Professional Experience

Research Positions

  • 2026.04 – PresentLead Research Scientist at Lexsi Labs, Lexsi.ai (Aurionpro Solutions Group), Remote
    • Post-Training & Alignment: Lead the lab’s post-training and alignment research, directing workstreams across interpretability, safety, model optimization, and agents; scaled AlignTune (37⭐) into a production-grade multi-backend ecosystem spanning multiple SFT/RL algorithms, model merging, and domain-specific alignment auditing for BFSI, legal, and healthcare; shared applied results in case studies on template-strict domain specialization and wealth-management alignment with AlignTune
    • Post-Training & Data Curation: Building CuratorKIT (24⭐), a provenance-grounded data-curation and synthetic-generation pipeline; verifiable long-form synthetic document generation, with further work under review
    • Post-Hoc Interpretability: Built unified post-hoc explainability tooling (LRP, Integrated Gradients, DL-Backtrace), including LRP- and DLB-based analysis of model refusal behavior
    • Mechanistic Interpretability: Lead circuit-level interpretability: built CircuitKIT (14⭐), a circuit discovery, evaluation, and application toolkit; co-led C-ΔΘ, a circuit-restricted weight-arithmetic method for selective refusal (ICML 2026 Workshop); advised machine unlearning via circuit attribution, with further work on circuit faithfulness under review
    • Safety & Steering: Improve safety alignment across the model lifecycle — during training, post-training recovery and fixing, and at inference time. Co-built SafeTune (7⭐), a unified library for auditing and repairing safety drift in fine-tuned LLMs; built an inference-time alignment-transfer method (ALIGNBEAM, ICML 2026 Workshop); a position paper on the limits of behavioral safety assurance for governance; audited compliance detectors and guard models; further work on safety-drift repair and safety-repair evaluation, under review
    • Model Optimization: Lead structured-pruning and quantization work for efficient model deployment
    • AI & Coding Agents: Explore agent and coding-agent architectures; early exploration of a self-improving post-training agent and autonomous-research (auto-research) agents
    • Evaluation: Built an internal LLM evaluation library
    • Research-to-Product Integration: Translate research into production — integrate interpretability, alignment, and safety tooling into the product stack
    • Team & Delivery: Lead the Model Science group (16 interns and 8 full-time researchers across India and Paris), covering grants, hiring, roadmap, and overall planning. Team output spans ICML 2026 workshop papers and submissions under review at A* venues (NeurIPS, ACL, and more)
    • Publications:
      • AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models. 2026. Pre-print.
      • CuratorKIT: Data Curation and Synthetic Data Generation for LLM Post-Training. 2026. Pre-print.
      • CircuitKIT: Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability. 2026. Pre-print.
      • SafeTune: A Unified Faithful Library for Auditing and Repairing Safety Drift in Fine-Tuned LLMs. 2026. Pre-print.
      • C-ΔΘ: Circuit-Restricted Weight Arithmetic for Selective Refusal. 2026. Mechanistic Interpretability Workshop, ICML 2026.
      • ALIGNBEAM: Inference-Time Alignment Transfer via Cross-Vocabulary Logit Mixing. 2026. AI for Good Workshop, ICML 2026.
      • Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution. 2026. Pre-Print.
      • Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands. 2026. Pre-print.
  • 2025.07 – 2026.03Research Scientist at Lexsi Labs, Lexsi.ai (Aurionpro Solutions Group) [Previously: AryaXAI, Arya.ai], Remote
    • Model Science Group: Led and scaled the lab’s Model Science group across post-training alignment, safety, and interpretability into a core research team driving the lab’s alignment and safety agenda
    • AlignTune: Built AlignTune, a modular toolkit for post-training alignment of LLMs spanning SFT, preference optimization, and safety methods (37⭐); formalized the Interpretability as Alignment framework (EurIPS 2025 Workshop) as a guiding design principle for the team’s alignment work
    • Interpretability: Led the lab’s interpretability tooling: rearchitected DLBacktrace v2 on a torch.export-based graph-capture design to keep it model-agnostic across architectures, with CUDA acceleration for LLMs and MoEs (26⭐); drove actionable interpretability into model optimization through interpretability-aware pruning for medical imaging (MICCAI Workshop 2025)
    • Tabular Foundation Models: Led problem framing, early model design, and benchmarking for the ORION tabular foundation-model series — Orion-BiX (WWW 2026) and Orion-MSP (AITD Workshop @ EurIPS 2025) — and drove its extension to regression
    • TabTune: Architected TabTune, an open-source toolkit for inference, fine-tuning, and regression with tabular foundation models (116⭐, WWW 2026 Demo), shipped with an accompanying fine-tuning study; later advised distillation, ensembling (ICML 2026 Workshops), and credit-risk prediction (FinDS @ ACM SIGMOD 2026, Oral) for health and enterprise deployment
    • Model Compression: Built an internal pruning and model-compression toolkit, bringing interpretability-guided compression into the production deployment pipeline
    • Team & Mentorship: Led the Model Science group (14 interns and 5 full-time researchers during this period across India and Paris) spanning tabular, alignment, and interpretability; worked on grants, hiring, and overall planning
    • Talks & Posters: Spotlight talk at EurIPS 2025 Workshop; poster at MICCAI Workshop 2025
    • Publications:
      • Interpretability-Aware Pruning for Efficient Medical Image Analysis. 2025. MICCAI Workshop 2025 (LNCS).
      • Interpretability as Alignment: Making Internal Understanding a Design Principle. 2025. Position Paper (Accepted at EurIPS Workshop on Private AI Governance).
      • TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models. 2026. Accepted at WWW 2026 (Demo).
      • Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning. 2025. AITD Workshop @ EurIPS 2025.
      • Orion-BiX: Bi-Axial Attention for Tabular In-Context Learning. 2026. Accepted at WWW 2026.
      • Exploring Fine-Tuning for Tabular Foundation Models. 2026. Accepted at WWW 2026.
  • 2024.07 – 2025.06Research Scientist at AryaXAI Alignment Labs (rebranded to Lexsi Labs in 2025), Remote / Mumbai, India
    • Research Focus: Working at the intersection of Explainable AI (XAI), AI alignment, and AI safety in high-stakes domains—interpreting black-box models, assessing XAI reliability, and developing foundation models for tabular data in fraud detection and mission-critical applications
    • Explainability: Enhanced the DL-Backtrace method by generalizing its mechanics for model-agnostic use; co-developed a benchmarking framework for the systematic evaluation of XAI techniques
    • XAI-Guided Optimization & Alignment: Investigating model-agnostic post-hoc optimization and alignment strategies across various model architectures—leveraging interpretability for safer, more reliable model behavior
    • Leadership & Mentorship: Mentored two research interns; led recruitment of interns and full-time scientists (Paris and India); authored technical and research documentation for stakeholders; initiated proof-of-concept (POC) projects to advance internal algorithmic capabilities
    • Representation: Served as R&D representative in client-facing engagements and presented AryaXAI solutions at industry forums, including the 5th MLOps Conference
    • Publications:
      • DL-Backtrace: A Model-Agnostic Explainability Method for Deep Learning Models. Accepted at IJCNN 2025.
      • XAI Evals: A Framework for Evaluating Post-Hoc Local Explanation Methods. Technical Report, 2025.
      • Bridging the Gap in XAI: Why Reliable Metrics Matter for Explainability and Compliance. Accepted at EurIPS Workshop on Private AI Governance, 2025.
  • 2024.01 – 2024.06Research Intern at Rolnick Lab, Mila Quebec AI Institute, Remote
    • Project: Computer vision and deep learning for geospatial applications targeting climate change
    • Focus: Detecting abandoned oil and gas wells from satellite imagery; created new geospatial dataset and benchmarked deep learning models
    • Mentor: Dr. David Rolnick (McGill University, Université de Montréal, Mila)
    • Outcome: Led to ICML 2025 publication on Alberta Wells Dataset
  • 2023.06 – 2023.10Computer Vision Research Intern at Robert Bosch Research and Technology Center India, Bangalore
    • Project: Vision-based generative AI for autonomous driving using Latent Diffusion Models
    • Focus: Generating additional data for difficult or misclassified samples to improve downstream task network optimization
    • Mentors: Mr. Koustav Mullick (CR/RDT-2), Dr. Amit Kale
  • 2021.03 – 2024.01Research Progression at Mars Rover Manipal
    • Advanced from Trainee to Senior Researcher and Mentor
    • Led AI research initiatives leading to multiple publications at NeurIPS, ACL, AAAI, CVPR, etc. with projects in Generative AI, Medical Image Analysis, and Climate Change.
    • Built a team of 10+ members and mentored them in their research.
  • 2023.04 – 2023.12Research Assistant under Dr. Abhilash K. Pai, Dept. of DSCA, MIT MAHE
    • Focused on medical image analysis and fairness in AI.
    • Worked on a study on effects of pretraining techniques on skin tone bias in skin lesion classification with support from MAHE Undergraduate Research Grant leading to a publication at Pre-Train Workshop at WACV 2024.

Research Collaborations

  • 2023.12 - 2024.01, Research Collaboration with Dr. Amit Agarwal, Wells Fargo AI COE
  • 2022.05 - 2023.12, Research Intern at KLIV Lab, IIT Kharagpur under Dr. Debdoot Sheet and mentored by Mr. Rakshith Satish. Worked on integrating domain knowledge in medical image analysis using Graph Convolutional Networks and Explainable AI for chest radiographs.
  • 2022.10 - 2023.03, Research Collaboration with IIT Roorkee.

Leadership Roles

Early Career Experience

  • 2022.06 - 2022.09, Research Assistant, Dept. of DSCA, MIT MAHE under Dr. Vidya Rao & Dr. Poornima P.K. working on International Cyber Security Data Mining Competition leading to a position of 5th out of 134+ teams.
  • 2022.03 - 2022.05, Machine Learning Intern, Eedge.ai
  • 2022.01 - 2022.02, Data Science (NLP) Intern, CUREYA