Site: kninad.github.io | GitHub: kninad | Email: ninadk.utd@gmail.com | LinkedIn: linkedin.com/in/kninad EDUCATION 2019 - 2024: UNIVERSITY OF TEXAS AT DALLAS, _Ph.D. in Computer Science_ - Intelligent Robotics and Vision Lab 2017 - 2019: UNIVERSITY OF MASSACHUSETTS, AMHERST, _M.S in Computer Science_ 2013 - 2017: INDIAN INSTITUTE OF TECHNOLOGY (IIT) KANPUR, _B.S. in Mathematics and Scientific Computing_ WORK EXPERIENCE Research and Development Internship Jun 2022 - Aug 2022: _Kitware Inc_ - Researched machine learning algorithms for approximating medial skeleton of point clouds & voxels - Implemented UNet based segmentation models for skeletonizing 2D images and adapted them for 3D setting - Demonstrated improved results via point-cloud skeletonization on data from hippocampi and leaflet regions Graduate Research Assistant Aug 2019 - Present: _University of Texas at Dallas_ - Researcher in Intelligent Robotics & Vision Lab, working on robot grasping, 3D vision and learning from humans - Concurrent research on interactive perception for unseen object segmentation in cluttered environments - Prior work on submodular information measures for machine learning problems in data selection & active learning - Involved in mentoring students, working as a teaching assistant and taking guest lectures in selected courses Mitacs Globalink Research Internship May 2016 - Jul 2016: _University of Manitoba, Winnipeg_ - Studied the problem of graph sampling and extracting relevant statistics like clustering coefficient - Implemented scale-down sampling with like Metropolis-Hastings and Jump random walks in R - Statistical models like ERGM were used for producing model fits and simulating random networks - Worked on second project for simulating team performance and biases in a football tournament structure TECHNICAL SKILLS PROGRAMMING LANGUAGES: Python, C/C++, R FRAMEWORKS/LIBRARIES: PyTorch, ROS, Unity, OpenGL, CUDA, OpenCV DEVELOPMENT TOOLS: Git/GitHub, Docker, VS Code, Vim, Tmux, LaTeX, Pandoc RESEARCH PROJECTS INTERACTIVE PERCEPTION | _Unseen Object Segmentation_ - Leveraging long term robot interaction with objects for real world unseen object segmentation - Proposed self-supervised data collection method to improved real world segmentation performance - Extended the method to utilize uncertainty in segmentation for minimizing number of interactions OBJECT MANIPULATION | _Robust Grasping & Skill Transfer_ - Learning a common representation across different robot gripper grasps for efficient skill transfer - Proposed object contact-based metric learning constraints for effective learning in common space - Demonstrated applications for human to robot grasp trasnfer via our encoding + retrieval pipeline ROBOT BENCHMARKING | _Perception, Grasping & Motion Planning_ - Developed an intuitive method for replicable, real-world scenes of objects for robot benchmarking - Implemented scene generation pipeline in simulation with focus on cluttered but graspable scenes - Extened 10 existing methods across pose estimation, segmentation and grasping for real world experiments SUBMODULAR INFORMATION MEASURES | _Robust Machine Learning_ - Proposed novel information theoretic measures for submodular set functionsin context for robust machine learning - Theoretical properties backed up with applications on outlier aware subsets, summarization & clustering tasks - Follow up works demonstrated computer vision applications in active learning for object detection VIRTEPEX | _Remote Strength Assessment_ - Design and development of a mixed reality system in Unity for Kinect-based force estimation of body movements - Utilized Kinect to track body joints and an inverse dynamics solver to infer force/torque estimates for an user RELEVANT PUBLICATIONS 1. RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features, _In IEEE International Conference on Robotics and Automation (ICRA) 2024_. 2. SceneReplica: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes, _In IEEE International Conference on Robotics and Automation (ICRA) 2024_. 3. CIS2VR: CNN-based Indoor Scan to VR Environment Authoring Framework, _In IEEE International Conference on AI & extended and Virtual Reality (AIxVR) 2024_. 4. Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction. _In Robotics: Science and Systems (RSS), 2023_. 5. Skeletal Point Representations with Geometric Deep Learning. _In IEEE International Symposium on Biomedical Imaging (ISBI), 2023._ 6. NeuralGrasps: Learning Implicit Representations for Grasps of Multiple Robotic Hands. _In Conference on Robot Learning (CoRL), 2022._ 7. Virtepex: Virtual Remote Tele-Physical Examination System. _In ACM SIGCHI Conference on Designing Interactive Systems (DIS), 2022._ 8. Submodular combinatorial information measures with applications in machine learning. _In International Conference on Algorithmic Learning Theory (ALT), 2021._ OTHER EXPERIENCE PROFESSIONAL SERVICE: - Reviewer for CoRL, ICRA, IROS, IEEE VR, ACM MM, ICMR, ICHI, IJCAI (external reviewer) - Workshop organizer for Workshop for Neural Representation Learning for Robot Manipulation at CoRL 2023 TEACHING ASSISTANT: Machine Learning, Robotics, Computer Graphics, NLP, Statistics for Data Science MENTORSHIP: Peer mentor for new PhD students at UT-Dallas and member of Counselling Service at IIT Kanpur COURSE PROJECTS FASTER INFERENCE FOR CHOW-LIU TREES | _Machine Learning_ - Developed approximation algorithms for faster inference in Chow-Liu tree probabilistic graphical model - Tried out sub-quadratic variants for minimum weight spanning tree computation & compared with optimal setting DATA SUBSET SELECTION | _Optimization Algorithms_ - Framed subset selection from training data as an optimization problem with minimal impact on validation loss - Utilized gradient approximation scheme to show utility on logistic regression and neural network models ACADEMIC ACHIEVEMENTS - Recipient of Inspire scholarship awarded by Indian Govt. for academic performance at IIT Kanpur. - Awarded the Mitacs Globalink scholarship for fully funded summer research internship in Canada. - Secured a percentile score of 97.7 in JEE (Advanced)-2013 and a percentile score of 99.8 in JEE (Main)-2013 national engineering entrance examinations.