Dr. Joseph Wang is the Distinguished Professor of Nanoengineering and the SAIC Endowed Chair at UC San Diego. He leads the NBE Lab and directs the Center of Wearable Sensors and the Center for Mobile-health Systems. With over 50 researchers in his team, his work focuses on nanomachines, wearable sensors, electrochemistry, and analytical chemistry. His global citation ranking places him #13 in Chemistry and #31 in Materials Science, with an H-index of 217 and over 180,000 citations. He has been a Highly Cited Researcher since 2014 and ranks #4 in Nanoscience & Nanotechnology in the 2025 World Top 100 Scientists list. His research has led to groundbreaking innovations, including microrobots for lung cancer treatment, multiplexed microneedle sensors, and wearable devices for real-time health monitoring. He has been honored with prestigious awards such as the 2024 ACS Award in Analytical Chemistry, IEEE Sensors Council Award, and IUPAC Medal. He holds honorary doctorates from Comenius University and Charles University, and Woxsen University named its Chemistry Department after him. Key contributions include pioneering work in biohybrid microrobots, self-healing wearable devices, and sweat-based health monitoring systems. His lab’s work has been featured in Nature, Science, and The Economist. He co-authored influential books like Analytical Electrochemistry (4th ed.) and Nanomachines , and his research spans clinical applications, environmental sensing, and personalized medicine.
Donald Lie is a Professor and the Keh-Shew Lu Regents Chair in Electrical and Computer Engineering at Texas Tech University's Whitacre College of Engineering. His research focuses on low-power RF/analog integrated circuits, System-on-a-Chip (SoC) design, and interdisciplinary applications in medical electronics, biosensors, and biosignal processing. PhD, Electrical Engineering, California Institute of Technology (1995) MS, Electrical Engineering, California Institute of Technology (1990) BS, Electrical Engineering, National Taiwan University (1987) Donald Lie's research bridges RF/analog circuit design with biomedical engineering, emphasizing millimeter-wave power amplifiers for 5G systems and non-contact vital signs monitoring using software-defined radio (SDR). His work explores CMOS FD-SOI, GaN HEMTs, and SiGe technologies for high-efficiency, linear RF front-end modules and wearable biosensors. His 15 most recent publications focus on 5G communication systems , millimeter-wave power amplifier design in CMOS FD-SOI and GaN , digital predistortion techniques, and non-contact biosensors . These works highlight advancements in wideband amplifiers for 5G FR2 bands and wireless power transfer for medical devices. Institute of Electrical and Electronics Engineers (2017) Excellent Paper Award Winner (2019) Best Student Poster Paper Award Winner (2019) Donald Lie has secured NSF Student Travel Grants for conferences like RFIC 2022 and 2020. He leads the RF/Analog System-on-a-Chip (SoC) Design Lab , which develops innovative solutions for 5G RF front-ends and biomedical sensing systems.
Erwin Schoof is an Associate Professor at the Department of Biotechnology and Biomedicine , Technical University of Denmark. He leads the Cell Diversity Lab and focuses on advancing proteomics and mass spectrometry technologies. Expertise in single-cell proteomics , stem cell niches , and bioinformatics . Active in myelofibrosis and leukemia research , with applications in UN Sustainable Development Goals . Research Trends from 2025–2024 include: Machine learning-driven peptide sequencing (InstaNovo, InstaNexus). Single-cell resolution tools for mapping hematopoietic stem cells and tumor microenvironments . Biomarker discovery in chronic diseases and respiratory conditions . Supervision : Mentors multiple PhD students on single-cell proteomics , omics data analysis , and biotherapeutic production . Labs & Collaborations : Collaborates with international teams on plasma proteomics , 3D bioengineering , and advanced mass spectrometry workflows .
Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, and Director of the 3D Systems Packaging Research Center . His research focuses on heterogeneous integration , electrical/photonic interconnects , thermal modeling , and electronics for healthcare , with over 180 publications and 12 U.S. patents. Research areas include: Advanced cooling and power delivery for emerging systems Biosensor-CMOS integration 2.5D/3D IC packaging Polylithic integration technology Nanofabrication for microsystems Scientific accolades include: 2018 IEEE EPS Exceptional Technical Achievement Award 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Outstanding Young Engineer Award Best paper awards at IEEE ECTC, IITC, and CICC 2020 Georgia Tech Doctoral Thesis Advisor Award His lab explores integrated 3D systems with emphasis on co-design of thermal, power, and electrical networks for machine learning and healthcare applications.
Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.
Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.
Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.
Jindal Shah is a Professor and holds the Anadarko Petroleum Chair in Chemical Engineering at Oklahoma State University, where he also serves as the Graduate Program Director. He is affiliated with the Department of Chemical Engineering within the College of Engineering at Oklahoma State University. Dr. Shah received his educational training from prestigious institutions worldwide. He earned his Ph.D. in Chemical Engineering from the University of Notre Dame in 2005, followed by an M.S. in Environmental Engineering from the University of Cincinnati in 1999, and completed his undergraduate education with a B.Tech. in Chemical Engineering from the Indian Institute of Technology (IIT) Bombay in 1996. Dr. Shah's research focuses on the application of molecular simulation methodologies to understand molecular-level interactions that give rise to macroscopic phenomena. His primary research interests include Monte Carlo and Molecular Dynamics Simulations, Phase Equilibria, Ionic liquids, and Dye-sensitized solar cells. A significant portion of his work centers on designing novel biodegradable ionic liquids with properties suitable for chemical processes, with applications in next-generation batteries and carbon capture. He also investigates molecular-level interactions responsible for device efficiency in dye-sensitized solar cells to rationally design novel dye molecules. Additionally, Dr. Shah employs data science and machine learning techniques to correlate properties of ionic liquids and generate new molecules with desired properties. An analysis of Dr. Shah's recent publications reveals a strong focus on ionic liquids and their applications in energy storage and carbon capture technologies. His work consistently bridges fundamental molecular-level understanding with practical applications, particularly in developing electrolytes for batteries and CO2 capture systems. A notable trend is the integration of machine learning techniques with traditional molecular simulation methods to accelerate materials discovery and optimization. His research demonstrates a progression from fundamental molecular simulations toward applied technologies with significant environmental impact, particularly in climate action (SDG 13) and affordable clean energy (SDG 7). Dr. Shah has secured substantial research funding from multiple prestigious sources including the National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, and industry partners. His funded projects include 'Collaborative Research: Cyber Training-Implementation, Medium, Establishing Sustainable Ecosystem for Computational Molecular Science Training & Education' (NSF), 'Ionic Liquids for Direct Air Capture of CO2 using Electric-Field-Mediated Moisture Gradient Process' (DOE), and 'CAREER: Computation-Enabled Rational Design of Cytochrome P450 for Ionic Liquid Biodegradation' (NSF). These grants support his research in computational molecular science, CO2 capture technologies, and the development of biodegradable ionic liquids. As an educator, Dr. Shah has been actively involved in teaching graduate courses including Principles of Chemical Engineering Thermodynamics, Doctoral Thesis supervision, and specialized courses such as Machine Learning for Chemical Processes and Introduction to Chemical Process Analytics. His teaching philosophy integrates cutting-edge research with educational practice, preparing students for the computational challenges of modern chemical engineering. He has also mentored numerous doctoral students through their dissertation research, contributing to the development of the next generation of chemical engineers and computational scientists.
Stefan Röhrl, M.Sc., is a research staff member at the Chair for Data Processing at the Technical University of Munich (TUM). His work focuses on applying artificial intelligence to biomedical imaging and computational creativity. Research Areas CellFace: AI-based analysis and diagnosis of blood cells using deep learning and digital holographic microscopy Computer & Creativity: Development of machine learning methods for artificial creativity Teaching Summer Term: Computer Vision Praktikum, Industry 4.0 Grundkurs C++, Monty Matlab, Daedalus Arduino, 3D-Druck Workshops, Python 4 EI Workshops Winter Term: Komputer & Creativität, Leistungskurs C++, Daedalus Arduino, 3D-Druck Workshops, Python 4 EI Workshops Contact Office: Z947, Arcisstraße 21, 80333 München Email: stefan.roehrl@tum.de Phone: +49 (0)89 289 23605
Dr. Alexandra Piotrowski-Daspit is an Assistant Professor in the Biomedical Engineering department and Internal Medicine – Pulmonary and Critical Care Medicine at the University of Michigan Medical School. She is a chemical/biological engineer with expertise in polymeric biomaterials for gene therapies, focusing on in vivo behavior of delivery vehicles and strategies to optimize biodistribution. Education: Ph.D. in Chemical/Biological Engineering Her research bridges polymer chemistry, gene delivery, and translational medicine, emphasizing in utero interventions and pulmonary targeting. Key areas include nanoparticle surface engineering, macrophage decoys, and computational pharmacokinetic modeling. Recent publications highlight systemic in utero gene editing for cystic fibrosis, poly(amine-co-ester) nanoparticle tunability, and mucosal vaccination platforms. She also explores miRNA therapies for congenital diaphragmatic hernia and triplex-forming PNAs for CFTR correction. PhRMA Foundation Awardee (2024) Her work involves interdisciplinary collaborations, DEI initiatives, and translational projects from postdoc foundations in W. Mark Saltzman’s lab. Labs like Saltzman and SCGE teams support her research.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Professor David Ackerley (Victoria University of Wellington) is a leading microbiologist and enzyme engineer specializing in directed evolution of bacterial enzymes for biotechnological applications. As Biotechnology Programme Director since 2006, he lectures in foundational courses like BTEC101 and BTEC201. Academic rank: Professor of Biotechnology Institutional affiliation: Victoria University of Wellington Research focus areas: Microbial Biotechnology, Drug Discovery, Synthetic Biology His research employs Darwinian evolutionary principles to engineer enzymes with enhanced activities, particularly targeting non-ribosomal peptide synthetases and nitroreductases for antibiotic development and cancer therapy. Recent work explores metagenomic domain substitution in pyoverdine biosynthesis and Purpuramine R from marine sponges. Key publications demonstrate innovations in metagenomic library construction , CRISPR screening for regeneration genes, and structural characterization of engineered enzymes. His team has developed NTR 2.0 , a high-efficacy nitroreductase for targeted cell ablation. Current research projects include: Clean solutions from dirty genes: Plastic-degrading enzyme discovery Engineering enzymes for CAR T-cell-chemotherapy synergy Repurposing niclosamide against Gram-negative superbugs Grants from the Health Research Council of New Zealand, Royal Society of New Zealand, and Cancer Society of NZ support his work. Collaborations span biomedical research, synthetic biology, and environmental applications.
Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.
Yanxi Liu is a Professor in the Department of Computer Science and Engineering and the Department of Electrical Engineering at Pennsylvania State University. She is affiliated with the Huck Institutes of the Life Sciences and holds multiple NSF grants focusing on computational symmetry, regularity perception, and human movement analysis. Her work bridges computer vision, cognitive science, and medical imaging. Education details not explicitly listed in provided text. Key research areas include computational symmetry, near-regular textures, human perception of patterns, and medical image analysis. Notable projects include 'RI: Medium: From Vision to Dynamics' (2023-2026) and 'INSPIRE: Symmetry Group-based Regularity Perception in Human and Computer Vision' (2012-2016). Her research emphasizes symmetry-driven approaches for urban scene analysis, medical diagnostics (e.g., brain asymmetry in Alzheimer's), and dynamic motion modeling. She has co-authored over 100 publications and pioneered methods like lattice-based tracking and symmetry-based mid-sagittal plane extraction in neuroimaging. Funding includes grants from NSF totaling over $5M, supporting interdisciplinary work in vision science and AI. Collaborations span neuroscience, biomedical engineering, and architectural pattern analysis.
João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .
