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Shweta Shinde is an Assistant Professor in the Department of Computer Science at ETH Zurich where she leads the Secure & Trustworthy Systems (SECTRS) research group. She is also a member of the Institute of Information Security and the ZISC Center at ETH Zurich. Her work bridges theoretical foundations with practical implementations in hardware and system security. Dr. Shinde's research focuses on the intersection of trusted computing, system security, and program analysis. Her group investigates both foundational aspects of security to protect phones, servers, and accelerators, as well as practical aspects of realizing these solutions through large-scale system building. Her work spans hardware security primitives, trusted execution environments, confidential computing, and secure system design across multiple platforms including mobile devices, cloud infrastructure, and blockchain systems. Her publication record demonstrates significant impact in the field, with numerous papers in top security venues including IEEE S&P, USENIX Security, CCS, and NDSS. Her research has uncovered critical vulnerabilities in major hardware security technologies like Intel SGX and AMD SEV-SNP while also proposing novel frameworks for building more secure systems. The work spans multiple domains including hardware security, cloud security, mobile security, and blockchain security, with a consistent thread of identifying and addressing fundamental security challenges in trusted computing. Among her notable achievements are multiple best paper awards including at SysTEX 2025 and nominations at DATE 2025, a Distinguished Paper Award at IEEE S&P 2024, and recognition as a Most Innovative Research Pwnie Award Nominee in 2024. Her work on WeSee received particular attention for breaking AMD SEV-SNP through novel interrupt-based attacks. Dr. Shinde actively mentors PhD students including Mark Kuhne, Supraja Sridhara, Mélisande Zonta-Roudes, Benedict Schlüter, Andrin Bertschi, and others. She teaches courses on Systems Security, Information Security Lab, Seminar on Systems Security, and Current Topics in Information Security at ETH Zurich. Her service to the community includes extensive program committee work for major security conferences including Oakland (S&P), NDSS, USENIX Security, and CCS, where she has served as track co-chair and program co-chair for several workshops.

• Trusted Computing
• System Security
• Hardware Security
• +4 more

Asaf Cidon is an Associate Professor at Columbia University, jointly affiliated with the Department of Electrical Engineering and Computer Science, and a member of the Data Science Institute. His research focuses on software systems , storage , large-scale machine learning , and cybersecurity . Stanford University - PhD in Electrical Engineering Stanford University - MS in Electrical Engineering Technion - BS in Computer and Software Engineering His work in distributed storage systems has been commercialized by companies such as Facebook, Tibco, and Rubrik. He has led projects like Sentinel and Forensics during his industry career. Recent publications highlight advancements in software-based radiation protection (ASPLOS'26), PCIe pooling with CXL (HotOS'25), and AI phishing detection (IMC'25). These reflect his expertise in system architecture , security , and networking . Scientific recognitions include best paper awards at OSDI, Usenix Security, CIDR, and ATC, along with NSF CAREER and ARO Young Investigator Awards . His papers Cookie Monster (SOSP'24) and Chablis (CIDR'24) received notable accolades. He has mentored numerous PhD and Master's students , including Edward Guo, Harry Wang, and Teng Jiang, many of whom now hold roles at Google, Meta, Amazon, and academic institutions. His lab at Columbia is actively recruiting CS and EE PhD students. Prior to Columbia, he founded and led the startup Sookasa to acquisition and served as Senior Vice President of Email Protection at Barracuda Networks , managing a $200M business with 100 engineers.

• Software Systems
• Storage Systems
• Machine Learning
• +4 more

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.

• Query Processing
• Big Data Analytics
• Database Systems
• +5 more

Shweta Shinde is an Assistant Professor at ETH Zürich's Department of Computer Science. She leads the Secure & Trustworthy Systems (SECTRS) group and is affiliated with the Institute of Information Security and the ZISC Center. Her research focuses on foundational and practical aspects of trusted computing, system security, and program analysis to protect devices ranging from smartphones to cloud servers. Key areas include AMD SEV-SNP, Arm CCA, Intel SGX, and hardware-software co-design for security. Dr. Shinde has made significant contributions to confidential computing, including frameworks like OpenCCA and tools for analyzing hardware vulnerabilities. Her work has been recognized with Best Paper Awards and Distinguished Paper Awards at top venues. She advises a group of PhD students, including Mark Kuhne, Supraja Sridhara, and Benedict Schlüter. Her service includes program committee roles at IEEE Security & Privacy, USENIX Security, and as Track Co-Chair for AsiaCCS 2026.

• Trusted Computing
• System Security
• Program Analysis
• +4 more

Jian Huang is an Associate Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), holding a 100% appointment since August 2024. He is also an affiliated Associate Professor in Computer Science and serves as the Y. T. Lo Faculty Fellow in ECE. Huang leads the Systems Platform Research Group and received his Ph.D. in Computer Science from Georgia Institute of Technology in August 2017, where his dissertation earned the College of Computing Dissertation Award. His research focuses on Computer Architecture, Memory and Storage Systems, AI Infrastructure, Distributed Systems, and Systems Security . Huang has pioneered work in sustainable AI infrastructures, modular data centers, neural processing unit virtualization, and ransomware-aware storage systems. His research has been featured in top-tier conferences including ISCA, MICRO, ASPLOS, OSDI, SOSP, and USENIX ATC, with over 50 publications and multiple patents issued worldwide. Huang's research has significant industry impact, with some work transferred into products and featured in popular media outlets including ACM CACM, TechXplore, and Science Daily. His work on non-volatile memory and storage systems has received extensive coverage, with some projects generating over 50 media reports. Among his notable achievements are the ACM SIGMICRO Early Career Award, NSF CAREER Award, IEEE Micro Top Picks (three times), USENIX Best Paper Award, and the MICRO Best Paper Runner Up in 2024. He has secured over $4.01M in grants ($11.12M with collaborators) from NSF, DARPA, Army Research Office, and industry partners. ACM SIGMICRO Early Career Award NSF CAREER Award IEEE Micro Top Picks (three times) USENIX Best Paper Award MICRO Best Paper Runner Up (2024) Google Faculty Research Award NetApp Faculty Fellowship Award Huang actively contributes to the academic community as program co-chair of NVMW'23 and organizer of HotInfra'23. He currently advises 12 Ph.D. students, 1 master student, and multiple undergraduates. His teaching innovations include developing ECE522: Emerging Memory/Storage System and revising ECE511: Computer Architecture with cutting-edge content.

• Computer Architecture
• Memory and Storage Systems
• AI Infrastructure
• +5 more

Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as the Director of the 3D Systems Packaging Research Center. His research focuses on heterogeneous integration of microsystems, including 2.5D and 3D ICs and packaging technologies, with significant contributions to advanced cooling systems, electrical and photonic interconnects, and biosensor integration with CMOS. Dr. Bakir's research interests span heterogeneous microsystem design and integration, advanced cooling and power delivery for emerging architectures, electrical and photonic interconnect technologies, biosensor technologies, and nanofabrication. His work addresses critical challenges in next-generation electronics, enabling polylithic integration that concatenates heterogeneous ICs of various functionalities while mimicking monolithic-like densities. His research particularly focuses on co-design of thermal technologies, power delivery networks, and signaling networks for silicon nanoelectronic systems. His recent publications demonstrate strong trends in fused-silica stitch-chip technology for heterogeneous integration, with particular emphasis on RF and mm-wave applications, power delivery for AI accelerators, and thermal management solutions. His work bridges electrical engineering, materials science, and thermal management to solve critical bottlenecks in computing performance and efficiency. 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Society Outstanding Young Engineer Award 2012 National Academy of Engineering Frontiers of Engineering Symposium Invited Participant 2015 IEEE CPMT Society Distinguished Lecturer 2014 Best Paper of the IEEE Transactions on Components Packaging and Manufacturing Technology More than 25 conference and student paper awards Twelve issued US Patents Dr. Bakir leads the Integrated 3D Systems Lab (I3DS) at Georgia Tech, which is actively researching advanced packaging, interconnects, electrical and thermal design, and system integration. His team has received significant recognition for their work, including multiple best paper awards from major conferences like ECTC, IITC, and CICC. The lab is currently seeking postdoctoral researchers and research faculty to advance next-generation electronics through collaborative research. His lab focuses on enabling the next phase of Moore's Law through polylithic integration, which concatenates heterogeneous ICs of various functionalities (digital, analog, photonic, and mm-wave) using advanced off-chip '2.5D' and '3D' heterogeneous interconnects and packaging. This work impacts applications in high-performance computing, machine learning, edge intelligence, autonomous vehicles, augmented/virtual reality, and healthcare.

• Heterogeneous Integration
• 3D IC Packaging
• Advanced Cooling Systems
• +7 more

Babak Falsafi is a Full Professor at the School of Computer and Communication Sciences (IC) at EPFL, leading the Parallel Systems Architecture Laboratory (PARSA). He is a renowned expert in computer architecture, datacenter systems, and cloud-native server design. His research focuses on post-Moore era computing, emphasizing heterogeneous architectures, energy efficiency, and scalable IT infrastructure. Falsafi is the founder of EcoCloud, an EC-sponsored industrial-academic consortium investigating sustainable information technology. He holds ACM and IEEE fellowships, a Sloan Research Fellowship, and has contributed to major projects like Optimus Prime (data transformation acceleration), AstriFlash (flash-based online service systems), and Midgard (virtual memory re-design). His work spans hardware-software co-design, memory systems, and security. Falsafi advises numerous PhD students and collaborates with industry partners such as Google and Cavium. Key achievements include pioneering scalable multiprocessor architectures, snoop filters in IBM BlueGene, and spatial memory streaming in ARM cores. His lab develops open-source tools like QFlex for server simulation. He frequently presents at top conferences (HPCA, ISCA, MICRO) and chairs workshops on post-Moore infrastructure. Teaching roles include leading courses in computer architecture and parallel systems across multiple EPFL departments (SIN, EDIC, SSC, SMA). His work addresses datacenter challenges like the 'data tax' and mitigating latency through specialized accelerators.

• Computer Architecture
• Datacenter Systems
• Cloud-Native Server Architecture
• +7 more

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +7 more

John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.

• Robotics
• Nonlinear control theory
• Mechanical systems control
• +6 more

Morteza Fayazi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah, with an adjunct position in the Kahlert School of Computing. His research focuses on Electronic Design Automation (EDA), applying machine learning to automate analog and mixed-signal circuit design, and developing high-performance computing systems. He holds a B.Sc. from Sharif University of Technology, and M.S.E./Ph.D. degrees from the University of Michigan. His research interests include AI-driven EDA, RF/circuit automation, and energy-efficient processors. Key achievements include the MEDAL lab’s work on terahertz radars, systolic-array processors (e.g., DAP and Versa), and open-source frameworks like FASCINET and Tablext. He has received awards such as the 2024 College of Engineering Dean’s ETR Fund and the 2017 Outstanding Undergraduate Thesis Award. Teaching responsibilities include multiple iterations of the Digital System Design course (ECE/CS 3700). His work spans over 15 peer-reviewed articles in IEEE Transactions, ACM, and top conferences like ICCAD and VLSI-SOC, emphasizing automation, efficiency, and AI integration in hardware design.

• Electronic Design Automation (EDA)
• Machine Learning (ML) in Analog Circuit Design
• System-on-Chip (SoC) Design Automation
• +3 more

Yannis Chronis is an incoming Assistant Professor at the Department of Computer Science , ETH Zurich (starting August 2025), where he will join the ETH Systems Group . Prior to this, he spent 3 years as a Systems Researcher at Google's Systems Research Group in Sunnyvale, USA. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison (advised by Prof. Jignesh Patel) and a Bachelor's/Master's from the University of Athens, Greece (advised by Prof. Yannis Ioannidis). His research focuses on optimizing databases and data processing for modern hardware through software-hardware co-design. Key areas include database efficiency in memory-centric architectures, learned query optimizers, and cloud resource management. His work is supported by a Facebook Fellowship and has been recognized with the EDBT 2016 Medal for best paper. Teaching: Taught CS 564 - Database Management Systems at UW-Madison (Spring 2022). Service: Served on program committees for SIGMOD, VLDB, CIDR, and others, and chairs the CIDR Proceedings Committee. Key Publications: His recent work includes studies on memory-centric computing for databases, cardinality estimation benchmarks, and adaptive query processing techniques.

• Database Systems
• Hardware-Software Co-design
• Data Processing Efficiency
• +3 more

Jingtong Hu is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh, where he also holds the William Kepler Whiteford Faculty Fellowship. His research focuses on Cyber-Physical Systems and Infrastructure Security, with significant contributions to embedded systems, non-volatile memory architectures, and hardware/software co-design for energy-constrained environments. Dr. Hu received his PhD from the University of Texas at Dallas (2007-2013) and his Bachelor of Engineering from Shandong University (2003-2007). His research spans multiple interdisciplinary areas including energy harvesting systems, non-volatile processors, FPGA acceleration, and machine learning at the edge. His recent publications demonstrate a strong focus on algorithm-hardware co-design, particularly for vision transformers, federated learning, and non-volatile memory systems. His work often addresses the challenges of implementing AI on resource-constrained edge devices, with emphasis on energy efficiency, reliability, and performance optimization. The trend in his publications shows increasing focus on sustainable AI processing, heterogeneous computing architectures, and personalized machine learning for IoT applications. Selected Awards: IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award (2021) ACM SIGDA Meritorious Service Award (2019) Multiple Best Paper Award nominations at top conferences including DAC, ASP-DAC, and CODES+ISSS Dr. Hu's research has been supported by multiple grants focusing on energy-efficient computing, non-volatile memory systems, and hardware acceleration for machine learning. His collaborative work spans numerous institutions and involves interdisciplinary teams working at the intersection of computer architecture, embedded systems, and artificial intelligence. His publications show extensive collaboration with researchers at the University of Pittsburgh, particularly with Albert K. Jones and Yiyu Shi. His laboratory work focuses on implementing practical systems for energy harvesting powered devices, non-volatile processors, and hardware accelerators for machine learning applications. Current projects appear to emphasize sustainable AI processing at the edge, heterogeneous FPGA acceleration, and personalized federated learning for health monitoring applications.

• Cyber-Physical Systems
• Infrastructure Security
• Non-Volatile Memory Systems
• +6 more

Peter Druschel is a Professor and founding Director of the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. He holds adjunct professorships at Saarland University and the University of Maryland. His research focuses on distributed systems, operating systems, and privacy-preserving technologies. He earned his Ph.D. from the University of Arizona in 1994 and has held roles at Rice University since 1994, including Professor of Computer Science (2002–2005). Education: Ph.D. in Computer Science, University of Arizona (1994) Research Interests: Distributed systems, operating systems, network security, accountable computing, and privacy technologies. Current projects include privacy compliance in data systems (Thoth), secure communication (EbN), and privacy-aware image capture (I-Pic). Awards: SIGOPS Mark Weiser Award (2008) NSF CAREER Award (1995) Member of Academia Europaea and German Academy of Sciences Leopoldina Grants & Leadership: Leads the ERC Synergy Project imPACT, chairs the Max Planck Society’s Chemistry, Physics, and Technology Section, and collaborates with institutions like Cornell and Google. Advises on policy issues related to technology and privacy. Labs/Teams: Distributed Systems Group at MPI-SWS, collaborations with Microsoft Research and MIT. Current team includes students and postdocs working on privacy, security, and distributed systems.

• Distributed Systems
• Operating Systems
• Network Security
• +3 more

Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.

• Data Management Systems
• Database Systems
• Distributed Systems
• +5 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.

• Data Management
• Database Systems
• Machine Learning
• +3 more