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Hokeun KimView profile

Assistant Professor

Arizona State UniversityUnited States

Hokeun Kim is an Assistant Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He previously held positions at Hanyang University (2021-2023) and worked in industry roles at Google, LinkedIn, and HP Labs. His research focuses on cyber-physical systems, IoT security, and computer architecture, with a particular emphasis on safety and security aspects of time-sensitive systems. Education: Ph.D. in EECS, University of California, Berkeley (2017) M.S. in EECS, Seoul National University (2012) B.S. in Computer Science and Engineering, Seoul National University (2010) Research Interests: Kim’s work spans secure IoT frameworks, real-time embedded systems, and edge computing. He develops tools like the Secure Swarm Toolkit (SST) and Lingua Franca, addressing challenges in distributed system security, interoperability, and performance. Key Contributions: Authored over 30 peer-reviewed publications in top venues like IEEE Transactions, ACM Conferences, and DATE. Received the ACM/IEEE Best Paper Award (IoTDI 2017) and IEEE Micro Top Picks Honorable Mention (2017). Active in organizing conferences (e.g., DATE, FDL) and serves on technical committees for top journals/conferences. Teaching: Courses include Computer Architecture I/II, Real-Time Embedded Systems, and IoT design at both undergraduate and graduate levels.

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Ram AlagappanView profile

Assistant Professor

University of Illinois Urbana-ChampaignUnited States

Ram Alagappan is an Assistant Professor at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science, Department of Computer Science. He co-leads the Distributed And Storage Systems Laboratory (DASSL) and focuses on improving reliability and performance in computer systems. PhD from University of Wisconsin-Madison (2019) Postdoctoral researcher at VMware Research (2020-2022) Research interests span storage systems, distributed systems, and operating systems, with emphasis on: Crash consistency and reliability Consensus protocols and fault tolerance Non-volatile memory optimization Datacenter storage abstractions Recent publications highlight advancements in shared log abstractions (LazyLog, SOSP 2024), replication for KV stores (IONIA, FAST 2024), and fault tolerance in disaggregated datacenters (SplitFT, EuroSys 2024). His work also includes foundational studies on crash vulnerabilities and consistency models. Scientific recognitions include: Best Paper Awards at SOSP 2024, FAST 2020, FAST 2018, FAST 2017 NSF CAREER Award (2023) NetApp Faculty Fellowship (2024) Teaching honors: Listed as Excellent/Outstanding Teacher at UIUC (CS598 Storage Systems, 2022-2023) Ranked 1st in student evaluations for CS739 Distributed Systems at UW-Madison (2020) Grants and service: NSF CAREER Award, IBM-IL Discovery Grant Program Committee roles at OSDI, SOSP, EuroSys, HotStorage Labs & Teams: Co-leader of DASSL research group at UIUC.

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Willy ZwaenepoelView profile

Professor

Graz University of TechnologyAustria

Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).

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Hamed Nabizadeh RafsanjaniView profile

Lecturer

University of GeorgiaUnited States

Hamed Nabizadeh Rafsanjani, Ph.D., P.E., ENV SP is a Lecturer at the University of Georgia (UGA) College of Engineering since 2018. He holds a Ph.D. and master’s degrees in Construction Engineering and Management from the University of Nebraska-Lincoln (UNL). Prior to UGA, he served as an assistant professor and visiting lecturer at various universities, earning accolades such as the Best Teacher Award and Best Researcher Award. His research focuses on IoT, AI, and Digital Twin technologies applied to Architecture, Engineering, and Construction (AEC) industries. As director of iSC-LAB, he develops IoT-based platforms to analyze building occupants’ energy-use behaviors and improve learning environments. His research interests span smart building systems, non-intrusive energy monitoring, and occupant behavior analysis. Key projects include a global occupant behavior database and an IoT-based smartphone energy assistant (iSEA). He has secured grants from agencies like the National Science Foundation (NSF), contributing to studies on energy efficiency, construction project management, and sustainability in built environments. Rafsanjani’s publications (2018–2023) emphasize AI-driven solutions for AEC, IoT applications in energy management, and adaptive learning systems. His work bridges technology and human behavior to optimize building performance and educational outcomes. Awards reflect his dual excellence in pedagogy and research, with ongoing contributions to the Sustainable Human-Building Ecosystems field.

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Sang-Woo JunView profile

Professor

University of California, IrvineUnited States

Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.

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Sebastian AngelView profile

Associate Professor

University of PennsylvaniaUnited States

Sebastian Angel is an Associate Professor and Chair of Undergraduate Curriculum in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in systems, security, privacy, and networking, with a focus on privacy-preserving systems, accountability in online services, consistency in distributed systems, and next-generation operating systems. Research Areas: Systems, Security, Privacy, Networking, Distributed Systems, Operating Systems Labs & Teams: Distributed Systems Laboratory, Security and Privacy Laboratory, Warren Center for Network and Data Sciences Education: Ph.D. in Computer Science from the University of Texas at Austin (2018), ACM SIGOPS Dennis M. Ritchie Dissertation Award, Bert Kay Best Dissertation Award Recent Publications (2025–2022) span serverless computing, zero-knowledge proofs, distributed transactions, privacy-preserving ad tech, and verifiable execution. His 2025 work includes serverless workflow optimization and structural logic verification, while 2024 focuses on caching frameworks and stateful serverless. Earlier 2023–2022 projects include secure federated learning, confidential cloud services, and private information retrieval. Scientific Awards: NSF CAREER Award (2021) JPMorgan Faculty Award (2021) ACM SIGOPS Dennis M. Ritchie Dissertation Award (2018) Bert Kay Best Dissertation Award (2018) ACM SIGMOD Research Highlights Award (2024) VLDB 2024 Best Paper Award Nominee Advising: Current PhD Students: Elizabeth Margolin, Jess Woods, Yuxuan Zhang Current Masters Students: Felix Adena, Sydnie Shea Cohen Alumni: Eleftherios Ioannidis (2025), Yiping Ma (2025), Haoran Zhang (2024), Ke Zhong (2024), Selin Butun (2025), Martin Sander (2025), Seungmin Han (2024), Andrew Beams (2022), Yifeng Mao (2021), Varad Deshpande (2020) Current Courses: CIS 4510/5510: Computer and Network Security (Fall 2025). He also organizes conferences like S&P, OSDI, SOSP, PETS, and EuroSys.

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Bruce D BakerView profile

Professor

University of MiamiUnited States

Bruce D. Baker serves as Professor in the Department of Teaching and Learning at the University of Miami's School of Education and Human Development. He is Co-director and creator of the School Finance Indicators Database, establishing himself as a leading national expert on public education funding with over 25 years of research experience. His research focuses on school finance systems, educational equity, and funding adequacy , with particular emphasis on racial disparities and constitutional compliance. Baker has authored influential books including Educational Inequality and School Finance: Why Money Matters for America’s Students and School Finance and Education Equity: Lessons from Kansas , with forthcoming work on housing discrimination and school funding inequality. His publication portfolio demonstrates consistent focus on state finance system evaluations, cost modeling methodologies, and policy reform frameworks , with recent work addressing pandemic impacts, voucher systems, and federal aid structures across multiple jurisdictions including New York, Oregon, and California. AERA Fellow (2024) Cited by U.S. Supreme Court for racial inequality research #59 in Education Week/RHSU Edu-Scholar Top 200 (2025) National Education Policy Center Senior Fellow Learning Policy Institute Senior Fellow Baker provides extensive policy advisory services, having consulted on school funding design with Vermont, New Hampshire, Kansas, Texas, Colorado, Wyoming, Washington, Virginia, Missouri, Delaware, and Oregon. His expert testimony has been instrumental in numerous state and federal court cases regarding school funding inequalities. Current collaborations include work with the World Bank on global educational adequacy indicators.

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Tilmann RablView profile

Professor

Schloss Dagstuhl - Leibniz Center for InformaticsGermany

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

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Oscar Romero MoralView profile

Professor

Polytechnic University of CataloniaSpain

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

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Michael D. BondView profile

Professor

National and Kapodistrian University of AthensGreece

Michael D. Bond is a Professor in the Department of Computer Science & Engineering at Ohio State University's College of Engineering. He leads the Programming Languages and Software Systems (PLaSS) Research Group, which focuses on designing program analyses and software and hardware systems that enhance computing reliability, scalability, and security. His academic service includes general chair for PLDI 2027, program committee membership for multiple top conferences, and committee roles in SIGPLAN Research Highlights (2024-2027). Professor Bond's research spans programming languages, systems, and security, with particular expertise in memory management, concurrency, hardware transactional memory, information flow control, and predictive race detection. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source projects accompanying his publications. The PLaSS group has made significant contributions to understanding and improving memory models, developing efficient garbage collection techniques for modern architectures, and creating novel approaches to secure programming in languages like Rust. Analysis of his recent publications reveals a clear trajectory toward addressing security and reliability challenges in modern computing systems, particularly through language-based approaches. His work increasingly focuses on Rust programming language security mechanisms, memory disaggregation for datacenters, and advanced techniques for detecting and preventing concurrency bugs. The research demonstrates strong continuity in exploring memory models and concurrency while adapting to emerging hardware trends and security challenges. Outstanding Teaching Award, Department of Computer Science and Engineering, Ohio State University (2018) Lumley Research Award, College of Engineering, Ohio State University (2016) OOPSLA 2015 Distinguished Paper and Artifact Awards NSF CAREER Award ACM SIGPLAN Outstanding Doctoral Dissertation Award Intel PhD Fellowship Professor Bond actively mentors several PhD students including Chujun Geng, Vincent Beardsley, Chris Xiong, Victor Chen, and Noah Charlton, with external co-advisee Zixian Cai at Australian National University. His research is currently supported by multiple NSF grants including SaTC-2348754 (2024-2027), CyberCorps-2336531 (2024-2029), and CSR-2106117 (2021-2025), reflecting sustained funding for his work in information flow control, security, and systems research. The PLaSS Research Group maintains a strong presence in both academic and industrial communities, with graduated PhD students securing positions at major technology companies like Google, Amazon Web Services, and Huawei, as well as academic positions at institutions like UIUC and IIT Kanpur. The group's work combines theoretical rigor with practical implementation, consistently producing open-source artifacts that enable reproducibility and further research in the systems and programming languages community.

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Kaisong HuangView profile

Researcher

Simon Fraser UniversityCanada

Kaisong Huang is a Researcher at the University of Calgary , where he leads the Data Systems and Infrastructure Lab (DSIL) . His work bridges database systems with modern hardware innovations, focusing on performance optimization and storage technologies. PhD, Computer Science, Simon Fraser University (Sep 2020 - May 2025) MMath, Computer Science, University of Waterloo (Sep 2018 - Aug 2020) BEng, Software Engineering, Nanjing University (Sep 2014 - Jun 2018) Huang's research spans database engines , transaction processing , data storage management , resource disaggregation , and AI/LLM-driven database systems . His work includes optimizing latency in database systems, leveraging non-volatile memory, and addressing cross-engine transaction consistency. Recent publications highlight trends in hardware-aware DBMS design, persistent memory indexing, and SSD performance trade-offs. Huang has received prestigious recognitions, including: ACM SIGMOD Best Paper Award (2025) ACM SIGMOD Student Travel Award (2025) ACM SIGMOD Research Highlight Award (2023) Simon Fraser University (SFU) Graduate Fellowship (2020-2024) SFU Computing Science Travel Awards (2022, 2025) SFU Professional Development Grant (2025) He actively contributes to academic service as: PC member for SIGMOD 2026, ICDE 2026, CIKM 2023-2025, VLDBJ 2025-2026 Reviewer for SIGMOD 2022 and the Journal of Systems Architecture Committee member for the SIGMOD Availability Committee

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Rob LiddiardView profile

Academic

University College LondonUnited Kingdom

Rob Liddiard is a Research Associate at University College London's Bartlett School Env, Energy & Resources. His work focuses on identifying and classifying energy use patterns in non-domestic buildings through the 3DStock model , a geospatial representation of the UK building stock. He contributes to dynamic energy models like SimStock and collaborates with organizations such as the Valuation Office Agency (VOA) and Ordnance Survey AddressBase Premium.

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Nikolaus AugstenView profile

Professor

University of SalzburgAustria

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

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Ursula EickerView profile

Professor

Concordia UniversityCanada

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

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Philippe Cudré-MaurouxView profile

Professor

Swiss Federal Institute of Technology in LausanneSwitzerland

Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.

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