Dr. Jaswinder Lota is a Reader in Engineering at the University of East London , School of Architecture, Computing and Engineering, Department of Engineering & Construction. He is also a Visiting Academic at University College London’s Department of Electronic and Electrical Engineering, and a Chartered Engineer with extensive industry and academic experience. Education: BSc BEng MEng PGCert HE PhD Research Interests: Dr. Lota specializes in signal processing, circuits and systems, wireless communication, and their applications in radar systems (weather/military), low-power sustainable networks beyond 5G/6G (robotics, automation, healthcare), and electronic technologies for hydrogen propulsion. His work integrates AI-driven channel modeling and impulsive noise analysis. Scientific Awards: IEEE CAS Society Certificate of Appreciation (2019) Grants and Collaborations: He has secured significant funding, including a £2.5K International Research Collaboration Award (2016), £2.5K Research Internship Award (2015), £76K Impact Grant (2014), and a £7M MoD-funded project (1999-2004). Collaborators include UCL and NYU. Leadership: Dr. Lota leads the Smart Cities Research group at UEL and contributed to the REF 2021 submission. He has served as Associate Editor for IEEE TCAS I and Guest Editor for multiple IEEE journals.
Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.
Dejan Markovic is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), holding the Mukund Padmanabhan Term Chair in Electrical Engineering and serving as Area Director for Circuits and Embedded Systems. His research spans implantable neuromodulation systems, domain-specific compute architectures, and energy-efficient design methodologies for biomedical and embedded applications. Dr. Markovic earned his PhD (2006) and MS (2000) from UC Berkeley and BS (1998) from the University of Belgrade, Serbia. His research focuses on ultra-low-power integrated circuits for neural interfaces, including artifact-free stimulation/sensing platforms and neural recording front-ends, alongside domain-specific architectures for sparse linear algebra in IoT and mobile computing. Recent work demonstrates strong trends in medical neuromodulation and energy-efficient VLSI for biomedical signal processing and wireless communications. His scientific contributions have been recognized with prestigious awards including IEEE Fellow (2021), ISSCC Lewis Award (2014), ISSCC Jack Raper Award (2010), and NSF CAREER Award (2009). Additional honors include UC Berkeley's David J. Sakrison Memorial Prize (2007) and multiple best paper awards. Dr. Markovic co-founded semiconductor IP startup Flex Logix Technologies (2014) and leads the DMGroup research lab at UCLA. He teaches core courses including ECE 115C (Digital Integrated Circuits), ECE M216A (VLSI Design), and BME M260 (Neuroengineering), mentoring students in cutting-edge VLSI design for medical and communication applications. The DMGroup lab specializes in developing implantable neuromodulation systems, domain-specific compute engines, and novel design methodologies, with recent projects featured in UCLA's coverage of brain-computer interface technology and Anari AI's $2M funding for personalized AI hardware.
Claire Prada is a CNRS Research Director at the Institut Langevin , specializing in laser ultrasound , guided wave propagation , and time-reversal acoustics . Her work bridges fundamental wave physics and applied nondestructive testing. Research Pillars : Zero-group-velocity (ZGV) Lamb modes for material characterization Anisotropic wave propagation and negative refraction phenomena Time-reversal operator decomposition for structural monitoring Passive acoustic defect localization with ambient noise Technological Innovations : Fourier-domain reconstruction algorithms for 3D imaging Single-pixel photoacoustic microscopy Adaptive projection methods for rib-cage ultrasound focusing Wave Physics Discoveries : Documentation of power flux skewing in anisotropic plates Identification of beating resonance patterns in elastic media Experimental validation of negative reflection in chaotic waveguides Medical & Industrial Applications : Quantitative elastography for tissue stiffness measurement Jet engine blade damage detection Cortical bone femoral neck assessment Thin layer thickness measurement via ZGV resonance shifts
Patrick McCormick is an Assistant Professor and Assistant Scientist in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Information Sciences (I2S) at the University of Kansas. He joined the university in 2021 after working at the Air Force Research Laboratory - Sensors Directorate (2018-2021). His research focuses on RF systems, signal processing, and waveform design for radar and communication systems. B.S. Mechanical Engineering, University of Kansas (2008) B.S. Electrical Engineering, University of Kansas (2013) Ph.D. Electrical Engineering, University of Kansas (2018) McCormick’s research areas include optimal emission design, multifunction transmissions, hardware characterization/compensation, and adaptive model-based parameter estimation. He leads the Radar Systems and Remote Sensing Laboratory (RSL) and has published extensively in radar waveform diversity, spectrum sharing, and adaptive signal processing. Recent work trends emphasize dual-function radar-communications co-design, low-cost systems, nonlinear hardware characterization, and digital array optimization. His publications span topics like waveform optimization, mutual coupling compensation, and power-efficient joint systems. IEEE Aerospace and Electronic Systems Society 2018 Robert T. Hill Best Dissertation Award He advises graduate students and collaborates with researchers at institutions like the Air Force Research Laboratory (AFRL), University of Oklahoma (OU), and international conferences. His service includes conference chairs, special session organization, and journal reviewing for IEEE Transactions on Signal Processing and Aerospace Systems. McCormick holds memberships in IEEE, Signal Processing Society, and Young Professionals. His lab seeks motivated graduate students for research in radar waveform design and RF systems.
Professor Elias Aboutanios is a distinguished academic at the University of New South Wales (UNSW), serving as Professor in the School of Electrical Engineering and Telecommunications. With a career spanning over two decades in academia and research, he has established himself as a leading expert in signal processing, radar systems, satellite technology, and NMR spectroscopy. Professor Aboutanios earned his BE in Electrical Engineering from UNSW in 1997 and completed his PhD from UTS in 2002, with research focused on frequency estimation for communications with low earth orbit satellites. Following his doctoral studies, he conducted postdoctoral research at the Institute for Digital Communications at the University of Edinburgh from 2003 to 2007, specializing in space-time adaptive processing for radar target detection. He joined UNSW as a senior lecturer in 2007, was promoted to associate professor in 2019, and achieved the rank of Professor in 2022. His research interests span a broad spectrum of signal processing domains including signal and image processing, parameter estimation, array signal processing, statistical signal processing, positioning and localization, radar and sonar signal processing, NMR signal processing, and space systems. Professor Aboutanios has developed significant expertise in nuclear magnetic resonance spectroscopy, global navigation satellite systems, radar target detection, biologically inspired signal processing, power systems and smart grids, and theoretical signal processing. His work bridges theoretical foundations with practical applications across multiple engineering disciplines. Professor Aboutanios's recent publications demonstrate a strong focus on integrated sensing and communication systems, radar technology, satellite applications, and advanced signal processing techniques. His research shows a clear trajectory toward dual-function radar-communication systems, massive MIMO architectures, CubeSat technology for air traffic monitoring, and innovative approaches to NMR spectroscopy. His work consistently addresses challenging problems in signal parameter estimation, adaptive processing, and system design across multiple application domains. Professor Aboutanios has made significant contributions to engineering education, having developed new courses in electrical engineering design and established the master's program in satellite systems engineering. His educational innovations focus on teaching signal processing through frequent and diverse design experiences, enhancing student learning outcomes in technical subjects. He has led significant space projects including UNSW's involvement in the European QB50 project and the UNSW-EC0 satellite mission, which successfully launched in 2017. As a member of the Space Industry Association of Australia's Legislation Working Group, he has contributed to shaping space policy through multiple submissions to the Australian Government's review of the Space Activities Act.
Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.
Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Laxmikant V. Kale is a Professor and the Paul and Cynthia Saylor Professor Emeritus at the University of Illinois at Urbana-Champaign , where he has been a faculty member since 1985. He directs the Parallel Programming Laboratory and is a Fellow of the ACM and IEEE . Educational Background: B.Tech, Electronics Engineering (1977), Banaras Hindu University M.E., Computer Science (1979), Indian Institute of Science Ph.D., Computer Science (1985), SUNY Stony Brook Research Interests include parallel computing with a focus on adaptive runtime systems , message-driven execution , and interdisciplinary applications such as biomolecular simulations (NAMD), computational cosmology (ChaNGa), and quantum chemistry (OpenAtom). His work integrates high-performance computing with distributed systems to improve scalability and efficiency. Recent Publications highlight advancements in exascale resilience , N-body simulations , power management , and fault tolerance via migratable objects , reflecting his commitment to scalable and robust parallel systems. Scientific Awards Gordon Bell Award (2002) for NAMD IEEE Sidney Fernbach Award (2012) for parallel software development HPCC Challenge Class 2 Award (2011) for Charm++ C. W. Gear Outstanding Junior Faculty Award (1990) ONR Young Investigator (1990-93) Students and Collaborators include Maya Taylor , Jessica Williams , Abhinav Bhatele , Gengbin Zheng , and James C. Phillips , who have contributed to projects like Charm++ , NAMD , and BigSim . Grants include funding from the NIH , NSF , DOE , and NCSA for projects such as NAMD , OPEN ATOM , and Blue Waters .
Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.
Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.
Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .
Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.
