Dr. Saree Alnaghy is an Honorary Associate Professor at the University of Wollongong's Faculty of Engineering and Information Sciences, affiliated with the Centre for Medical Radiation Physics. She holds a Bachelor of Medical and Radiation Physics (Honours) and a PhD in Physics from the University of Wollongong (2009–2017). Her research focuses on advanced medical imaging and radiation therapy technologies, including photon counting detectors, dosimetry systems, and robotic motion phantoms for quality assurance. Key research areas include: Development of novel X-ray detectors for radiotherapy guidance High-resolution dosimetry techniques using silicon and polymer-based systems Integration of real-time imaging in radiation therapy Robotic systems for motion management in oncology Her work has been supported by grants such as 'Sharper Targeting, Brighter Future' (2024) and 'Bringing Colour to Radiotherapy' (2021–2025). She currently supervises PhD and MRes students on projects involving photon counting CT scanners and radiotherapy imaging. Alnaghy also serves as a Radiation Oncology Medical Physics Registrar at the Nelune Comprehensive Cancer Centre.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
John A. Rogers is the Louis Simpson and Kimberly Querrey Professor at Northwestern University, holding joint appointments in Materials Science and Engineering, Biomedical Engineering, Mechanical Engineering, Chemistry, and Neurological Surgery. He directs the Querrey-Simpson Institute for Bioelectronics. His work bridges soft materials science, bio-integrated electronics, and nanotechnology, with a focus on wearable medical devices, bioresorbable systems, and neural interfaces. Educations: B.A. & B.S. from University of Texas Austin (1989), S.M. from MIT (1992), Ph.D. in Physical Chemistry from MIT (1995). Prior roles include Director of Bell Labs' Condensed Matter Physics Department and faculty positions at University of Illinois at Urbana-Champaign. Research emphasizes soft materials for bio-inspired electronics, including flexible sensors, microfluidic platforms, and bioresorbable implants. His lab has pioneered epidermal electronics, injectable optoelectronics, and neural interfacing systems. Over 1000 peer-reviewed publications and 100+ patents highlight his contributions to nanotechnology and biomedical engineering. Awards include the Benjamin Franklin Medal (2019), MRS Medal (2018), and MacArthur Fellowship (2009). He is a member of the National Academies of Sciences, Engineering, and Medicine. Current projects span bioelectronic medicines, wearable health monitors, and advanced medical imaging tools.
Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.
Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.
Adam Khalifa is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Florida. His research focuses on low-power analog/RF/Mixed-mode ASIC design, miniaturization of biomedical devices, wireless powering solutions, and neural stimulation/recording techniques in animal models. He holds a PhD from Johns Hopkins University and degrees from The Hong Kong University of Science and Technology. His work emphasizes implant packaging, electrode microfabrication, and coil design for medical applications. Key research areas include developing energy-efficient wireless systems for implanted devices, such as magnetoelectric antennas and galvanic body-coupled powering. He has pioneered advancements in miniaturized implantable devices, including the 'Microbead' stimulator. His NIH T32 Fellowship (2019) and Ferdinand H. Fellowship (2018) reflect his impactful contributions. Publications highlight innovations in wireless power transfer, metamaterials for biomedical implants, and injectable microdevice fabrication. His work spans from circuit-level modeling to in vivo validation, emphasizing both technical and biological integration challenges. Collaborative efforts address challenges like implant migration tracking via MRI and energy harvesting for battery-free systems.
Andre Berger is an Associate Professor of Operations Research at Maastricht University, affiliated with the QE Operations Research department within the School of Business and Economics. He holds a PhD in Mathematics from Emory University (2006) and completed a postdoc at Technical University Berlin. His research focuses on optimization algorithms, combinatorial optimization, and their applications in scheduling, network design, and operations research. Notable contributions include work on the many-visits Traveling Salesman Problem and cluster editing algorithms. Berger’s recent publications span scheduling theory, MRI-based clinical research collaborations, and theoretical advancements in facility location models. He is based at Tongersestraat 53, Maastricht, and can be reached via a.berger@maastrichtuniversity.nl. Education: MSc in Mathematics, Emory University (2003) PhD in Mathematics, Emory University (2006) Research Interests: Berger’s work bridges theoretical computer science and practical applications in operations research, emphasizing algorithm design for complex optimization problems. Key areas include scheduling algorithms, network flow optimization, and geometric optimization challenges such as the Apollonius problem in facility location. His interdisciplinary approach integrates mathematical programming with real-world scenarios in telecommunications and healthcare.
Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).
Tural Khudiyev is an Assistant Professor at the Department of Materials Science and Nanotechnology , National University of Singapore (NUS), focusing on multimaterial electronic fibers for human-fabric interaction, soft robotics, and neural probes. He previously worked as a postdoctoral researcher at MIT's Research Laboratory of Electronics. B.Sc. in Nuclear Engineering, Hacettepe University Ph.D. in Materials Science and Nanotechnology, Bilkent University His research spans implantable and wearable electronics , stimuli-responsive drug delivery , and AI in healthcare , with a strong emphasis on scalable nanomanufacturing techniques. His publications in journals like Nature , Nature Nanotechnology , and Advanced Materials highlight innovations in thermally drawn fibers and soft bioelectronic interfaces . Key trends include flexible electronics , machine learning in textiles , and bio-integrated devices , with applications in neural engineering and power textiles . His work has been featured in media outlets such as Reuters and Business Insider . Selected as MIT’s top research story of 2021 #1 Most Read Article in Applied Optics (2015) Featured in Virtual Journal of Nanoscale Science & Technology Khudiyev's contributions to thermally drawn fibers and soft robotics demonstrate interdisciplinary collaboration between materials science, neuroscience, and wearable technology.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.
Rikky Muller is an Associate Professor of Electrical Engineering and Computer Sciences at UC Berkeley, holding the S. Shankar Sastry Professorship in Emerging Technologies. She is Co-director of the Berkeley Wireless Research Center (BWRC), a Core Member of the Center for Neural Engineering and Prostheses (CNEP), and an Investigator at the Chan-Zuckerberg Biohub. Her research focuses on implantable/wearable medical devices, low-power wireless systems, and neurotechnology for neurological applications. Education: PhD (2013), UC Berkeley; BS and M.Eng. (2004), MIT, all in EECS. Prior roles include IC designer at Analog Devices and co-founder of Cortera Neurotechnologies (acquired). Research interests include neural interfaces, closed-loop neuromodulation, and biomedical microelectronics. Notable contributions include Neural Dust (ultrasonic implants), wireless EEG systems, and seizure prediction hardware. Awards: MIT TR35 Innovator, NAE Gilbreth Lectureship, NSF CAREER Award, IEEE SSCS New Frontier Award Grants: Bakar Fellows, Hellman Fellowship, NSF CAREER Labs: Muller Lab (UC Berkeley EECS), Chan-Zuckerberg Biohub collaborations
Wenfeng Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at Binghamton University. He holds a PhD from the National University of Singapore (2014) and BS/MS degrees from Huazhong University of Science and Technology (2007-2009). Prior to this role, he conducted postdoctoral research at the University of Minnesota's Biomedical Engineering Department. His research focuses on neural engineering, compressed sensing, ultra-low-power VLSI systems, and in-memory computing. Key areas include hardware security, biomedical signal processing, and energy-efficient computing architectures. His work spans applications in neural interfaces, cryptographic hardware, and IoT edge devices. Recent publications highlight advancements in block-cipher-in-memory architectures, emotion recognition via EEG analysis, and energy-efficient FPGA accelerators for neural networks. His research also addresses challenges in cryogenic memory systems and MRI-compatible neural recording devices. Zhao's contributions emphasize interdisciplinary approaches at the intersection of hardware design, signal processing, and cybersecurity. His lab develops novel solutions for low-power embedded systems and trustworthy IoT infrastructure.
Stuart Long is an associate dean of undergraduate research and faculty member at the Honors College of the University of Houston, where he serves as the academic adviser for all honors students majoring in Electrical and Computer Engineering. He teaches courses on electromagnetic waves and conducts research in antenna design and applied electromagnetics. Education: Received his doctorate from Harvard University. Stuart Long's research focuses on biomedical applications of electromagnetics, particularly MRI safety testing for implantable medical devices. His work addresses RF-induced heating, electromagnetic compatibility, and safety protocols for devices such as orthopaedic implants and active implantable systems. Recent publications emphasize computational modeling, machine learning, and historical advancements in antenna design. His scholarly contributions include the 2024 Distinguished Achievement Award, the 2018 Chen-To Tai Distinguished Educator Award, and the 2014 John Kraus Antenna Award. These honors reflect his leadership in electromagnetic safety research and engineering education. Stuart Long has actively contributed to improving pedagogy in engineering education, particularly through collaborative learning and retention workshops for diverse student populations. His academic advising role supports the integration of rigorous technical training and interdisciplinary research opportunities for honors students.
Sean B. Andersson is a Professor in the Department of Mechanical Engineering at Boston University's College of Engineering. His research focuses on optimal estimation, system identification, single particle tracking, robotics, and control theory. He earned his Ph.D. from the University of Maryland, College Park. Education : Ph.D. in Mechanical Engineering (University of Maryland, College Park) His work integrates control algorithms with applications in microscopy, nanofabrication, and multi-agent systems. Recent research trends highlight persistent monitoring, trajectory optimization, MRI reconstruction, and dip-pen nanolithography. He has mentored numerous graduate and undergraduate students, many of whom now hold positions at institutions like MIT Lincoln Labs, University of Pennsylvania, and Juniper Networks. Scientific Contributions : Developed robust multi-agent control policies for data harvesting Advanced single particle tracking with real-time feedback Innovated in non-raster scanning probe microscopy Optimized sensor scheduling via minimax and semidefinite programming His lab team combines theoretical and applied research in robotics and control systems, with alumni contributing to academia, industry, and research labs globally.
