Turning research into something that actually works in the real world sounds straightforward, but it rarely is. There’s a gap between discovery and impact that trips up even the most promising ideas, and it often has less to do with the science and more to do with how the problem is framed, understood, and communicated. My guest today is Marc Filerman, Chief Business Officer at Start2 Group, a global accelerator working across academia, startups, and government to help early-stage ventures de-risk, grow, and scale.

Marc brings a unique perspective shaped by his background as an MIT-trained engineer, corporate leader, and serial startup founder. At Start2 Group, he helps run major programs like BARDA-supported VITAL and NSF-backed Stride Ventures, supporting thousands of companies and contributing to billions in follow-on funding. We talk about what actually separates startups that move forward from those that stall out, including the importance of defining a clear problem, building a real value proposition, and choosing a focused foothold market instead of trying to do everything at once.

We also get into where universities and tech transfer offices have an opportunity to better prepare founders, especially when it comes to early validation and adopting a commercial lens alongside strong IP. Marc shares practical insights on funding pathways beyond venture capital, common mistakes he sees again and again, and how small shifts in thinking can dramatically improve a startup’s chances of success.

In This Episode:

[04:05] Marc shares the three through-lines guiding his career: building things, solving complex problems, and teaching others.

[06:15] He explains his shift from engineering to startups through a passion for translating real-world problems into solutions.

[08:45] Introduction to Start2 Group and its global footprint supporting startup de-risking and commercialization.

[11:20] Breakdown of government-backed programs including BARDA and NSF partnerships, and how they fund innovation.

[14:10] Differences between VITAL (biotech, pandemic preparedness) and STRIDE (deep tech, materials reclamation).

[17:48] A major funding opportunity is highlighted, including a $100M antiviral development initiative.

[21:52] Marc outlines three core startup success factors: clear problem, strong value proposition, and focused market entry.

[24:10] Why poorly defined problem statements derail startups before they gain traction.

[26:30] He explains value propositions as benefits divided by adoption hurdles, not just features or outcomes.

[29:15] Common adoption barriers emerge, especially inertia and resistance to workflow disruption in healthcare systems.

[32:05] The hidden impact of IT integration and organizational complexity on startup adoption.

[34:45] Why many “real problems” never get solved due to low prioritization despite clear need.

[42:33] The importance of choosing a foothold market and resisting the urge to over-expand too early.

[44:10] Academic founders struggle with narrowing focus due to fear of limiting platform potential.

[46:00] Defining a true foothold market as one where customers urgently demand the solution.

[48:10] The biggest gap in university commercialization is lack of early validation and commercial thinking.

[49:20] Tech transfer offices can better support founders by encouraging market validation beyond IP development.

[50:30] Not all startups fit the VC model, alternative funding paths like angels and bootstrapping are critical.

[51:30] Closing reflections on improving startup success through clearer strategy and commercialization readiness.

Resources:

AUTM

Start2

Marc Filerman - LinkedIn

One of the toughest calls in technology transfer isn’t deciding whether a discovery is interesting or even promising. It’s deciding whether that discovery should become a company at all. There’s a big difference between a strong piece of research and something that can support a venture-backed startup, and most of the real work happens in that space in between.

My guest today is Omar Zahr, Chief Technology Officer at TandemLaunch, a venture creation firm that builds companies around university technologies in collaboration with global industry and academic networks. Omar started his career as a researcher at McGill University, where he completed a Ph.D. in Materials Chemistry before moving into venture development. Over the years, he’s worked at the intersection of deep tech, founders, and university IP, helping shape early-stage research into companies that are actually investable and positioned for growth.

We talk about how to evaluate whether a technology is truly “company-ready,” what it takes to build a business around early-stage science, and why not every invention should become a startup. Omar also shares how TandemLaunch approaches founder pairing, how investor expectations shape deep tech timelines, and where tech transfer offices can make or break momentum in the process. It’s a practical look at what happens between invention disclosure and a funded company, and why getting that middle stage right matters so much.

In This Episode:

[05:08] Omar Zahr walks through his transition from materials science at McGill into venture creation at TandemLaunch.

[06:14] He explains how curiosity, not a fixed plan, led him away from academia and toward commercialization.

[07:22] The moment he began to understand the broader gap between research and real-world impact started to take shape.

[08:35] Omar outlines how TandemLaunch differs from traditional incubators by acting as a company builder, not just a funder.

[09:48] The venture creation model is described as an end-to-end process from invention to seed-stage startup.

[11:02] He shares what makes a university technology “company-ready,” starting with proof of core scientific validity.

[12:16] The idea of building a business narrative first comes into focus as a key decision-making tool.

[13:29] Working backward from a successful exit helps determine whether a startup path even makes sense.

[14:41] Omar explains how some technologies are better suited for licensing when a full company story can’t be formed.

[15:54] The role of tech transfer offices is highlighted as essential for structuring deals and protecting all sides.

[17:08] Early IP clarity is discussed, focusing on capturing the true differentiator behind the invention.

[18:21] He breaks down how licensing complexities like field of use and background IP are handled in practice.

[19:37] Getting involved early allows TandemLaunch to influence patent strategy and support broader filings.

[20:49] The conversation shifts to founder selection, including how technical and product leads are identified.

[22:03] Omar explains why experienced CEOs are critical, even when other team members are first-time founders.

[23:17] Managing expectations between inventors and startup teams often comes down to relationship dynamics.

[24:28] Deep tech timelines and investor expectations are explored, with a focus on reducing risk step by step.

[25:32] Omar closes on a key challenge: slow negotiations introduce risk and can quietly kill otherwise strong deals.

Resources:

AUTM

TandemLaunch

Omar Zahr - LinkedIn

Omar Zahr - AUTM

Some of the most meaningful industry–academic partnerships don’t begin with a breakthrough headline or a flashy piece of technology. More often, they take shape around a real research need, a practical solution, and a willingness to keep showing up and working through the details over time. That’s the kind of collaboration we’re exploring here, including how ideas move from early-stage science into something researchers can actually use, and what it takes to make those relationships last.

My guest today is Mark Fairey, Senior Licensing Manager at STEMCELL Technologies, Canada’s largest biotech company known for its high-quality reagents, instruments, and tools used by life science researchers around the world. Mark has spent two decades at STEMCELL, moving through roles in R&D, scientific sales, business operations, and now licensing and business development. That range of experience gives him a grounded, practical perspective on what it really takes to turn academic discoveries into reliable, scalable products, and why the strongest partnerships often start long before anything is ready for market.

We talk about what actually bridges the gap between a promising idea in the lab and something that can be reproduced, scaled, and trusted in labs globally. Mark shares how STEMCELL evaluates technologies, why understanding real-world workflows matters just as much as the science itself, and where academic teams often underestimate the challenges of usability and scale. We also get into the role of tech transfer offices, what makes early conversations productive, and why consistent communication is still the backbone of any successful long-term partnership.

In This Episode:

[02:03] Mark Fairey reflects on his 20-year path at STEMCELL Technologies, from research into sales, operations, and licensing.

[03:11] He explains how time spent working directly with researchers gave him a clearer view of how products perform in the real world.

[04:07] The conversation turns to STEMCELL’s “scientists helping scientists” philosophy and how that mindset still shapes the company today.

[05:02] Mark discusses what helps move a promising academic insight toward something that can become a dependable product.

[06:18] He says early-stage science needs more than exciting data. It also needs a real commercial niche and a practical use case.

[07:26] A simple muffin-baking analogy captures why scaling a process is much harder than just repeating what worked in the lab.

[08:39] Mark shares how his exposure to customers and end users affects the way he evaluates technologies for licensing.

[09:47] Trust, strong science, and a shared commitment to improving research workflows all factor into lasting academic partnerships.

[10:56] He points to communication as one of the most important ways tech transfer offices can keep partnerships productive over time.

[12:04] Regular check-ins, clear expectations, and timely replies all make it easier for industry and academia to stay aligned.

[13:16] Not every collaboration leads to a license, and Mark explains why smaller, informal relationships can still be worthwhile.

[14:28] The discussion highlights what academic teams sometimes miss about usability, shelf stability, and large-scale reproducibility.

[15:42] Mark broadens the lesson beyond life sciences, arguing that commercialization always starts with solving a real-world problem.

[16:54] He reflects on how the volume of university innovation has grown and how both academia and industry have become more fluent in each other’s needs.

[18:06] When researchers or tech transfer offices first reach out, a solid non-confidential overview helps make the conversation more productive.

[19:02] Mark closes with his biggest takeaway for tech transfer professionals: communication, empathy, and active listening matter most in building relationships.

Resources:

AUTM

STEMCELL Technologies

Mark Fairey - LinkedIn

If you've ever wondered what's actually going on inside a company's head when a university comes knocking with a new technology, today's episode is for you. We're getting into the real mechanics of university-industry partnerships and what makes them work, what slows them down, and where the biggest opportunities are being left on the table.

My guest today has lived this from just about every angle imaginable. He started his career in Ecuador, where he built the country's first university tech transfer office essentially from scratch. He then co-founded an ed-tech startup that turned profitable in its first year, led digital innovation licensing at Duke University, and now sits on the industry side at Samsung Research America, where he manages university collaboration programs and serves as a bridge between academic research and one of the world's largest tech companies.

In this conversation, we get into what Samsung actually looks for when a university brings an opportunity forward, how they think about technology at different stages of readiness, and why the human factor in these relationships matters more than most people realize. We also talk about how fast-moving fields like AI are changing the rules of the game for tech transfer professionals, and he shares some really practical advice on how to position technologies so companies lean in rather than walk away.

In This Episode:

[03:12] David Chang shares how curiosity and a belief in innovation as an engine for economic development shaped his global career in tech transfer.

[03:58] His path spans building Ecuador’s first tech transfer office, founding a startup, working at Duke, and now leading university partnerships at Samsung.

[04:41] Early work in Ecuador showed how innovation ecosystems develop slowly through trust and incremental collaboration.

[05:36] In emerging markets, university partnerships often begin with student projects before growing into research and commercialization efforts.

[06:44] David explains how seeing both the university and corporate sides of tech transfer reshaped his perspective.

[08:09] Relationships between tech transfer offices and industry partners often drive successful collaborations more than databases or programs.

[09:47] Industry timelines can be tight, and lengthy contract edits can create friction in university–industry partnerships.

[11:13] At Samsung’s LeapU program, three factors help advance a university technology: differentiation, clear milestones, and strategic fit.

[12:08] Demonstrations that spark an internal “aha moment” can help companies rally support for a new technology.

[13:27] Samsung evaluates proposals through a balance of technology push and market demand.

[14:16] The company organizes partnerships by technology readiness through the START, LeapU, and LeapS programs.

[14:58] START accepts early research ideas, while LeapU and LeapS rely on trusted relationships and invitations.

[15:43] Strong university partners often begin with deep expertise in a specific research area.

[16:29] Tech transfer offices add value by mentoring researchers on IP strategy and identifying entrepreneurial investigators.

[17:52] Emerging technologies like AI and robotics are pushing companies toward new collaboration models.

[18:41] Development speed matters in AI, where innovations can become obsolete within a short time.

[19:36] Platform technologies with modular components are often easier for companies to adopt than standalone inventions.

[21:18] Cultural factors such as flexibility and ongoing dialogue often distinguish the best university partners.

[22:44] Researchers interested in collaborating with Samsung should highlight their research background and concrete collaboration ideas.

[24:03] Combining technical depth with a strong business case can help tech transfer professionals position inventions more effectively.

[25:32] Industry conferences like AUTM provide valuable opportunities to build long-term collaboration networks.

[26:18] Reflecting on his career, David notes how working on both sides of tech transfer deepened his understanding of how innovation moves to market.

Resources:

AUTM

Samsung Research America

START

LEAP-U

LEAP-S

One of the biggest challenges in tech transfer isn't generating innovation — it's helping promising technologies move from early success into sustained, real-world use. That pattern shows up across industries, but today we're going to explore it through one fast-moving example: AI in healthcare. My guest is Adam Brickman, a healthcare innovation leader and part of the team behind Vega Health, a company focused on helping organizations identify, implement, and scale validated AI solutions.

Adam brings a practitioner's perspective to a problem that's becoming harder to ignore. Technologies that show real promise, sometimes even strong clinical results, can still end up stuck at their site of origin, never reaching the patients and health systems that need them most. Vega Health was built to change that by creating a new commercialization pathway that connects proven AI models from leading academic medical centers and health systems with the community hospitals that make up the vast majority of healthcare in this country.

We discuss why AI that works at one institution doesn't automatically translate somewhere new, and what it actually takes to bridge that gap. We talk about workflow discovery, the importance of testing models against local patient data before full deployment, and why user experience and staff buy-in are just as critical as the technology itself. Adam also shares what Vega Health looks for when evaluating whether an AI solution is ready to scale and has some pointed thoughts for tech transfer offices on licensing strategy in an increasingly crowded market.

In This Episode:

[02:29] Adam describes why many AI innovations remain trapped at their site of origin, even after demonstrating strong clinical or operational results.

[03:10] The conversation breaks down four traditional commercialization paths and introduces Vega Health’s role as a fifth, scale-focused alternative.

[04:05] A common assumption is challenged: the belief that only large academic medical centers can access or afford high-quality AI solutions.

[04:48] Adam explains why success in one health system rarely translates directly, emphasizing that implementation context and workflow differences are critical.

[05:32] Vega Health’s approach is outlined, including retrospective data testing to determine which models perform best in a specific patient population.

[06:40] The typical AI purchasing process is critiqued, highlighting the risks of committing to full deployment before validating real-world performance.

[07:31] The shift from “technology that works” to “technology that is used daily” is framed as a human and organizational challenge, not just a technical one.

[08:12] Adam stresses that technology must adapt to clinicians and staff workflows rather than expecting already-burdened users to change behavior.

[09:05] Validation is defined through live clinical deployment combined with peer-reviewed evidence, reducing the risks of first-time real-world testing.

[10:18] Transparency gaps in AI documentation are addressed, with Vega Health advocating standardized reporting on training data, origins, and performance.

[12:02] Adam reflects on the disconnect between innovation teams solving local problems and vendors pursuing only the most prestigious institutions.

[13:15] The imbalance in vendor strategy is highlighted, noting that most AI companies target a small percentage of elite hospitals while community systems remain underserved.

[14:10] Non-technical barriers take center stage, including alert fatigue, workflow friction, and the outsized importance of thoughtful UI and UX design.

[18:18] A story of initial resistance illustrates how skepticism can soften when end users feel heard through collaborative workflow discovery.

[20:31] Evaluation expands beyond model accuracy to include adoption metrics, clinical outcomes, administrative impact, and measurable return on investment.

[22:23] Adam offers strategic guidance to tech transfer offices: determine whether an innovation stands alone as a company or functions better as a feature.

[24:40] The risks of mandatory exclusivity are discussed, especially in a rapidly crowding AI market likely to experience consolidation.

[26:05] The episode closes with a reflection on why scaling innovation is difficult, resource-intensive, and still deeply worth pursuing.

Resources:

AUTM

Adam Brickman - LinkedIn

Vega Health