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<title>About Chandra Vikram</title>
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<div class="eyebrow">Healthcare AI · Models, evidence, and connected care</div>
<h1>Chandra Vikram Singh</h1>
<p class="lead">I work on AI that can make sense of clinical information and fit into the systems people use to deliver care.</p>
<p class="muted">Healthcare AI engineer · MS in Health Informatics, Indiana University · Background in pharmaceutical sciences</p>
<nav aria-label="Find me online">
<a href="https://chandravikram.com/" target="_blank" rel="noopener noreferrer">Portfolio &amp; research notes ↗</a>
<a href="https://github.com/supermax01" target="_blank" rel="noopener noreferrer">GitHub ↗</a>
<a href="https://x.com/thuggeshwar_" target="_blank" rel="noopener noreferrer">X / Twitter ↗</a>
<a href="https://huggingface.co/Supermax01" target="_blank" rel="noopener noreferrer">Hugging Face ↗</a>
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<h2 id="about">About me</h2>
<p>My route into AI began with pharmaceutical sciences and continued through graduate study in health informatics. That combination shapes the questions I bring to engineering: What information is missing? What evidence supports the answer? How will the result reach a patient or clinician?</p>
<p>I work across clinical language models, retrieval, interoperability, and applications for patients and care teams. I’m especially interested in the connections between them: grounding a model in the right evidence, carrying context from the health record, and making its uncertainty visible to the people using it.</p>
<p>Healthcare is my main focus, and I also explore broader applied AI. I value systems whose behavior can be inspected, tested, and improved.</p>
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<h2 id="interests">What I’m exploring</h2>
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<article class="card"><h3>Clinical language &amp; reasoning</h3><p>Medical question answering, clinical NLP, and reasoning across longitudinal patient records.</p></article>
<article class="card"><h3>Retrieval &amp; knowledge graphs</h3><p>Clinical RAG, guideline retrieval, patient context, and whether an answer’s citations actually support its claims.</p></article>
<article class="card"><h3>Fine-tuning &amp; alignment</h3><p>Adapting open language models for clinical tasks and evaluating what changes after training.</p></article>
<article class="card"><h3>Interoperability</h3><p>HL7 FHIR, SMART on FHIR, and the infrastructure connecting AI applications with health records.</p></article>
<article class="card"><h3>Agents, voice &amp; telehealth</h3><p>Clinical workflows, conversational interfaces, and useful handoffs between AI systems and care teams.</p></article>
<article class="card"><h3>Evaluation &amp; safety</h3><p>Grounded benchmarks, privacy, uncertainty, failure analysis, and keeping clinicians involved.</p></article>
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<h2 id="work">Selected work &amp; notes</h2>
<article class="work"><h3><a href="https://huggingface.co/Supermax01/qwen3-14b-tpn-finetuned" target="_blank" rel="noopener noreferrer">Qwen3 fine-tuning experiment ↗</a></h3><p>A public Qwen3 model adapted from Unsloth’s 4-bit Qwen3-14B checkpoint, trained with Unsloth and Hugging Face TRL.</p></article>
<article class="work"><h3><a href="https://github.com/supermax01/Carepal" target="_blank" rel="noopener noreferrer">Carepal ↗</a></h3><p>A patient-facing healthcare application exploring AI assistance, patient-reported outcomes, and connections to clinical data systems.</p></article>
<article class="work"><h3><a href="https://chandravikram.com/" target="_blank" rel="noopener noreferrer">Clinical AI working notebook ↗</a></h3><p>Project ideas and preliminary manuscripts spanning FHIR infrastructure, clinical benchmarks, voice agents, knowledge graphs, retrieval, and alignment.</p></article>
<p class="note">The notebook describes work in progress. Project proposals, evaluation plans, and targets should be read in that context.</p>
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<footer>Find project details and updates at <a href="https://chandravikram.com/" target="_blank" rel="noopener noreferrer">chandravikram.com</a>.</footer>
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