جستجوی استاد راهنمای دانشگاهی

Chengmo Yangمشاهده پروفایل

استاد

• Embedded Systems
• Design Automation
• Computer Architecture
• +۶ مورد دیگر

Dr. Chengmo Yang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware, with a joint appointment. She received her PhD and MSc in Computer Science from the University of California, San Diego, and a BSc in Microelectronics from Peking University, China. PhD, University of California, San Diego MSc, University of California, San Diego BSc, Peking University, China Her research focuses on enhancing security, reliability, non-volatility, and energy efficiency in embedded systems, cyber-physical systems (CPS), and Internet-of-Things (IoT). She leads the Computer Architecture, Reliability, and Security (CARES) group and has authored over 70 peer-reviewed publications. Recent publications emphasize neural network accelerators, non-volatile memory (NVM) optimization, and security frameworks for embedded systems. Her work explores fault tolerance, wear leveling, and side-channel attack mitigation in NVM-based FPGAs, STT-RAM, and SSDs. National Science Foundation Career Award (2013) University of Delaware Mid-Career Faculty Excellence in Scholarship Award (2020) Best paper award at ICESS (2019) Three best paper nominations at DAC, DATE, and GLSVLSI (2019) Dr. Yang serves as Associate Editor for IEEE TCAD and ACM TODAES. She is the Diversity & Inclusion Representative for IEEE CEDA and has organized numerous conferences including Technical Program Chair for CODES-ISSS 2022. Her lab has hosted current and former students like Fanruo Meng (PhD candidate), Fateme Hosseini (Qualcomm), and Patrick Cronin (Intel), focusing on graduate-level research in hardware security and reliability.

Tianqi Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +۶ مورد دیگر

Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

Yingyan Linمشاهده پروفایل

دانشیار

• Efficient Machine Learning
• Cross-Layer Innovations
• Green AI
• +۷ مورد دیگر

Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Andrew Davisonمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• SLAM
• +۳ مورد دیگر

Professor Andrew Davison holds the position of Professor of Robot Vision at Imperial College London's Department of Computing. He leads the Dyson Robotics Laboratory and the Robot Vision Research Group, focusing on advancing SLAM (Simultaneous Localization and Mapping) and Spatial AI. His groundbreaking work includes the MonoSLAM algorithm (2003), enabling real-time 3D vision for robotics and AR/VR. Current research emphasizes scalable, semantic-rich Spatial AI systems, as outlined in his FutureMapping papers (2018–2019). Education: BA in Physics (Oxford, 1994), D.Phil. (Oxford, 1998). Postdoctoral work at AIST, Japan (1998–2000), followed by a lectureship at Imperial (2002–present). Industrial collaborations include SLAMcore, a Spatial AI startup, and Dyson Robotics Lab. Over 18 PhD students supervised, many now leading roles at Meta, NVIDIA, SLAMcore, and academia. Notable contributions include DTAM, KinectFusion, and Event Camera SLAM. Recognized for software tools like SceneLib and contributions to robotics benchmarks (SLAMBench). Active on Twitter (@AjdDavison) for research updates.

Chris Donahueمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Music Technology
• +۵ مورد دیگر

Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .

Hyesoon Kimمشاهده پروفایل

استاد

• Computer Architecture
• GPU
• Compilers and Runtime Systems
• +۳ مورد دیگر

Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).

Tushar Krishnaمشاهده پروفایل

دانشیار

• Computer Architecture
• Interconnection Networks
• Networks-on-Chip
• +۴ مورد دیگر

Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, with a courtesy appointment in the School of Computer Science. He earned his PhD in Electrical Engineering and Computer Science from MIT in 2014, an MSE in Electrical Engineering from Princeton University in 2009, and a B.Tech in Electrical Engineering from IIT Delhi in 2007. His research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems, with a focus on optimizing data movement in modern computing platforms. His work is funded by NSF, DARPA, IARPA, SRC, Department of Energy, Intel, Google, Meta, Qualcomm, and TSMC. His papers have been cited over 17,000 times, with three receiving IEEE Micro's Top Picks recognition, one earning an honorable mention, and four winning best paper awards. Dr. Krishna leads the Synergy Lab at Georgia Tech and has developed several influential tools including ASTRA-sim for distributed AI/ML training, MAESTRO and SCALE-sim for accelerator design space exploration, and Garnet2.0 for NoC simulation. His recent work focuses on large language model acceleration, distributed training systems, and neuro-symbolic AI architectures. He has received numerous teaching and research awards including induction into the HPCA Hall of Fame (2022), the Class of 1940 Teaching Effectiveness Award (2018), and the Roger P. Webb Outstanding Mid-career Faculty Award (2024). HPCA Hall of Fame Inductee (2022) Roger P. Webb Outstanding Mid-career Faculty Award (2024) Richard M. Bass/Eta Kappa Nu Outstanding Junior Teacher Award (2023) Roger P. Webb Outstanding Junior Faculty Award (2021) Class of 1940 Course Survey Teaching Effectiveness Award (2018) Dr. Krishna currently serves as Associate Director for the Center for Research into Novel Computing Hierarchies (CRNCH) and co-chair of the Chakra Execution Traces and Benchmarks Working Group. He has held the ON Semiconductor (Endowed) Junior Professorship at Georgia Tech (2019-2021) and has been a visiting professor at MIT EECS, Harvard University CS, and a researcher at Intel's VSSAD group.

Celestine Mendler-Dünnerمشاهده پروفایل

استاد

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +۶ مورد دیگر

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

Kenneth P. Birmanمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Machine Learning Systems
• +۵ مورد دیگر

Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.

Kian-Lee Tanمشاهده پروفایل

استاد

• Query Processing
• Big Data Analytics
• Database Systems
• +۵ مورد دیگر

Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.

Priyanka Rainaمشاهده پروفایل

استادیار

• Domain-Specific Architectures
• Hardware/Software Co-design
• Energy-Efficient Computing
• +۳ مورد دیگر

Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. She leads the Stanford Accelerate research group, focusing on domain-specific hardware architectures and agile hardware-software co-design. Her work emphasizes high-performance, energy-efficient accelerators for emerging technologies. Education: B.Tech. from IIT Delhi (2011), M.S. and Ph.D. from MIT (2013/2018). Postdoctoral experience includes NVIDIA Research (2018) and Amazon Visiting Academic (2023–present). Awards include the Sloan Research Fellowship (2024), NSF CAREER Award (2023), and Terman Faculty Fellowship (2018). Research Interests: Domain-specific architectures, near-memory computing, design productivity, and machine learning acceleration. Her group develops frameworks like AHA (Agile Hardware) for efficient accelerator design and compilers. Awards: Over 10 major awards, including best paper recognitions at VLSI, MICRO, and JSSC. Active roles as Associate Editor for IEEE JSSC and Program Chair for IEEE Hot Chips (2020). Advising & Teaching: Supervises multiple PhD and MS students in hardware design, CGRAs, and ML accelerators. Teaches VLSI design courses (EE271/272/372) and oversees independent studies in embedded systems and chip design. Labs & Collaborations: Affiliated with Stanford PORTAL Center, AHA Center, and SystemX Alliance. Projects include MINOTAUR (edge AI accelerator), Amber (CGRA-based SoC), and EMBER (RRAM macros).

Sarita V Adveمشاهده پروفایل

استاد

• Computer Architecture
• Extended Reality Systems
• Heterogeneous Computing
• +۴ مورد دیگر

Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.

Bahar Asgariمشاهده پروفایل

استادیار

• Computer Architecture
• Domain-Specific Architecture Design
• Near Memory Processing
• +۳ مورد دیگر

Bahar Asgari is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in CS, UMIACS, and ECE, and directs the Computer Architecture and Systems Lab (CASL). Her research focuses on domain-specific architecture design, near memory processing, and reconfigurable computing. Before joining UMD, she worked at Google, contributing to system optimization and memory utilization strategies. Education: Ph.D., Georgia Institute of Technology, 2021. Research Interests: Developing energy-efficient architectures for sparse problems, scientific computing acceleration, FPGA optimizations, and reconfigurable hardware systems. Her work addresses challenges in memory efficiency, parallelization, and hardware-software co-design across domains like machine learning and IoT. Key Awards: 2024 UMD Excellence in Teaching Award 2023 DOE Early Career Award Advising & Grants: Supervises 9 graduate students and leads projects sponsored by NSF, DOE, and industry partnerships. Her lab explores systolic arrays, near-data processing, and fault-tolerant computing. Labs/Teams: Directs CASL, which builds next-generation computing systems through hardware innovation and algorithm-architecture co-design.

Xupeng Miaoمشاهده پروفایل

استادیار مهمان

• Machine Learning Systems
• Distributed Computing
• Data Management
• +۵ مورد دیگر

Xupeng Miao is a Visiting Assistant Professor in the Department of Computer Science at Purdue University, holding the Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professorship. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University's Catalyst Group under Professors Zhihao Jia and Tianqi Chen. He earned his Ph.D. in Computer Science from Peking University (2022) and a Bachelor’s degree from Northeastern University. His research focuses on machine learning systems, distributed computing, and data management, with notable contributions to systems like Hetu (a distributed deep learning framework) and innovations in large language model (LLM) serving (e.g., SpotServe, SpecInfer). He has received awards including the 2024 WAIC Yunfan Award and an Outstanding Paper Award at ACL 2024. Current projects emphasize efficient systems for generative AI, including optimizing LLM training and inference on heterogeneous hardware, fault-tolerant distributed training, and scalable graph-based machine learning. He teaches CS 59200-MLS: Machine Learning Systems at Purdue and actively mentors students in PhD/MS programs and internships. Key achievements include NVIDIA Academic Awards, IEEE Micro Top Picks recognition, and leadership in international conference roles (e.g., Artifact Evaluation Co-Chair for KDD 2025 and MLSys 2025). His work bridges system design, algorithmic innovation, and hardware-aware optimizations to advance scalable AI infrastructure.