Our research focuses on creating next-generation architected soft and living materials by leveraging our expertise in mechanical design, materials science, stem cell biology, fluid mechanics, 3D printing, and biomanufacturing. The overarching goal of our laboratory is to formulate the fundamental science and engineering basis behind autonomous systems driven by sequential decision algorithms that will help us achieve our research vision at a fraction of the cost and speed compared to the traditional Edisonian research paradigm.


Lab of the Future

There is a growing interest in leveraging data-driven models to help discover new materials, accelerate material optimization, and lower costs by reducing expensive laboratory measurements. However, building data-driven models and eliminating experiments are often mutually exclusive ideas in scientific discovery. The strength of data-driven models is directly proportional to the amount and quality of data that they are trained on, and experiments are where these data are produced. A major aspect of our lab is the rapid prototyping of customized modular scientific instrumentation for materials processing and characterization. Examples include powder and liquid handling instruments and low-cost metrology instrumentation for the characterization of mechanical and rheological properties of polymeric materials.


Low-Cost Powder Dispenser

Automated precise powder-type agnostic dispenser for chemistry and materials science labs:


Experimental Intelligence

The goal of optimal design is to make the most efficient use of limited resources (such as time, budget, or available experiments) to achieve specific objectives, such as building accurate models, estimating parameters, or minimizing uncertainty. Our lab focuses on the application of sequential decision algorithms and optimal learning in experimental intelligence. We are engaged in developing methods that improve decision-making processes through iterative cyber-physical experimentation and learning. Our approach is based on adapting and refining strategies based on previous outcomes, enhancing both efficiency and precision. Every experimental workflow is and can be mathematically formulated as a sequential decision problem with uncertainty. This work has practical applications in various areas, including 3D printing optimization and resource management. Our goal is to combine theoretical research with practical problem-solving, contributing to advancements in scientific understanding and offering solutions to real-world challenges.


Machine Intelligence

"In our 'Machine Intelligence' research initiative, we focus on designing intelligent manufacturing machines and devices, like 3D printers and energy harvesting buoys, to solve pressing industry and societal needs. Our goal is to develop self-calibrating machines that intuitively adapt to new materials, environments, or operational changes. This is achieved through the integration of advanced machine learning algorithms, enabling machines to adjust settings in real-time for optimal performance. These innovations are designed to complement human expertise, streamlining the manufacturing process and improving productivity. Our approach is to create a harmonious collaboration between machine intelligence and human skill, where each benefits from the other's strengths. Within the wider initiative, we are closely working with industry experts concerning the UN sustainability goals to create tools that will help the adoption of additive manufacturing technologies.

Learning of E-Jet Printing Dynamics with Physics-Informed Gaussian Processes:


Optimal Wave Body-Interaction for Renewable Energy Production:

Under Patent Submission 

(contact Technology Licensing Office of NCSR Demokritos)


Self-Driving Labs

In the 'Self-Driving' research initiative, we work on integrating the cutting-edge tools and methodologies developed across all previous initiatives to establish self-driving laboratories. These state-of-the-art labs are engineered to revolutionize the discovery, optimization, and standardization processes in the field of soft and living materials. By harnessing the power of machine intelligence, experimental intelligence, and advanced automation, our self-driving labs automate material synthesis, characterization, and manufacturing. This integration allows for continuous closed-loop autonomous experimentation by scientific robotic agents, driven by sequential decision agents. This paradigm shift not only accelerates the pace of discovery but also ensures a higher level of reproducibility and standardization in research outcomes. Our vision is to transcend traditional boundaries in scientific research, opening new horizons for exploration and innovation in soft and living material science and engineering.


Self-Driving Sustainable Thin-Film Biopolymer Lab

Robotics and automation Infrastructure for accelerating the development of materials for the circular economy