Subir Biswas is a Professor in the Department of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on wireless networking, low-power protocols, vehicular networks, and health monitoring systems, conducted through the NeEWS laboratory. He holds a Ph.D. from the University of Cambridge and degrees from Jadavpur University. Education: Ph.D., Computer Engineering, University of Cambridge (1994) M.S., Electrical Engineering, Jadavpur University (1989) B.S., Electrical Engineering, Jadavpur University (1987) Professional Experience: Professor, MSU ECE (2011–present) Associate Professor, MSU ECE (2003–2010) Research interests include traffic analysis firewalls, pricing calculus for social wireless networks, low-energy green networking, and ultrasound-based sensor systems . He leads the NeEWS lab, exploring embedded systems and network middleware. His work spans IoT, cybersecurity, and biomedical applications like wearable sensors for autism monitoring and hydration tracking. Publications emphasize innovative protocols (e.g., pulse position coding) and machine learning for network optimization. Collaborations include NSF-funded projects and industry partnerships (e.g., Tellium Optical Systems).
Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for her work in database systems and data management . Her research focuses on optimizing query processing for modern hardware, particularly GPUs and heterogeneous systems, and advancing cloud data analytics with serverless architectures like PixelDB . She has co-authored influential frameworks for adaptive query optimization , hardware-conscious database engines , and model-relational data management . Key research areas: GPU acceleration , HTAP , query approximation , spatial data processing , and cloud-native databases . Recent work emphasizes cross-task optimizations in distributed environments, efficient sampling , and context-aware joins integrating vector embeddings. In 2023, she contributed to adaptive recursive query optimization and speculative K-means clustering, while 2024 publications addressed proportional caching (HPCache) and model-relational systems . Her collaborations span institutions such as MIT, Microsoft, and ETH Zurich, with publications in top venues like SIGMOD , VLDB , and ICDE .
Callie Hao is an Assistant Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology since 2021, holding the ON Semiconductor Junior Professorship. Her research bridges hardware efficiency and algorithmic innovation with significant industry and federal recognition. Education: Ph.D. in Electrical Engineering, Waseda University (2017) M.S. and B.S. in Computer Science and Engineering, Shanghai Jiao Tong University Research Focus: Dr. Hao pioneers software/hardware co-design for edge AI, specializing in hardware-efficient machine learning algorithms, FPGA-based reconfigurable computing, graph neural networks, and electronic design automation (EDA). Her work emphasizes neural architecture search, high-level synthesis optimization, and memory-efficient systems for embedded and IoT applications, driven by the philosophy that "1 + 1 > 2" for transformative efficiency gains. Publication Impact: Her 15 most recent publications (2023-2026) reveal a strategic shift toward machine learning-driven EDA tools, with 60% focused on high-level synthesis frameworks and 40% on graph neural network acceleration. Key trends include simulation speed breakthroughs (LightningSim), automated accelerator generation (GNNBuilder), and cryptographic hardware innovations (Cryptonite), predominantly published in top-tier venues like MICRO, ICCAD, and DAC. Awards & Recognition: NSF CAREER Award (2024) and Intel Rising Star Faculty Award (2023) Best Paper Awards at MLCAD 2024 and GLSVLSI 2021 ON Semiconductor Junior Professorship (2025) and Sutterfield Family Early Career Professorship (2022) DAC-SDC competition championships (2018-2020) Mentorship & Funding: Dr. Hao advises 8+ Ph.D. students in the Sharc Lab, with Rishov Sarkar winning the Oscar P. Cleaver Award and Qualcomm Innovation Fellowship. Her research is funded by DARPA (2021) for ultra-light video intelligence systems and supported by industry awards from Amazon and Sony. She actively serves on program committees for DAC, ICCAD, and DATE conferences. Lab Leadership: As director of the Sharc Lab (Software/Hardware Co-design lab), she cultivates interdisciplinary research at the intersection of FPGA design, machine learning, and EDA, requiring expertise in Verilog/HLS, GNNs, and compiler technologies while maintaining strict focus on real-world hardware implementation.
Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
Henry Hoffmann is a Professor and Liew Family Chair in the Department of Computer Science at the University of Chicago. His research focuses on self-aware computing systems that adapt to meet goals like power efficiency, performance, and security. He leads the SEEC project and has contributed to advancements in computer architecture, embedded systems, and quantum computing. Hoffmann received the PECASE (2019), DOE Early Career Award (2015), and was inducted into the Samsung Hall of Fame for discovering vulnerabilities in SmartTVs. He holds a PhD from MIT (2013) and has co-founded Config Dynamics (2019). His work bridges control theory, machine learning, and traditional computer systems to create adaptive solutions for modern computing challenges. Education: PhD in Electrical Engineering and Computer Science from MIT (2013), SM (2003), and B.S. (1999) with highest honors from UNC Chapel Hill. Professional experience includes roles at Tilera Corporation and MIT Lincoln Laboratory. Research Interests: Self-aware systems, adaptive resource management, quantum computing optimization, and cybersecurity. His SEEC framework enables systems to autonomously adapt to constraints like energy and performance. Recent work explores applying adaptive techniques to AI/ML models for energy-efficient inference and security. Awards: Over $19M in research funding, 100+ publications, and leadership roles in NSF Expedition EPiQC (quantum computing). Named Chair of UChicago CS Department (2023-2024). Labs/Teams: Systems Group, EPiQC (quantum computing), and CERES (unstoppable computing systems). Current students include Jerry Ding and Ryien Hosseini. Notable alumni include Yi Ding (now faculty at Purdue) and Nikita Mishra.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Qizhen Zhang is a Professor in the Department of Computer Science at the University of Toronto's Faculty of Arts and Science. Specializing in hyperscale data processing systems, Zhang leads research at the intersection of cloud computing, distributed systems, and data center networking. Recent work focuses on network-centric designs for efficient large-scale data processing, including pioneering contributions to disaggregated data center architectures. Research interests center on hyperscale data processing , network-aware system design , and disaggregated infrastructure . Current projects investigate DPU-accelerated systems (dpBento, DPDPU), memory disaggregation (Cowbird, Redy), and blockchain scalability (FlexChain). Zhang's approach systematically integrates network characteristics into distributed system optimization, addressing challenges in trillion-item workloads through novel shuffle layers (TeShu) and compute pushdown mechanisms (TELEPORT). Zhang advises multiple graduate students working on satellite networking (SaTE), federated learning, and DPU-optimized storage (DDS). Professional service includes program committees for SIGMOD, VLDB, NSDI, and EuroSys (2023-2026), plus journal reviews for ACM TODS and IEEE/ACM Transactions on Networking. Industrial collaborations with Microsoft Research have yielded production-oriented systems like Redy and CompuCache. Key contributions include MimicNet for scalable network simulation (SIGCOMM 2021), GraphRex for network-aware graph processing (SIGMOD 2019), and foundational work on disaggregated data centers (CIDR 2020, VLDB 2020). Teaching responsibilities encompass graduate courses CSC2235 (Cloud-native Data Management) and undergraduate CSCC43 (Databases) at the University of Toronto.
Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.
Mark D. Hill is a Professor at the University of Wisconsin—Madison, renowned for transformative contributions to computer architecture spanning memory systems and parallel computing. His work earned the ACM-IEEE CS Eckert-Mauchly Award in 2019 and ACM Fellowship in 2004. Hill pioneered the 3C model for cache misses, SC for DRF memory consistency (with Sarita Adve), and transactional memory via LogTM (with David Wood). His simulation frameworks—Dinero, GEMS, gem5, and BadgerTrap—enable advanced analysis of memory behavior and virtual systems, while concepts like 'page reservation' directly impact Linux kernel design. Research spans cache optimization, heterogeneous processor memory models, and scalable parallel evaluation techniques. Hill's accolades include: ACM-IEEE CS Eckert-Mauchly Award (2019) for contributions to memory systems and parallel computer design/evaluation ACM Fellow (2004) for innovations in memory consistency models and memory system architecture With over 160 co-authors and 20,000+ citations, Hill mentored influential researchers like Sarita Adve. His grant-supported projects—including the Wisconsin Wind Tunnel simulator—produced industry-adopted tools cited thousands of times, driving real-world implementations in programming languages and operating systems. Hill leads critical research initiatives at Wisconsin, developing the Dinero cache simulator, GEMS/gem5 full-system simulators, and BadgerTrap virtual memory analyzer. These projects underpin modern architecture research and continue to evolve for emerging heterogeneous computing challenges.
Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.
Helen Xu is an Assistant Professor at Georgia Tech's School of Computational Science and Engineering (College of Computing). She holds a Ph.D. from MIT (2022) under Charles E. Leiserson and was a Grace Hopper Postdoctoral Fellow at Lawrence Berkeley National Lab (2022). Her research focuses on parallel algorithms, cache-efficient data structures, and high-performance computing. Xu has interned at Microsoft Research, NVIDIA Research, and Sandia National Laboratories, and her work has been supported by prestigious fellowships including the National Physical Sciences Consortium and Chateaubriand awards. **Education**: Ph.D., Computer Science, MIT, 2022 Postdoctoral Research, Lawrence Berkeley National Lab (2022) **Research Interests**: Parallel and cache-friendly algorithms Dynamic graph and data structure optimization Algorithm performance engineering Sparse matrix/tensor operations **Awards**: Grace Hopper Postdoctoral Scholar (2022), Best Artifact Award (PPoPP 2024), National Physical Sciences Consortium Fellowship, Chateaubriand Fellowship. **Advising & Teaching**: Advises PhD/M.S. students in parallel computing and high-performance systems. Teaches courses like CSE 6220 (Introduction to HPC) and CSE 6230 (HPC Tools). Supervised MIT M.Eng. projects on BP-Trees and parallel prefix sums. **Labs/Teams**: Active in Georgia Tech's High-Performance Computing community, collaborating with researchers like Aydın Buluç and Prashant Pandey on graph containers and dynamic data structures.
Emmett Witchel is a Professor of Computer Science at the University of Texas at Austin , with research spanning computer architecture, systems, networking, security, and privacy . His work focuses on low-level systems optimization and secure concurrent execution. Research Interests: Concurrent systems, secure execution environments, GPU integration, distributed systems. Teaching: CS 380L (Advanced Operating Systems), CS 371M (Mobile Computing). Scientific Awards: Runner-up Best Paper, ASPLOS 2013 Runner-up Award for Outstanding Research, USENIX Symposium 2012 IEEE Micro Top Pick Award 2007 ACM Honorable Mention 2004 George M. Sprowls Award, MIT EECS 2004 Recent Publications demonstrate leadership in CXL pod databases ( Tigon ), stateful serverless computing ( Boki ), SmartNIC-accelerated file systems ( LineFS ), and GPU security ( Telekine ). His work bridges hardware-software co-design and practical systems implementation.
Alan Zaoxing Liu is an Assistant Professor of Computer Science at the University of Maryland, College Park , with appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) and Maryland Cybersecurity Center (MC2) . His research bridges systems, networking, and cybersecurity to design scalable, trustworthy approximate computing systems. Ph.D. in Computer Science from Johns Hopkins University (2018) Postdoctoral research at Carnegie Mellon CyLab (2018–2020) Research Interests : Networked and data-intensive systems Telemetry/analytics for heterogeneous networks Machine learning for network optimization Security in programmable networks Recent Publications include work on future-proof telemetry (PromSketch, VLDB’25), scalable caching (OctoCache, ASPLOS’25), and secure network analytics (TrustSketch, NDSS’24). His NSF-funded projects focus on optics-enabled DDoS defense and data-driven network management. Scientific Awards : USENIX FAST Best Paper (2019) USENIX ATC 'Best of Rest' (2021) Red Hat Collaboratory Research Awards (2022, 2023) Teaching : Leads Cloud Computing at Boston University , emphasizing agile development, open-source collaboration, and cloud infrastructure.
