CV

Work experience

• Research Scientist, September 2022 - Present
  • Meta, Seattle, USA
  • Data sub-sampling algorithms for Facebook Ads
• Research Intern, Machine Learning, Summer 2019
  • Adobe Research, San Jose, CA, USA
  • Optimal Bidding Strategy in Real-time Bidding System
  • Supervisor: Dr. Vishy Swaminathan, Dr. Saayan Mitra, Dr. Somdeb Sarkhel
• Research Intern, Data Science, Summer 2018
  • Adobe Research, San Jose, CA, USA
  • Bid landscape forecasting in Real-time Bidding System
  • Supervisor: Dr. Vishy Swaminathan, Dr. Saayan Mitra, Dr. Somdeb Sarkhel
• Software Engineer 2, July 2014 - August 2017
  • Microsoft Corporation, Bangalore, India
  • Selection and Relevance Algorithms in Sponsored Search (Bing Ads/ Microsoft Advertising)
  • Supervisor: Rahul Agrawal

Education

• Ph.D in Computer Science, University of Massachusetts, September 2017- August 2022
  • Advisor: Professor Andrew Lan
• M.S. in Electrical Engineering, Indian Institute of Science, August 2012- July 2014
  • Advisor: Professor P.S. Sastry

Awards

• Won the grand prize in 2021 NAEP Automated Scoring Challenge, Jan 2022. [Press Release]
• 2021 Duolingo Dissertation Award, November 2021.
• Best Student Paper Award at IEEE BigData Conference, December 2020.
• Winner of Task 4 NeurIPS 2020 Education Challenge, November 2020.
• Student Travel Grant Award in KDD 2020, July, 2020.
• Student Travel Grant Award in SDM 2020, February, 2020.
• Microsoft BingAds Bangalore Q3 Innovation Award Winner, FY 15-16.
• N.R Khambhati Memorial Medal for Best Student Award in SSA Department of Electrical Engineering, Indian Institute of Science, July 2014.

Skills

• Python, C, Java, C#, Matlab, R
• PyTorch, Tensorflow

Publications

• Learning from Sequential User Data: Models and Sample-efficient Algorithms

  Aritra Ghosh.

  Doctoral Dissertation. 2023 (UMass Amherst).

• DiFA: Differentiable Feature Acquisition

  Aritra Ghosh, and Andrew Lan.

  Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. 2023 (AAAI).

  Code

• DiPS: Differentiable Policy for Sketching in Recommender Systems

  Aritra Ghosh, Saayan Mitra, and Andrew Lan.

  Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence. 2022 (AAAI).

  Code

• Automated scoring for reading comprehension via in-context bert tuning

  Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard Baraniuk, and Andrew Lan.

  Proceedings of the 23rd International Conference on Artificial Intelligence in Education. 2022 (AIED).

• BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

  Aritra Ghosh, and Andrew Lan.

  Proceedings of the 30th International Conference on Artificial Intelligence, 2021 (IJCAI).

  Code

• Contrastive Learning Improves Model Robustness Under Label Noise

  Aritra Ghosh, and Andrew Lan.

  Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021 (CVPR WS).

  Code Video

• Option Tracing: Beyond Correctness Analysis in Knowledge Tracing

  Aritra Ghosh, Jay Raspat, and Andrew Lan.

  International conference on artificial intelligence in education, 2021 (AIED).

  Code

• Do We Really Need Gold Samples for Sample Weighting under Label Noise?

  Aritra Ghosh, and Andrew Lan.

  Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision, 2021 (WACV).

  Code Video

• Skill-based Career Path Modeling and Recommendation

  Aritra Ghosh, Beverly Woolf, Shlomo Zilberstein, and Andrew Lan.

  Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), 2020 (IEEE BigData).

  Best Student Paper Award Code

• Context-Aware Attentive Knowledge Tracing

  Aritra Ghosh, Neil Heffernan, and Andrew S Lan.

  Proceedings of the 2020 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020 (KDD).

  Code Video

• Optimal Bidding Strategy without Exploration in Real-time Bidding

  Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, and Viswanathan Swaminathan.

  Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020 (SIAM SDM).

• Scalable Bid Landscape Forecasting in Real-time Bidding

  Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu, and Viswanathan Swaminathan.

  Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2019 (ECML-PKDD).

• Generative Sequential Stochastic Model for Marked Point Processes

  Abhishek Sharma, Aritra Ghosh, and Madalina Fiterau.

  ICML Time Series Workshop. 2019.

• Robust Loss Functions for Deep Neural Networks

  Aritra Ghosh, Himanshu Kumar, and P. S. Sastry.

  Thirty-First AAAI Conference on Artificial Intelligence. 2017 (AAAI).

• On the Robustness of Decision Tree Learning Under Label Noise

  Aritra Ghosh, Naresh Manwani, and P. S. Sastry.

  Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2017 (PAKDD).

• A Preference Approach to Reputation in Sponsored Search

  Aritra Ghosh, Dinesh Gaurav, and Rahul Agrawal.

  Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016 (CIKM).

• Making Risk Minimization Tolerant to Label Noise

  Aritra Ghosh, Naresh Manwani, and P. S. Sastry.

  Neurocomputing 160 (2015): 93-107.

Service

• Program Committee Member: AAAI 2021-2022, IJCAI 2021, WACV 2021-2022
• Conference Volunteer: ICML 2020-2021, KDD 2020, NeurIPS 2020