Shengzhe Xu
PhD Candidate, under Prof. Naren Ramakrishnan
Sanghani Center for AI & Analytics, Department of Computer Science
Virginia Tech
Email: shengzx vt edu
VTRC-Arlington, Room 5-165
|
|
Shengzhe Xu
PhD Candidate, under Prof. Naren Ramakrishnan
|
Deep generative model, tabular data, large language model; AI in nextG wireless application; programming language analysis.
Facebook (Ads Core ML: time-series video-clip summarization)[2020], Microsoft Research Asia (ML3: deep reinforcement learning)[2018], Google (input method engine)[2015], Tsinghua National Laboratory (Tsinghua-Waterloo Joint Research Center for Internet Information Acquisition: medical science text mining)[2015], B.E. (telecommunication) from BUPT[2012-2016].
Can an LLM find its way around a Spreadsheet?
Cho-Ting Lee, Andrew Neeser, Shengzhe Xu, Jay Katyan, Patrick Cross, Sharanya Pathakota, Marigold Norman, John C. Simeone, Jaganmohan Chandrasekaran, Naren Ramakrishnan
Proceedings of the 47th IEEE/ACM International Conference on Software Engineering (ICSE)
Ottawa, Canada, April 2025.
[C]
Data Augmentations to support Speculative Reasoning in LLMs
Raquib Bin Yousuf, Nicholas Defelice, Mandar Sharma, Shengzhe Xu, and Naren Ramakrishnan
Proceedings of the 2024 IEEE International Conference on Big Data (Big Data)
Washington DC, Dec 2024.
[C]
Forecasting Migration Patterns and Land Border Encounters
Raquib Bin Yousuf, Shengzhe Xu, Patrick Butler, Brian Mayer, Nathan Self, David Mares, and Naren Ramakrishnan
Proceedings of the 2024 IEEE International Conference on Big Data (Big Data)
Washington DC, Dec 2024.
[C]
Are LLMs Naturally Good at Synthetic Tabular Data Generation?
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma, Raquib Bin Yousuf, Nikhil Muralidhar, Naren Ramakrishnan
Jun 2024.
[arXiv]
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yousuf, Naren Ramakrishnan
Proceedings of the 1st Conference on Language Modeling (COLM)
Philadelphia, PA, Oct 2024.
[C]
[pdf]
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan
IEEE Network, July 2024.
[J]
[pdf]
ML-assisted Optimization of Securities Lending
Abhinav Prasad, Prakash Arunachalam, Ali Motamedi, Ranjeeta Bhattacharya, Beibei Liu, Hays Mccormick, Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan
Proceedings of the 4th ACM International Conference on AI in Finance (ICAIF)
Brooklyn, NY, Nov 2023.
[C]
[pdf]
STAN: Synthetic Network Traffic Generation with Generative Neural Models
Shengzhe Xu, Manish Marwah, Martin Arlitt, Naren Ramakrishnan
Proceedings of Deployable Machine Learning for Security Defense (MLHat)
in conjunction with ACM SIGKDD conference on knowledge discovery & data mining (KDD)
Virtual Event, Aug 2021.
[W]
[pdf]
[website]
[code]
Meditor: inference and application of API migration edits
Shengzhe Xu, Ziqi Dong, Na Meng
Proceedings of the 27th International Conference on Program Comprehension (ICPC)
Montreal, QC, Canada, May 2019.
[C]
[pdf]
[code]
[C] Conference Proceeding | [J] Journal Article | [W] Workshop Proceeding