Piotr Łuczak is a Researcher at the Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology. His work bridges computer engineering, machine learning, and biomedical applications. Research spans VLSI systems for radar-based health monitoring, thermal imaging for industrial control, hyperdimensional computing frameworks, and neuromorphic approaches. Recent publications demonstrate strong interdisciplinary focus on hardware-efficient AI implementations. Article analysis shows consistent evolution in: (1) Radar-based biomedical sensing with novel signal processing; (2) Thermal imaging for industrial automation; (3) Hybrid AI models combining neural networks with symbolic methods; (4) Neuromorphic computing for edge devices; (5) Optimization techniques for efficient neural architectures; (6) Human-machine interaction systems.
Maryam Parsa is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. Her research focuses on neuromorphic computing, Bayesian optimization, and algorithm-hardware co-design, aiming to develop energy-efficient, secure, and resilient AI systems for edge computing applications. She holds a PhD from Purdue University, supported by an Intel/SRC fellowship, and previously worked at Oak Ridge National Lab. Education: PhD in Electrical and Computer Engineering, Purdue University (2020) MS in Civil Engineering, Purdue University (Year unknown) MS in Electrical and Computer Engineering, University of Ottawa (Year unknown) BS in Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology (Year unknown) Research Interests: Neuromorphic learning and bio-inspired robotics Bayesian optimization for materials discovery Privacy-preserving spiking neural networks Edge AI and real-time embedded systems Major Achievements: Lead a $2.4M 3-year project on 3D chip creation (2023) Recipient of Intel/SRC PhD Fellowship Current Work: Developing neuromorphic architectures for smart healthcare and cyber-physical systems Pioneering causal machine learning for materials innovation Advancing privacy & security in neuromorphic systems Labs/Teams: Active in Mason's neuromorphic computing research group with collaborations in national labs and industry partners.
Kathryn Simone is a Sessional Lecturer at the University of Waterloo, focusing on interdisciplinary research at the intersection of neuroscience, neuroengineering, and computational systems. Her work emphasizes neurophysiological mechanisms, biologically plausible algorithms, and innovative neurotechnology development. Research interests center on stress neurobiology, neural coding mechanisms, and the development of advanced optical systems for in vivo neural monitoring. She explores topics such as CRH neuron activity in memory formation, motion artifact correction in fiber photometry, and neuromorphic computing inspired by biological systems. Her publications span 2015–2024, with a strong emphasis on optoelectronic systems for neuroimaging, reinforcement learning algorithms, and behavioral neuroscience applications. Notable contributions include open-source fiber photometry systems and studies on hypothalamic stress response dynamics. No scientific awards are explicitly mentioned in the provided materials. Advising and grant information is not available in the current text. Her work involves collaboration with rodent models and employs cutting-edge tools like calcium imaging and vector symbolic algebra for neural data analysis.
Jeff Orchard is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo , with cross-appointments to the Department of Applied Mathematics and Department of Education . He directs the Neurocognitive Computing Lab and is a core member of the Centre for Theoretical Neuroscience . In 2022, he completed a 6-month sabbatical at the International Centre for Neuromorphic Systems (Western Sydney University). Education: PhD in Computing Science, Simon Fraser University (2003) MSc in Applied Mathematics, University of British Columbia (1996) BMath in Applied Mathematics, University of Waterloo (1994) Dr. Orchard's research focuses on understanding the brain through computational and mathematical modeling, particularly through neural networks , predictive coding , and vector symbolic architectures . His work bridges neuroscience , machine learning , and neuromorphic engineering , seeking mechanistic rules governing cognition and behavior. Earlier research involved medical image processing , including MRI motion compensation , image denoising , and retinal vessel segmentation . Recent publications (2022-2025) highlight his exploration of biologically-plausible learning algorithms , neuromorphic computing , and AI alignment through active inference models. His work on predictive coding networks and hyperdimensional computing demonstrates interdisciplinary innovation across computer science , mathematics , and cognitive neuroscience .
Diego Cabello Ferrer is a Full Professor in the Department of Electronics and Computer Science at the University of Santiago de Compostela (USC). He graduated in Physics from the University of Granada in 1978 and earned his PhD in Physics from USC in 1984, receiving the PhD extraordinary prize. His career spans decades, including roles as an assistant professor at the University of Granada, associate professor at USC, and full professor since 1997. Education: BS in Physics, University of Granada (1978) PhD in Physics, University of Santiago de Compostela (1984) His research focuses on efficient CMOS architectures and intelligent electronic design for computer vision, particularly early vision processing. He has contributed to projects involving mixed-mode signal CMOS chips, hyperdimensional computing, and low-power embedded AI solutions. Scientific awards include the 2003 Best Paper Award from the European Circuit Society. He has been actively involved in academic leadership, serving as Dean of the Faculty of Physics (1977–2002) and Chair of the Department of Electronics and Computer Science (2002–2006). He has authored or co-authored approximately 170 publications and participated in organizing committees for conferences, including co-chairing the 11th IEEE International Workshop on Cellular Neural Networks and Their Applications in 2008.
Vasant Gajanan Honavar is a Professor and Edward Frymoyer Chair of Information Sciences and Technology at Penn State University . He is affiliated with multiple institutes: the Institute for Computational and Data Sciences (ICDS), Huck Institutes of the Life Sciences, Clinical and Translational Science Institute (CTSI), One Health Microbiome Center, and the Neuroscience Institute. Research Interests : Machine Learning, Predictive Modeling, Ontology, RNA-Protein Interactions, and Data Science. Projects : NSF-funded research on topics like interatomic potentials, AI alignment, and diffusion models. His work spans interdisciplinary domains, contributing to computer science, bioinformatics, and materials science. Recent publications focus on preference optimization and hyperdimensional learning. He collaborates globally on projects involving predictive modeling and collaborative AI.
Sara Achour is an Assistant Professor jointly appointed in both the Computer Science and Electrical Engineering Departments at Stanford University's School of Engineering. Her work bridges computer science and electrical engineering, focusing on enabling end-users to develop computations for emerging computing platforms with analog behaviors. Dr. Achour received her PhD in Computer Science from the Massachusetts Institute of Technology in 2021. Her academic journey led her to Stanford where she currently teaches courses including Introduction to Essential Software Systems and Tools (CS 104), Software Engineering (CS 295), and Software Techniques for Emerging Hardware Platforms (CS 349H/EE 349). Her research program centers on developing new programming languages, compilers, and runtime systems that address the challenges of emerging computing platforms. She specializes in creating tools that help developers harness the potential of analog and non-traditional hardware systems. Her work spans quantum computing, analog computing paradigms, hyperdimensional computing, and memory systems, with a particular emphasis on compiler techniques and hardware-aware optimization. Analysis of her recent publications reveals a strong focus on bridging the gap between software and emerging hardware platforms. Her work spans quantum computing (qubit/qutrit circuits), analog computing paradigms, hyperdimensional computing, and novel memory systems. A recurring theme is developing compiler techniques that optimize for specific hardware characteristics while maintaining programmer productivity. Dr. Achour actively mentors students across multiple levels of their academic careers. She serves as a Doctoral Dissertation Advisor, Co-Advisor, Reader, and Master's Program Advisor for numerous students working on cutting-edge research in compilers and emerging hardware. Her teaching portfolio includes both foundational courses like Introduction to Essential Software Systems and specialized advanced courses focused on emerging hardware platforms. She appears to be building a research group focused on programming languages and compilers for non-traditional computing architectures, with students working across quantum computing, analog systems, and memory technologies.
Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.
Steven Latré is a Professor in the Department of Computer Science at the University of Antwerp, specializing in network management, software-defined networking, and network function virtualization. His research spans wireless communications, Internet of Things, machine learning for networking, and quality of experience optimization. With over 246 publications from 2007 to 2025, he has established himself as a leading researcher in network intelligence and management. His research focuses on developing intelligent approaches for network management, with particular emphasis on applying machine learning techniques to solve complex networking challenges. His work covers adaptive video streaming, resource allocation in heterogeneous networks, wireless sensor networks, and vehicular communications. His research often bridges theoretical advances with practical implementations, as evidenced by numerous collaborations with industry partners and participation in European research projects. His recent publications demonstrate a growing interest in applying AI and machine learning to networking problems, with significant contributions in network function virtualization, software-defined radio access networks, and intelligent resource allocation. His work shows a clear trajectory from traditional network management toward more intelligent, self-optimizing network systems that leverage the latest advances in artificial intelligence. His scientific contributions have been recognized through numerous publications in top-tier networking venues including IEEE Communications Magazine, IEEE Transactions on Network and Service Management, and IEEE Internet of Things Journal. His research has consistently addressed real-world networking challenges while pushing theoretical boundaries in network management and orchestration. Professor Latré has actively supervised numerous PhD students and collaborated extensively with researchers worldwide, particularly with colleagues at the University of Antwerp including Jeroen Famaey, Filip De Turck, and Tom De Schepper. His collaborative network spans multiple continents, reflecting the international impact of his research.
Bernard De Baets is a Senior Full Professor and Head of the Department of Data Analysis and Mathematical Modelling at the Faculty of Bioscience Engineering, Ghent University. He leads the Knowledge-based Systems research unit KERMIT. With interdisciplinary expertise in mathematics, computer science, and knowledge engineering, his research bridges fundamental and applied domains in biological sciences. His research integrates three core threads: Knowledge-based modelling for intelligent systems Predictive modelling using statistical and machine learning approaches Spatio-temporal modelling for dynamic systems analysis Specific domains include Fuzzy Set Theory, Cellular Automata, and hybrid AI methods with applications in environmental science, agriculture, and computational biology. He has authored over 550 peer-reviewed journal papers (accumulating ~25,000 citations) and earned multiple best paper awards. As Editor-in-Chief of Fuzzy Sets and Systems and editorial board member for several journals, he significantly contributes to the scientific community. Major honors include: Honorary Professor at Budapest Tech Doctor Honoris Causa (University of Turku) Professor Extraordinarius (University of South Africa) Fellow of the International Fuzzy Systems Association EUSFLAT Scientific Excellence Award
Nicholas F. Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University's College of Science. His academic journey includes a Ph.D. in Applied Mathematics from Yale University (2019) and a B.S. in Mathematics from Clarkson University (2014), with additional research experience at Princeton University as an NSF Postdoc. Ph.D. in Applied Mathematics, Yale University, 2019 B.S. in Mathematics, Clarkson University, 2014 His research focuses on the interplay between analysis, geometry, and probability, particularly as applied to data science challenges. Current investigations include harmonic analysis on geometric domains, randomized algorithms for linear systems, and mathematical frameworks for cryo-electron microscopy. His work bridges pure mathematical theory with computational applications in imaging and machine learning. Analysis of his recent publications reveals strong trends in computational harmonic analysis, with significant contributions to fast algorithms for spherical and disk harmonics, randomized linear solvers with momentum acceleration, and geometric approaches to hyperdimensional computing. His work consistently connects abstract mathematical concepts to practical computational problems in imaging and data science. Dr. Marshall actively mentors graduate students including Wyatt Whiting, Peter Cowal, and Heather Fogarty, and has supervised notable undergraduate research projects leading to publications in SIAM journals. His current teaching portfolio includes advanced courses in probability theory, numerical linear algebra, and data science mathematics. He maintains active research collaborations with institutions including Princeton University and Yale, focusing on applications in cryo-EM imaging and computational geometry. Personal interests include skiing (learned in Vermont) and kayaking along the Oregon Coast.
Jonas Kantic is a Ph.D. student and Researcher at the Chair of Integrated Systems within the Faculty of Electrical Engineering and Information Technology at Technical University of Munich . Holding a Master of Science in Technical Informatics from Leibniz University Hannover, his work focuses on AI acceleration architectures and embedded systems design. Education Master's in Technical Informatics (2017-2020), Leibniz University Hannover Chinese Language Studies (2018-2019), Beijing Foreign Studies University Bachelor's in Technical Informatics (2013-2017), Leibniz University Hannover Research Focus Specializes in reservoir computing architectures Expertise in FPGA-based AI acceleration Investigates temporal/spatial compression techniques Develops efficient edge AI inference systems Applies machine learning to motorcycle control systems Works on hyperdimensional computing models Supervision Mentored 5+ students in RNN accelerators, CNN optimization, and stochastic computing Collaborates with industry partners (BMW Motorrad, NXP Semiconductors) Publications 2024 - Complex & Intelligent Systems: Cellular Automata for Reservoir Computing 2024 - IEEE NorCAS: FPGA Implementation for High-Speed Reservoir Models 2021 - Current Directions in Biomedical Engineering: Hearing Aid CNN Optimization
Arya Mazumdar is the HDSI Endowed Chair Professor in AI at the Halıcıoğlu Data Science Institute (HDSI) , Computer Science and Engineering , and Electrical and Computer Engineering departments at University of California San Diego. She co-leads NSF's AI Institute for Learning-enabled Optimization at Scale (TILOS) and serves as UCSD Site Lead for EnCORE: Institute for Emerging CORE Methods of Data Science. Her research bridges machine learning , information theory , and optimization with applications in distributed learning, clustering, and data science. Distinguished Lecturer, IEEE Information Theory Society (2023-2024) Co-PI, NSF AI Institute for Learning-enabled Optimization at Scale Formerly: Associate Professor at University of Massachusetts Amherst (2015-2021) Research focus areas include: Learning-enabled optimization algorithms Communication-efficient distributed systems Neural auto-associative memory Locally repairable codes Key scientific recognitions: ECE Distinguished Alumni Award, University of Maryland (2025) EURASIP JASP Best Paper Award (2020) NSF CAREER award
Pat Pannuto is an Assistant Professor in Computer Science Engineering at the University of California, San Diego. His research bridges digital systems and the physical world, focusing on resource-constrained computing, embedded systems, and IoT infrastructure. He leads projects on sustainable computing, energy harvesting, social interaction tracking, and city-scale sensing. Research Interests: Dr. Pannuto designs systems for extreme resource constraints (microwatts power, kilobytes memory) that enable reliable sensing/actuation. Key areas include: Embedded OS development (Tock OS) Energy harvesting from unconventional sources (soil MFCs, corrosion systems) Human interaction tracking for epidemiology/psychology Large-scale infrastructure monitoring (GridWatch) Localization and ultra-low-power communication Awards & Fellowships: National Defense Science & Engineering Graduate (NDSEG) Fellow (2013) NSF Graduate Research Fellowship (2013) Qualcomm Innovation Fellow (2013) Teaching & Service: He teaches courses in computer architecture, embedded systems, and IoT. As faculty advisor, he mentors 25+ graduate students and oversees undergraduate research programs (ERSP/TRELS). He chairs conferences including TockWorld 7 and serves on TPCs for MobiCom, SenSys, and IPSN. Labs & Teams: Leads the UCSD Embedded Systems Lab, co-founded the UCSD Embedded Systems Seminar, and collaborates with the TerraSwarm and CONIX research centers. Current projects include security for embedded OS, hyperdimensional computing libraries, and renewable energy systems.
Ersin Çine is a Researcher and former PhD candidate in the Computer Engineering Department at İzmir Institute of Technology. He holds a BSc from Süleyman Demirel University (2015), and MSc and PhD degrees from İzmir Institute of Technology (2018 and 2024). His research focuses on artificial intelligence, machine learning, automated reasoning, and medical imaging. He teaches courses such as CENG 213 (Theory of Computation) and CENG 463 (Introduction to Machine Learning). His research spans topics like syllogistic logic integration into knowledge bases, efficient image matching algorithms, and bioengineering applications using lensless holographic microscopy. His work combines theoretical advances in logic with practical applications in medical diagnosis and space biotechnology. No scientific awards or grants are listed, and there are no formal research projects or advisees documented. His professional contact is based at İzmir Institute of Technology’s Computer Engineering Department.
