Vineeth Rakesh Mohan

Research Scientist IV

Research areas:  Probabilistic and interpretable machine learning, recommender systems, natural language processing and active learning

Headshot of Vineeth Rakesh Mohan. Visa Research Scientist.

Dr. Vineeth Rakesh Mohan joined Visa Research as a Research Scientist IV in December 2021.  Vineeth received his Ph.D. in Computer Engineering from Wayne State University in 2017. He then did his postdoctoral research at Arizona State University from 2017-2018. Prior to joining Visa, he was a researcher at  Interdigital. He was responsible for the research and development of projects on search and information retrieval systems. Before joining Interdigital, he worked as a researcher at Technicolor on topics such as user behavior modeling and personalized recommendation systems.  

As a member of the Transaction Insight team, Vineeth’s research interests are broadly in probabilistic machine learning, generative models, natural language processing, recommendation systems, graph neural networks and their various business applications in deep authentication and anomaly detection. Vineeth brings extensive knowledge in applied machine learning as well as experience in industry. He has published over 20 research papers at top-tier conferences such as WWW, WSDM and CVPR. He is excited to develop solutions to the real-world sequence and graph learning problems and advance AI empowered payment systems.

Publications

  1. Jha, Akshita, Vineeth Rakesh, Jaideep Chandrashekar, Adithya Samavedhi, and Chandan K. Reddy. "Supervised Contrastive Learning for Interpretable Long-Form Document Matching." ACM Transactions on Knowledge Discovery from Data (TKDD),  2022. 
  2. Vineeth Rakesh, Swayambhoo Jain. “Efficacy of Bayesian Neural Networks in Active Learning,” Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Event, 2021. 
  3. Vineeth Rakesh, Ajith Pudiyaveti, Jaideep Chandrasekar. “User Modeling and Churn Prediction in Over-the-top Media Services,” 14th ACM Conference on Recommender Systems (RecSys), Virtual Event, Brazil, 2020.
  4. Tian Shi, Vineeth Rakesh, Suhang Wang and Chandan K. Reddy. “Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts,” 28th ACM International Conference on Information and Knowledge Management (CIKM), Beijing, China, 2019.
  5. Vineeth Rakesh, Suhang Wang and Huan Liu. “Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items,” ACM International Conference on Web Search and Data Mining (WSDM), Melbourne, Australia, 2019.
  6. Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh. “Matrix Completion in the Unit Hypercube via Structured Matrix Factorization,” 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2019.
  7. Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, and Huan Liu. “Linked  Causal Variational Autoencoder for Inferring Paired Spillover Effects,” 26th ACM International Conference on Information and Knowledge Management (CIKM), Turin, Italy, 2018.
  8. Vineeth Rakesh, Weicong Ding, Nikhil Rao, Yifan Sun, Chandan K. Reddy. “A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews,” ACM International Conference on World Wide Web (WWW), Lyon, France, 2018.
  9. Vineeth Rakesh, Niranjan Jadhav, Alexander Kotov, Chandan K. Reddy. “Probabilistic Social Sequential Model for Tour Recommendation,” ACM International Conference on Web Search and Data Mining (WSDM), Cambridge, UK, 2017.