MS Computer Science · Peer-reviewed publication (Impact Factor 4.5) · MS thesis at above 98% accuracy · 42 shipped engineering projects · 450+ hours of teaching. This page is written for prospective supervisors: everything needed for an initial assessment is here, without requesting documents first.
My research has circled one problem from two very different directions, and I did not notice the pattern until recently.
In my MS thesis on fabric defect detection, the obstacle was never the network. Reviewing two decades of prior work, auto-encoders, transfer learning, regional CNNs, U-Net, attention mechanisms, showed strong reported accuracies that each collapsed to a narrow set of defect classes or a single fabric pattern. The binding constraint was the dataset: defects are rare by definition, so real collections are small, imbalanced and inconsistently labelled. My response was to stop treating the data as fixed. I built a generator that synthesises defects into pristine fabric with controlled variation in type, size, shape, orientation and lighting, producing 100,000 exactly-labelled images across ten classes. The resulting model reached above 98% accuracy, beating SVM, Random Forest and a baseline CNN on the same task.
Independently, I later built BitWithBite AVS, a virtual production engine that turns a written prompt into a rendered film. It exists because I wanted to make animated educational content and could not afford a studio. Only after it worked did I recognise the same instinct at work: where the data does not exist, generate it under controlled conditions. AVS synthesises entire scenes rather than defect patches, but the epistemics are identical, and so are the open questions.
Those open questions are what I want to spend a PhD on. Synthetic data always carries a domain gap, and my thesis is honest about not having measured its own rigorously. When a model is trained on generated examples, what exactly has it learned, the phenomenon, or the generator? How do we measure that gap, and can generative models close it rather than widen it? These questions matter most precisely where data is scarcest: industrial inspection, medical imaging, rare-event detection, domains where the examples that matter are the ones you have least of.
I bring an unusual combination to this: research training and shipping discipline. I have written a peer-reviewed paper and a thesis, and I have also built and maintained 42 working systems, several running in production at zero marginal cost on commodity hardware. I care about work that runs, and about being straightforward regarding what it does not yet do.
Producing training data for rare events. Measuring and closing the synthetic-to-real domain gap; comparing parametric injection against diffusion-based generation.
Reference-free anomaly detection that generalises past known patterns, reconstructing expected appearance and flagging deviation, rather than classifying a fixed defect taxonomy.
LLM-driven scene synthesis: turning natural language into structured 3D scene representations, camera language and lighting decisions that hold up as coherent visual narrative.
Closing the loop between generating a form and simulating how it behaves. Coupling single-view 3D reconstruction with building energy modelling means a designer's sketch gets evaluated rather than just rendered, and the trade-off can be shown as a Pareto front instead of collapsing into one number.
Composing specialised models, writer, designer, critic, into pipelines where intermediate state stays inspectable and human-correctable rather than opaque.
Capable systems on commodity CPU hardware without paid API dependencies. A research equity question as much as an engineering one, who gets to do this work.
Building on my immunoinformatics publication: computational methods that reduce expensive wet-lab search spaces before physical experimentation begins.
These are directions rather than a fixed proposal, I would expect them to change substantially in conversation with a supervisor whose programme they intersect.
When defects are injected parametrically, a classifier may learn the injection process rather than the physical phenomenon. I would like to develop diagnostics that separate the two, measuring how much of reported accuracy survives transfer to real data, and identifying which generator parameters drive genuine generalisation versus shortcut learning. My own thesis is a natural first case study, since I can rerun it honestly.
My thesis assumed a known reference pattern, which works for repeating printed fabric and fails on irregular weaves or fabric under tension. A reconstruct-and-compare formulation would generalise, but raises its own question: how do you set a deviation threshold when you have never seen the anomaly class? In industrial settings recall on defects matters far more than accuracy, a false alarm costs a second look, a miss costs a roll.
AVS produces films from prompts. There is no correct output to compare against, which makes evaluation genuinely hard, and this is the shared problem across generative systems. I am interested in evaluation frameworks for open-ended generation, including whether a critic model can substitute for human judgement and where that substitution breaks down.
I build for CPU-only machines by necessity, which has made the trade-offs concrete. I would like to characterise them properly: quantisation, distillation and architectural choices measured not on benchmark accuracy alone but on task-level outcomes under a fixed compute budget, the constraint most of the world actually operates under.
No need to request these separately.
42 shipped projects, several in production. Ideas become working systems rather than staying in slide decks, useful for a group that needs prototypes, tooling or infrastructure to actually exist.
AVS was conceived and built alone across six development phases with no external direction. I can be given a hard problem and a long horizon.
450+ hours of one-to-one instruction across every level. TA duties, demonstrating and supervising undergraduate projects are already familiar territory.
My own case studies document what my work does not yet do and what I would change. I would rather report a real limitation than defend a number.
5+ years with teams in Canada and the United States across time zones, asynchronous collaboration is my default mode, not an adjustment.
Actively seeking funded positions, scholarships and assistantships. Previously awarded PEEF, EHSAAS and eHunar scholarships on academic merit.
If any of these directions overlap with your group's work, I would welcome a conversation, and I am glad to adapt them substantially toward an existing programme.
irfanaaslam@bitwithbite.com · Lahore, Pakistan