Efficient stimulus independent timing abstraction model based on a new concept of circuit block transparency. - Martin Foltin; Brian Foutz; Sean Tyler (DAC, 2002)

Genetic Algorithm Based Adaptive Control Of An Electromechanical MIMO System. - Ivan Sekaj; Martin Foltin; Michal Gonos (GECCO, 2002)

Hardware-Software Co-Design for an Analog-Digital Accelerator for Machine Learning. - Joao Ambrosi; Aayush Ankit; Rodrigo Antunes; Sai Rahul Chalamalasetti; Soumitra Chatterjee; Izzat El Hajj; Guilherme Fachini; Paolo Faraboschi; Martin Foltin; Sitao Huang; Wen-Mei Hwu; Gustavo Knuppe; et al. (ICRC, 2018)

Regular Expression Matching with Memristor TCAMs. - Catherine E. Graves; Wen Ma; Xia Sheng; Brent Buchanan; Le Zheng; Sity Lam; Xuema Li; Sai Rahul Chalamalasetti; Lennie Kiyama; Martin Foltin; Matthew P. Hardy; John Paul Strachan (ICRC, 2018)

Regular Expression Matching with Memristor TCAMs for Network Security. - Catherine E. Graves; Wen Ma; Xia Sheng; Brent Buchanan; Le Zheng; Sity Lam; Xuema Li; Sai Rahul Chalamalasetti; Lennie Kiyama; Martin Foltin; John Paul Strachan; Matthew P. Hardy (NANOARCH, 2018)

PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference. - Aayush Ankit; Izzat El Hajj; Sai Rahul Chalamalasetti; Geoffrey Ndu; Martin Foltin; R. Stanley Williams; Paolo Faraboschi; Wen-mei W. Hwu; John Paul Strachan; Kaushik Roy 001; Dejan S. Milojicic (ASPLOS, 2019)

FPGA Demonstrator of a Programmable Ultra-Efficient Memristor-Based Machine Learning Inference Accelerator. - Martin Foltin; Glaucimar Aguiar; Rodrigo Antunes; Plínio Silveira; Gustavo Knuppe; Joao Ambrosi; Soumitra Chatterjee; Jitendra Onkar Kolhe; Sunil Lakshiminarashimha; Dejan S. Milojicic; John Paul Strachan; Craig Warner; et al. (ICRC, 2019)

PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-Efficient ReRAM. - Aayush Ankit; Izzat El Hajj; Sai Rahul Chalamalasetti; Sapan Agarwal; Matthew J. Marinella; Martin Foltin; John Paul Strachan; Dejan S. Milojicic; Wen-Mei Hwu; Kaushik Roy 001 (IEEE Trans. Computers, 2020)

Data-Aware Storage Tiering for Deep Learning. - Cong Xu; Suparna Bhattacharya; Martin Foltin; Suren Byna; Paolo Faraboschi (PDSW@SC, 2021)

Tree-based machine learning performed in-memory with memristive analog CAM. - Giacomo Pedretti; Catherine E. Graves; Can Li; Sergey Serebryakov; Xia Sheng; Martin Foltin; Ruibin Mao; John Paul Strachan (CoRR, 2021)

Self-learning Data Foundation for Scientific AI. - Annmary Justine; Sergey Serebryakov; Cong Xu; Aalap Tripathy; Suparna Bhattacharya; Paolo Faraboschi; Martin Foltin (SMC, 2022)

Dataset Efficient Training with Model Ensembling. - Yeonju Ro; Cong Xu; Agnieszka Ciborowska; Suparna Bhattacharya; Frankie Li; Martin Foltin (CVPR Workshops, 2023)

Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning. - Gayathri Saranathan; Martin Foltin; Aalap Tripathy; Maxim A. Ziatdinov; Ann Mary Justine Koomthanam; Suparna Bhattacharya; Ayana Ghosh; Kevin Roccapriore; Sreenivas Rangan Sukumar; Paolo Faraboschi (SC Workshops, 2023)

Workflows Community Summit 2022: A Roadmap Revolution. - Rafael Ferreira da Silva; Rosa M. Badia; Venkat Bala; Debbie Bard; Peer-Timo Bremer; Ian Buckley; Silvina Caíno-Lores; Kyle Chard; Carole A. Goble; Shantenu Jha; Daniel S. Katz; Daniel E. Laney; et al. (CoRR, 2023)

X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs. - Giacomo Pedretti; John Moon; Pedro Bruel; Sergey Serebryakov; Ron M. Roth; Luca Buonanno; Tobias Ziegler 006; Cong Xu; Martin Foltin; Paolo Faraboschi; Jim Ignowski; Catherine E. Graves (CoRR, 2023)

Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability. - Revathy Venkataramanan; Aalap Tripathy; Martin Foltin; Hong Yung Yip; Annmary Justine; Amit P. Sheth (IEEE Internet Comput., 2023)

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments. - Gayathri Saranathan; Ayana Ghosh; Martin Foltin; Ann Mary Justine Koomthanam; Aalap Tripathy; Maxim A. Ziatdinov; Suparna Bhattacharya; Kevin Roccapriore; Paolo Faraboschi (SC Workshops, 2024)

LLM-Guided Counterfactual Data Generation for Fairer AI. - Ashish Mishra 005; Gyanaranjan Nayak; Suparna Bhattacharya; Tarun Kumar; Arpit Shah; Martin Foltin (WWW (Companion Volume), 2024)

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization. - Revathy Venkataramanan; Aalap Tripathy; Tarun Kumar; Sergey Serebryakov; Annmary Justine; Arpit Shah; Suparna Bhattacharya; Martin Foltin; Paolo Faraboschi; Kaushik Roy 009; Amit P. Sheth (Frontiers Big Data, 2024)

Common Metadata Framework: Integrated Framework for Trustworthy Artificial Intelligence Pipelines. - Ann Mary Justine Koomthanam; Aalap Tripathy; Sergey Serebryakov; Gyanaranjan Nayak; Martin Foltin; Suparna Bhattacharya (IEEE Internet Comput., 2024)

SubLIME: Subset Selection via Rank Correlation Prediction for Data-Efficient LLM Evaluation. - Gayathri Saranathan; Cong Xu; Mahammad Parwez Alam; Tarun Kumar; Martin Foltin; Soon Yee Wong; Suparna Bhattacharya (ACL (1), 2025)