Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
James C. Hoe is Professor of Electrical and Computer Engineering at Carnegie Mellon University (College of Engineering). He is on sabbatical at MangoBoost and directs research in computer architecture, reconfigurable computing, and high-level hardware design. Education Ph.D., Electrical Engineering and Computer Science, MIT (2000) M.S., Electrical Engineering and Computer Science, MIT (1994) B.S., Electrical Engineering and Computer Science, UC Berkeley (1992) Research Interests Professor Hoe’s work spans computer architecture , reconfigurable computing , FPGA architectures , and high-level hardware synthesis . His group created the CoRAM abstraction for virtualized FPGA computing and leads efforts in power-efficient accelerators, in-network computing, and security-oriented FPGA systems. Scientific Awards IEEE Fellow (2013) Intel Outstanding Researcher Award (2021) Research Funding & Projects Intel / VMware Crossroads 3D-FPGA Academic Research Center – co-leading exploration of FPGA roles in future datacenters. DARPA BRASS program ($2.7 M, 4 years) – ensuring long-lived software systems remain robust to resource changes. Pigasus open-source IDS – world’s fastest FPGA-accelerated intrusion-detection system (100 Gb/s on one server). Labs & Teams He heads activities within the Computer Architecture Lab at Carnegie Mellon (CALCM) , supervising graduate researchers on CoRAM++, SPIRAL autotuning, and FPGA overlays for stream processing.
Yao Yang is an Assistant Professor in the Department of Chemistry and Chemical Biology at Cornell University's College of Arts and Sciences. His research focuses on developing multimodal operando electron microscopy and synchrotron X-ray methods to probe electrochemical dynamics at solid-liquid interfaces for energy materials. PhD, Cornell University (2021) Miller Postdoctoral Fellow, UC Berkeley (2021-2024) Research interests span fundamental electrochemistry and energy material interfaces, particularly CO2 reduction, clean H2 production, and rechargeable batteries. The Yang group specializes in operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and correlative synchrotron X-ray methods at Cornell Center for Materials Research (CCMR) and Cornell High Energy Synchrotron Source (CHESS). Recent publications highlight atomic-scale imaging of catalyst dynamics, Tafel slope analysis, and epitaxial growth techniques for enhanced electrocatalysts. Articles demonstrate interdisciplinary approaches combining electrochemistry, nanoscience, and advanced characterization. Scientific Awards: 2025 ACS Materials and Interfaces Outstanding Presentations by Young Investigators Award 2024 Journal of Materials Research Distinguished Invited Speaker Miller Postdoctoral Fellowship (2021-2024) 2023 Best Early Career Presentation at MRS Spring 2022 ACS AC/DC Rising Stars in Analytical Chemistry Contact: yaoyang@cornell.edu
Tobias Bolch is a full Professor at the Institute of Geodesy, Graz University of Technology (TU Graz), Austria, specializing in cryospheric research with a focus on High Mountain Asia and the Tibetan Plateau. His work integrates advanced geodetic and remote sensing methodologies to address critical climate change impacts on glaciers and related systems. His research spans: Glacier dynamics and mass balance monitoring using InSAR and optical satellite data Rock glacier kinematics and permafrost interactions Glacial lake evolution and outburst flood hazards Climate-glacier-hydrology linkages in Central Asian river basins Long-term cryosphere reconstruction using historical satellite archives Analysis of his 2023-2025 publications reveals dominant trends in quantifying ice loss acceleration, particularly from lake-terminating glaciers, and the application of multi-sensor approaches (Sentinel, ASTER, UAV) for decadal-scale change detection. His work consistently emphasizes the hydrological consequences of glacier retreat for downstream water security across High Mountain Asia. No scientific awards or specific grant programs were documented in the provided materials. While student supervision details are absent, his position at TU Graz's Institute of Geodesy indicates active mentorship within geospatial and cryospheric research programs. His homepage (https://ifg.tugraz.at) serves as the primary hub for research dissemination through the university's PURE portal.
André DeHon is the Oliver C. Boileau Jr. and Nan Eleze Boileau Professor of Electrical Engineering at the University of Pennsylvania, with affiliations in the Electrical and Systems Engineering (ESE) and Computer and Information Science (CIS) departments. He founded the Penn Computer Engineering (CMPE) program and directs the DARPA-sponsored CyberSavvy Security Center. His research spans reconfigurable computing, FPGA architecture, interconnect design, hardware security, and energy efficiency, with a focus on adapting to fabrication imperfections and runtime variability. His work explores programmable substrates (e.g., CMOS VLSI, molecular electronics) and algorithmic mappings to create resilient, efficient systems. Current projects include runtime feedback mechanisms (REFINE), partial reconfiguration (ExHiPR), and security frameworks like SCALPEL and μSCOPE. Recent publications highlight advancements in FPGA compilation (PLD, HiPR), security (SCALPEL, μSCOPE), and interconnect optimization (Asymmetry in NoC). These studies intersect computer architecture, security, and embedded systems. Scientific awards and recognitions: 2023 IEEE Fellow 2020 IEEE Mary Kenneth Keller Teaching Award 2018 ACM Fellow TCFPGA Hall of Fame (2019) Best Paper at ICFPT 2015 Academic leadership: He chairs the ACM/SIGDA Technical Committee on FPGAs and Reconfigurable Computing, previously held faculty roles at Caltech (1999–2006), and was a postdoc at UC Berkeley (1996–1999). Education includes MIT SB (1990), SM (1993), and PhD (1996) in Electrical Engineering and Computer Science.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Dr. Hiren Patel is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a Doctorate in Computer Engineering from Virginia Tech and previously worked as a postdoctoral fellow at UC Berkeley under Edward A. Lee. His research focuses on real-time embedded systems, computer architecture, machine learning hardware, and cybersecurity. He teaches courses like ECE 150 (Programming), ECE 320/429 (Computer Architecture), and ECE 327 (Digital Systems). Research Interests: Cyber-physical systems and hybrid architectures Hardware/software co-design methodologies Predictable cache coherence protocols IoT and edge computing systems Security in embedded and real-time systems Recent work emphasizes cache coherence solutions for safety-critical systems and GPU acceleration strategies. His publications address challenges in multicore predictability, FPGA bandwidth optimization, and autonomous robotics orchestration. No specific awards are listed, though his extensive publication record indicates significant contributions to embedded systems research. He currently oversees graduate student applications focusing on his core research areas.
Dr. Simon Beaumont is an Associate Professor in the Department of Chemistry at Durham University , with additional responsibilities as Associate Dean (PGR) in the Faculty of Science. His research program integrates heterogeneous catalysis , nanomaterials , and in situ spectroscopic techniques to develop sustainable chemical processes. BA & MSci Natural Sciences, University of Cambridge (2003-2007) PhD in Heterogeneous Catalysis, University of Cambridge (2010) Postdoctoral Fellowship at UC Berkeley (2010-2012) Research foci include mechanistic studies of catalytic processes, nanoparticle synthesis , and in situ characterization via X-ray absorption (NEXAFS), DRIFTS, and Raman. His work addresses challenges in CO2 hydrogenation , biomass conversion , and environmental remediation , supported by national/EU/industrial funding. Recent publications highlight trends in selective hydrogenation (furfural), multi-functional catalysts (acid-base systems), and nanoparticle stability under reactive conditions. All studies emphasize molecular-level understanding for practical catalyst design. Scientific awards include Leverhulme Trust and Addison Wheeler fellowships. Teaching portfolio spans first-year laboratories , organic chemistry tutorials , and advanced catalysis lectures . Supervision of five research postgraduates and leadership of industry-funded projects further demonstrate his academic impact.
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.
Paolo Ienne is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), where he leads the Processor Architecture Laboratory (LAP) within the School of Computer and Communication Sciences. His research focuses on advancing reconfigurable computing systems through innovative FPGA architectures and high-level synthesis methodologies. His primary research domains include reconfigurable computing, FPGA architecture design, dynamically scheduled dataflow circuits, and hardware acceleration techniques. Recent work emphasizes memory system optimization for FPGAs, formal verification of circuit transformations, and rapid C-to-hardware compilation flows. He has pioneered approaches for handling thousands of outstanding memory misses in FPGA accelerators and developed novel techniques for switch-block exploration without explicit pattern enumeration. Analysis of his 2023-2025 publications reveals a strong trend toward practical FPGA deployment challenges, with increasing focus on HBM integration, virtual memory systems for PCIe-attached devices, and formally verified circuit transformations. His work consistently targets real-world bottlenecks in high-level synthesis toolchains while maintaining theoretical rigor in dataflow architecture design. Professor Ienne's laboratory receives support from the Swiss National Science Foundation and industry partners including Huawei, enabling cutting-edge research in FPGA-based acceleration. His collaborative network spans major semiconductor companies and academic institutions worldwide, with frequent co-authorship on conference proceedings and journal publications in IEEE and ACM venues.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Hiroshi Ishikawa is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering. He also serves as a Visiting Professor at the National Institute of Informatics since 2016. Previously, he held positions at Nagoya City University from 2004-2010 as Assistant Professor, Associate Professor, and Professor. His academic journey includes being a JST PRESTO Researcher from 2009-2013 and an Associate Research Scientist at New York University's Courant Institute of Mathematical Sciences from 2000-2001. Ph.D. in Computer Science, New York University (2000) Master of Science, Kyoto University Bachelor's degree in Mathematics, Kyoto University Faculty of Science (1991) Hiroshi Ishikawa's research spans perceptual information processing, computer vision, artificial intelligence, deep learning, and discrete optimization. His work focuses on developing algorithms for image restoration, segmentation, and understanding, with significant contributions to energy minimization techniques in computer vision. He has pioneered approaches in sketch simplification, medical image segmentation, and higher-order graph cuts. His research bridges theoretical advances in mathematical optimization with practical applications in medical imaging, computer graphics, and consumer electronics. Ishikawa's recent publications demonstrate a strong focus on leveraging deep learning for image enhancement and understanding. His work spans super-resolution techniques, colorization methods, human avatar generation, and medical image analysis. A notable trend is the increasing integration of attention mechanisms and generative models to solve complex vision problems, with growing emphasis on real-world applications in medical imaging and computer graphics. His research group consistently produces high-impact work that appears in top-tier computer vision conferences. 75th Annual IEICE Best Paper Award (2019) Innovative Technologies 2016 Special Prize for Culture (Ministry of Economy, Trade and Industry) MIRU Nagao Award (Best Paper Award) (2009) Young Author Award (IEEE Computer Society Japan Chapter, 2006) MIRU2006 Excellent Paper Award (2006) Harold Grad Memorial Prize (Courant Institute of Mathematical Sciences, NYU, 2000) As a professor at Waseda University, Ishikawa has mentored numerous students who have become active researchers in computer vision, including Yuya Masuda, Edgar Simo-Serra, and Satoshi Iizuka. His research has been supported by various grants, including JST PRESTO funding from 2009-2013. He has served on editorial boards for prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and has held leadership roles in major computer vision conferences such as ICCV, CVPR, and ACCV. Ishikawa leads a vibrant research group at Waseda University focused on computer vision and image processing. His laboratory collaborates extensively with researchers at Nagoya City University, National Institute of Informatics, and international institutions. The group maintains strong connections with industry partners, particularly in medical imaging and consumer electronics sectors, translating theoretical advances into practical applications.
Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.
Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.
