Rebeca Garcia Fandiño is a Full Professor in the Department of Organic Chemistry at the Faculty of Biology, University of Santiago de Compostela. She leads the SupraNanoBioMol research group focused on supramolecular systems, nanobiomimetics and molecular biophysics. Develops cyclodextrin-based therapeutics for age-related diseases Studies cyclic peptide nanotubes for antimicrobial applications Specializes in molecular dynamics simulations of biomolecular systems Her research explores hierarchical membrane structures, water model effects in nanoconfined environments, and membrane-targeted therapies. She has published extensively on: Toxic oxysterol removal using cyclodextrin dimers Antimicrobial D,L-α-cyclic peptide interactions Quantum-classical simulation hybrid approaches Post-COVID condition molecular characterization Augmented reality applications in education The SupraNanoBioMol group at CIQUS center utilizes experimental and computational techniques including DSC, ATR-FTIR and MD simulations. Recent work addresses antimicrobial resistance through membrane disruption mechanisms and AI-driven drug discovery.
Hazem Ali is a Senior Lecturer at Halmstad University's School of Information Technology. He holds a Ph.D. in Electrical and Computer Engineering from Faculdade de Engenharia da Universidade do Porto (FEUP) and an M.Sc. in Computer Science and Engineering from Halmstad University. His research focuses on embedded systems, real-time systems, and dataflow programming models. He has expertise in hardware/software co-design, parallel computing, and optimization of real-time applications. Education: Ph.D. in Electrical and Computer Engineering (FEUP, Portugal) M.Sc. in Computer Science and Engineering (Halmstad University, Sweden) Recent publications highlight his work in cybersecurity for autonomous vehicles, GPU acceleration of MIMO systems, and optimization of dataflow models. His projects include ELLIIT B02 (Beyond 5G Wireless) and CyberInfra (Cybersecure Traffic Infrastructure). Proficiency in tools includes MATLAB, C/C++, Java, VHDL, and dataflow languages like CAL and Sigma-C, with extensive international experience in Sweden, Portugal, and Egypt.
Neda Maleki is a Senior Lecturer at the Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, starting in September 2024. Her research focuses on Applied IoT, Edge-Cloud Computing, Distributed Systems (Hadoop/Spark), Artificial Intelligence, and Machine Learning for data analysis. PhD in Computer Engineering (2014–2021), Science and Research Branch of Islamic Azad University, Tehran, Iran Master of Science in Computer Engineering (2009–2013), Ghazvin Islamic Azad University Bachelor of Science in Hardware Engineering (2004–2008), Ghazvin Islamic Azad University Her research explores IoT applications in energy forecasting, environmental conservation, and SME digital transformation. Key contributions include frameworks for power-aware Hadoop acceleration, energy-efficient IoT data formats, and predictive models for fuel consumption and city load forecasting. Recent publications highlight collaborations with industry partners in Sweden and international conferences across Qatar, Italy, Denmark, and the Netherlands. She teaches courses in Data Structures, Algorithms, IoT, and programming at the bachelor's and master's levels. 1DV018: Data Structures and Algorithms 1DV501: Introduction to Programming 4DV119: Applied IoT Competence (Master level) Final Thesis supervision
Talip Tolga Sarı is a Research Assistant at the Department of Computer Engineering , Istanbul Technical University , where he has been affiliated since 2019. He holds a PhD in Computer Engineering (2021), a Master's in Computer Engineering (2018), and dual Bachelor's degrees in Computer Engineering and Electronics & Communication Engineering (2015). Education PhD in Computer Engineering, Istanbul Technical University (2021) Master's in Computer Engineering (2018) Bachelor's in Computer Engineering (2015) Bachelor's in Electronics & Communication Engineering (2015) Research interests span Computer Networks , Wireless Communication , and Unmanned Aerial Vehicles (UAVs) , with a focus on semantic communication , UWB localization , and topology control for resilient networks. His recent publications address UAV swarm coordination , smart campus frameworks , and assisted localization systems , reflecting interdisciplinary applications of computer science in real-world problems. Key trends in his 15 most recent articles include decentralized UAV networks, semantic communication architectures, and UWB-based indoor positioning. His work combines machine learning, cross-layer routing, and software-defined networking paradigms, with citations spanning Computer Science , Robotics , and Network Security . Awards : Highest Performance Award, Istanbul Technical University (2025)
Maurizio Valle serves as Full Professor and PhD Program Coordinator at the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) of the University of Genoa. His teaching portfolio includes graduate-level courses such as Digital Systems and Electronic Devices for the Master's program in Electronic Engineering, with active instruction scheduled through the 2025-2026 academic year. His research program centers on embedded machine learning systems for tactile sensing applications, with particular emphasis on FPGA implementations of neural networks for real-time sensor data processing. Key focus areas include neuromorphic engineering approaches to tactile texture classification, PVDF sensor analysis for slippage detection, and hardware-efficient implementations of convolutional networks for hand-gesture recognition in prosthetic systems. This work bridges electronic engineering, robotics, and biomedical applications through the development of electronic skin technologies and sensor fusion methodologies. Analysis of his 15 most recent publications (2024-2025) reveals a concentrated research trajectory in real-time embedded solutions for tactile perception systems. Dominant themes include hardware acceleration of neural networks on FPGA/microcontroller platforms, biomimetic sensor design inspired by cutaneous mechanoreceptors, and practical implementations for the Hannes prosthetic hand. The publications demonstrate methodological rigor in sensor characterization while prioritizing computational efficiency for resource-constrained embedded deployment. As PhD Program Coordinator at DITEN, Professor Valle oversees doctoral research in electronic engineering disciplines, mentoring students in specialized areas including embedded AI systems and tactile sensor development. His academic leadership extends to curriculum development for graduate engineering programs at the University of Genoa. His laboratory work focuses on integrated electronic skin systems for prosthetic applications, featuring custom sensor arrays, embedded processing units, and neuromorphic computing architectures. Current projects involve multimodal sensory feedback systems for upper-limb prostheses and real-time object recognition frameworks using wearable sensor gloves, indicating active collaboration with biomedical engineering and robotics research groups.
Michael Zuzak is an Assistant Professor in the Department of Computer Engineering at the Rochester Institute of Technology's Kate Gleason College of Engineering. His research focuses on hardware security, digital VLSI, and electronic design automation with an emphasis on provably secure system design. Prior to his academic career, he worked at the U.S. Naval Research Laboratory as the lead digital designer for a fielded naval system. Dr. Zuzak's research interests span hardware security , digital VLSI/CAD , and computer architecture . His work centers on designing secure, trustworthy, and reliable electronic systems with a particular focus on addressing security challenges in outsourced IC fabrication. His research uniquely explores solutions with provable, system-level security guarantees through security-aware design automation algorithms and theoretical modeling of hardware security. His recent publications (2023-2025) demonstrate continued focus on hardware security challenges, particularly in logic locking, neural network integrity, hardware trojan detection, and system-level obfuscation techniques. His work shows a strong trend toward integrating machine learning with hardware security and addressing emerging threats in secure IC design. ARCS Scholar Future Faculty Fellow Edison Memorial Graduate Fellow Best Paper nomination at the 2021 Design Automation Conference (DAC) Dr. Zuzak teaches courses including CMPE-110 Introduction to Computer Engineering, CMPE-160 Digital System Design I, CMPE-361 Introduction to Hardware Security, and CMPE-630 Digital Integrated Circuit Design. His research is supported by various sponsors focusing on hardware security challenges in the globalized IC supply chain. He received his PhD in Electrical Engineering from the University of Maryland, College Park in 2022.
Chris Misa is a post-doctoral researcher at the University of Oregon's Department of Computer Science, affiliated with the Oregon Networking Research Group and Oregon Cybersecurity Center of Excellence. His work bridges programmable network hardware, virtualization, and security research. PhD in Computer Science (University of Oregon, mentor: Rejaie & Durairajan) Key research: hardware-accelerated network telemetry, DDoS defense via ZAPDOS, cloud networking Publications in top venues: USENIX NSDI, IEEE Security & Privacy, IEEE Transactions on Networking His research focuses on optimizing network measurement systems by combining programmable switch hardware, machine learning, and innovative algorithmic approaches. Recent projects like ZAPDOS demonstrate his emphasis on addressing high-volume DDoS threats through prefix-level traffic analysis and dynamic resource allocation. Current work spans runtime telemetry, multi-cloud management, and optical network security solutions. Scientific awards include: Ripple Faculty Fellowship Ripple Graduate Fellowship NSF grant CNS 1850297 (as team member) As a postdoc, he contributes to sponsored research collaborations with Broadcom and leads academic advising for junior researchers. His projects integrate network testbeds, open-source tools (BONSAI), and interdisciplinary approaches to network science.
Dr. Amer Dawoud serves as an Associate Professor at the University of Southern Mississippi, where he bridges computer engineering with defense technology and medical diagnostics through innovative hardware-software integration. His research program spans electrochemical sensing systems, drone-based surveillance, and advanced image processing algorithms with real-world deployment focus. His educational foundation includes: PhD in Engineering from University of Waterloo (2003) MS from Kuwait University (1998) BS from Yarmouk University (1988) Dr. Dawoud's research centers on defense-oriented electrochemical sensing and hardware security for IoT infrastructure . Recent work (2022-2024) pioneers drone-mounted potentiostats for remote chemical warfare agent detection, while his hardware security research develops FPGA-based PUF designs resistant to machine learning attacks. His longstanding expertise in medical image processing features Markov Random Fields and Type-2 fuzzy logic techniques for lung segmentation in radiographs and dermoscopic analysis, demonstrating methodological continuity across domains. Current projects emphasize field-deployable systems that merge drone mobility with embedded electrochemistry for battlefield applications. Analysis of his publication trajectory reveals strategic evolution from foundational image processing (2009-2015) toward defense technology (2021-2024), with consistent focus on robust, real-world implementations . The integration of Markov Random Fields across medical imaging and document analysis demonstrates cross-domain applicability of his core methodologies. His recent shift to chemical threat detection represents both technological advancement and response to contemporary security challenges.
Dr. Eishi Arima is a researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM). His work focuses on cutting-edge computer architecture and high-performance computing systems, with particular expertise in power-aware computing, resource management, and heterogeneous systems. He actively contributes to numerous international conferences and collaborative research projects addressing challenges in modern computing infrastructure. Dr. Arima's research spans multiple critical areas in computer architecture including memory and storage systems, performance modeling and optimization, hardware/software codesign, and processor microarchitectures. His work demonstrates particular strength in addressing energy efficiency challenges in high-performance computing environments, with numerous publications on power capping, resource partitioning, and sustainable computing approaches. His research bridges theoretical concepts with practical implementations, often incorporating machine learning techniques to optimize system performance under various constraints. Analysis of Dr. Arima's publication record reveals a strong focus on addressing the energy efficiency challenges in modern computing systems. His work consistently targets the intersection of hardware architecture and system-level resource management, with particular emphasis on heterogeneous computing platforms combining CPUs, GPUs, and emerging memory technologies. Over time, his research has evolved from traditional cache and memory system optimizations toward more holistic approaches incorporating machine learning for resource management in power-constrained environments. Recent publications demonstrate increasing attention to sustainability aspects of computing, reflecting broader industry trends toward greener computing solutions. Dr. Arima has served in various organizational capacities for major international conferences including as Program Committee member for SC, IPDPS, and Cluster conferences, and as Program Co-Chair for ACM CF'20. His journal review activities span multiple prestigious publications including IEEE TPDS and Elsevier FGCS. This extensive service demonstrates his recognition as a respected member of the international computer architecture research community. Dr. Arima has mentored numerous students through bachelor's theses, master's theses, and guided research projects. His students have produced research on topics including reinforcement learning for resource management, job scheduling optimization, memory system improvements, and power-aware computing techniques. Several student projects have resulted in publications at reputable conferences, indicating the high quality of research conducted under his supervision. His mentoring covers both theoretical aspects of computer architecture and practical implementation challenges in real-world systems. Dr. Arima is actively involved in multiple research projects including SEANERGYS (EuroHPC), PlasmaPEPS, OpenCUBE, DaREXA-F, ScalNEXT, PDexa, MUNIQC-ATOMS, BB-KI_Chips, QuaST, and Q-DESSI. These projects address various aspects of high-performance computing, from energy efficiency to quantum computing integration. His work contributes to the development of next-generation computing infrastructure that balances performance requirements with sustainability concerns.
Rolf Drechsler is a Full Professor and Head of the Group of Computer Architecture at the University of Bremen since 2001, with additional role as Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He holds a Dr. phil. nat. in computer science from Johann Wolfgang Goethe University Frankfurt and has held academic positions at Albert-Ludwigs University Freiburg and Siemens AG. Education: Diploma and Dr. phil. nat. in Computer Science (Johann Wolfgang Goethe University Frankfurt, 1992-1995) Previous Roles: Albert-Ludwigs University Freiburg (1995-2000), Siemens AG Munich (2000-2001) His research focuses on formal verification , circuit/system design , and data structures for hardware validation . He has pioneered work on polynomial verification methods, BDD-based synthesis, and quantum computing verification frameworks. Recent publications analyze memristor-based in-memory computing, RISC-V security, and LLM-assisted hardware validation. Key awards include ACM Fellow IEEE Fellow Berninghausen Teaching Award (2018) Multiple best paper awards at ICCAD, DATE, DAC, and DDECS He serves as Associate Editor in journals like IEEE Transactions on CAD and ACM Journal on Emerging Technologies. He co-founded the Graduate School of Embedded Systems and Data Science Center at University of Bremen.
Tiziano Bianchi is a Full Professor in the Department of Electronics and Telecommunications at Politecnico di Torino (Polytechnic of Turin), Italy. He serves as the Course Coordinator for the Master's Degree in ICT Engineering for Smart Societies and is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Previously, he was an Assistant Professor at the same institution starting in 2012, having joined from the University of Florence where he worked as a Research Assistant from 2005 to 2012. Dr. Bianchi's research focuses on multimedia security technologies, with particular expertise in multimedia forensics, signal processing in the encrypted domain, and security aspects of compressed sensing. His work spans adversarial machine learning, image and video forensics, deep learning for video compression, secure authentication based on deep learning, and satellite image compression. He has made significant contributions to privacy-preserving data processing through compressed sensing techniques. His recent publications demonstrate a strong emphasis on applying deep learning to solve complex problems in image processing, security, and data compression. The research shows a clear trajectory toward efficient, secure, and privacy-preserving processing techniques, particularly for resource-constrained environments like wearable devices and satellite systems. Among his notable recognitions are the Journal of Visual Communication and Image Representation Best Editor of 2022 Award, The SIGMM Test of Time Paper Honorable Mention in Multimedia Security and Privacy, and the 2019 Best Paper Award from IEEE MultiMedia. He has also received multiple editorial awards for his work as Senior Area Editor for the Elsevier Journal of Visual Communication and Image Representation and Associate Editor for the IEEE Transactions on Information Forensics and Security. Dr. Bianchi is the co-founder of ToothPic, a startup offering breakthrough technologies for camera identification, and has co-invented four patents related to cryptographic key obfuscation, user authentication, and camera recognition techniques. He has authored over 100 papers in international journals and conference proceedings, demonstrating sustained scholarly productivity throughout his career.
Erdal Aksoy is an Associate Professor at Halmstad University 's School of Information Technology , with visiting scholar roles at Volvo Group Trucks Technology and Zenseact AB . His work focuses on semantic scene perception , action semantics , and environment understanding for autonomous systems including robots and unmanned vehicles. Education : PhD from University of Göttingen, postdoctoral research at Karlsruhe Institute of Technology (KIT) and University of Göttingen Research Interests : Semantic scene perception, action semantics, autonomous systems, human-robot interaction, LiDAR processing, and deep learning applications His recent publications demonstrate expertise in LiDAR data processing (SalsaNet, SalsaNext), semantic state estimation for robotic cloth manipulation, and multimodal failure detection systems. He has coordinated the HORIZON Europe ROADVIEW consortium with 15 partners. Scientific recognition includes Best Student Paper Award at IJCAI 2021 AI4AD workshop Best AI Master’s Thesis Award from Swedish AI Society (SAIS) Wimanska Prize for best bachelor thesis Distinguished Service Award as IEEE Robotics and Automation Letters Associate Editor His advising track record shows mentorship of award-winning students across bachelor, master, and PhD levels. Erdal Aksoy maintains active collaborations between academia and industry, particularly with automotive technology companies.
Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.
Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset
Arya Mazaheri is a Research Leader at PanocularAI, affiliated with the Technische Universität Darmstadt. His work bridges high-performance computing (HPC) and machine learning, focusing on optimizing large-scale computational systems. Based at Hochschulstr. 10, Darmstadt, Germany, he contributes to GPU acceleration, neural network pruning, and parallel processing. PhD in Performance Engineering of Data-Intensive Applications (2022) Key areas: HPC, Machine Learning, GPU Computing, Neural Network Pruning Research Trends: Mazaheri's publications from 2015-2024 reveal expertise in: Accelerating LLM inference through pipelined speculation Topology-aware network pruning with reinforcement learning GPU-based spacecraft trajectory simulations Performance portability in tensor operations Hardware-independent communication metrics for parallel systems
