Liangcan He | Design of Experiments (DOE) | Research Excellence Award

Dr. Liangcan He | Design of Experiments (DOE) | Research Excellence Award

Harbin Institute of Technology | China

Dr. Liangcan He is a distinguished Professor at the School of Medicine and Health, Harbin Institute of Technology, China, where he leads advanced interdisciplinary research programs focused on biomaterials, nanomedicine, multimodal imaging, and bio-nano interface engineering. His scientific work spans the design, synthesis, and biomedical translation of organic–inorganic nanoclusters, functional nanomaterials, therapeutic hydrogels, DNA nanotechnology, and stimuli-responsive nanosystems for diagnostic and therapeutic applications. Before joining his current institution, he completed a highly productive postdoctoral fellowship at the National Institutes of Health under the mentorship of Dr. Xiaoyuan (Shawn) Chen, where he developed cutting-edge nanoplatforms for NIR-II imaging, X-ray-mediated dynamic immunotherapy, responsive nanoparticle assemblies, and cascade-reaction-based photodynamic therapy. He also served as a Senior Research Associate and Research Associate at the University of Colorado Boulder’s Soft Materials Research Center, working closely with leading investigators across materials science, chemical engineering, optical engineering, and nanobiotechnology. During this period, he contributed significantly to DNA-programmable nanoparticle crystallization, MOF-based hybrid therapeutic systems, plasmon-enhanced upconversion luminescence, single-particle photophysics, engineered UCNP–Au nanostructure assemblies, and DNA-origami-directed enzymatic cascade architectures. Dr. He received his Ph.D. in Chemistry from Tsinghua University and conducted parallel research at the National Center for Nanoscience and Technology under the supervision of Prof. Zhiyong Tang and Prof. Yadong Li, where he worked extensively on plasmonic nanoparticle@MOF core–shell systems, catalytic nanomaterials, graphene-oxide-integrated hybrid structures, yolk–shell nanostructures for controlled release, supraparticle-based photocatalysis, and chiral coordination polymer assemblies. His current research continues to advance critical frontiers such as photothermal therapy, radiosensitization, microenvironment-responsive nanoreactors, injectable biomaterials for tissue repair, bioinspired catalytic systems, and multifunctional hydrogel-based therapeutic platforms. Dr. He has authored more than 76 peer-reviewed publications, achieving over 6,130 citations and an H-index of 33, underscoring his global impact in nanobiotechnology, functional materials, and translational biomedical engineering. He has also contributed as Guest Editor for the Special Issue “Research Progress of Bioimaging Materials” in the International Journal of Molecular Sciences. With a strong interdisciplinary foundation bridging chemistry, nanoscience, materials engineering, and biomedical translation, he is recognized as a leading innovator in the development of functional nanomaterials for precision imaging, sensing, biosystem modulation, and advanced therapeutic strategies.

Profiles: Scopus Orcid

Featured Publications

Zhang, R., Ma, Q., Zheng, N., Wang, R., Visentin, S., He, L., & Liu, S. (2025). Plant polyphenol-based injectable hydrogels: Advances and biomedical applications.

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

Dr. Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

University of Lisbon | Portugal

Dr. Rasool Taban, Ph.D, is a distinguished Data Scientist currently affiliated with Technical University Institute – University of Lisbon, where he continues to advance the frontiers of Artificial Intelligence and Data Science. His academic journey began in Computer Engineering and evolved into a profound focus on Artificial Intelligence during his M.Sc. studies at the University of Tehran, where he graduated with honors in Artificial Intelligence and Robotics. His early research centered on developing an automated screening system designed to assist in diagnosing Autism Spectrum Disorder in children, demonstrating his ability to merge technology with meaningful social impact. Dr. Taban recently earned his Industrial Ph.D. at Institute – University of Lisbon, funded by the prestigious Marie Curie BIGMATH project, where his research specialized in addressing one of the most persistent challenges in statistical learning-imbalanced data. He successfully developed three novel balancing techniques, each tailored to optimize performance across different variable classes, making significant contributions to data reliability and analytical accuracy in machine learning models. With two published journal papers indexed in Scopus and SCI, Dr. Taban’s scholarly work reflects both academic rigor and applied innovation. He has also participated in multiple research and industry projects, collaborating with institutions such as the SDG Group, CIF/N26, Evenco International, and CTAD–Tehran Autism Center. His involvement as part of the editorial team for the International Conference on Robotics and Mechatronics (ICRoM) further underscores his leadership in advancing interdisciplinary research. Dr. Taban’s primary research interests include imbalanced data, statistical learning, data science, and financial data modeling. His contributions have not only expanded methodological knowledge in statistical computing but have also bridged the gap between theoretical frameworks and real-world data-driven applications, reflecting his commitment to excellence in both academia and industry.

Profiles:  Google Scholar | Linked In

Featured Publications

Taban, R., Nunes, C., & Oliveira, M. R. (2023). RM-SMOTE: A new robust balancing technique.

Taban, R., Nunes, C., & Oliveira, M. R. (2025). Mixed-robROSE: A novel balancing technique tailored for mixed-type datasets.

Bozorgnia, F., Arakelyan, A., & Taban, R. (2023). Graph-based semi-supervised learning for classification of imbalanced data. Submitted to Conference ENUMATH.

Shahri, M. A., & Taban, R. (2021). ML revolution in NLP: A review of machine learning techniques in natural language processing. Journal of Applied Intelligent Systems & Information Sciences (JAISIS), 2(1), 2.

Taban, R., Parsa, A., & Moradi, H. Tip-toe walking detection using CPG parameters from skeleton data gathered by Kinect. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 9).