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, using 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
  • 2021.06 – 2023.12Researcher, AI Research Wing at Mars Rover Manipal, Manipal, India
    • Progression: Rose from Student Trainee to Senior Researcher leading the AI Research Wing, driving the team’s technical roadmap across computer vision, multimodal AI, and medical imaging
    • Recruitment & Training: Spearheaded recruitment that raised applicant quality; designed and ran training programs for incoming ML researchers
    • Mentorship: Mentored 10+ undergraduates (12+ total collaborators) under Dr. Ujjwal Verma; co-authored their publications, including workshop papers at NeurIPS, CVPR, ICLR, and MICCAI, an AAAI student abstract, and a Nature Scientific Reports journal paper
  • 2023.04 – 2023.12Undergraduate Researcher under Dr. Abhilash K. Pai, Dept. of Data Science & Computer Application, MIT MAHE, Manipal, India
    • Medical AI & Fairness: Studied the impact of pretraining techniques on skin-tone bias in skin lesion classification, funded by the MAHE Undergraduate Research Grant
    • Outcome: Led to a publication at the Pre-Train Workshop, WACV 2024

Research Collaborations

  • 2024 – 2026Independent Research Collaborator with academic collaborators across IIT Kharagpur, IISc Bangalore, and MAHE, Remote
    • Medical AI: Lesion-aware MAE for glaucoma detection from retinal context, with Siddhant Bharadwaj and Chandra Sekhar Seelamantula (MIDL 2025 Short Papers)
    • LLM Safety: Introspective LLM detection of multi-person mental manipulation in conversations, with Danush Khanna, Sidhaarth Sredharan Murali, and collaborators (ACL 2025 & NAACL-SRW Workshop)
  • 2023.12 – 2024.01Research Collaborator with Dr. Amit Agarwal, Wells Fargo AI Center of Excellence, Remote
    • AgriLLM: Built seq2seq LLMs (BART, T5, Flan-T5) for agricultural queries from Indian farmers on a highly noisy real-world dataset
    • Outcome: Published at the NLP4PI Workshop, EMNLP 2024, and the Undergraduate Consortium at KDD 2024
  • 2022.05 – 2023.12Research Intern at KLIV Research Group, IIT Kharagpur (PI: Dr. Debdoot Sheet; mentored by Rakshith Satish), Remote
    • Healthcare AI: Developed an attention-driven dynamic Graph Convolutional Network for noisy, multi-label, comorbidity-aware chest radiograph screening
    • Interpretability: Investigated sanity checks for class activation maps in multi-label chest X-ray classification
  • 2022.10 – 2023.03Research Collaborator with Dr. Vijay Kumar BR, NEC Labs & IIT Roorkee, Remote
    • Focus: Deep metric learning with self-supervised and contrastive learning; vision-based attention models and hyperbolic representation learning

Leadership Roles

  • 2022.08 – 2023.09Co-President & AI Research Mentor, Research Society MIT Manipal, Manipal, India
    • Organization: Led a 90+ member undergraduate research organization across 10+ technical domains; managed recruitment from 250+ applicants and ran member-development pathways
    • Mentorship: Mentored 10+ undergraduates and co-authored the RSM-NLP team’s shared-task papers
  • 2022.11 – 2023.09Co-founder & Head of Artificial Intelligence and Machine Learning, The Data Alchemists, Manipal, India
    • Co-founding: Founded and led the AI/ML club, growing it to 30+ members and establishing a lasting community of practice
    • Programming: Ran workshops and technical events on ML fundamentals and projects

Early Career Experience

  • 2022.06 – 2022.09Undergraduate Research Assistant under Dr. Vidya Rao & Dr. Poornima P.K., Dept. of Data Science & Computer Application, MIT MAHE, Manipal, India
    • Cybersecurity & AI: Multi-class malware classification at the intersection of cybersecurity and AI
    • Outcome: Placed 5th of 134 teams in the 13th International Cyber Security Data Mining Competition
  • 2022.03 – 2022.05Machine Learning Intern at Eedge.ai, a deep-learning talent-design platform, Remote
    • Synthetic Data & Interpretability: Built generative models for synthetic tabular data to fine-tune downstream models; analyzed the effect of synthetic data on the interpretability of the fine-tuned models
    • Mentor: Ananta Mahapatra, CTO & Co-Founder
  • 2022.01 – 2022.02Data Science (NLP) Intern at CUREYA, Aspexx Health Solutions, Remote
    • Healthcare AI: Built NLP pipelines for Reyana, a conversational healthcare chatbot: text classification, summarization, BERT models, and LSTM-based NLG for clinical dialogue