Ayan AcharyaSenior Data Scientist
Google Scholar Page: [Link]
About MeI am working as a senior data scientist at Netflix Inc. If you love ML, NLP and are interested in applying your knowledge in solving challenging real-world problems associated with health-care, retail services, text mining, image, and audio analytics, do drop me an email and I would be happy to collaborate.
I graduated from Department of Electrical and Computer Engineering at University of Texas at Austin, USA in July 2015, where I was associated with Intelligent Data Exploration and Analysis Laboratory (IDEAL) led by Dr. Joydeep Ghosh and Machine Learning Research Group led by Dr. Raymond J. Mooney. In the past, I have also collaborated with Dr. Mingyuan Zhou from McCombs School of Business and with people from Applied Research Laboratories. My research focus as a graduate student was on developing generative models and efficient inference algorithms for solving transfer learning problems prevalent in text document analysis, social network study, recommender systems, object recognition from images and evolution of financial data. As an intern, I have worked in places like eBay Research Lab, Qualcomm Inc and Yahoo! Research, Sunnyvale, CA and Cognitive Scale, Austin, TX. Before joining UT Austin, I completed my Bachelor's degree in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata, India in the year 2009.
- Paper accepted in ICDM 2015
- Paper accepted in KDD SRS Workshop 2015
- Paper accepted in KDD MiLeTS Workshop 2015
- Paper accepted in ECML 2015
- Paper accepted in AISTATS 2015
- Nominated by UT Austin Graduate School as an oustanding graduate student to present research at ITA Workshop, La Jolla, CA.
- Received SDM 2014 Student Travel Scholarship
- Paper accepted in SDM 2014
- Paper accepted in NIPS 2013 Topic Model Workshop
- Two papers accepted in ECML 2013
- Paper accepted in SDM 2013 [Link].
- Deep Learning for Image Segmentation and Classification
- Transfer learning for text classifcation and object recognition from images
- Bayesian non-parametric dynamic state space models
- Combination of classification and clustering ensemble for non-transductive semisupervised and transfer learning
- Theoretical guarantees for algorithms of solving matrix completion problems
Research SummaryIn several applications, scarcity of labeled data is a challenging problem that hinders the predictive capabilities of machine learning algorithms. Additionally, the distribution of the data changes over time rendering models trained with older data less capable of discovering useful structure from the newly available data. Transfer learning is a convenient framework to overcome such problems where the learning of a model specific to a domain can benefit the learning of other models in other domains through either simultaneous training of domains or systematic sequential transfer of knowledge from one domain to the others. In my thesis, in all the approaches related to simultaneous learning, a low dimensional space is maintained that is shared across multiple domains and transfer of information may take place via such shared space. For sequential knowledge transfer, parameters of the model trained with data from an older domain are carefully adapted to fit the new distributions. Applications of such frameworks in problems like text classification, object recognition from images, network modeling for community detection and evolution of count data have shown promising results so far. Simultaneous knowledge transfer has also been integrated with active learning to gain additional benefits in domains where labeled data is expensive to obtain. In my research, numerous probabilistic low-rank factorization models (both parametric and non-parametric) are used which help capture dependencies across multiple related domains in a shared low dimensional space. A significant amount of research effort is also directed towards the development of efficient inference mechanisms for the proposed models. Please check the Research page for further details.
Scholarly Service Activities
- Reviewer for ACM TKDD, ACM TKDE, INS Information Sciences, ICDM.
- Host for WNCG Seminar Series in Spring 2013.