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 .
Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.
Michael P. Bradley is a Professor in the Department of Physics and Engineering Physics at the University of Saskatchewan, affiliated with the College of Arts and Science. He holds a Ph.D. from MIT and is a Professional Engineer (P.Eng.). His research focuses on precision measurement techniques, plasma-based nanofabrication, and quantum metrology, including work on diamond NV-centre magnetometers and superconducting watt balance systems. He leads the University of Saskatchewan Plasma Physics Laboratory (U of S PPL) and has received a Canada-UK Joint Quantum Technology grant for quantum sensor development. Education BSc (Honours) in Applied Physics, University of New Brunswick Ph.D. in Physics, Massachusetts Institute of Technology (MIT) Research Interests Bradley specializes in quantum magnetometry , plasma processing , semiconductor nanostructures , and precision electromagnetic measurements . His lab develops novel techniques for materials characterization and fabrication, including plasma immersion ion implantation (PIII) for micro- and nano-scale engineering, graphene doping, and silicon photonics. Recent work includes advancements in diamond NV-centre magnetometry for quantum technologies. Grants & Collaborations Recipient of a prestigious Canada-UK Joint Quantum Technology grant (2023). Collaborated internationally, including at the Bureau International des Poids et Mesures (BIPM) in France, where he contributed to superconducting watt balance prototypes for redefining mass standards. Teaching Teaches courses in optics, thermodynamics, and planetary astronomy, including EP421: Optical Systems & Materials and ASTR104: Planetary Astronomy .
Daria Camilla Boffito is a Full Professor in the Department of Chemical Engineering at Polytechnique Montréal , holding the Tier-2 Canada Research Chair in Intensified Mechano-chemical Processes for Sustainable Biomass Conversion. Her research spans process intensification , catalysis , sonochemistry , photocatalysis , and metal extraction , with a focus on sustainability. Education: B.Sc. and Ph.D. in Industrial Chemistry from the University of Milan, M.Sc. in Industrial Chemistry and Management Current Research: Developing ultrasound-assisted extraction , CO2 conversion , and floating photocatalysts for wastewater treatment Collaborations: Works with Canadian and international companies on sustainable chemical processes Scientific Awards include the Canada Research Chair Tier-2 (2016-2021), NSERC Banting Postdoctoral Fellowship (2013-2016), and FRQNT PBEEE Postdoctoral Fellowship (2013-2016). Advising has seen 5 Ph.D. and 9 Master's students graduate. She leads the Engineering Process Intensification and Catalysis (EPIC) Laboratory and is a member of the Institut de génie biomédical .
Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants
G. Kane Jennings is a Professor of Chemical and Biomolecular Engineering at Vanderbilt University's School of Engineering, where he also serves as Director of Graduate Recruiting. His research focuses on molecular design of smart surfaces and biohybrid materials for applications in solar energy conversion, responsive coatings, and nanoscale lubrication. He leads the Jennings Lab, training students in bioinspired materials science. Jennings holds a Ph.D. from MIT and specializes in self-assembly techniques and surface-initiated polymerizations. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology M.S., Chemical Engineering, Massachusetts Institute of Technology B.S., Chemical Engineering, Auburn University Research Interests: Jennings develops adaptive materials such as anionic chameleon coatings, biohybrid solar systems using Photosystem I proteins, and high-throughput membrane fabrication via spin coating-ROP integration. His group explores nanoscale defect detection in 3D-printed materials and corrosion-resistant surface treatments. Lab Innovations: Highlights include the mMSIP micromolding technique for customizable superhydrophobic coatings and the scROMP method enabling rapid polymer film synthesis. Collaborations with civil engineering and chemistry departments advance energy-minimizing surfaces and bioelectrochemical systems. Awards: No explicit awards listed, though his work has been funded through interdisciplinary initiatives at Vanderbilt's VINSE and Process Innovation Center.
Gillian Hayes is the Vice Provost for Academic Personnel and a Chancellor’s Professor at the University of California, Irvine (UCI). She holds joint appointments in the Department of Informatics (Donald Bren School of Information and Computer Sciences), School of Education, and School of Medicine. Her research focuses on human-computer interaction, assistive technologies, and digital health interventions for children with ADHD and autism. She leads the STAR Group, which designs technologies to address real-world challenges in healthcare and education. Education: PhD in Computing from Georgia Tech, and degrees from Vanderbilt University. Professional roles include Vice Provost for Graduate Education, Dean of the Graduate Division, and faculty director of multiple programs, including the Master of Human Computer Interaction and Design. Research interests include child development, health informatics, and ethical technology deployment. Notable projects include the CERES initiative and work on smartwatch interventions for ADHD. Over 100 peer-reviewed publications and 6 books/chapters highlight her contributions. Scientific awards include NSF CAREER Award, ACM SIGCHI Academy, and recognition for teaching and innovation in accessibility. Advises graduate students in informatics, computer science, and education. Active in leadership roles at UCI and the Computing Research Association (CRA Board member). Labs/Teams: STAR Group, Intel Science and Technology Center (ISTC) collaborator, and co-founder of AVIAA and Tiwahe Technology. Focuses on interdisciplinary research with global collaborators.
Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.
Yongxiang Li is a Professor in the School of Engineering at RMIT University, Australia, specializing in advanced materials and nanotechnology. His research focuses on piezoelectric materials, 2D functional materials, and energy harvesting systems. He is actively involved in supervision of PhD and Master’s projects, including topics like flexible piezoelectric nanogenerators, 2D heterostructures, and low-temperature co-fired ceramic (LTCC) sensors. Research Interests: Materials Engineering, Electrical Engineering, Nanotechnology, Condensed Matter Physics, and Functional Ceramic Design. Key projects include developing lead-free piezoelectric materials, integrating sensors for lithium-ion batteries, and exploring gas sensors using liquid metal-derived oxides. Teaching Interests: Dielectric materials, ferroelectric systems, sensor technologies, and LTCC applications. His interdisciplinary work bridges materials science with optoelectronics and energy storage. Publications span over 400 outputs, emphasizing machine learning-driven materials discovery, defect engineering in 2D materials, and sensor innovation. He leads projects in collaboration with industry, focusing on practical applications of advanced ceramics and nanomaterials.
Truong Q. Nguyen is a Professor in the Electrical and Computer Engineering (ECE) Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds positions at the Center for Wireless Communications and the California Institute for Telecommunications and Information Technology. His research focuses on image/video processing, wavelets, 3D video technology, and applications in healthcare and robotics. He has authored influential textbooks like Wavelets & Filter Banks and pioneered low-power video processing algorithms for mobile devices. Nguyen earned his B.S., M.S., and Ph.D. in Electrical Engineering from the California Institute of Technology (1985–1989). He held roles at MIT Lincoln Laboratory and Boston University before joining UCSD in 1998. His honors include the IEEE Signal Processing Paper Award (1992), NSF Career Award (1995), IEEE Fellow (2005), and UCSD’s Distinguished Teaching Award (2019). His research interests span 3D video processing, machine learning for health monitoring, and biomedical imaging. Notable contributions include wavelet-based compression techniques and AI-driven medical image analysis. He leads the UCSD Video Processing Lab, exploring computer vision, robotics, and generative AI applications. Nguyen is committed to educational innovation, co-creating programs like the Hands-on Curriculum, Summer Research Internship Program (SRIP), and Project-in-a-Box (PIB) for K-12 students. Nguyen’s work bridges academia and industry, with patents in wavelet design and signal analysis. Recent projects include NSF-funded initiatives to develop inclusive engineering curricula and collaborate on graduate pathways programs through the Inclusive Engineering Consortium (IEC).
Shima Abdullateef is a Postdoctoral Research Fellow at the Centre for Medical Informatics within the Usher Institute, College of Medicine and Veterinary Medicine at the University of Edinburgh. Her work bridges biomedical engineering and clinical medicine through computational modeling and data science applications. Education: PhD in Biomedical Engineering, Brunel University London (2016-2020) MSc in Biomedical Engineering, University of Surrey (2014-2015) BSc in Biomedical Engineering (Bioelectrics), Science and Research IA University (awarded 2013) Research Focus: Dr. Abdullateef specializes in two interconnected domains: computational hemodynamics modeling arterial wave propagation and reflection phenomena, and machine learning-driven seizure detection using minimal-density EEG montages. Her arterial research investigates how vascular geometry impacts blood pressure dynamics, while her neuroscience work develops practical clinical tools for critical care seizure monitoring that reduce electrode requirements by 50-75% compared to standard EEG setups. Publication Trends: Her 15 most recent publications (2018-2025) reveal a strategic shift from pure cardiovascular modeling toward integrated neurological applications, with 60% focusing on seizure detection algorithms. The work consistently applies one-dimensional computational models and phase-synchrony analysis to solve clinical monitoring challenges, particularly in resource-constrained pediatric intensive care settings. Active Projects: A Window in the Brain: Developing a novel seizure detection tool for pediatric critical care (since 2020), funded through University of Edinburgh research channels Collaborative Environment: She operates within the Centre for Medical Informatics' interdisciplinary ecosystem, collaborating with clinicians from Edinburgh BioQuarter and data scientists to translate engineering solutions into clinical practice, with particular emphasis on making neurocritical care monitoring more accessible through reduced-sensor EEG technology.
Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.
Dr. Kezhi (Ken) Li is an Associate Professor of AI in Healthcare at the Institute of Health Informatics, University College London (UCL). He leads the AI for Health research group and has established himself as a leading expert in applying artificial intelligence to solve complex problems in healthcare, with over 130 publications in leading journals (total impact factor greater than 400). Dr. Li earned his Doctor of Philosophy from Imperial College of Science, Technology and Medicine in 2013, followed by research positions at the Medical Research Council (2015-2017), University of Cambridge (2014-2015), and Royal Institute of Technology (KTH) (2013-2014). His academic journey reflects a consistent trajectory from technical AI research toward increasingly healthcare-focused applications. Dr. Li's research focuses on solving physiological, medical, clinical, and operational problems in healthcare using AI techniques. His specific expertise includes AI in healthcare using electronic health records (EHR), biomedical time series analysis using monitors/wearables, diabetes management, large language models (LLM) in healthcare (especially mental health), patient flow optimization, and digital health with federated learning. His work bridges the gap between cutting-edge AI methodologies and practical healthcare applications, with a strong focus on improving patient outcomes and healthcare system efficiency. Analysis of Dr. Li's publication history reveals a strong emphasis on diabetes management technologies, particularly blood glucose prediction systems using advanced neural network architectures. More recently, his work has expanded into mental health applications of large language models, blockchain-based federated learning for healthcare data, and mortality prediction in critical care settings. His research demonstrates a clear evolution from purely technical AI development toward increasingly clinically impactful applications, with growing emphasis on explainability, privacy preservation, and real-world implementation challenges. Dr. Li has received numerous prestigious awards recognizing his contributions to healthcare AI: Best Application Award of IEEE Global Blockchain Conference (2025) Fellow of British Computer Society (2025) Fellow of the Royal Society for Public Health (2024) Healthcare Partnership of the Year category at the London Higher Awards (2024) ECR Promising Project Award (2023) Gallivan Award finalists (2022) Stylianos Kalaitzis PhD Award Winner (2022) HDR UK Team of the Year (COVID-19) Award (2021) As an educator, Dr. Li serves as the Director of MRes study (AI-enabled Healthcare Systems) at UCL. He leads multiple key modules including Healthcare Artificial Intelligence Journal Club, Dissertation in Artificial Intelligence Enabled Healthcare, and Advanced Machine Learning for Healthcare. His supervision extends across dissertation projects and junior researchers in his AI for Health group. His research has been supported by various grants, including those from HDR UK, focusing on translating AI innovations into practical healthcare solutions. Dr. Li leads the AI for Health research group (https://ai4hucl.github.io/ai4h_webs/), which comprises researchers with diverse expertise in machine learning, healthcare systems, and clinical domains. The group maintains strong collaborations with healthcare providers and industry partners to ensure their research addresses real-world healthcare challenges and can be effectively translated into clinical practice.
Prof. Dr. Aliaksandr Bandarenka is a Professor at the Technical University of Munich (TUM) in the TUM School of Natural Sciences , leading the Assistant Professorship of Physics of Energy Conversion and Storage . His research focuses on electrochemical surface science and energy materials development. Education: PhD in Chemistry from Belarusian State University (2005) Key Collaborations: Ruhr University Bochum, University of Twente, Technical University of Denmark Prof. Bandarenka's research explores: Design of electrocatalytic materials via bottom-up approaches Characterization of electrified interfaces Development of sustainable energy conversion/storage systems Surface structure-activity relationships in catalysis Recent article trends (2024) include: ORR electrocatalyst optimization using ZIF-8 templating Advanced impedance spectroscopy for battery/electrolyzer diagnostics Mesoporous oxide materials for energy applications Surface structure effects on double layer capacitance Scientific Recognition: Ernst Haage-Prize (2016) Hans-Jürgen Engell Award (2013) He teaches graduate courses on: Electrified interfaces Energy materials science Electrocatalysis fundamentals Hands-on experiments in battery technology
Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.
