offering courses like CS165 (Data Systems) and CS265 (Big Data Systems). The lab emphasizes reproducible research and industry-relevant solutions.

• Distributed systems
• Operating systems
• Networking
• +۳ مورد دیگر

Charlie Hu is the Michael and Katherine Birck Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His work focuses on distributed systems, operating systems, networking, parallel computing, and signal/image processing. He holds a BS from the University of Science and Technology of China (1989), and MS/MPhil from Yale (1992), and PhD from Harvard (1997). Research emphasizes optimizing mobile and edge computing systems, particularly around 5G networks, AR/VR technologies, and energy-efficient systems. Recent work includes studies on 5G uplink performance, multi-carrier access potential, and camera parameter tuning for video analytics. He has developed systems like CAMTUNER and Elixir to improve video analytics accuracy and data quality. His contributions span energy profiling methodologies (e.g., GfxDoctor) and network traffic modeling frameworks. Awards and recognitions are not explicitly listed here, but his extensive publication record reflects sustained impact in systems research. Advising activities and grants are not detailed in the provided text, though his lab likely focuses on next-gen wireless systems and embedded computing challenges.

• Database Systems
• Query Compilation
• High-Performance Computing
• +۳ مورد دیگر

Ruby Tahboub is a Teaching Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (UIUC), since 2022. She previously served as a Visiting Assistant Professor at Purdue University (2019–2022). She holds an M.S. (2016) and Ph.D. (2019) in Computer Science from Purdue University. Her research focuses on query compilation, spatial databases, and high-performance computing, with notable contributions to Apache Spark optimization and educational tools like LIMO for programming education. She teaches courses such as Introduction to Data Mining (CS 412) and Introduction to Programming for Engineers and Scientists (CS 101). Ruby has received the Raymond Boyce Graduate Teacher Award (Purdue, 2016) and the Best Demo Award at ACM SIGSPATIAL (2015) for her work on LIMO. Her research spans database systems, compiler design, and distributed computing, emphasizing practical applications in both industry and education. Her work bridges theoretical advancements in query processing with real-world scalability challenges, particularly in spatial and heterogeneous workloads. She also advocates for innovative teaching methods to enhance programming literacy through interactive tools like map-based activities.

Dirk Eddelbuettelمشاهده پروفایل

استاد بالینی

• Statistical Computing
• R Programming
• Parallel Computing
• +۴ مورد دیگر

Dirk Eddelbuettel is a Clinical Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC), where he teaches courses such as STAT 447: Data Science Programming Methods. He holds a PhD from EHESS (France, 1997), an MA from EHESS (France, 1992), and an MSc from Karlsruhe Institute of Technology (Germany, 1992). His research focuses on statistical computing, R and C++ integration, and open-source software development. He is renowned for creating the Rcpp package, which bridges R and C++, and co-creating the Rocker Project (Docker containers for R). He also leads the r2u initiative, providing CRAN packages as Ubuntu binaries. His work emphasizes reproducible research, high-performance computing, and statistical software engineering. Notable contributions include the RcppArmadillo, RProtoBuf, and RInside packages. He actively contributes to the R community through publications, workshops, and open-source projects. His teaching and research highlight interdisciplinary approaches, combining statistical theory with computational methods. He maintains a personal website (dirk.eddelbuettel.com) and is active on social platforms discussing R, C++, and open-source technologies.