Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.
Dimitris Mitropoulos is an Assistant Professor at the National and Kapodistrian University of Athens (NKUA) in the Department of Business Administration, where he teaches courses on Distributed Ledger Technologies, Data Security and Privacy, Algorithms and Business Analytics, and Introduction to Programming. He also serves as Head of the Reliability Engineering Directorate at the National Infrastructures for Research and Technology (GRNET), Greece's national research and education network organization. Previously, he was a Postdoctoral Researcher in the Computer Science Department at Columbia University. Dr. Mitropoulos received his Ph.D. degree in Secure Software Development Technologies from the Athens University of Economics and Business (AUEB) in 2014. His doctoral research was supported by the Heracleitus II Scholarship, co-financed by the European Union and Greek national funds. He is a member of prestigious professional organizations including ACM, IEEE, and USENIX. Dr. Mitropoulos conducts pioneering research at the intersection of software engineering and cybersecurity, with particular expertise in secure software development, vulnerability analysis, and blockchain security. His work spans multiple dimensions of software security including code injection attacks, infrastructure as code security, smart contract analysis, and dependency management in software ecosystems. His research methodology combines static and dynamic analysis techniques with empirical studies of real-world software systems, particularly focusing on Java, Python, and Solidity ecosystems. His recent work has made significant contributions to understanding security vulnerabilities in modern software development practices and infrastructure management. Dr. Mitropoulos has received numerous prestigious awards for his research contributions, including the Research Excellence Award from NKUA (2025), Distinguished Paper and Artifact Awards at PLDI '22, Best Data Showcase Award at MSR 2018, and multiple postdoctoral research funding scholarships. His work on "Finding typing compiler bugs" was recognized with both Distinguished Paper and Artifact Awards at PLDI '22, highlighting the significance and reproducibility of his research. He has also received recognition for his service to the academic community, including a Certificate of Appreciation from ESEC/FSE '21 for his contributions to conference organization. Dr. Mitropoulos has been actively involved in securing research funding and leading significant research projects. He currently serves as Principal Investigator for the SecOPERA project (2023-Today), funded by the European Commission under Horizon Europe. Previously, he contributed to several major EU and US-funded projects including eSSIF-Lab (2019-2022), FASTEN (2019-2022), PRIViLEDGE (2018-2021), CERTCOOP (2017-2020), PANORAMIX (2016-2019), and TREDISEC (2016-2018). His research has been supported by diverse funding sources including the European Commission's Horizon 2020 program, the National Science Foundation, and the Defense Advanced Research Projects Agency (DARPA). Dr. Mitropoulos plays an active role in the international research community through various leadership positions. He serves on program committees for top-tier conferences including OOPSLA (2026), ICSE (2026), ESEC/FSE (2025), and ISSTA (2025). He has previously served as Workshop Co-Chair for ISSTA 2025 and Student Volunteer Chair for ESEC/FSE 2021. His contributions to mentoring the next generation of researchers include serving as a mentor for the ICSE Student Mentoring Workshop (2022) and supervising Google Summer of Code projects (2017).
Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.
Rabi N. Mahapatra is a Professor in the Department of Computer Science & Engineering at Texas A&M University, within the College of Engineering. His research focuses on embedded systems, reconfigurable architectures, real-time systems, and semantic networks. He holds a Ph.D. in Computer Engineering from the Indian Institute of Technology (1992), an M.S. in Electrical Engineering (Sambalpur University, 1984), and a B.S. in Electronics & Communication (Sambalpur University, 1979). His research interests include Network-on-Chip (NoC), data analytic co-design, IoT protocols, and temperature-aware energy management. His work emphasizes hardware-software co-design for complex systems, with applications in many-core processors, semantic search engines, and real-time embedded systems. Key publications highlight contributions to collaborative filtering on many-core architectures, low-jitter clock distribution circuits, and energy-efficient scheduling. He has been recognized as an IEEE Computer Society Distinguished Visitor (2005–2007) and received the BOYS-CAST Indo-US Young Scientist Award. He leads the Codesign Embedded Systems group at Texas A&M, exploring cutting-edge topics such as photonics NoC, reservoir computing, and IoT security. His research bridges theory and practice, addressing challenges in scalable systems and embedded applications.
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.
Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .
Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on compilers and programming systems, particularly program optimizations for heterogeneous computing and emerging architectures, with applications in machine learning and graph processing. He joined Mines in 2014 after earning a Ph.D. from The College of William and Mary and earlier degrees from Central South University in China. Education : B.S. in Computational Science and Technology (Central South University, 2005) M.S. in Computer Science (Central South University, 2008) Ph.D. in Computer Science (The College of William and Mary, 2014) Research Interests : Wu's work emphasizes enhancing data locality in heterogeneous systems, GPU scheduling, and optimizing applications for emerging architectures. His contributions include frameworks like GraphZero for efficient graph mining and FLEP for GPU preemption. Awards & Grants : NSF SPX Award (2018) NSF CAREER Award (2018) Supercomputing Best Paper Award (2015) Multiple NSF grants for GPU-related research Advising & Grants : Wu has led several NSF-funded projects and actively participates in conference program committees (e.g., PPoPP, SC, ICS). His research spans compiler optimizations, parallel computing, and high-performance systems. Labs & Teams : While specific labs aren’t named, his work involves collaborations on GPU-based systems, graph processing frameworks, and compiler toolchains.
Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.
Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.
Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.
Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.
Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.
