Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale
A. Rathore, S. Palande, J. S. Anderson, et al.
Resume
Education
- Ph.D. in Computer Science, University of Utah (Expected graduation: May 2022)
- B.Tech in Computer Science and Engineering, Indian Institute of Technology (IIT) Kanpur, 2016
Skills
- Research - Machine Learning, Deep Neural Networks, Topological Data Analysis, Ethics and Fairness in ML
- Programming Languages - Python, Javascript, C
- Data Science - Tensorflow, Pytorch, Scikit-Learn, Numpy, Pandas, Apache Spark (PySpark), SQL, Jupyter
- Frameworks - Vue, Angular, Docker, Terraform, Hadoop, Spark, AWS (ECS, Fargate, API Gateway)
Research
I work on understanding ML models through the lens of Computational Topology and Visualization. I believe that the strong mathematical foundations of topological analysis and interactive capabilities through visualization techniques can help traverse the inherently complex structures of ML models, especially in the context of deep learning. I also create frameworks for Topological Data Analysis (TDA) and visualization to analyze and reason about deep learning models, as a step towards explainable and interpretable ML.
Work experience
- Summer 2021: ML Engineer Intern @ OpenSesame Inc, OR
- Built a data pipeline and NLP based chatbot service to provide course recommendation to learning admins.
- Created the architecture and APIs to deploy trained models in a scalable and secure manner.
- Planned and coordinated integration of the chatbot service with the recommendation system and frontend UI.
- Chatbot prototype identified as one of the key goals for the data science arm of the organization.
- Summer 2019: Research Intern @ VISA Research, CA
- Proposed a method to create auto-encoding models using Recurrent Neural Networks for financial transactions.
- Developed models to create embeddings for entities under weak labels.
- Implemented an end-to-end pipeline in PyTorch to interface with fraud detection and recommendation systems.
- Implemented efficient models capable of handling more than a million data points using pruning and distillation.
- Jun 2016 - July 2017 @ Samsung Research, Bangalore
- Built and optimized ETL pipelines for processing device logs for Samsung smartphones using Apache Spark.
- Proposed and implemented methods to find recurrent temporal patterns in smartphone usage and app activities.
- Deployed distributed processing pipelines for 500 million daily users with nearly 5 billion data points.
- Integrated the output from above into the intelligence module for Bixby in low-resource high-throughput setting.
Publications
TopoAct: Exploring the Shape of Activations in Deep Learning
A. Rathore, N. Chalapathi, S. Palande, B. Wang.
Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data.
Y. Zhou, N. Chalapathi, A. Rathore, Y. Zhao and B. Wang.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
A. Rathore, S. Dev, J.M. Philips , S. Srikumar, et al.
Talks
A Visual Tour of Bias Mitigation Techniques for Word Representations
Talk at AAAI 2021, Virtual
A Visual Tour of Bias Mitigation Techniques for Word Representations
Talk at KDD 2021, Singapore (Virtual)
(Upcoming) Exploring the topology of fine-tuned language models
Talk at Michigan State University, Michigan (Virtual)
(Upcoming) An Interactive Visual Demo of Bias Mitigation Techniques for Word Representations
Talk at NeurIPS 2021, Virtual