You started out as a scientist and now you've moved into a leadership role. Was that what you always intended to do?
Honestly, I did not expect to be in this role; it’s something that just kind of evolved over time. Early on, I remember my parents saying I was always asking “why” – to the point of annoyance, I think. Why is that? How does this work? I used to constantly take things apart, always interested in how and why things worked. That kind of natural curiosity drew me into the sciences. The scientific process – the constant challenging of the status quo and the discovery of new truths – really appealed to me.
I always assumed I’d end up in a lab, working as a bench scientist. That was the career I pictured. But as I progressed, I also developed a deep interest in human behaviour and psychology. I started reading a lot on those topics in my personal time, and it sparked an interest in how to build and structure innovative teams.
I’ve always been comfortable taking educated risks. I’ve taken on some quite challenging assignments – things that others might have seen as risky – but I saw them as opportunities. One of those was stepping into a management role. When I was offered the chance to leave the lab and become a manager, I actually spent an entire Christmas thinking about it. My boss at the time was surprised – I think he expected me to jump at the opportunity. But I knew it was a pivotal moment, a fork in the road for my career, and I wanted to be sure.
Ultimately, I felt I could have more impact in a leadership role than I could by staying at the bench. It gave me a chance to combine my passion for science and innovation with my growing interest in psychology and team dynamics. That combination has really shaped my career and brought me to where I am today.
Are you more driven by raw curiosity or by the desire to have an impact on the world – and has that changed over time?
From the very early stage of my career, I wanted to do something that had an impact – that’s always been a driving force for me. I want to do work that matters. I have two daughters now, and I want them to be proud of what I do, to be excited to tell their friends, “Hey, my dad’s working on this.”
And I find what we do as an organization incredibly motivating and impactful. When I talk to our customers – scientists – and hear about the challenges they’re trying to solve and the potential that has for improving human health, it’s deeply motivating. That impact is a huge part of what drives me, and I think it drives the team I work with as well.
That said, individual curiosity still plays a strong role. When I took on this role, I made it very clear to my boss at the time that I didn’t want to just sit in an office and shuffle paperwork. That can easily happen to senior leaders as they move up in an organization – suddenly all you're dealing with is budgets and admin. I said no, what drives me is the science, the technology, and the impact, so I want to stay close to that.
I still take part in brainstorming sessions with our technical team. I still get into the details of technical barriers we’re trying to overcome. I enjoy getting in the mix and thinking through ideas with the team.
A while ago, I had the chance to meet the CEO of NVIDIA at a Danaher conference. He gave a talk and afterward we chatted one-on-one. That conversation really stuck with me. He said that every week, no matter what, he made time to talk with the scientists and engineers working on key projects – just to hear directly from them about what they were doing, what challenges they were facing. He wanted to stay connected to the heart of the work.
I’ve taken that with me ever since. No matter where I am in the organization, I want to stay grounded and connected to the people doing the work – to understand their challenges and help where I can.
What is the role of innovation in an established company like SCIEX – as opposed to a startup instrument business, for example?
I think it goes back to culture – you have to create the right environment for the type of work you're trying to do. I’ve had many opportunities to explore how you integrate what is essentially a startup mentality into a more established organizational framework. You want that innovative mindset – creative, agile behaviour that enables new ideas and fresh approaches. You want to capture the value that brings. But you also have to couple that with a regimented product development process that delivers reliable, high-quality products that meet customer expectations. Those are very different processes.
And what you come to realize – and this ties into my interest in human behaviour – is that some people really excel in that startup-style environment, while others thrive in the execution and quality-focused mindset. You shouldn’t treat those as interchangeable roles.
As you work with more and more people, you begin to recognize that everyone is wired differently. We all have different strengths and weaknesses. Some people are comfortable with risk and ambiguity, while others are better suited to detailed execution and structured environments. Some are highly creative; others prefer having clear guidelines.
Understanding where people fit along that innovation continuum is critical to structuring an effective R&D organization. That’s a big part of talent management: recognizing how someone is wired and identifying the best place for them to contribute. It’s like assembling a jigsaw puzzle – when you get it right, all the pieces fit together, and you end up with something cohesive and complete.
Just because two people have the same title – say, mechanical engineer – doesn’t mean they should be treated the same. They may be fundamentally different in how they work and what environment allows them to thrive. So figuring that out and aligning people with the right roles is essential.
That’s been a passion of mine over the years – understanding that range across the innovation pipeline and, as we hire or develop people, making sure we place them where they can bring the most value. That’s how you capture both the startup mentality and the structured, high-quality execution required to bring a great product to market – because those are radically different skill sets.
Are there any other lessons from psychology that you’ve implemented in how you build your team?
The idea of psychological safety is really important to me. It’s a core tenet of how I lead. I want the team to feel comfortable bringing up ideas or expressing contrary opinions. I’ve always told them: I’ll never get upset if someone tells me they think something’s a bad idea. But I will be frustrated if no one says anything in the moment – and then several months later says, “Yeah, I knew that wouldn’t work.”
We need to be able to talk openly about concerns and risks, and get the data around those concerns so we can make informed, logical decisions together as a team. That openness is critical. Many of us have worked in environments where people were afraid to speak up or challenge the status quo – where they felt like they couldn’t say something that might go against what the boss had said.
Creating an environment where people feel safe to speak their minds is incredibly empowering – not just for individuals, but for the team as a whole. It fosters a lot of creativity. You’d be surprised at the ideas people come up with when they feel safe and supported. That sense of psychological safety and mutual respect has become a real strength of our culture, and I think it plays a huge role in driving our innovation mindset.
The Tricorder Moment: Mass Spec in the Age of AI
Could AI soon allow users to operate instruments using natural language, without understanding how to build a method or process data?
Are there any emerging trends on your radar?
Probably not surprisingly, I’d say AI is the big one right now. Pretty much every time I speak publicly, I get asked about what we’re doing with AI – and internally, too, it’s something the team brings up regularly. I really believe it's going to be transformational.
The speed at which AI is evolving right now is incredible. Sure, there’s the fun side of AI – people using it for entertainment – but I believe this is one of those step-change moments in human development. You could compare it to the advent of the personal computer, or the cell phone, or the shift from analog to digital photography. AI is on that level.
One of the big impacts is how it can democratize complex technologies. When I started my career, the people buying mass spectrometers often had PhDs specifically in mass spectrometry. The instruments were complex and required deep expertise to operate. But over the years, that’s changed. Mass spec has become more accessible, easier to use, and now people from a much wider range of backgrounds – like biotechnology, food science, or engineering – are using it not because they’re experts in mass spectrometry, but because it helps them get answers they need to move their work forward.
AI has the potential to take that further. It can compress the time it takes to go from problem to answer. It can provide deeper insight into complex data and reduce the need for highly specialized expertise in every use case.
We're moving toward a future where someone can use natural language to describe what they want to learn from a sample. Ideally, they wouldn’t need to understand the inner workings of the instrument, how to build a method, or how to process the data. They could simply walk up, load the sample, and the instrument would figure it out.
I often think of the mental image of the tricorder from Star Trek. You walk up to something, scan it, and it tells you what's there and in what quantity. That’s what I think a lot of customers would love. They want to put in a sample and just have the system say: “Here’s what’s in it, here’s how much, and here’s a report you can use.” AI absolutely has the potential to deliver that. There’s still work to do, but the trajectory is clear – and it’s exciting.
This applies not just to mass spec, but to a whole range of analytical technologies. AI could fundamentally change how we understand biology, how we approach precision medicine, how we develop drugs – both biopharmaceutical and small molecule. We’re already seeing AI used extensively in pharma, including with some of our partners who are applying it to transform their internal capabilities.
The pace of innovation in AI is what amazes me the most. It feels like every week there’s something new. I don’t see that pace slowing down anytime soon, and we’re going to be part of that transformation with the tools we’re developing.
What is missing today from the mass spec toolkit? Are there any major problems that we can’t fully address with the current technology?
First, something that's been interesting to observe throughout my career – I've been working in mass spectrometry for more than 27, 28 years now – is what I’d call an “arms race” between technology developers and customers. As we evolve the technology, customers immediately find new ways to use it and then run into new bottlenecks. That cycle continues – we push the technology, and then they push the boundaries of what they want to do with it.
A good example of that is high-resolution accurate mass technology. Initially, customers used it to identify what was in their sample – say, a protein digest. Then they quickly moved to asking, "How much of these proteins are there?" Once they had ID and quantification, they wanted to know how things changed under different conditions – so they wanted to run multiple samples, get IDs, quantitation, and do it with high precision and repeatability. And they wanted to do it faster, more robustly, and at scale.
Every time we’ve thought that increased performance might mean fewer instrument purchases or reduced sample throughput, it’s been the opposite. Customers find more questions to ask, go deeper into their samples, and extend their research. So that continual drive for deeper insights – faster, more quantitative, more robust, more reliable – that’s very much still alive. We're not done yet. There's still lots of innovation needed.
Accessibility is another big theme. As we discussed earlier, customers increasingly don’t want mass spec to be the focus – they want easy, intuitive access to results. That’s where AI has huge potential, by lowering the barriers to entry and making the technology more approachable to non-specialists.
The second major gap I’d point to is data integration. Many customers are using multiple analytical technologies in their workflows, and at some point, they need to bring that data together. There’s no single, dominant tool that does that really well today – nothing that seamlessly integrates all those different modalities into a coherent whole.
Ironically, what we see most often is that people are still using Excel and Word as makeshift integration tools. They’re flexible, familiar, and can pull in different types of information, but they’re clearly not purpose-built for this kind of scientific data integration. So there’s a big opportunity there: how do we combine multimodal results – mass spec, chromatography, spectroscopy, imaging, whatever it may be – into one unified platform that gives scientists a “single source of truth” they can actually use to make decisions? That’s a big challenge still waiting to be solved.
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