Brain and body connectivity is remarkable when you think about it. The complexity involved in building and storing models of the external world and then applying them to coordinate animal body systems is a natural function often taken for granted. And yet, the opposite holds when we look at human-made technology with similar functionality. You can’t help admiring how lab automation workflow integration platforms coordinate output and conserve resources.
Part three of our series of parallel interviews on Strategic Lab Automation: Unlocking Efficiency, Sustainability, and ROI Across the Research Lifecycle opens our eyes to the potential of accelerating research via the integration of software, data and robotics. Labconscious thanks Yousef Baioumy and Jesse Mayer, who will be at the upcoming Future Labs West Summit, in San Diego, California, for sharing their experience leveraging these platforms in life science labs!
integration Streamlines lab Workflows
Q1: What is your expertise, and what types of technologies do you focus on to advance research and discovery?
Yousef Baioumy: As an Automation Engineer, I specialize in integrating robotic platforms, software, and data systems to streamline laboratory workflows. My expertise lies in bridging biology and engineering to design workflows that improve reproducibility, throughput, and data quality. I focus on laboratory automation systems, liquid handling robotics, scheduling software, and data integration platforms. Increasingly, I also work with AI-augmented analytics and cloud-based lab management tools to connect instruments with decision-making systems.
Our proprietary immune profiling platform reveals and translates insights from our adaptive immune systems with extraordinary scale and precision. We work with drug developers, clinicians and academic researchers to help develop products to transform treatment for disease.
Jesse Mayer, Ph.D: I have a degree in biochemistry, but interestingly enough, I began my career in genomics with desert plants at the University of Nevada. My research was on uncovering genetic reasons behind their ability to use less water and grow in drier, saltier climates. I’ve been an application scientist since graduating, starting at Thermo Fisher to support reagents around NGS prep and extraction. My experience with tricky plant samples made me pretty knowledgeable on dealing with tough tissue types. When the pandemic hit, I shifted to COVID testing and automation which led me to my current role. At Automata, I support scientists looking to automate, whether for the first time or for an expansion of existing automation.
Q2: How do you work to leverage lab automation in life science research?
Yousef Baioumy: I work to design workflows that are both scalable and adaptable. This includes helping researchers choose the right instruments, integrating software for seamless scheduling and data capture, and ensuring the automated process maps closely to biological needs. The goal is to free scientists from repetitive tasks so they can focus on higher-level problem-solving.
Jesse Mayer: On the hardware side, our team provides benches for your lab equipment and a robotic arm that can pick up plates or other items from one instrument to another. On the software side, we have a cloud-based scheduler that plans the route of each plate and tells the devices what to do. It may sound a little bit intimidating at first, but the process is simple to draw out using the software. We’ll work with you to build out your workflows and determine what success looks like. The final phase of a project is physically building and testing the systems, and then training users on how to use them day-to-day. Once the system is up and running, don’t worry, we’ll still be around to help refine your protocols or add additional ones.
Q3: Do you see a relationship developing between biological research, lab automation, and sustainability goals?
Yousef Baioumy: Absolutely! Automation reduces reagent waste, lowers error rates, and makes experiments more efficient. Combined with careful process design, it allows labs to operate with fewer resources while accelerating discovery—directly supporting sustainability goals in science and industry.
Jesse Mayer: Yes, definitely. The consistency that comes with lab automation can allow you to dial in the experiment to use the least amount of tips and reagents possible. A perfect example is the compound screening space, where experiments that started out in 96-well plates were miniaturized down to 1536-well plates. This lets you get more data with the same plates and reagent volume. It may also reduce the number of times you need to re-do an assay.
There are a lot of great instruments that have brought sustainability into the lab. The Grenova tip washers allow you to re-use tips multiple times, and non-contact dispensers have removed the need for tips for certain workflows. These devices can be expensive, but in most automated applications, they can pay themselves off compared to tips.
Q4: What makes pursuing this technology worthwhile? How do you gauge the potential value in projects?
Yousef Baioumy: The value comes from measurable improvements: reduced costs, faster turnaround times, higher data quality, and increased reproducibility. I gauge potential value by asking how the automation will impact research velocity, reduce human error, and improve scalability across projects.
Jesse Mayer: Next generation sequencing is a good case study in the value of automation. If you think back to sequencing the first human genome, it cost something like $100 million. Then we were striving for the $10,000 genome and the $1,000 genome. Now that we’ve got systems that can sequence very quickly and very cheaply, the sequencer is no longer the bottleneck. The biggest opportunity for improvement is getting from the patient sample into high-quality, ready-to-sequence DNA. Automating this sample prep helps further lower the cost per sample, democratizing sequencing and the valuable health data that comes with it.
There are a few things we look at to gauge the value of an automated project. How many samples do you need to run a day? Are your workflows consistent from one run to the next? How automation-friendly is your workflow and labware? With these and a few other questions, we can determine if automation is a good fit for your lab.
Q5: Are there any common barriers the life science industry faces in integrating lab automation systems?
Yousef Baioumy: Yes—cost, complexity, and change management are the most common. Instruments are expensive and not always modular, integration with existing lab systems can be challenging, and teams often need training to adopt new workflows. Building a culture that embraces automation is as important as the technology itself.
It is important to ask, “How do we ensure lab automation remains accessible to smaller research groups and not just large institutions?” Democratizing automation—through modular systems, open-source tools, and scalable solutions—is key to making sure innovation reaches every corner of the scientific community.
Jesse Mayer: The biggest barrier usually sits around adapting existing protocols or instruments to an automated workflow. I can’t stress enough the value of planning ahead for automation. Even if you think you’ll never run more than a plate a week, you never know when your product will become a success or when a major event like COVID will strike. All of a sudden, a need can arise to do 10 times or 50 times what you were processing before. If you’ve already thought about how your lab will scale as your throughput grows significantly, this process can be seamless. This isn’t to say that your existing manual workflow can’t be automated, but it may require additional validation or switching to more automation-friendly instrumentation.
Q6:What kinds of skillsets are beneficial to biologists in automation lab settings?
Yousef Baioumy: Biologists benefit from having some exposure to coding (Python or other common scripting languages), data analysis, and an understanding of automation logic. Strong troubleshooting skills and adaptability are equally important, since automation often requires bridging biology with engineering and IT.
Jesse Mayer: Some skills that are already present in most biologists, like questioning the outputs of an experiment or deciding how to further test a hypothesis, remain important in the automated lab. Being comfortable around hardware and knowing how to troubleshoot a faulty instrument can be helpful. Some basic software skills go a long way as well. A lot of the skillset previously required for lab automation, like being able to script in a programming language, have become less important as schedulers have become more advanced. The goal is to make automation as approachable as possible for scientists.
Q7: How are AI algorithms helping in the lab and clinical medicine?
Yousef Baioumy: Yes, AI is accelerating everything from image analysis to predictive modeling. In the lab, AI helps optimize protocols, detect anomalies in real time, and manage large data sets. In clinical medicine, it supports diagnostics, patient stratification, and even drug development pipelines.
Jesse Mayer: There are labs looking to do what we refer to as DMTA (design, make, test, analyze) or more simply “lab in the loop”. The idea is that you start an experiment, data comes out, the AI reads it and comes up with the next experiment, and the system starts over. This is still in its early stages but stands to greatly reduce the amount of experimental iteration required by scientists to get the intended final outcome.
Q8: What is unique about the “Future Labs, Automation, and Technology” summit?
Yousef Baioumy: It brings together both scientific and technical perspectives in one forum. You don’t often see biologists, engineers, software developers, and industry leaders exchanging ideas so openly. That mix of expertise is exactly what’s needed to drive innovation in labs of the future.
Jesse Mayer: Being a smaller event with less structured talks and more discussion-based sessions, it’s a great event to dive into the nitty-gritty details of how we can improve lab automation. You can come with some ideas of how to automate your lab, or issues you are facing with an already automated lab, and find a group of experienced automation engineers and scientists who are happy to help.
Q9: Could you envision a future where an AI scientist or AI lab technician supports research projects?
Yousef Baioumy: Yes, I envision AI copilots that handle experimental planning, error detection, and even suggest protocol optimizations. Rather than replacing scientists, these AI systems will act as highly skilled assistants—augmenting human creativity, accelerating discoveries, and making labs more accessible.
Jesse Mayer: Absolutely. If you look at how AI is being used today to help write articles or code, there is still a human element to it. AI can provide the start that an experienced technical writer or software developer can refine into something great. I think that we’ll see something similar in the lab space, where AI can help to plan an experiment or an automated protocol and a scientist can refine the output to reach their desired outcomes.
Interested in learning more perspectives?
Connect in person at the summit, and read the Q&As on lab automation and sustainability!
Part One of a series of parallel interviews features Avinash Gill, Ph.D., Senior Principal Scientific Manager, gRED, Genentech, whose team generates target antigens for small and large molecule drug discovery, and Part Two interviews Ilja Kuesters, Ph.D. Generate:Biomedicines, whose team builds “agent augmented” labs.
Use the discount code: PARTNER15