جستجوی استاد راهنمای دانشگاهی

• Computer Networks
• Programmable Infrastructures
• Cloud Computing
• +۵ مورد دیگر

Dr. Theophilus A. Benson is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with additional responsibilities at Carnegie Mellon University-Africa. His research group focuses on improving network performance and availability through models, algorithms, and frameworks that manage network state semantics. Key application areas include addressing the digital divide, optimizing microservices/cloud systems, software-defined networks, and CDN designs. Education: Ph.D., University of Wisconsin, Madison (2012) M.S., University of Wisconsin, Madison (2008) B.S., Tufts University (2004) Research Focus: Professor Benson's work spans three core domains: Democratizing Web Performance (measurements and optimizations for developing regions), Systems Abstractions for Programmable Infrastructures (eBPF/P4 frameworks), and Self-Managing Networks (ML-driven configurations). His African Internet Observatory initiative analyzes Africa's internet ecosystem to address digital inequity through assessment probes and statistical methods. Publication Trends: Recent works (2021-2024) demonstrate a strong focus on programmable networks (eBPF/P4 management), web performance in developing regions, and data-driven cloud/CDN optimizations. Earlier foundational work established expertise in SDN fault tolerance, network updates, and video streaming characterization. Awards & Honors: SIGCOMM Test of Time Award NSF CAREER Award NEC Faculty Award Google Faculty Award Facebook Faculty Award (2x) DARPA ISAT Study Group Member Grants & Advising: Secured funding from NSF (CAREER, NeTS), Google, Facebook, and Yahoo. Current advisees include 4 PhD/MS students working on programmable networks and web performance. Actively recruiting post-docs and students for African connectivity and eBPF projects. Leadership: Co-chairs NSDI'25 and ApNet'24 conferences. Leads the NetLab research group developing deployable systems adopted by web-scale companies and open-source communities.

Franz Franchettiمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Parallel Computing
• Algorithm and Hardware Co-Synthesis
• +۵ مورد دیگر

Franz Franchetti is the Kavčić-Moura Professor of Electrical & Computer Engineering at Carnegie Mellon University. He serves as Associate Dean for Research and Director of the Engineering Research Accelerator at CMU. Education: Ph.D. in Computational Mathematics (Vienna University of Technology, 2003) M.Sc. in Technical Mathematics (Vienna University of Technology, 2000) His research interests focus on automatic performance tuning and program generation for emerging parallel computing platforms , including multicore CPUs , GPUs , and 3DIC chip design . He leads the SPIRAL effort to automate highly optimized software libraries and explores domain-specific compiler transformations in HPC applications for smart grids and material sciences . Recent work extends SPIRAL to quantum computing . The scientific awards Franchetti has received include the Gordon Bell Prize (2006) , HPC Challenge Class II Award (2010) , and the CIT Dean's Early Career Fellowship (2013) . He and his students have won multiple Best Paper Awards at HPEC, DAC, and ISPA ACM TODAES Best Paper (2014) Student Research Competition wins (PACT 2024, CGO 2023) Franchetti has advised students like Richard Veras and Thom Popovici . He has secured significant grants from agencies such as DARPA, DOE, NSF, and industry partners (Intel, NVIDIA, Mercury). He co-founded SpiralGen, Inc. and holds leadership roles in organizations like ASciNA Western Pennsylvania and as Honorary Consul of Austria in Pittsburgh.

Vyas Sekarمشاهده پروفایل

استاد

• Networking
• Cybersecurity
• Network Security
• +۹ مورد دیگر

Vyas Sekar is the Tan Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Computer Science Department. He is affiliated with CyLab and co-directs the Future of Enterprise Security initiative. His research focuses on networking, cybersecurity, distributed systems, and IoT security, with an emphasis on data-driven approaches and network verification. Education: Ph.D. in Computer Science (2010) from CMU; B.Tech. from IIT Madras (President of India Gold Medal recipient). Professional roles include Chief Scientist at Conviva and co-founder of Rockfish Data. Research Interests: Cybersecurity, network security, software-defined networking (SDN), IoT security, DDoS defense, privacy-preserving data sharing, and network performance optimization. Recent work includes developing tools like Pigasus (FPGA-accelerated intrusion detection), Nomad (cloud side-channel mitigation), and frameworks for anomaly detection in IoT networks. Articles Trends: Recent publications address advanced threats like LLM-driven network attacks, stealthy automotive network exploits (CANDid), and optical-layer DDoS defenses. Emphasis on practical solutions (e.g., SketchPlan for telemetry, Pryde for firewall evasion detection). Awards: ACM SIGCOMM Test of Time Award (2022), IIT Madras Young Alumni Achiever Award (2022), Intel Outstanding Researcher Award (2021), and NSF CAREER Award (2016). Recognized for contributions to intrusion prevention, network security, and IoT resilience. Grants & Projects: Led NSF-funded ONSET project (optical-layer DDoS defense), CyLab's Secure IoT Initiative, and collaborations with industry partners like Intel, Facebook, and Nokia Bell Labs. Advises graduate students in cybersecurity and networking. Labs & Teams: Active contributor to CyLab, co-developer of frameworks like Lumos (hidden IoT device detection) and KalKi (IoT security platform). Engages in interdisciplinary research across CMU’s Robotics Institute and Software Engineering Institute.

Benjamin Eysenbachمشاهده پروفایل

استاد

• Reinforcement Learning
• Self-Supervised Learning
• Machine Learning
• +۲ مورد دیگر

Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.

Joshua Bardمشاهده پروفایل

دانشیار

• Robotic Fabrication
• Architectural Design
• Human-Machine Collaboration
• +۴ مورد دیگر

Joshua D. Bard is an Associate Professor and Associate Head for Design Research at Carnegie Mellon University's School of Architecture. His work bridges traditional craft and cutting-edge robotics, focusing on human-machine collaboration in construction domains. He leads Archolab, an award-winning research group exploring digital fabrication methods like 'Morphfaux' (robotic plaster techniques) and 'Spring Back' (parametric steam bending). Education: M.Arch (Distinction) from University of Michigan; B.A. in Literature & Philosophy from Wheaton College. Professional affiliations include the Manufacturing Futures Institute and rob|arch. Research emphasizes reviving historical crafts through digital tools, such as augmented reality interfaces for architectural education and thermal-tuned concrete panels via robotic processes. His teaching includes generative modeling and architectural robotics labs. Awards: Architect Magazine R+D Award, Canadian Wood Council Merit Award Key Projects: Plaster ReCast AR app, Thermally Informed Robotic Concrete Panels Collaborators: Dana Cupkova, Garth Zeglin, Steven Mankouche Current courses include 62-225 Generative Modeling and 48-555 Introduction to Architectural Robotics. His work is featured in venues like the Carnegie Museum of Art and academic journals like International Journal of Architectural Computing .

• Statistical Machine Learning
• Algorithmic Statistics
• Robust Statistics
• +۱۱ مورد دیگر

Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.

Gianluca Piazzaمشاهده پروفایل

استاد

• MEMS/NEMS
• Nanotechnology
• Piezoelectric Devices
• +۷ مورد دیگر

Gianluca Piazza is the STMicroelectronics Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Mechanical Engineering. He directs the John and Claire Bertucci Nanotechnology Laboratory (CMU Nanofab). Previously, he was the Wilf Family Term Assistant Professor at the University of Pennsylvania. His research focuses on piezoelectric micro/nano electromechanical systems (M/NEMS) for RF communication, optomechanics, chemical/biological sensing, and mechanical computing. Key projects include nanorelays for low-power computing, ultrasound-based wireless powering, and piezoelectric MEMS for energy harvesting. Education: PhD (2005) in Electrical Engineering from UC Berkeley; MS (2001) from University of Texas at Austin and Politecnico di Milano (Italy). Research Interests: M/NEMS design, micro/nano fabrication, piezoelectric materials, mechanical switches, and energy-efficient electronics. His work bridges fundamental science and applied engineering, with patents in micromechanical resonators and awards including the IBM Young Faculty Award (2006) and multiple IEEE Best Paper Awards. Grants & Collaborations: NSF LEAP-HI grant ($2M) for nanorelay development (2020); CMU Kavčić-Moura Endowment funding. Collaborates with Maarten de Boer (Mechanical Engineering) and institutions like the University of Pennsylvania and City University of Hong Kong. Labs & Teams: Leads the Piazza Micro and Nano Systems Laboratory, focusing on NEMS/MEMS innovation. Active in CMU’s Center for Silicon System Implementation and Engineering Research Accelerator.

Sean Welleckمشاهده پروفایل

استادیار

• Large Language Models
• Mathematical Reasoning
• Code Generation
• +۴ مورد دیگر

Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.

Yongjie Jessica Zhangمشاهده پروفایل

استاد

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +۵ مورد دیگر

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

• Artificial Intelligence
• Supply Chain
• Sustainability
• +۶ مورد دیگر

Evelyn Xiaoyue Gong is an Assistant Professor of Operations Management at the Tepper School of Business , Carnegie Mellon University . She holds a PhD in Operations Research from MIT (2023) and a BS in Honors Mathematics and Interactive Media Arts from New York University (2017) . Research Interests : Developing artificial intelligence solutions for supply chains and sustainability with provable performance guarantees. Online assortment optimization, pure exploration in reinforcement learning, and data-driven decision-making . Recent Work Trends : Her publications focus on applying reinforcement learning and optimization algorithms to supply chain sustainability, server deployment under uncertainty, and resource management. Key themes include AI-driven environmental impact reduction and theoretical advancements in online inventory models . Scientific Recognition : Best Dissertation Prize , Supply Chain Conference (2023) Accenture Fellowship (2022-2023) Bayer Women in Operations Research Scholarship (2021) Professional Service : Reviewer for Management Science , NeurIPS 2025 , and IPCO 2025 . Committee member for INFORMS Public Sector Operations Research Best Paper Award (2025).

Yihan Sunمشاهده پروفایل

استادیار

• Parallel Computing
• Algorithms
• Data Structures
• +۱۲ مورد دیگر

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

Amritanshu Pandeyمشاهده پروفایل

استادیار

• Grid Simulation and Optimization
• Grid Cybersecurity
• Modeling of Future Energy Systems
• +۳ مورد دیگر

Amritanshu Pandey is an Assistant Professor in Electrical Engineering at the University of Vermont, with a part-time adjunct appointment in Electrical and Computer Engineering at Carnegie Mellon University. His research focuses on enhancing the efficiency, reliability, and security of electric grids through methods in circuit theory, optimization, and machine learning. He pioneered the SUGAR simulation engine for power systems and collaborates globally on grid challenges. Research Interests: Pandey's work spans renewable integration, grid cybersecurity, digital twins, and decarbonization. Key projects include developing algorithms for large-scale grid optimization, anomaly detection, electric vehicle infrastructure modeling, and cyber-resilient energy systems. His research addresses real-world challenges in rapidly evolving grids across Asia and Africa. Awards: Best Paper Award, IEEE PES General Meeting (2017, 2021) Best-of-the-Best Paper Award, IEEE PES General Meeting (2021) Best Student Paper Runner-up, ECML-PKDD (2018) Students & Labs: He advises 7 PhD students and has graduated 11 advisees (PhD/MS/BS). His lab focuses on power systems innovation, including the SUGAR simulation framework and projects on grid cybersecurity and sustainable electrification.

Shawn Litsterمشاهده پروفایل

استاد

• Energy Conversion Technologies
• Fuel Cells
• Batteries
• +۱۵ مورد دیگر

Shawn Litster is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University, where he leads cutting-edge research in sustainable energy conversion technologies. He is affiliated with the Wilton E. Scott Institute for Energy Innovation and serves as a Scott Institute Energy Fellow, contributing to major national initiatives in hydrogen and fuel cell systems. His work is supported by significant funding from the U.S. Department of Energy (DOE), ARPA-E, and the Office of Naval Research. Education: Ph.D. in Mechanical Engineering, Stanford University (2008) Master of Applied Sciences, University of Victoria (2005) Bachelor of Engineering, University of Victoria (2004) His research focuses on micro- and nanoscale transport phenomena in electrochemical energy systems such as fuel cells, batteries, and electrolyzers. Key interests include electrochemistry, multiphase flow in porous media, microfluidics, catalytic gasification, and computational fluid dynamics . He pioneers innovations in ionomer-free electrodes, high-oxygen-permeability materials, and low-iridium anodes to improve efficiency, durability, and cost-effectiveness. His recent publications (2021–2025) reveal a strong trend toward advanced diagnostics, operando characterization, machine learning integration, and multiscale modeling of fuel cell and electrolyzer systems. These works emphasize performance optimization, degradation analysis, and material innovation for heavy-duty and transportation applications. Scientific Awards: George Tallman Ladd Research Award, Carnegie Mellon University National Science Foundation CAREER Award Lieutenant Governor’s Silver Medal, University of Victoria Best Paper/Presentation Award, The Electrochemical Society Best Paper/Presentation Award, American Society of Mechanical Engineers (ASME) Litster has secured over $50 million in research funding as a sub-awardee in DOE hydrogen projects and led a $3.2M ARPA-E OPEN 2021 project on disruptive fuel cell electrodes. He is an inventor on two U.S. patents related to fuel cell design. He advises graduate students and leads the Laboratory for Transport Phenomena in Energy Systems , where his team develops novel materials and diagnostics for next-generation energy technologies.

Gary L Millerمشاهده پروفایل

استاد

• Spectral Graph Theory
• Algorithms
• Computational Geometry
• +۴ مورد دیگر

Gary L. Miller is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. His research focuses on Spectral Graph Theory, Algorithms, Computational Geometry, and Scientific Computing. He has developed influential methods in graph partitioning, mesh generation, and numerical linear algebra solvers. Teaching includes advanced courses like Spectral Graph Theory, Algorithms, and Computational Geometry. Active in publishing, recent work involves weighted Cheeger inequalities, exact manifold metric computations, and adaptive graph sketching techniques. Projects include Orasis, 3D Meshing Software, Tumble, and Sangria. His work bridges theoretical foundations with applications in machine learning, image processing, and scientific computing. Current projects emphasize efficient graph algorithms and scalable numerical methods.

Minchen Liمشاهده پروفایل

استادیار

• Computer Graphics
• Machine Learning
• Robotics
• +۴ مورد دیگر

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.