Info
I’m a UX researcher and product builder with a background in cognitive science and HCI. I’m especially drawn to ambiguous, early-stage problems where the product direction is still taking shape and research can turn a fuzzy hypothesis into something real.
My work has taken me across AI, enterprise tools, advertising, and transportation, often in roles that sit between research, design, and product. I like getting close to the problem, making ideas tangible, and working with teams to turn evidence into something they can act on.
Outside of work, I’m usually taking photos with my collection of early 2000s CCD sensor digicams, looking for a sweet treat, or reading something slightly too existential.
Experience
AnswerLab
Trust & Safety / Fintech
Databricks
Agentic AI / Enterprise
Advertising / Mobile
Amtrak
Sustainability / Service Design
Background
I’ve worked with consulting teams supporting companies like Meta, Wikipedia, The Metropolitan Museum of Art, and Civian.
Prior to that, I worked in academic and healthcare research, improving digital health technologies, systems, and treatments.
Skills
UX research
Product strategy
Interaction design
Rapid prototyping
Co-creation
Contact
Work
I led research at Databricks for an early-stage agentic marketing product, turning ambiguity into clear direction.
I helped converge competing design directions for LinkedIn’s first mobile Boosting experience.
I translated frontline research at Amtrak into strategic opportunities that informed executive-level prioritization.
Introduction
Product context
Research progression
Initial findings
From insight to interaction
What the work converged on
Impact
Reflection
Introduction
Research brief
Mobile context
Concept comparison
Research findings
Direction
Future product strategy
Impact
Reflection
Introduction
Project context
Reframing the problem
Approach
Field research
Research under real conditions
Synthesis
The central tension
Translating research
Participatory design
Prioritization
Outcome
Impact
Reflection
CustomerLake
Shaping an agentic AI experience from ambiguity to a clearer product direction.
I led three rounds of research for an early-stage agentic AI product, turning workflow insights into testable interaction models. The work clarified where AI should assist and people should retain control, informing 17 roadmap items and eight in-flight design changes.
Role
UX Research Intern
Timeline
May–August 2026
What I did
Research · Co-creation · Interactive prototyping
Partners
Design · Product · Engineering
CustomerLake / Agentic CDP
Early product direction
01 / Introduction
CustomerLake brings agentic AI into the customer-data workflow.
I was the primary researcher on an early-stage agentic AI initiative for marketing and data practitioners. Across three rounds of generative and evaluative research, I helped the team clarify how people should work with an AI agent and where trust, control, and governance needed to shape the experience.
Between studies, I worked with design and product to turn findings into interactive concepts, using prototyping and AI-assisted development to make emerging directions concrete enough to test.
Product
CustomerLake — Agentic Customer Data Platform
Role
UX Research Intern
Timeline
May–August 2026
Partners
Design · Product · Engineering
02 / Product context
A new collaboration model between marketing and data teams.
CustomerLake is Databricks’ Agentic Customer Data Platform, built natively in Databricks. It brings core CDP capabilities like Customer 360, identity resolution, audience building, campaign automation, activation, and personalization into the same governed data and AI environment enterprises already use.
A central part of the product vision is giving marketers agentic interfaces to work with trusted customer context, while data teams retain governance over the underlying data and models.
Where I came in
The product direction was ambitious, but the interaction model was still open.
I joined while that experience was still taking shape. My role was to reduce uncertainty through iterative research, then carry the learning forward into product directions we could test.
How should marketers express intent to an agent?
What should the agent do on its own?
What would users need to inspect, verify, or approve?
How could the experience reduce operational friction without weakening governance?
Initial framing
What would need to be true for users to adopt and trust this kind of AI-assisted workflow?
Core product tension
Business intent had to become governed technical logic without disappearing inside the agent.
Input
Business intent
→
Interpreter
AI / agent
→
Execution boundary
Governed technical logic
03 / Research progression
Each phase narrowed the next product question.
01
Test the emerging direction
Concept testing + interviews
Question
Which capabilities resonate, and what could block adoption?
Shift
Promising capabilities were not yet adding up to a coherent workflow.
02
Investigate the riskiest assumption
Generative interviews + workflow research
Question
Would greater self-service actually solve the underlying problem?
Shift
The technical task was not the real bottleneck.
03
Make the findings tangible
Co-creation + concept development + prototyping
Question
What would the research imply if we turned it into an interaction?
Shift
Research principles became product hypotheses we could test.
04
Test the emerging model
Workflow research + concept testing
Question
Where should AI assist, what needs verification, and where should users stay in control?
Shift
A clearer model emerged around translation, verification, control, and governance.
04 / Initial findings
Powerful AI capabilities weren’t enough.
The product needed to serve people with very different levels of technical fluency. Marketers thought in business goals and campaign outcomes; technical counterparts were responsible for making those goals executable, governed, and safe.
Does it fit how we work?
Can we trust it?
Can we actually work this way?
How might an AI agent make complex data work more accessible to non-technical users without losing the controls that make it trustworthy?
Turning point / Workflow
The first prototypes had features, but not yet a workflow.
01
Goal
02
Build
03
Review
04
Act
05
Learn
Shift / prototype research
Participants saw value in individual capabilities, but the experience did not yet map cleanly to how they worked.
The research pointed toward greater guidance, room to iterate, human review around consequential actions, and better visibility into what the agent was doing.
From
Which AI capabilities matter?
To
How should these capabilities work together as an experience?
Turning point / Bottleneck
We were optimizing the wrong bottleneck.
The team was exploring greater self-service as a way to make a slow workflow faster. Research showed that the technical build itself represented only a small portion of the delay. Most friction came from requirements translation, cross-team coordination, handoffs, queueing, and governance.
From
How can we make creation faster?
To
How can we compress the translation + governance loop?
Automating execution alone would make one step faster without fixing the system around it.
Artifact / current workflow + bottleneck
The opportunity was a tighter loop between business intent, technical interpretation, evidence, and accountable action—not simply faster execution.
01 · Ambiguous goal
Request
02 · Intent → requirements
Translate
03 · People + systems
Coordinate
04 · Technical step
Build
05 · Evidence + risk
Validate
06 · Accountability
Approve
Translation + coordination
Most elapsed work
Build
Small step
Validation + governance
Necessary friction
Implications
The build wasn’t the bottleneck.
The workflow needed translation, visible correctness, embedded review, and autonomy calibrated to risk.
Translate intent
Turn ambiguous business language into structured logic.
Make correctness visible
Help users inspect what the system understood.
Embed review
Make approval and accountability part of the experience.
Scale autonomy with risk
Give users more freedom during exploration than during consequential actions.
05 / From insight to interaction
I turned the research into a shared product question.
Rather than handing off a set of recommendations, I brought the findings into a cross-functional co-creation session with design, product, and engineering. We explored what the research meant in the broader workflow, then translated the strongest directions into two contrasting interaction models for testing.
01
Research findings
Evidence from the workflow.
02
Cross-functional co-creation
Design · Product · Engineering.
03
Two interaction hypotheses
Contrasting models of trust.
STRUCTURED
CONVERSATIONAL
04
Prototype + test
Compare the models in research.
↔
Prototype study
Make interpretation visible, editable, and verifiable.
Business intent gets lost when it has to become precise system logic. The product question became: how might the agent help with that translation without making its reasoning opaque?
Example research finding
Business intent gets lost when it has to become precise system logic.
Product question
How might the agent help with translation without making its reasoning opaque?
Testing question
Which model better helps users understand, verify, and confidently proceed?
Starting from the team’s existing concept, I forked the direction, independently redesigned the interaction model, and vibe-coded this alternative prototype myself.
01 / Structured interaction
Inspectability and control. Explicit stages make the agent’s interpretation visible and correctable.
01 · Goal
02 · Build
03 · Verify
04 · Constraints
05 · Proceed
Step 03 / Review the agent’s interpretation
Is this what you meant?
Goal
Editable interpretation
Evidence
Visible source signal
Constraint
Review required
Edit
Confirm interpretation
Same research finding.
A deliberately different interaction hypothesis.
01 ↔ 02
02 / Conversational interaction
Lower-friction understanding. Intent and interpretation evolve together through dialogue.
Abstracted dialogue sequence
User
Agent / Clarify
User
Agent / Proposed interpretation
Evidence + assumptions surfaced
Together, the two concepts turned the research into a comparative study: one model prioritized inspectability and control; the other tested whether dialogue could create a lower-friction path to the same understanding. In other words, the prototypes were competing hypotheses we could put back into research.
Product details are abstracted to protect confidential work.
The conversational direction was developed collaboratively with the designer.
Turning point / Judgment
The deeper workflow was translation and judgment.
The final research round zoomed back out. The actual job began before someone manipulated controls: teams clarified the business goal, negotiated what their data could support, worked around incomplete information, validated risk, and decided whether it was safe to act.
The opportunity: help users turn an ambiguous goal and imperfect data into a defensible decision.
AI could help with
Drafting · translating · suggesting · explaining · checking
People needed to retain
Judgment · approval · consequential decisions
01 / Frame
Intent
Ambiguous goal
02 / Interpret
Translation
Make meaning explicit
03 / Ground
Evidence
Surface signals
04 / Decide
Review
Human judgment
05 / Commit
Action
Accountable execution
AI assistance
Draft · translate · reason · check
Human accountability
Inspect · judge · approve · act →
06 / What the work converged on
A clearer interaction model for agentic AI.
Translation
Show how user intent becomes system logic. The agent needs to expose what it believes the user means.
Verification
Give users enough evidence to decide whether an output is credible.
Control
Make editing, rejection, approval, and recovery normal parts of the interaction.
Governance
Increase autonomy only when the consequences allow it.
07 / Impact
Research insights shaped product decisions.
17
roadmap items touched
9
roadmap bets validated / strengthened
8
design changes in-flight
15+
cross-functional stakeholders engaged
Product direction
Helped shift isolated capabilities toward a more coherent workflow.
Roadmap
Strengthened and redirected product bets.
Design
Research and concept testing informed active interaction changes.
Team learning
Co-creation and prototyping sustained an ongoing product-learning loop.
08 / Reflection
What I’m taking forward.
The question can evolve with the research
As the research progressed, the most useful product questions often became clearer too. I learned not to treat the initial framing as fixed, and to treat research as a continuous loop of discovery.
Product building: making can extend research
Turning findings into prototypes extended the research loop and closed the gap with design and engineering. Making became a way to carry insights further into the product process.
Bringing people in creates momentum
Co-creation gave my team space to engage, share perspectives, and build on the findings together. Collaboration makes research into something teams can actually move forward with.
Next project
Mobile Boosting
Three plausible paths to mobile Boosting. Research had to establish the right direction.
I led formative research for LinkedIn’s first mobile Boosting experience, examining how SMB advertisers make promotion decisions. The study helped the team converge from three competing directions to one refined concept and informed 21 design changes across three iterations.
Role
UX Research Intern
Timeline
2025
What I did
Interviews · Concept testing · Synthesis
Partners
Product · Design · Data Science · Engineering · Research Ops
01 / SINGLE
02 / STEPPED
03 / SIMPLE
THREE ALTERNATIVES / ONE PRODUCT QUESTION
01 / Introduction
Boosting worked on desktop. But advertisers were increasingly mobile.
Boost lets LinkedIn members pay to extend an organic post’s reach beyond their network. It was an important entry point for new advertisers, particularly small and medium-sized businesses. Yet Boost was unavailable on mobile, where much of LinkedIn activity already happened.
80%
of LinkedIn sessions were mobile
40%
of weekly posts were mobile
$35M
estimated incremental revenue opportunity in the Mobile Boosting PRD
02 / Research brief
What would make Boost work on mobile?
The team already had three concepts to evaluate. I combined contextual interviews with think-aloud testing to understand mobile behavior, compare the approaches, and identify what helped advertisers feel confident in their decisions.
01
What changes on mobile?
How do mobile context, behaviors, and needs affect design and feature prioritization?
02
Which direction?
How do advertisers respond to the three interaction models?
03
What creates confidence?
What information proves a Boost is worth the investment?
03 / Mobile context
Mobile was for quick engagement. Setup demanded more.
Participants checked notifications, replied, and browsed on mobile, but often moved detailed work to desktop. Eight of nine described lightweight interactions as typical mobile tasks; only two typically posted on mobile. Bringing Boost to mobile meant supporting a demanding setup task alongside behaviors that already felt natural there.
Mobile Boosting had to support more than setup.
BEFORE BOOST
Configure the Boost
Choose a post, decide to promote it, and set up the Boost.
PRODUCT NEED
Make setup easier without hiding what the choices mean.
DURING BOOST
Manage engagement
Monitor activity and respond to engagement.
PRODUCT NEED
Make timely monitoring and replies easy on mobile.
AFTER BOOST
Evaluate the return
Review results against the audience and outcomes intended.
PRODUCT NEED
Help advertisers judge whether their spend delivered value.
04 / Concept comparison
Three fundamentally different ways to translate Boosting to mobile.
The three concepts predated the study. Seen against participants’ mobile habits, they exposed a useful tension: reducing effort could also remove context needed to make a decision. The comparison tested how each approach handled that tradeoff.
01 / SINGLE
Long-scroll configuration
Desktop-like structure. The most information and configuration choices appear in one continuous scroll.
02 / STEPPED
Focused steps
Minimal information and decisions per screen, but without upfront estimated-results context.
03 / SIMPLE
Preset packages
Two decisions upfront. Priced packages show estimated results based on the selections above.
What does a genuinely mobile-native Boosting experience need to do differently?
05 / Research findings
Advertisers couldn’t configure a Boost confidently without understanding what their choices would produce.
Advertisers weighed the audience and business outcomes they hoped to reach against money and time invested. Some tracked previous Boosts in spreadsheets to learn which configurations worked. Seeing estimated results alongside setup choices helped participants make that value judgment without leaving the flow.
RESEARCH MENTAL MODEL / HOW ADVERTISERS JUDGE VALUE
Perceived ROI =
“[I look at] the quality of the engagement – is it the right audience?”
AUDIENCE
Did my post reach the right people?
“Success is increasing user base, engagement, and tangibly that means more conversations”
GOAL
Did my Boost generate meaningful outcomes?
BUDGET + DURATION
How much did I spend, over what period of time?
“Let me think about how much I really do want to spend on this and is it worth it?”
Product implication
Show expected outcomes while users configure the Boost by dynamically updating estimated results as variables change.
Finding / Guidance
More control wasn’t necessarily more empowering.
Participants wanted help interpreting their options. They described smart assistance and suggested actions as ways to reduce guesswork, especially when they lacked the marketing knowledge to choose an audience or duration confidently. That made guidance part of the configuration experience.
Product implication
Turn setup from a configuration form into a guided decision-making experience: recommendations, smart defaults, and contextual assistance.
Post-research prototype / audience guidance and suggested targeting.
Finding / Complexity
Mobile changed the acceptable level of complexity.
The desktop-like long-scroll model created excessive interaction and cognitive overhead
Post-research prototype / compressed configuration sections.
Reduce cognitive load
Less content and scrolling at once.
Reduce typing
Favor tap-to-select, steppers, and lightweight controls.
Reduce simultaneous decisions
Focus attention on one configuration choice at a time.
Finding / Confidence
Confidence depended on what happened after the spend.
Participants wanted to know whether a Boost reached the right people and supported their business goals. Engagement totals alone could not answer that. They also treated the forecast’s lower bound as an expectation: falling short without context left them questioning the investment.
POST-RESEARCH PROTOTYPE / ESTIMATED RESULTS
“I look at the range as essentially a promise”
Head of Talent Acquisition, Financial Services / research participant
The forecast supported a purchase decision. Participants described its lower bound as the minimum they would expect a Boost to deliver.
01 / EXPECTATION
Judge the likely return
Estimated results help advertisers decide whether to spend. The lower bound can become a baseline expectation.
02 / EVIDENCE
See who and what changed
Report the audience actually reached and business-relevant outcomes alongside impressions and engagement.
03 / NEXT ACTION
Explain and guide
When results miss expectations, explain the shortfall and offer realistic next steps.
06 / Direction
Three directions became one.
The comparison established a shared criterion: reduce decision effort while keeping enough context to judge value. The post-research prototype paired focused configuration with guidance and visible estimated results. The broader recommendations extended beyond setup to how advertisers followed engagement and evaluated success.
POST-RESEARCH PROTOTYPE / SELECTED DETAILS
Focused setup. Guidance in context. Outcomes in view.
Post-research prototype
21 design changes
Estimated across three concept iterations. The deck documents convergence and selected changes, not an itemized list of all 21.
Iteration detail / tap-to-select reduces typing.
3 / ALTERNATIVES →
DOCUMENTED TRADEOFFS →
1 / RESOLVED DIRECTION
Single / long scroll
Stepped / focused screens
Simple / priced packages
Long-scroll setup increased cognitive and interaction load.
Focused screens lacked upfront estimated-results context.
Seeing expected outcomes alongside choices supported decisions.
One research-informed mobile concept.
Research reframed the task: reduce decision effort while making likely outcomes understandable.
07 / Future product strategy
A direction for the product, and a research agenda beyond it.
The study pointed toward assistance that could reduce configuration effort while preserving informed choice. I presented that longer-term vision alongside the next step of validating the refined experience.
PRODUCT VISION / EXPLORE
Natural-language setup assistance
The shareout proposed AI configuration assistance that could use natural language to help set up a Boost. It suggested exploring whether capabilities used by Sales Assistant could support this direction.
NEXT RESEARCH / VALIDATE
Test simplicity against informed choice
Evaluate the refined design with mobile-first creators. Check whether advertisers have enough context to make informed choices without feeling overwhelmed, addressing the desktop-heavy sample in this study.
08 / Impact
Research changed the product direction.
3 → 1
Convergence from divergent directions to one refined concept.
21
Design changes across three iterations.
MVP
Priority features shaped the roadmap and MVP PRD.
$35M
Estimated incremental revenue opportunity in the PRD, not revenue measured by this study.
09 / Reflection
Research isn’t neutral facilitation. It requires a point of view.
I entered a project with three plausible product directions rather than a blank canvas. My role wasn’t to simply ask users to choose a design. It was to dig into what lay underneath — what information, guidance, control, and feedback made the experience valuable — and use that evidence to help my team decide where the product needed to go.
Research, at its best, helps teams choose a direction with a clearer understanding of what will make it work. I learned that doing that well requires really owning your insights and being opinionated about what the evidence reveals.
Qasim Malik / UX Research
Next project
Historic Stations of the Future
Using frontline research to shape
sustainable modernization at Amtrak.
I studied frontline work at Amtrak’s Wilmington Station and translated nine findings into three strategic opportunity areas. The research informed employee proposals and gave leadership an evidence base for prioritizing sustainable station improvements.
ROLE
UX Research Intern
TIMELINE
2024
WHAT I DID
Ethnography · Participatory design · Business Strategy
PARTNERS
Innovation · Sustainability · Executive leadership
JOSEPH R. BIDEN JR. STATION / WILMINGTON, DELAWARE
CONTENT
Project context
Reframing
Approach
Field research
Research conditions
Synthesis
Central tension
Translating research
Participation
Prioritization
Outcome
Impact
Reflection
01 / Introduction
Grounding historic-station modernization in frontline experience.
Historic Stations of the Future was an enterprise-wide challenge led by Amtrak Innovation and Sustainability to modernize historic stations sustainably while preserving their cultural value.
As a UX Research Intern, I studied frontline work at Wilmington Station and translated the findings into opportunities that informed employee proposals and organizational prioritization.
ROLE
UX Research Intern
YEAR
2024
LOCATION
Wilmington, Delaware
ORGANIZATION
Amtrak Innovation
02 / PROJECT CONTEXT
A modernization challenge with an organizational ambition.
Amtrak’s historic stations serve both as working transportation infrastructure and as cultural landmarks. Improvements needed to support sustainable operations while respecting preservation requirements.
The project also aimed to strengthen how Amtrak approached innovation: bringing employee experience and human-centered research into the decisions shaping forward-facing bets.
Sustainability
The project supported Amtrak’s Net-Zero by 2045 ambition.
Historic preservation
More than 500 stations served; roughly one-third listed on the National Register of Historic Places.
Organizational change
Research helped leadership prioritize, while employees contributed ideas.
STRATEGIC QUESTION
How might Amtrak modernize its historic stations sustainably while preserving their cultural identity?
03 / REFRAMING THE PROBLEM
Not just an infrastructure problem: a system to improve.
The original challenge focused on buildings and sustainability. But frontline work crossed physical spaces, digital systems, customer interactions, and informal team dependencies. Improving the station required understanding how all of these elements worked together, not simply identifying new technology to install.
INITIAL FRAMING
What sustainable upgrades could Amtrak introduce?
RESEARCH FRAMING
What must sustainable modernization improve for the people operating the station?
04 / APPROACH
Discovery → Participatory Design → Prioritization
Rather than moving directly into solution generation, the project connected frontline evidence to enterprise decision-making.
RESEARCH PROGRESSION / EVIDENCE → INTERPRETATION → DECISION
01 / FIELDWORK
2 days · 5 roles
Frontline observations
Study frontline work and identify operational friction, unmet needs, and sustainability opportunities.
02 / SYNTHESIS
9 findings
Themes → opportunity statements
Turn observations into themes, role profiles, and actionable opportunity areas.
03 / ORGANIZATIONAL SELECTION
1 initiative
Employee ideas → evaluation → selection
Use the research to guide employee ideas and evaluate directions for desirability, viability, feasibility, and impact.
05 / FIELD RESEARCH
To understand a historic station, I needed to understand the people who keep it running.
I coordinated two days of ethnographic research at Joseph R. Biden Jr. Station in Wilmington, Delaware. Together, five interconnected roles revealed how customer service, station information, safety, baggage operations, and facilities depend on one another.
FIELDWORK / STATION INFORMATION AND DAILY OPERATIONS
FIVE INTERCONNECTED ROLES
01
PIDS operator
02
Customer service representative
03
Baggageman
04
Amtrak police officer
05
Extraboard employee
06 / RESEARCH UNDER REAL CONDITIONS
Research inside an active train station meant being dynamic and adaptable.
Employees were serving customers, monitoring trains, and responding to unexpected events while participating. I structured the study around the Jobs to Be Done framework, then classified questions as essential or optional so each session could be flexible without losing the most important knowledge gaps.
Goals
Tasks and indicators of success
Dependencies
Tools, information, and team handoffs
Friction
Operational and environmental pain points
Context
Historic status and sustainability behaviors
07 / SYNTHESIS
Nine findings converged into three opportunity areas.
Instead of treating each finding as an isolated issue, I organized the evidence into three strategic areas that leadership and employees could act on.
NINE FINDINGS → THREE STRATEGIC AREAS
From station evidence to directions for change.
FIELD FINDINGS
STRATEGIC AREA
01
The station’s historic character is a valued asset.
Preserve the station as a cultural asset
Retain the historic character that gives the station its value.
02
Unclear signage creates confusion for customers.
03
Aging infrastructure affects staff and passengers.
04
Internal screens are difficult for employees to navigate.
05
Teams need stronger alignment.
06
Employees are deeply committed to serving customers.
Modernize the operational foundation
Improve the systems and coordination that support frontline service.
07
Paper-heavy processes create unnecessary waste.
08
Poor energy efficiency limits sustainable operations.
09
Employees are essential to putting sustainability into practice.
Enable sustainable action
Pair infrastructure improvements with practical support for employees.
08 / THE CENTRAL TENSION
The station’s history created value and friction at the same time.
Historic architecture gave the station a distinctive identity and attracted public interest. But its layout, infrastructure, and preservation constraints also contributed to customer confusion and operational burden. The opportunity was not to choose between preservation and modernization—it was to make them reinforce one another.
Wayfinding
Poor signage generated frequent employee interruptions.
Infrastructure
Aging HVAC, ventilation, and plumbing affected comfort and safety.
Systems
Outdated tools and information gaps increased cognitive load.
Sustainability
Paper-heavy processes and inefficient energy use persisted in routine work.
09 / TRANSLATING RESEARCH
Beyond a report: the research became a decision-making tool.
I translated findings into opportunity statements that connected observed problems to possible interventions. These gave employees and leadership a shared foundation for imagining solutions grounded in frontline reality.
OBSERVATION
Customers struggled with wayfinding and repeatedly interrupted employees for assistance.
OPPORTUNITY
Improve station orientation and signage so customers can navigate independently while employees remain focused on critical work.
10 / PARTICIPATORY DESIGN
Frontline insights became an enterprise-wide design challenge.
Amtrak invited employees across the organization to propose ways of modernizing historic stations sustainably. The research gave participants and evaluators a shared view of problems occurring on the ground. I also helped two frontline employees translate ideas surfaced during fieldwork into formal submissions.
The strongest proposals advanced from open submission into a structured evaluation process.
Frontline evidence
Observed work and unmet needs
Opportunity areas
Research-backed strategic territories
Employee ideas
Enterprise participation
Executive prioritization
Evidence-informed selection
11 / PRIORITIZATION
Research became the lens for choosing what moved forward.
An Innovation Advisory Committee narrowed the submissions to three finalists. My research report gave evaluators evidence about the most pressing needs on the ground, helping them assess ideas against both frontline reality and organizational constraints.
Desirability
Does it solve an important human problem?
Viability
Does it make sense for the business?
Feasibility
Can Amtrak realistically deliver it?
Impact
Would it meaningfully improve the system?
AMTRAK / INNOVATION
Initiative home Browse
Browse ideas
View the contributions of the innovation community.
FILTER
All ideas
IDEA MANAGEMENT / ILLUSTRATIVE RECONSTRUCTION
Desirability
PEOPLE
Feasibility
DELIVERY
Viability
ORGANIZATION
Impact
SYSTEM BENEFIT
INNOVATION
SWEET SPOT
INNOVATION SWEET SPOT / ADAPTED FOUR-CRITERION FRAMEWORK
12 / OUTCOME
Power Save Mode
The selected direction made sustainability visible and actionable.
The winning concept was Power Save Mode: a digital platform designed to help stations understand progress toward Amtrak’s sustainability goals and access practical resources for improvement. It directly addressed a major finding: employees were motivated to act sustainably but lacked clear goals, shared definitions, and visible feedback.
RESEARCH NEEDS → CONCEPT RESPONSE
Making sustainability actionable.
RESEARCH NEED
CONCEPT RESPONSE
Motivation to contribute
→
Practical resources for action
Unclear goals and definitions
→
Shared sustainability goals
Limited visibility into progress
→
Visible station-level progress
SELECTED DIRECTION
Power Save Mode
ORGANIZATIONAL SELECTION
Amtrak’s Sustainability team selected the concept as its next large-scale initiative, with Innovation supporting development through research and iterative design.
13 / IMPACT
Research connected frontline reality to enterprise strategy.
The work grounded sustainability decisions in operating realities and created reusable research infrastructure for future modernization efforts.
1 initiative
Power Save Mode selected for development
25% participation
of idea-platform visitors submitted proposals — above industry average
5 role profiles
created as reusable research assets for future work
9 themes
translated into opportunity areas
14 / REFLECTION
The biggest research impact was confident alignment.
This project reinforced that research can create impact before a product exists. By connecting frontline work, organizational goals, and evaluation criteria, I helped the team move from a broad question about future stations to an aligned direction grounded in the realities of operating them.
Design research around the environment
While a researcher’s curiosity is endless, the time you have with participants is not. Flexible sessions and prioritized knowledge gaps preserved research intent without asking employees to step outside their environments.
Connect needs to business decisions
Insights gain influence when they are framed connected to the decisions leaders face. Framing employee experiences against feasibility, organizational priorities, and long-term service outcomes made them easier to act on.
Rapport is key for quality research
Meaningful research participation depends on trust. Rapport and transparency about how ideas would be used helped employees contribute more candidly and constructively, especially in a context where corporate hierarchies and social dynamics created obstacles.
QASIM MALIK / UX RESEARCH
Next project
← Work
My work spans AI, technical, and emerging products. With a background in cognitive science and design, I connect research with prototyping to test ideas and shape product direction.
In other words:
I investigate the problem, shape the product, and build enough to learn what happens next.
Profile
Qasim Malik — UX researcher, turning ambiguity into product direction.
qasimhmalik1@gmail.com
Resume
© 2026 Qasim Malik