Kartik Sharma

PhD student · UCLA

I am a PhD student in Electrical and Computer Engineering at UCLA, advised by Prof. Vwani Roychowdhury. My current focus is post-training for reasoning models with teacher interventions. I am also interested in world models: the internal structures that let a system build useful abstractions of tasks, environments, and other agents.

Before UCLA, I completed my BS-MS (Research) in Physics at IISc, working with Prof. Shayan Srinivasa Garani on quantum-enhanced Viterbi decoding. I also worked with Prof. B. Ananthanarayan from the Centre for High Energy Physics on analytic methods for expressing Feynman integrals in computable closed forms.

My recent work has been more applied and method-driven:

LLM decision support for epilepsy care Recommendation and deferral models for underrepresented clinical settings, where local practice and uncertainty matter.
Multimodal models for seizure semiology Video/audio feature extraction for interpretable seizure classification and clinically meaningful event descriptions.
Time-resolved neuronal dynamics in organoid models Network features from calcium imaging to distinguish pathological and control brain assembloid states.
Optimization methods for nonnegative matrix factorization Exterior optimization methods for structured low-rank factorization under nonnegativity constraints.

Blogs

Auditing the Teaching Signal in On-Policy Self-Distillation Jun 2026

A token-level look at what a privileged teacher's dense feedback actually transfers in on-policy self-distillation: surface-level formatting, or a genuine teaching signal?

All Posts →

Selected Papers

Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care

NeurIPS 2026 (under review)

Shreyas Rajesh*, Kartik Sharma*, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder

We study LLM decision support for anti-seizure medication management in underrepresented epilepsy care, where systems must adapt to local prescribing practice and know when to defer. We introduce Manana, a multi-agent prompt-learning framework for site-specific prescribing corrections, and Bayesian prompt averaging, which treats learned prompt states as an ensemble to produce prescription probabilities, confidence scores, and deferral decisions for specialist review.

An Exterior Method for Nonnegative Matrix Factorization

ICML 2026

Qiujing Lu*, Tonmoy Monsoor*, Ehsan Ebrahimzadeh, Kartik Sharma, Vwani Roychowdhury

We propose eNMF, an exterior optimization framework for nonnegative matrix factorization that separates low-rank approximation from nonnegativity enforcement. The method starts from the optimal unconstrained factorization, rotates toward the nonnegative orthant, and then refines the solution; across real and synthetic benchmarks, it improves reconstruction error and speed over a large set of NMF baselines.

OpenReview Code

Automated Seizure Classification Using Multimodal Large Language Models

medRxiv 2025

Lina Zhang, Richard Jiang, Tonmoy Monsoor, Jessica N. Pasqua, Colin M. McCrimmon, Prateik Sinha, Kartik Sharma, Muayad Alzuabi, Victor Morales, Hailey M. Miranda, Chaya Manjeshwar, Vwani Roychowdhury, Rajarshi Mazumder

We build an MLLM pipeline for seizure video and audio that extracts clinically relevant semiological features and classifies epileptic versus nonepileptic events. The study evaluates feature extraction against expert annotations across 90 events from 29 patients and frames the system as an interpretable video-based decision-support approach.

PubMed

Time-Resolved Neuronal Network Dynamics Distinguish Pathological States in Organoid Models

ICASSP 2026

Colin M. McCrimmon, Prateik Sinha, Qing Cao, Tonmoy Monsoor, Kartik Sharma, Mehmet Yigit Turali, Ranmal A. Samarasinghe, Vwani Roychowdhury

We analyze two-photon calcium imaging from human brain assembloids using time-resolved functional network features. The pipeline extracts dynamic biomarkers from neuronal activity and uses them to distinguish pathological and control organoid states, connecting biomedical signal analysis with interpretable disease-model characterization.

IEEE Xplore

Closed Form Expressions for Certain Improper Integrals of Mathematical Physics

European Physical Journal Special Topics 2024

B. Ananthanarayan, Tanay Pathak, Kartik Sharma

We derive closed-form expressions for families of improper integrals in mathematical physics, including Ising, box, and associated integrals. The work combines the method of brackets, Mellin-Barnes representations, conic-hull residue methods, and analytic continuation to express these integrals in terms of multivariable hypergeometric functions.

EPJ ST

* denotes equal contribution.

Projects

Reddit Slop LLM finetuning · LoRA · Reddit A small LoRA fine-tuning study on how Reddit-derived content changes Llama-3.1-8B-Instruct. I trained adapters on absurdist problem/solution posts from r/fifthworldproblems and mundane slice-of-life posts from r/benignexistence, then compared them against the base model on reasoning, factuality, creativity, personality, and safety-style evaluations. The main result was content-dependent: Fifth World data improved GSM8K in the tested low-data setting, while other benchmarks often degraded or shifted the model's style. Writeup Code

Refusal Reasoning Experiments LLM safety · Refusal · Response generation A codebase for studying refusal behavior, reasoning traces, and response generation across open and API-backed language models. It organizes harmful and harmless prompt datasets, runs think versus no-think generation, collects residual activations, labels outputs with safety judges, and analyzes pass@k-style behavior across generated responses. The goal is to make refusal behavior easier to inspect as both a generation problem and a representation-level phenomenon. Code

Reviewing

Mech Interp Workshop @ ICML '26