As UX/UI Designer at Genemod, I led design across a scientific information management platform spanning web, mobile, and tablet design.

In a fast-paced startup, I wore multiple hats — from research and product design to light project management. This close collaboration with customers and internal teams gave me firsthand insight into how scientists actually use our platform and what they needed most.

As Genemod expanded, my work spanned both proactive feature design and ongoing UX optimization.

While designing new platform features to enhance the product’s capabilities, I also managed ongoing customer-reported tickets and feedback, ensuring we continuously improved existing experiences in parallel with building new ones.

This balance between feature creation and UX refinement is essential in a startup environment — allowing us to deliver rapid value while maintaining usability and visual consistency at scale.

Common insights and challenges included:

• Difficulty navigating between modules (Freezer, ELN, Orders, etc.)
• Gaps in workflows — missing analytics or sample traceability
• Outdated or inconsistent UI patterns across web and mobile

Balancing new feature development with iterative improvements ensured that every design decision moved the platform closer to a cohesive, scalable, and user-centered system.

Working closely with customers, product managers, and engineers, I use multiple feedback channels to guide my design priorities:

1

Customer Reported Tickets

I analyze customer-reported tickets to identify patterns, prioritize by workflow impact, and translate insights into meaningful UX improvements.

Rather than treating tickets as isolated issues, I evaluate them collectively to uncover recurring usability challenges and opportunities for product evolution.

My approach:

• Categorize & cluster: Group tickets by theme (feature type, visibility).
• Prioritize by impact: Identify which pain points affect the largest number of users or key workflows.
• Translate into design action: Convert high-impact tickets into scoped UX improvements or feature concepts.

3

Internal Testing & Validation

Beyond user research, I play an active role in validating design quality through internal testing before new features or fixes reach production—verifying flows, identifying inconsistencies, and revalidating resolved tickets to prevent regressions. This process ensures design consistency, reduces user frustration, and minimizes customer-reported bugs after release.

Because Genemod operates in a fast-paced startup environment, I work closely with engineers and the CEO to perform in-depth QA testing on both new features and previously completed tickets.

My focus includes:

• Testing new feature builds before release to verify that user flows, interactions, and visuals align with the intended design.
• Identifying and documenting bugs in existing workflows (functionality issues, layout inconsistencies, accessibility gaps).
• Revalidating resolved customer tickets marked “done” to ensure that fixes are implemented correctly and that no regressions occur.
• Providing design updates or UI recommendations when fixes require layout or interaction improvements.

This proactive testing process helps reduce post-release issues and ensures users experience the product as designed.

Example of internal testing documentation for a UI fix — validating the visual container border and interaction behavior before release.

4

Workflow Mapping

Before jumping into design concepts and to strengthen my understanding of how scientists interact with our platform end-to-end, I created detailed workflow maps covering Genemod’s core modules — including Freezers, Consumables, Notebook, Orders, and Protocols.

These maps visualize each user journey step, decision point, and task dependency across the platform, allowing the product and engineering teams to clearly see how one workflow impacts another.

By referencing these workflows, I was able to identify dependencies between modules, prioritize high-impact tickets, and ensure that new features integrated smoothly into existing user processes.

By establishing this system-wide map, I was able to:

• Quickly identify high-impact pain points that affected multiple features.
• Prioritize customer tickets that blocked core workflows (like sample tracking or data imports).
• Provide engineers and PMs with a shared reference for system behavior, improving sprint clarity and design alignment.

User workflow mapping for Freezers — used to define core scientific processes and identify high priority tickets.

Once priorities are identified, I move into rapid ideation — creating low- to high-fidelity mockups in Figma and running internal design reviews with the product and engineering teams.

• User needs (pain points, desired functionality)
• Business goals (feature adoption, retention, conversion)
• Technical feasibility (alignment with current system constraints)

When users requested better visibility into sample analytics, I designed an Analytics Dashboard within our LIMS system.

It provided a visual overview of sample usage, freezer capacity, and consumable stock levels — giving lab managers actionable insights at a glance.

A look into Genemod’s Analytics feature for Freezers, Orders and Consumables

Simultaneously, I worked on ticket-driven fixes such as improving our barcode scanning UI and allowing users to move items from one place to another.

• Select more label size options
• Adjust their printer resolution
• Edit their Barcode ID if they were importing barcodes from another software
• Customize the content on their label

Before

After

• Simplify repetitive workflows: Reduced the time scientists spent rebuilding existing racks across freezers
• Decrease manual labor: Eliminated the need for duplicate setup steps, streamlining sample management
• Improve flexibility: Enabled effortless location switching to support evolving lab layouts and sharing needs

→

Genemod’s “Move Rack & Category” feature: designed to support faster reorganization and cross-freezer flexibility

Gave lab managers a real-time view of sample usage, freezer capacity, and consumable stock—enabling faster, more informed decisions without leaving the platform.

• High-level view of lab activity across all modules
• Lab managers can track usage trends and freezer capacity
• Modular data cards designed for future scalability

Redesigned labeling to support flexible sizes, custom IDs, and printer control—reducing friction across lab workflows.

Created a feature to move Racks & Categories to eliminate redundant setup steps, making cross-freezer organization faster and more intuitive as lab needs evolve.

Introduced color-coded statuses to help users quickly locate and identify samples, reducing time spent searching and identifying specific samples.

I collaborate daily with engineers to ensure designs are implemented accurately and efficiently. Using Figma components, shared libraries, and annotated handoff specs, I make it easy for developers to access states, variants, and spacing logic.

In parallel, I manage some project coordination tasks in Jira, helping organize sprints, prioritize user tickets, and ensure visual QA before launch.

Post-launch, I revisit tickets to monitor whether design updates resolved the original issue and gather feedback for continuous improvement.

• Reduced post-release bugs through improved internal QA
• Positive enterprise feedback citing improved clarity and consistency
• Faster adoption for new modules like Analytics
• Improved design and engineering collaboration through structured design systems

Working at Genemod taught me that great design isn't just about visual polish — it's about balance. Balancing feature innovation with ongoing improvements deepened my ability to prioritize, empathize, and design for scalability.

This experience deepened my understanding of how thoughtful design decisions can transform complex scientific workflows into intuitive, human-centered experiences.

Inflerra is an AI-powered platform built to support both creators and brands equally — helping each side find partnerships that genuinely align with their goals, values, and audiences.

As a founding designer, I’m leading the end-to-end product experience for an early-stage startup focused on creating more intentional, transparent, and mutually beneficial collaborations.

Influencer marketing is often inefficient and misaligned.

Brands struggle to find creators who truly match their audience, voice, and campaign goals. At the same time, creators spend a lot of time applying to partnerships that don’t reflect their values, interests, or long term direction.

Discovery today is manual, noisy, and optimized for quantity, not fit.

• Brands need a faster way to evaluate creator fit beyond surface-level metrics
• Creators need more relevant opportunities instead of high-volume, low-alignment outreach
• Both sides need a process that feels more transparent, intentional, and mutually beneficial

Inflerra uses AI-driven matching to connect brands and creators based on meaningful signals such as audience alignment, content style, pricing, brand values, and campaign goals.

The platform is designed to work both ways: helping brands discover creators who are a true fit, while also helping creators find brands they actually want to work with.

Our goal is to replace transactional partnerships with high-quality, aligned collaborations on both sides.

As the Founding UX/UI Designer, I’m responsible for shaping the product experience for two distinct user groups, ensuring clarity, trust, and balance across the platform.

• Defining end-to-end flows for creators and brands
• Designing the desktop-first web experience
• Establishing the early design system and visual language
• Collaborating closely with our founders, engineers and PMs on AI-assisted UX patterns
• Translating complex matching logic into clear, intuitive interfaces

A key focus of my role is designing our AI-powered experience to feel transparent, human, and empowering — not opaque or one-sided.

Inflerra is currently in private beta and actively evolving. Due to our current stage, detailed workflows and logic are not publicly shared yet.

This page reflects the product vision, UX approach, and design leadership behind our work.

Around Town was created during the COVID-19 pandemic to help small, locally owned businesses stay visible and connected to their communities at a time when foot traffic, revenue, and business stability were under pressure.

The app was geared toward serving and empowering small, locally owned businesses by creating a resource that would allow consumers to easily discover businesses, support outreach and sales, and stay engaged with the places they care about most.

Due to the COVID-19 pandemic, many small businesses were struggling or shutting down because of reduced customers, tighter restrictions, and capacity limits. As those constraints increased, the survival of local businesses became harder and harder.

That pressure affected not only individual owners, but also the families and local economies that depended on those businesses staying open.

With the need for financial stability and visibility becoming more urgent, we wanted to design a solution that could help small businesses stay active, gain recognition in the community, and create stronger incentives for people to shop locally.

The product centered on features that could help both users and businesses connect more easily, discover nearby opportunities, and build social motivation around supporting local commerce.

We used low-fidelity wireframes to define structure, clarify the core experience, and map the most important user flows before moving into visual polish. This phase helped us pressure-test information hierarchy and feature scope early.

The visual system was designed to feel approachable, trustworthy, and easy to read. We wanted colors that conveyed trust while still standing out against a white background, paired with a clean font system that stayed accessible across screens.

• Easy, clear-to-read typography for a wide range of users
• Colors that represent trust yet maintain contrast against a white interface

The high-fidelity work translated the concept into a fuller mobile product experience, including onboarding, account creation, search, business profiles, reviews, promotions, social feeds, and settings. The goal was to make local business discovery feel useful, social, and actionable.

Getting Onboarded

Using the search engine

Viewing the restaurants profile

Socialize with other users

• Create an account based on whether you are a business or a consumer
• Search and discover local businesses by category
• View business profiles with reviews and hours
• Create and post reviews, then browse your review history
• Browse and activate promotions and local deals
• Follow friends and view community recommendations in a social feed
• Personalize settings for content and visibility

Around Town taught me how product design can respond to urgent real-world conditions with a balance of empathy, clarity, and practical incentives. It also strengthened my ability to work across project management, interaction design, and collaborative product thinking within a small team.

This project taught me how to shape a user-centered concept from the ground up, organize a team around a meaningful problem, and move from low-fidelity exploration to a polished mobile experience with a clear feature strategy and visual system.

Alumnify was designed to strengthen the relationship between alumni, current students, and university stakeholders through networking, mentorship, shared interests, and community groups.

The project focused on building a platform that could support long-term engagement after graduation while also creating clearer pathways for career development, mentorship, and alumni involvement.

We started by defining a clear gap: alumni often do not know how to update their information or stay connected with their organization after graduation. Many also want to mentor students or reconnect with their university community, but lack a dedicated platform to do so.

At the same time, students need better access to mentorship and employment opportunities, while university leadership and partners need more visibility into alumni outcomes and stronger ways to foster long-term engagement.

Our research process focused on defining the problem, identifying the people most affected by it, and understanding how the current alumni ecosystem was fragmented across different user groups.

After gathering initial research, the team synthesized findings through affinity clustering to identify the themes that mattered most. Each team member wrote down important Alumnify-related terms on sticky notes, then grouped them into larger categories based on similarity and relevance.

We then voted on the categories and ideas we believed were most important to carry into the design phase. This allowed us to ground feature decisions in a shared understanding of user needs rather than assumption.

Design Priorities

• Surface mentorship and networking opportunities more clearly
• Build around shared interests and group participation
• Support both alumni and student-specific goals without fragmenting the product
• Create a system that university leadership could also use for engagement and insight

To better understand intended features, system behavior, and relationships between different user roles, we created use case workflow diagrams. These diagrams helped us map important actions, dependencies, and functionality before moving further into interface design.

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The design goal was to create a web platform that felt user-friendly, simplistic, and informative while still supporting a broad set of networking and organizational needs.

• Role-based onboarding for alumni, students, and staff
• User profiles centered on interests, background, and career development
• Community posting, events, and group-based interaction
• Search and filtering to support discovery and connection
• Administrative views that support university organization and outreach

This project sharpened my ability to translate broad stakeholder needs into a clearer product strategy, especially when multiple audiences needed different outcomes from the same platform.