• AI Research Engineer with a Ph.D. and 8+ years of industry-related experience. I am interested in opportunities relating to applied artificial intelligence (AI) research and product development.
  • Experienced in applying deep reinforcement learning to several real-world problems:
    • Control fan speeds in a storage server towards optimizing efficiency (Advantage Actor-Critic)
    • Automate bid strategies in digital marketing channels towards maximizing profits (Policy Gradients)
    • Personalize store recommendations to mall customers (Multi-Armed Bandit / Thompson Sampling)
    • Adjust path weights in a fabrication laboratory network towards minimizing transport times (DQN)
    • Facilitate accurate baseline estimation in deep reinforcement learning algorithms (TRPO, TNPG)
  • Current role involves employing classical as well as modern machine learning techniques to improve various facets of customer service experiences such as automatically detecting and repairing outages before they happen, and finding the best solutions to customer questions and issues.
  • Strong machine learning fundamentals and a sound mathematical understanding of deep and reinforcement learning concepts, including CNNs, RNNs/LSTMs, GANs, VAEs, NLP, multi-arm bandits, Thompson sampling, deep Q-learning, policy gradient algorithms and actor-critic methods.
  • Experienced in working on a wide range of machine learning problems such as object detection and classification, semantic segmentation, transfer learning, collaborative and content-based filtering approaches for recommender systems, sentiment analysis using word2vec models, semi-supervised video classification, time series data analysis using sequence models, and continual decision making for control systems using deep reinforcement learning.
  • Proven track record of publications and patents: authored 5 journal manuscripts, 14 conference papers and 13 invention disclosures. My publications have garnered over 750 citations.
  • Ph.D. in electrical engineering with a strong emphasis on probabilistic analysis, optimization and stochastic modeling. My thesis work focused on the design and analysis of optimal scheduling and routing algorithms for multi-hop wireless networks.
  • Solid knowledge of probability and random processes, statistics, optimization, detection and estimation theory, signal processing, digital communications and information theory.
  • Over 3.5 years of experience in the semiconductor industry involving algorithm design and performance evaluation for SerDes (Serializer/Deserializer) transceivers that are used in high-speed digital communications for storage and networking applications.
  • Proficient in data structures, algorithms and coding in Python.