Staff design engineer with expertise in developing reinforcement learning algorithms to automate analog IC design process. Additionally, I have worked on side projects pertaining to other areas of deep learning such as NLP and computer vision -- some of which are highlighted in this repository.

Relevant Course Work: Advanced Analog Design, Power Electronics, Deep Reinforcement Learning, Deep Learning for NLP, Deep Learning for Computer Vision, Advanced Robotics, Intro to AI, Intro to Machine Learning, Algorithms & Data Structures, Software Fundamentals for Engineering Systems

• Transformer based networks such as BERT and GPT as well as traditional LSTM based networks
• Improve inference performance using graph optimization and weight quantization
• CNN architectures such as AlexNet, VGGNet, GoogLeNet for image classification as well as object detection using YOLO and SSD
• Semantic segmentation using Fully Convolutional Networks (https://amitp-ai.medium.com/fcn-571881788e70)
• Synthesize new images using Generative Adversarial Networks (GAN) and Variational Auto Encoders (VAE)

• Dynamic Programming, Bayesian Optimization, Thompson Sampling, Monte-Carlo (MC) learning
• Temporal Difference (TD) learning: SARSA, Q-Learning, Expected SARSA, Deep Q Network (DQN), Double DQN
• Policy Gradient Methods: Advantage Actor Critic (A2C), Deep Deterministic Policy Gradient (https://medium.com/@amitp-ai/policy-gradients-1edbbbc8de6b)

• Text pre-processing methods such as Tokenization, Stemming, Lemmatization, etc
• Traditional feature extraction methods such as bag-of-words, TFIDF, word embeddings like word2vec, Glove, etc
• Deep learning based models for NER, POS, Sentiment Analysis, Dependency Parsing
• Advanced deep learning models for Text Summarization (https://github.com/amitp-ai/Text_Summarization_UCSD)
• Machine Translation and Question-Answering System (https://github.com/amitp-ai/CS224n_Stanford_NLP)

• Trained a robot to pick the maximum number of good bananas while avoiding bad bananas.
• Received a reward of +1 for picking a good banana and -1 for picking a bad banana.
• State augmentation by including previous observations to transform the problem from POMDP to MDP.
• Trained the agent (end-to-end) from raw pixels to q-values using CNN based double DQN learning algorithm.
• For faster training, batch normalization technique was used.
• Trained using PyTorch on Google Cloud, achieving a 100-episode average reward of 12.

• Input text was first pre-processed followed by data wrangling and data exploration.
• Thereafter experimented with various encoder-decoder type of models using LSTM, attention based LSTM, transformers, and memory efficient transformers. Memory efficient transformers performed the best with Rouge-1 and Rouge-2 scores of 38.3 and 13.3.
• Productionized using a Docker container deployed on an AWS EC2 instance and served using a Flask based API.

• As part of Stanford’s CS224N’s final project, I experimented with a few different architectures for this task.
• Using Bi-Directional Attention Flow (BiDAF) network, achieved an F1 score of 62 on the validation set.
• Then added character level embeddings (in addition to word embeddings) to BiDAF and achieved F1 of 65.
• Thereafter built the transformer based QANet to further improve the F1 score to 70.
• Lastly used a pretrained BERT network to further improve the F1 score.