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Papers by Tanmoy Chowdhury

arXiv (Cornell University), Feb 3, 2023

2022 IEEE International Conference on Data Mining (ICDM)

Cornell University - arXiv, Nov 19, 2022

Analogical reasoning is the process of discovering and mapping correspondences from a target subj... more Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theorydriven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and realworld datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods. The code and data are available at: https://github.com/triplej0079/DeepGAR.

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

Biometric authentication is increasingly being used for large scale human authentication and iden... more Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful "fuzzy" cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics-e.g., multiple fingerprints-of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints-fingerprints of ten fingers-called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database. CCS CONCEPTS • Security and privacy → Biometrics;

Computational and Mathematical Methods in Medicine, 2015

A cochlear implant (CI) is an auditory prosthesis that enables hearing by providing electrical st... more A cochlear implant (CI) is an auditory prosthesis that enables hearing by providing electrical stimuli through an electrode array. It has been previously established that the electrode position can influence CI performance. Thus, electrode position should be considered in order to achieve better CI results. This paper describes how the electrode position influences the auditory nerve fiber (ANF) response to either a single pulse or low- (250 pulses/s) and high-rate (5,000 pulses/s) pulse-trains using a computational model. The field potential in the cochlea was calculated using a three-dimensional finite-element model, and the ANF response was simulated using a biophysical ANF model. The effects were evaluated in terms of the dynamic range, stochasticity, and spike excitation pattern. The relative spread, threshold, jitter, and initiated node were analyzed for single-pulse response; and the dynamic range, threshold, initiated node, and interspike interval were analyzed for pulse-tra...

Proceedings of the Great Lakes Symposium on VLSI 2022

This paper presents RAPTA, a customized Representation-learning Architecture for automation of fe... more This paper presents RAPTA, a customized Representation-learning Architecture for automation of feature engineering and predicting the result of Path-based Timing-Analysis early in the physical design cycle. RAPTA offers multiple advantages compared to prior work: 1) It has superior accuracy with errors std ranges 3.9ps∼16.05ps in 32nm technology. 2) RAPTA's architecture does not change with feature-set size, 3) RAPTA does not require manual input feature engineering. To the best of our knowledge, this is the first work, in which Bidirectional Long Short-Term Memory (Bi-LSTM) representation learning is used to digest raw information for feature engineering, where generation of latent features and Multilayer Perceptron (MLP) based regression for timing prediction can be trained end-to-end. CCS CONCEPTS • Hardware → Static timing analysis; • Computing methodologies → Neural networks.

Institute of Electrical and Electronics Engineers, 2022

In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions ... more In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions of an individual user's activities and the textual information of the posts over time in online health forums and learning how these correspond to the health stage development. To achieve this, we first formulate the transition of user activities as a dynamic attributed graph with multi-attributed nodes that evolves over time, then formalize the health stage inference task as a dynamic attributed graph to sequence learning problem. Our proposed model consists of a novel dynamic graph encoder along with a two-level sequential encoder to capture the semantic features from user posts and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs as well as user posts to a sequence of target health stages. We go on to propose new dynamic graph regularization and dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis on health stage prediction tasks demonstrates both the effectiveness and the interpretability of the proposed models.

arXiv (Cornell University), Feb 3, 2023

2022 IEEE International Conference on Data Mining (ICDM)

Cornell University - arXiv, Nov 19, 2022

Analogical reasoning is the process of discovering and mapping correspondences from a target subj... more Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theorydriven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and realworld datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods. The code and data are available at: https://github.com/triplej0079/DeepGAR.

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

Biometric authentication is increasingly being used for large scale human authentication and iden... more Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful "fuzzy" cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics-e.g., multiple fingerprints-of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints-fingerprints of ten fingers-called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database. CCS CONCEPTS • Security and privacy → Biometrics;

Computational and Mathematical Methods in Medicine, 2015

A cochlear implant (CI) is an auditory prosthesis that enables hearing by providing electrical st... more A cochlear implant (CI) is an auditory prosthesis that enables hearing by providing electrical stimuli through an electrode array. It has been previously established that the electrode position can influence CI performance. Thus, electrode position should be considered in order to achieve better CI results. This paper describes how the electrode position influences the auditory nerve fiber (ANF) response to either a single pulse or low- (250 pulses/s) and high-rate (5,000 pulses/s) pulse-trains using a computational model. The field potential in the cochlea was calculated using a three-dimensional finite-element model, and the ANF response was simulated using a biophysical ANF model. The effects were evaluated in terms of the dynamic range, stochasticity, and spike excitation pattern. The relative spread, threshold, jitter, and initiated node were analyzed for single-pulse response; and the dynamic range, threshold, initiated node, and interspike interval were analyzed for pulse-tra...

Proceedings of the Great Lakes Symposium on VLSI 2022

This paper presents RAPTA, a customized Representation-learning Architecture for automation of fe... more This paper presents RAPTA, a customized Representation-learning Architecture for automation of feature engineering and predicting the result of Path-based Timing-Analysis early in the physical design cycle. RAPTA offers multiple advantages compared to prior work: 1) It has superior accuracy with errors std ranges 3.9ps∼16.05ps in 32nm technology. 2) RAPTA's architecture does not change with feature-set size, 3) RAPTA does not require manual input feature engineering. To the best of our knowledge, this is the first work, in which Bidirectional Long Short-Term Memory (Bi-LSTM) representation learning is used to digest raw information for feature engineering, where generation of latent features and Multilayer Perceptron (MLP) based regression for timing prediction can be trained end-to-end. CCS CONCEPTS • Hardware → Static timing analysis; • Computing methodologies → Neural networks.

Institute of Electrical and Electronics Engineers, 2022

In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions ... more In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions of an individual user's activities and the textual information of the posts over time in online health forums and learning how these correspond to the health stage development. To achieve this, we first formulate the transition of user activities as a dynamic attributed graph with multi-attributed nodes that evolves over time, then formalize the health stage inference task as a dynamic attributed graph to sequence learning problem. Our proposed model consists of a novel dynamic graph encoder along with a two-level sequential encoder to capture the semantic features from user posts and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs as well as user posts to a sequence of target health stages. We go on to propose new dynamic graph regularization and dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis on health stage prediction tasks demonstrates both the effectiveness and the interpretability of the proposed models.