CV

Ph.D. candidate at CMU Robotics Institute. On the job market for research scientist roles (graduating late 2026). Download the PDF for the latest version.

Contact Information

Name Gautam Rajendrakumar Gare
Professional Title Ph.D. Candidate, Robotics Institute, Carnegie Mellon University
Email ggare@andrew.cmu.edu

Professional Summary

I make frozen vision-language models do new things without touching their weights, treating adaptation as an inference-time problem: optimizing prompts (DetPO, ECCV 2026), selecting image-conditioned attributes, and reading activations (ACL Findings 2026). I study when and why multimodal models generalize (ICML 2024 Oral) and prove these methods in medical imaging (5 ISBI papers, 4 U.S. patents). On the job market for research scientist roles (graduating late 2026).

Education

  • 2021 - 2026 (expected)

    Pittsburgh, PA

    Ph.D. (candidate)
    Carnegie Mellon University
    Robotics
    • Advisors: Prof. John Galeotti and Prof. Deva Ramanan.
    • Focus: inference-time adaptation and alignment of vision-language models; generalization; medical imaging AI.
  • 2019 - 2020

    Pittsburgh, PA

    M.S.
    Carnegie Mellon University
    Electrical and Computer Engineering
    • Courses: SLAM, Computer Vision, Machine Learning, Optimization, Deep Learning.
  • 2012 - 2016

    Bangalore, India

    B.E.
    B.M.S. College of Engineering
    Electronics and Communication

Research Experience

  • Soft-Prompt Tuning of Vision-Language Models for Few-Shot Object Detection · Robotics Institute, CMU · Nov 2025 – Present. Designed a soft-prompting framework that learns continuous prompt embeddings via gradient-based optimization, enabling task adaptation without modifying VLM weights. Outperformed LoRA fine-tuning in the 10-shot setting, showing lightweight prompt optimization can match or exceed parameter-efficient fine-tuning at low data regimes.
  • Iterative Prompt Optimization for Few-Shot Object Detection (DetPO) · Robotics Institute, CMU · Sep 2025 – Jan 2026. Developed an iterative prompt-optimization framework using false-positive and false-negative feedback to improve few-shot detection. Achieved state-of-the-art results with contemporary VLMs (e.g., Qwen3, Gemini3) on the RF20 dataset, outperforming specialist models such as GroundingDINO in a black-box setting.
  • Causal Discovery for Classification · Robotics Institute, CMU · Jan 2023 – Aug 2025. Designed a Markov-blanket-based causal discovery algorithm for feature selection. Demonstrated improved accuracy and interpretability on medical datasets.
  • Generic Human-Interpretable Lung Ultrasound Biomarkers · Biomedical Image Guidance Lab, CMU · 2021 – 2025. Developed interpretable lung ultrasound biomarkers enabling causal and contrastive learning for diagnosis. Showed biomarker-based features match deep features in downstream performance.

Professional Experience

  • AI Research Intern · Robert Bosch LLC (Bosch Research), Pittsburgh, PA · May 2026 – Aug 2026. Researched grounding of vision-language-action (VLA) models for autonomous-vehicle applications, connecting language-conditioned action reasoning to visual scene evidence.
  • Computer Vision Research Intern · DawnLight Inc., Palo Alto, CA · Jun 2021 – Aug 2021. Developed efficient multimodal action recognition networks (RGB + IR) for clinical monitoring.
  • Senior Data Scientist & Software Engineer · Sling Media R&D (Dish Networks), Bangalore, India · Jul 2016 – Jul 2019. Led analytics pipeline for user churn modeling and AirTV Player Android app interface optimization. Improved image caching, reducing app startup time by 20%.
  • Summer Research Fellow · IIEST, Shibpur, India · Jun 2015 – Jul 2015. Built a real-time tele-operated robotic arm system with RGB-D feedback.

Patents

  • US12154354B2: System and Method for Labeling Ultrasound Data (granted 2024)
  • US20240177000A1: System and Method for Training Machine-Learning Models with Probabilistic Confidence Labels (2024)
  • US20240177445A1: System and Method for Direct Diagnostic and Prognostic Semantic Segmentation of Images (2024)
  • US20240249498A1: System, Method, and Computer Program Product for Extracting Features from Imaging Biomarkers with ML Models (2024)
  • US10547891B2: Apparatuses, Systems, and Methods for Adding Functionalities to Control Buttons on a Remote Control Device (granted 2020)
  • IN201641006819: An Improved Faucet with Automatic Flow Control Using Proximity Sensor (2016)
  • IN201641036243: Add-on Smart Device for Charging Portable Devices (2016)

Fellowships and Awards

  • Liang Zhao Endowed Fellowship, CMU (2025)
  • CMLH Translational Fellowship, CMU (2022)
  • NSF XSEDE Compute Grant, 130,500 GPU hours (2020)
  • Neocortex EUP Compute Award, NSF PSC (2020)

Teaching and Service

  • Guest Lecturer, 16-720 Computer Vision, CMU (Fall 2026)
  • Teaching Assistant, 16-720 Computer Vision, CMU (Spring 2022, Fall 2021)
  • Reviewer, ECCV (2026), eXCV Workshop at ECCV (2026), MICCAI (2022-2024), HSMR (2023)
  • Invited Panelist, LL-COVID19 MICCAI Workshop (2021)
  • Mentor, CMU AI Mentoring Program (2021)

Media Coverage

  • “CMU and others propose a highly practical ‘model generalization’ metric, improving out-of-distribution accuracy by 6% | ICML 2024 Oral”, New Zhiyuan (Dec 2024), covering LCA-on-the-Line. article