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Author Information
Parikshit Ram (Infosys, Ltd.)
Sijia Liu (MIT-IBM Watson AI Lab)
Sijia Liu is a Research Staff Member at MIT-IBM Watson AI Lab, IBM research. Prior to joining in IBM Research, he was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor. He received the Ph.D. degree (with All University Doctoral Prize) in electrical and computer engineering from Syracuse University, NY, USA, in 2016. His recent research interests include deep learning, adversarial machine learning, gradient-free optimization, nonconvex optimization, and graph data analytics. He received the Best Student Paper Finalist Award at Asilomar Conference on Signals, Systems, and Computers (Asilomar'13). He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'17). He served as a general chair of the Symposium 'Signal Processing for Adversarial Machine Learning' at GlobalSIP, 2018. He is also the co-chair of the workshop 'Adversarial Learning Methods for Machine Learning and Data Mining' at KDD, 2019.
More from the Same Authors
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2021 : FlyNN: Fruit-fly Inspired Federated Nearest Neighbor Classification »
Parikshit Ram · Kaushik Sinha -
2021 : Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance »
Parikshit Ram · Alexander G Gray · Horst Samulowitz -
2021 : Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization »
Akihiro Kishimoto · Djallel Bouneffouf · Radu Marinescu · Parikshit Ram · Ambrish Rawat · Martin Wistuba · Paulito Palmes · Adi Botea -
2023 : End-to-end Differentiable Clustering with Associative Memories »
Bishwajit Saha · Dmitry Krotov · Mohammed Zaki · Parikshit Ram -
2023 Poster: End-to-end Differentiable Clustering with Associative Memories »
Bishwajit Saha · Dmitry Krotov · Mohammed Zaki · Parikshit Ram -
2021 : Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance »
Parikshit Ram -
2020 Poster: Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing »
Sanghamitra Dutta · Dennis Wei · Hazar Yueksel · Pin-Yu Chen · Sijia Liu · Kush Varshney -
2020 Poster: Proper Network Interpretability Helps Adversarial Robustness in Classification »
Akhilan Boopathy · Sijia Liu · Gaoyuan Zhang · Cynthia Liu · Pin-Yu Chen · Shiyu Chang · Luca Daniel -
2020 Poster: Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks »
Sijia Liu · Songtao Lu · Xiangyi Chen · Yao Feng · Kaidi Xu · Abdullah Al-Dujaili · Mingyi Hong · Una-May O'Reilly -
2020 Poster: Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case »
shuai zhang · Meng Wang · Sijia Liu · Pin-Yu Chen · Jinjun Xiong -
2019 Poster: Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications »
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse -
2019 Oral: Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications »
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse