CryptoNAS: Private Inference on a ReLU Budget. - Zahra Ghodsi; Akshaj Kumar Veldanda; Brandon Reagen; Siddharth Garg (NeurIPS, 2020)

NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs. - Akshaj Kumar Veldanda; Kang Liu 017; Benjamin Tan 001; Prashanth Krishnamurthy; Farshad Khorrami; Ramesh Karri; Brendan Dolan-Gavitt; Siddharth Garg (CoRR, 2020)

On Evaluating Neural Network Backdoor Defenses. - Akshaj Kumar Veldanda; Siddharth Garg (CoRR, 2020)

Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection. - Hao Fu; Akshaj Kumar Veldanda; Prashanth Krishnamurthy; Siddharth Garg; Farshad Khorrami (CoRR, 2020)

NNoculation: Catching BadNets in the Wild. - Akshaj Kumar Veldanda; Kang Liu 017; Benjamin Tan 001; Prashanth Krishnamurthy; Farshad Khorrami; Ramesh Karri; Brendan Dolan-Gavitt; Siddharth Garg (AISec@CCS, 2021)

Millimeter Wave Wireless-Assisted Robotic Navigation with Link State Classification. - Mingsheng Yin; Akshaj Kumar Veldanda; Amee Trivedi; Jeff Zhang 001; Kai Pfeiffer; Yaqi Hu; Siddharth Garg; Elza Erkip; Ludovic Righetti; Sundeep Rangan (CoRR, 2021)

A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation. - Hao Fu; Akshaj Kumar Veldanda; Prashanth Krishnamurthy; Siddharth Garg; Farshad Khorrami (IEEE Access, 2022)

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale. - Akshaj Kumar Veldanda; Ivan Brugere; Jiahao Chen; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (CoRR, 2022)

Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification. - Mingsheng Yin; Akshaj Kumar Veldanda; Amee Trivedi; Jeff Zhang 001; Kai Pfeiffer; Yaqi Hu; Siddharth Garg; Elza Erkip; Ludovic Righetti; Sundeep Rangan (IEEE Open J. Commun. Soc., 2022)

Path Planning Under Uncertainty to Localize mmWave Sources. - Kai Pfeiffer; Yuze Jia; Mingsheng Yin; Akshaj Kumar Veldanda; Yaqi Hu; Amee Trivedi; Jeff Zhang 001; Siddharth Garg; Elza Erkip; Sundeep Rangan; Ludovic Righetti (ICRA, 2023)

Application of BadNets in Spam Filters. - Swagnik Roychoudhury; Akshaj Kumar Veldanda (CoRR, 2023)

Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT. - Akshaj Kumar Veldanda; Fabian Grob; Shailja Thakur; Hammond Pearce; Benjamin Tan 001; Ramesh Karri; Siddharth Garg (CoRR, 2023)

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. - Akshaj Kumar Veldanda; Ivan Brugere; Jiahao Chen; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (Trans. Mach. Learn. Res., 2023)

LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models. - Akshaj Kumar Veldanda; Shi-Xiong Zhang 001; Anirban Das; Supriyo Chakraborty; Stephen Rawls; Sambit Sahu; Milind R. Naphade (CoRR, 2024)

Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. - Akshaj Kumar Veldanda; Ivan Brugere; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (Trans. Mach. Learn. Res., 2024)