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Research engineer · Dubai, UAE

Research,
made runnable.

I turn open questions across AI, data systems, and spatial computing into working, instrumented software—so ideas can be tested, measured, and improved.

Explore the experiments Talk research

Available for full-time rolesResearch Engineer · Applied Research Engineer · Research Software Engineer

BUILDEVIDENCE
01 Question02 Prototype03 Observe04 Refine
02live research experiments
1Mrows in a browser-native study
16program-synthesis benchmarks
16+years building emerging technology

01 Runnable research

Start with the evidence.

Both experiments run locally in the browser. Open them, change the conditions, and inspect the result.
01 / 02Interactive data systems · HCI Live experiment

Progressive Query Explorer

Does showing an approximate answer now help people explore more effectively than making them wait for an exact answer?

Progressive Query Explorer walkthrough00:29 · Silent screen recording

An instrumented, browser-native research prototype for studying how response latency and progressive refinement affect exploratory analysis. Users cross-filter one million property transactions while switching between immediate sample-based feedback and conventional exact queries.

Experimental variable
Exact results versus progressive sample-to-exact refinement
Research system
Five linked views over partitioned Parquet, queried locally with DuckDB-WASM
Observable evidence
Timings, interactions, and exploration traces can be exported for analysis
1Mrecords
05linked views
02query modes
0server queries
Evaluation path

A controlled study can compare time-to-first-action, breadth of exploration, abandonment, and confidence across exact and progressive conditions.

Open live prototype
02 / 02Program synthesis · Human–AI interaction Live experiment

Query by Example

Can a system infer the data transformation a person intends from only the result they demonstrate?

Query by Example walkthrough00:58 · Silent screen recording

A deterministic symbolic program synthesiser that turns small input–output examples into ranked, executable query candidates. It searches a compact transformation language in a worker, keeps every program consistent with the demonstration, and exposes ambiguity rather than hiding it.

Inference engine
Enumerative synthesis across filters, joins, groups, strings, sorting, and projection
Transparent output
Every surviving candidate is inspectable as a structured program and generated SQL
Evaluation harness
Benchmark tasks test intent recovery on full tables the examples never reveal
16benchmark tasks
57engine checks
01web worker
100%local execution
Evaluation path

The central measure is not example fit but generalisation: whether the top-ranked candidate recovers the intended transformation on unseen rows, and where ambiguity remains.

Open live prototype

02 Research direction

Questions I want to push further.

My strongest work sits where a technical system and a human decision meet.
A

Human-centred intelligent systems

Interfaces that make AI reasoning inspectable, steerable, and useful—especially when intent is incomplete or ambiguous.

Program synthesisExplainabilityInteraction
B

Interactive analytics at scale

Data systems that respond early, refine visibly, and help people think without obscuring uncertainty or computational cost.

Progressive computationHCIData systems
C

Spatial intelligence & digital twins

Research prototypes connecting real-time 3D, operational data, IoT, simulation, and AI to decisions in the physical world.

Digital twinsXRApplied AI

03 Research method

From uncertainty
to useful evidence.

01

Frame

Turn a broad problem into a precise question and a falsifiable claim.

02

Build

Create the smallest credible system that makes the question testable.

03

Instrument

Capture behavior, performance, uncertainty, and failure—not just outputs.

04

Evaluate

Use evidence to refine the system and identify the next useful question.

Research stance

“The prototype is not the end of the research. It is the instrument that lets us ask a better question.”

04 Industry × academia

Engineering depth.
Research discipline.

I bring production constraints into research and research thinking into production.

I have spent more than sixteen years building emerging technology across the UAE, UK, India, and Malaysia—from games and real-time 3D to enterprise digital twins, AI, computer vision, and immersive systems.

Alongside industry leadership, I have taught at the University of Westminster and currently lecture part-time at Middlesex University Dubai. That combination makes me particularly effective where a research idea needs to become robust software, a demonstrable system, or a meaningful study.

MBA Business Analytics · BITS PilaniBA Digital Media—Game Design · University of Wales
LecturerMiddlesex University Dubai

Immersive technology, game development, computer vision, and emerging technology.

CTOOrtmor Agency · Dubai

AI, digital twins, computer vision, immersive systems, and multidisciplinary delivery.

LecturerUniversity of Westminster · London

Computer vision, cybersecurity, AR/VR, game development, and student research mentoring.

Technical LeadWipro · London

Digital-twin product research and development using real-world data, IoT, Azure, and real-time 3D.

Looking for a research engineer who can make the idea real?

Give me the question.
I’ll build the way to test it.

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