جست‌وجوی استاد و پژوهشگر

Hui Luمشاهده پروفایل

استادیار

• Operating Systems
• Systems Virtualization
• Cloud Computing
• +۶ مورد دیگر

Dr. Hui Lu is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2023. Prior to joining UTA, he was an Assistant Professor at SUNY Binghamton from 2017 to 2023. His academic journey includes a Ph.D. in Computer Science from Purdue University (2017), and Master’s and Bachelor’s degrees in Electronic Engineering from Shanghai Jiao Tong University. Ph.D., Computer Science, Purdue University, 2017 M.S., Electronic Engineering, Shanghai Jiao Tong University, 2009 B.S., Electronic Engineering, Shanghai Jiao Tong University, 2006 Dr. Lu's research centers on systems software with a focus on operating systems, virtualization, cloud computing, file and storage systems, and computer networks. His work emphasizes performance optimization and security in cloud-native environments. He has collaborated with leading industrial research labs including HPE Labs, IBM Research, Microsoft Research, AT&T Labs, and NEC Labs. His recent publications span top-tier venues such as OSDI, SOSP, USENIX ATC, and VLDB. The article trends reflect a strong emphasis on secure container technologies, memory tiering, packet processing optimization in virtualized networks, and efficient cloud storage systems. His work increasingly integrates hardware-aware optimizations and lightweight security mechanisms. NSF CAREER Award (2023) UT System Rising STARs Award (2023) Summer Faculty Fellowship, Air Force Research Lab (2019) Dr. Lu has successfully advised multiple Ph.D. students, including Jiaxin Lei, who is now an Assistant Professor at Kean University. His research is supported by major grants from the National Science Foundation (NSF) and the Air Force Research Lab (AFRL), focusing on secure containers, non-volatile memory management, and cloud-native virtualization. He has served as Principal Investigator (PI) on multiple funded projects, demonstrating strong leadership in research and innovation. He is actively involved in teaching core courses such as Operating Systems and advanced topics in systems and architecture. He mentors a growing group of Ph.D. students and welcomes motivated individuals to join his research group.

Jia Raoمشاهده پروفایل

دانشیار

• Operating Systems
• Distributed Systems
• Parallel Computing
• +۱۳ مورد دیگر

Dr. Jia Rao is an Associate Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, College of Engineering. He previously served as an Assistant Professor at the University of Colorado, Colorado Springs from 2012 to 2016. His research spans operating systems, distributed and parallel computing, cloud computing, virtualization, and machine learning. Education: Ph.D., Computer Engineering, Wayne State University, 2011 M.S., Computer Science, Wuhan University, 2006 B.S., Computer Science, Wuhan University, 2004 Dr. Rao's research focuses on building adaptive, scalable, and efficient computer systems for cloud and data center environments. His interests include resource management, performance modeling, adaptive scheduling, and quality-of-service (QoS) guarantees in virtualized and containerized systems. He combines machine learning and feedback control techniques with low-level system design to improve efficiency, fairness, and predictability in heterogeneous and multi-tenant environments. An analysis of his recent publications reveals a strong trend toward memory and resource management innovations in cloud-native systems. His work explores tiered memory architectures, secure container deployment, preemptive multitasking for deep learning, and efficient packet processing in container networks. These efforts reflect a consistent focus on optimizing system-level performance, security, and scalability in modern data centers. Scientific Awards: NSF CAREER Award (2019) Best Paper Award, APSys (2016) Best Paper Award, ICAC (2013) Best Paper Nomination, HPCA (2013) Best Paper Nomination, HPDC (2013) Best Paper Award, Middleware (2021) Researcher of the Year, UCCS (2014) Dr. Rao actively advises students and serves on dissertation and thesis committees for numerous Ph.D. and Master’s candidates. He leads federally funded research projects supported by the National Science Foundation, including a major CAREER grant on virtualized architectures and collaborative big data initiatives. His research has been sponsored by NSF, IEEE, and Intel Corporation, reflecting strong industry and academic collaboration. He leads and contributes to major research labs and teams focused on cloud systems, operating systems, and performance optimization. His team has produced high-impact work in top-tier venues such as OSDI, SOSP, ATC, EuroSys, and ICDCS. Current and future work includes next-generation memory architectures using CXL, intelligent resource provisioning, and resilient container networking.

Al Amjad Tawfiq Isstaifمشاهده پروفایل

پژوهشگر

• Cloud and Edge Computing
• Microservices and Serverless
• Operating Systems and Middleware
• +۵ مورد دیگر

Al Amjad Tawfiq Isstaif is a Research Associate in the Systems Research Group (SRG) at the Department of Computer Science and Technology (also known as "The Computer Lab") at the University of Cambridge, working with Professor Richard Mortier. He is a member of Clare College. In 2018, he was awarded the Said Foundation Cambridge Scholarship to study the MPhil in Advanced Computer Science (ACS) at the Computer Lab. Before moving to Cambridge, he co-founded several projects for empowering engineering students in Syria, including the award-winning education enterprise: Wikilogia. His research interests include: Cloud and Edge Computing Microservices and Serverless Operating Systems and Middleware Networked and Distributed Systems Machine Learning Systems Human-Centred Computing Machine Learning and Artificial Intelligence Systems and Networking Isstaif's primary research focus is on building self-scaling network services on top of next generation infrastructure (IoT/5G/edge), leveraging lightweight virtualization techniques and distributed tracing. His work falls under the Compute First Networking research project. He has deep passion for social entrepreneurship and the role of digital infrastructure in addressing development and sustainability challenges across the globe. His technical expertise bridges the IT and telecom worlds, with emphasis on cloud-native and open source movements shaping future infrastructure. His recent publications demonstrate a strong focus on optimizing serverless computing environments, with particular attention to scheduling algorithms, resource management, and latency optimization in Linux-based systems. The trajectory of his work shows progression from database performance modeling to cutting-edge research in edge computing and network infrastructure. Notable awards and recognitions: Said Foundation Cambridge Scholarship (2018) Co-founded Wikilogia, an award-winning education enterprise for engineering students in Syria Isstaif has teaching experience in Operating Systems (LT 2021) and maintains an active research profile within the Systems Research Group. His work connects theoretical computer science with practical applications in cloud infrastructure, particularly focusing on how these technologies can serve broader societal needs in developing contexts. He is located in Room FN07 at the William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD.

Jovan Stojkovicمشاهده پروفایل

استادیار

• Cloud Computing
• Datacenters
• Cloud-Native Workloads
• +۷ مورد دیگر

Jovan Stojkovic is an incoming Assistant Professor at the Department of Computer Science at the University of Texas at Austin, set to join in Fall 2026. Prior to his appointment at UT Austin, he will spend a year at Meta working with the AI and Systems Co-design group. His research focuses on cloud computing and datacenters, with particular emphasis on cloud-native workloads and machine learning inference. Education: PhD in Computer Science from the University of Illinois at Urbana-Champaign, advised by Professor Josep Torrellas Undergraduate studies at the School of Electrical Engineering, University of Belgrade, Serbia, where he was recognized as the best student of the Computer Engineering and Information Theory Department every year from 2017-2020 Research Interests: Jovan's research focuses on cloud computing and datacenters , with two primary domains: Cloud-native workloads , such as microservices and serverless computing. He investigates how to co-design novel hardware platforms and software systems that deliver orders-of-magnitude improvements in performance, energy efficiency, and resource utilization for these emerging workloads. Machine Learning (ML) inference , particularly large language models (LLMs). His work addresses the challenges of ML inference through smart scheduling, workload placement, and system-level configuration tuning to reduce energy, power, and thermal overheads while maintaining performance and accuracy guarantees. Publication Trends: Jovan's publications demonstrate a strong focus on optimizing cloud infrastructure for emerging workloads. His research spans across serverless computing, microservices, and large language model inference. A clear trend emerges in his work: addressing the performance, energy efficiency, and resource utilization challenges of modern cloud workloads through innovative hardware-software co-design approaches. His most recent work shows increasing focus on LLM inference optimization, particularly in the areas of thermal management, power efficiency, and scheduling for many-adapter environments. Awards and Honors: HPCA Best Paper Award (2025) IEEE MICRO Top Picks Honorable Mention (2024) 6 patents with IBM and Microsoft on: Serverless systems, Processor overclocking in the cloud, and Energy-efficient LLM inference W. J. Poppelbaum Memorial Award (2025) for hardware and architecture innovation Mavis Future Faculty Fellowship (2024–2025) Invited to present at 11th Heidelberg Laureate Forum (2024) Kenichi Miura Award (2022) for excellence in High Performance Computing Multiple student travel grants to ISCA, MICRO, ASPLOS, and HPCA Advising and Grants: Jovan is actively seeking prospective PhD students for his research group at UT Austin. His research has been supported through collaborations with major tech companies including IBM, Microsoft, and Meta. His six patents with IBM and Microsoft demonstrate the practical impact of his research in serverless systems, processor overclocking, and energy-efficient LLM inference. His work on serverless computing (MXFaaS, EcoFaaS) and LLM inference optimization has received significant recognition in top-tier computer architecture conferences. Research Groups: During his PhD at UIUC, Jovan worked with Professor Josep Torrellas on cloud infrastructure research. He has collaborated extensively with researchers at IBM Research (particularly Hubertus Franke) and Microsoft (particularly Íñigo Goiri and Ricardo Bianchini). His upcoming position at UT Austin will establish his independent research group focused on cloud computing and datacenter systems. His year at Meta working with the AI and Systems Co-design group will further strengthen his expertise in AI infrastructure.

• Spatial Databases
• Geoinformatics
• Internet of Things (IoT)
• +۵ مورد دیگر

Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.

Charith Mendisمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• Machine Learning for Systems
• +۳ مورد دیگر

Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.

Eric Gamessمشاهده پروفایل

دانشیار

• Network Performance Evaluation
• IPv6
• Cybersecurity
• +۴ مورد دیگر

Eric Gamess is an Associate Professor in the Department of Mathematical, Computing, and Information Sciences at Jacksonville State University (JSU), Alabama. He holds a Ph.D. in Computer Science from the Central University of Venezuela (2000), an M.Sc. in Industrial Computing from INSA Toulouse (1989), and an Engineering Degree in Automatics, Computer Science, and Electronics from the same institution (1989). His academic career spans roles at universities in South America and the U.S., including Universidad del Valle (Colombia) and the University of Puerto Rico. Dr. Gamess specializes in network performance evaluation, cybersecurity, vehicular networking, and IoT. He has authored over 80 publications, edited 27 conference proceedings, and directs the Venezuelan Journal of Computing. His leadership roles include Vice-President of the Venezuelan Society of Computing and steering committee member of the ACM Southeast Conference. At JSU, he leads the Center of Academic Excellence in Cyber Defense Education (CAE-CD) and coordinates the Master of Science in Computer Systems and Software Design (CSSD) program. His research emphasizes network simulation, IPv6, and embedded systems performance (e.g., Raspberry Pi). Recent work explores containerization technologies, MQTT resilience, and IoT protocol optimizations. He teaches courses ranging from programming fundamentals to advanced cybersecurity and networking.

• Database Systems
• Big Data Analytics
• Cloud Database Systems
• +۵ مورد دیگر

Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.

Christina Delimitrouمشاهده پروفایل

استادیار

• Computer Architecture
• Computer Systems
• Datacenter Resource Efficiency
• +۷ مورد دیگر

Christina Delimitrou is an Assistant Professor in the Electrical and Computer Engineering Department at Cornell University, where she leads the SAIL research group and is a member of the Computer Systems Laboratory (CSL). She holds the John and Norma Balen Sesquicentennial Faculty Fellowship and will join MIT EECS and CSAIL as a professor starting September 2022. Dr. Delimitrou earned her Ph.D. and M.S. in Electrical Engineering from Stanford University, working with Christos Kozyrakis, and completed her undergraduate studies at the National Technical University of Athens. Her research focuses on computer architecture and systems, particularly on improving resource efficiency in large-scale datacenters through QoS-aware scheduling, resource management techniques, efficient server architectures, distributed performance debugging, and cloud security. Her publication record demonstrates consistent high-impact research in datacenter systems, with recurring themes in microservices architecture, machine learning for systems, and QoS-aware resource management. Her work bridges theoretical computer architecture with practical cloud computing challenges, resulting in multiple IEEE Micro TopPicks awards and best paper recognitions at major architecture conferences. Dr. Delimitrou has received numerous prestigious awards including: Sloan Research Fellowship in Computer Science NSF CAREER Award Microsoft Research Faculty Fellowship Intel Rising Star Award 2020 IEEE TCCA Young Computer Architect Award Multiple Google Faculty Research Awards Facebook Faculty Research Award Cornell Excellence in Research and Teaching Awards She actively mentors PhD, MS, and undergraduate students in the SAIL research group, focusing on cloud computing and computer architecture. Her research has been supported by significant grants from NSF, Google, Microsoft, Facebook, and Intel. She teaches ECE5710: Datacenter Computing at Cornell, exploring hardware, systems software, and distributed systems technology in modern datacenters. The SAIL research group develops innovative solutions for cloud infrastructure challenges, spanning from hardware acceleration to machine learning-driven resource management, with strong emphasis on practical implementation and real-world impact.

• Edge Computing
• Distributed Systems
• Container Networking
• +۹ مورد دیگر

Tom Goethals is an FWO Junior Postdoctoral Fellow affiliated with the Department of Information Technology at Ghent University , where he conducts research in edge computing, container networking, and decentralized systems. Current role: IMEC Postdoctoral Researcher Research focus: Secure and intelligent edge service management for decentralized IoT applications His work explores edge intelligence , orchestration frameworks , and AI-driven network optimization , with trends in lightweight virtualization (e.g., Feather), Kubernetes adaptation for edge environments, and intent-based decentralized orchestration. Publications emphasize scalability, security, and energy efficiency in fog-native workflows. Scientific Awards : FWO Junior Postdoctoral Fellowship He collaborates with researchers like Bruno Volckaert and Filip De Turck on projects funded by the Research Foundation - Flanders (FWO) , including grants for edge container networking and decentralized learning frameworks. His projects align with Ghent University’s focus on smart city infrastructure and edge-to-cloud systems.

Christina Delimitrouمشاهده پروفایل

دانشیار

• Computer Architecture
• Cloud Computing
• Systems & Networking
• +۶ مورد دیگر

Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.

• Software Engineering
• Energy-Efficient Systems
• Software Architecture
• +۴ مورد دیگر

Ivano Malavolta is an Associate Professor in Software Engineering at the University of Urbino , focusing on energy-efficient software, software architecture, and model-driven engineering (MDE). His research bridges robotics, mobile systems, and sustainability, emphasizing empirical methods and industrial applicability. Key Research Areas: Energy-efficient software, microservices, robotics systems, collaborative modeling, mobile application performance. Affiliations: Department of Software Engineering, University of Urbino. His recent publications highlight empirical studies on energy consumption patterns in robotics, mobile apps, and AI systems. He has contributed to frameworks like the Green Software Measurement Model (GSMM) and tools for architectural technical debt analysis. Notably, he has co-authored 15+ peer-reviewed articles in venues such as Journal of Systems and Software , Information and Software Technology , and ACM/IEEE conferences . His work often involves cross-platform comparisons (e.g., Electron vs. Web, Pandas vs. Polars) and systematic mappings of software engineering practices.

Muhammad Usmanمشاهده پروفایل

مدرس ارشد

• Cloud Computing
• Edge Computing
• Distributed Systems
• +۷ مورد دیگر

Dr. Muhammad Usman is a Senior Lecturer at the Department of Mathematics and Computer Science, Karlstad University, Sweden. His research focuses on cloud and edge computing, distributed systems, performance observability, DevOps automation, and networked systems. He holds a B.S. from NUST (Pakistan), and integrated M.S./Ph.D. from GIST (South Korea). He teaches master's-level courses in Distributed Systems and Cloud Computing. His research interests include optimizing container orchestration for edge-IoT workloads, developing AI-driven frameworks for industrial IoT (e.g., AIDA), and enhancing observability of distributed systems through frameworks like DESK and SmartX. His work often addresses challenges in resource efficiency, scalability, and fault detection in edge and cloud environments. Notable contributions include benchmarking lightweight container orchestration platforms, designing cost-effective testbeds for DevSecOps, and creating visualization frameworks for SDN-enabled clouds. His publications span topics from IIoT lifecycle management to intent-based network control. Usman actively collaborates internationally, with projects involving institutions like GIST, Aalto University, and the IEEE community. His work bridges theoretical research and practical applications in edge computing and cloud-native systems.

Adlen KSENTINIمشاهده پروفایل

استاد

• Mobile and Wireless Networks
• Software Defined Networking (SDN)
• Mobile Edge Computing (MEC)
• +۷ مورد دیگر

Adlen KSENTINI is a Professor at EURECOM's Communication Systems department, specializing in advanced networking technologies. His research focuses on Mobile and Wireless Networks, Software Defined Networking (SDN), Mobile Edge Computing (MEC), Network Function Virtualization (NFV), and Content Delivery Networks (CDN), with an emphasis on performance evaluation and network virtualization. He has contributed to projects like AC3 and 6G-BRICKS, exploring cloud-edge continuum integration and 6G infrastructure. Key research interests include virtualized mobile core networks, carrier cloud systems, and AI-driven network management. He has received Best Paper Awards at IEEE WCNC 2018 and IWCMC 2016 for works on network slicing and LTE modeling accuracy. His work often integrates machine learning for optimization, sustainability, and security in 5G/6G networks. Distinctions: Two Best Paper Awards Labs/Teams: Involved in EU-funded projects like AC3 and 6G-BRICKS Grants: Not explicitly listed, but active in collaborative research initiatives

• Information Management
• Data Security
• Scientific Databases
• +۵ مورد دیگر

Marianne Winslett is a Professor at the University of Illinois' Siebel School of Computing and Data Science, affiliated with the Department of Computer Science since 1987. Her research focuses on data security, information management, and privacy in cyber-physical systems. She co-led the TrustBuilder project, advancing access control and authentication in open computing environments, and directed the Advanced Digital Sciences Center (ADSC) in Singapore from 2009–2013, addressing challenges in data analytics and smart grids. Her work includes pioneering methods to ensure privacy in biomedical data analysis. Education: Earned her doctorate in Computer Science from Stanford University and worked at Bell Labs before joining Illinois. Awards: ACM Fellow (2006), NSF Presidential Young Investigator (1989), University Scholar, and Stanley H. Pierce Award for advising. She has supervised 24 PhD theses and mentored numerous graduate students, particularly supporting female scholars. Research Interests Secure data management in distributed systems Privacy-preserving techniques for biomedical data Adversarial attack detection in cyber-physical systems like smart grids Elastic resource scheduling in cloud environments Query optimization under differential privacy constraints Key Contributions Developed frameworks for self-supervised learning in smart grid cybersecurity Pioneered causal mechanism transfer networks for mechanical system domain adaptation Advanced auto-scaling strategies for real-time stream processing (DRS/Elasticutor systems) Labs & Teams Former Director of the Advanced Digital Sciences Center (ADSC), a University of Illinois research outpost in Singapore focusing on data analytics and IoT applications.