Nilmani Singh is a postdoctoral researcher at the University of Illinois Urbana-Champaign working with robotics and AI to improve laboratory automations. In this Postdoc Portrait, he shares how working at this interface helps to advance research autonomous biological research platforms and thus empower creative discovery.
Q | How did you first get interested in your field of research?
My fascination with science began as a child, reading science fiction stories in a magazine called Vigyan-pragati. I was captivated not just by the imaginative worlds, but by the questions they raised about how the physical world works and how living systems sustain themselves. This curiosity eventually led me to develop a deep admiration for the complexity and brilliance of the physical world and more so the living systems, culminating in pursuing a PhD in cell biology. During my PhD, I performed thousands of DNA cloning experiments and countless molecular assays such as PCR, qPCR, and minipreps. While these experiences honed my technical skills, the repetitive pipetting and long hours in the lab were often tedious and not my favorite part, which often left me wishing for faster, more efficient solutions.
After my PhD, I discovered the potential of laboratory automation and was instantly captivated. Suddenly, I could merge my molecular biology expertise with robotics, instrumentation, and programming. Experiments that once took days or weeks could be completed in hours, with greater reproducibility and scalability. Integrating AI/machine learning (ML) tools added another layer of power, enabling predictive insights to guide the next iteration of experiments. This convergence of biology, automation, and AI has transformed my approach to research and opened new possibilities for discovery and scalability.
Q | Tell us about your favorite research project you’re working on.
My favorite project centers on designing autonomous platforms, or self-driving systems, for protein engineering. Proteins hold immense potential for revolutionizing biomanufacturing and medicine, but their broader adoption is constrained by inherent performance inefficiencies at industrial scales and the slow, trial-and-error nature of traditional improvement methods. In my recent work, I integrated AI with automated robotics and synthetic biology to significantly enhance the performance of two industrially important enzymes, while creating a streamlined, user-friendly process broadly applicable to many proteins. This enzyme engineering platform minimizes human decision-making and specialized protein knowledge requirements, requiring only a protein sequence as input. It also eliminates the tedious manual work through seamless automated robotic workflows.
I am also focused on accelerating the design-build-test-learn (DBTL) paradigm, an iterative framework for optimizing biological systems. By integrating AI/ML tools and biofoundry workflows, I aim to accelerate discovery cycles, reduce costs, and improve reproducibility. A key challenge is that biofoundries, despite their transformative potential for the bioeconomy, remain difficult for new users to access, program, and operate. To address this, I’m developing remote-enabled, cloud-based biofoundries assisted by AI agents that simplify robotic programming and workflow design, ultimately establishing biofoundries as accessible national infrastructure that democratizes cutting-edge biotechnology capabilities for a broad research community.
Q | What has been the most exciting part of your scientific journey so far?
The most exciting aspect has been the realization that I am working at the cutting edge of human knowledge, particularly at the intersection of multiple disciplines. Having extensive experience with manual molecular biology work, it has been remarkable to witness the transformation of traditional assays into automated robotic workflows. While my PhD training focused primarily on biological techniques, I now find tremendous excitement in learning robotics and programming to automate various biological assays.
My recent autonomous protein engineering project exemplified this transformation, it was extraordinary to rapidly screen thousands of protein variants with AI/ML tools handling all design components, and biofoundries running automated experiments. The ability to witness months of traditional work compressed into weeks through intelligent automation has been profoundly rewarding. I particularly enjoy the hands-on process of working with robots, refining protocols, and creating robust workflows that bridge the gap between computational design and physical experimentation. This interdisciplinary approach, combining wet lab expertise with cutting-edge technology, represents the future of biological research and continues to inspire me.
Q | If you could be a laboratory instrument, which one would you be and why?
As someone working extensively with instrumentation at the Illinois Biofoundry, I would choose to be an Echo Acoustic Liquid Handler. This instrument can transfer volumes as small as 2.5 nanoliters with extraordinary precision and accuracy—a feat that never ceases to amaze me. The technology behind it is genuinely fascinating: using focused sound waves to dispense liquid droplets in a contactless manner, eliminating cross-contamination and reducing plastic waste.
Having worked with this instrument for several years, I’ve come to deeply appreciate its exceptional qualities. It operates with remarkable consistency, requires minimal maintenance, and has low consumable costs, making it both scientifically powerful and practically efficient. The Echo has proven reliable across all skill levels, from newcomers learning basic protocols to expert researchers pushing the boundaries of miniaturized assays.
I appreciate how the Echo embodies the qualities I value most in scientific work: precision, reliability, and accessibility. Just as this instrument bridges the gap between automation and experimentation with elegant simplicity, I’m drawn to facilitating breakthrough discoveries through dependable innovation that makes sophisticated biotechnology accessible.
Responses have been edited for length and clarity.
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