Adam S. Charles

48 papers A* 5A 1Misc 6Journal 29Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior. CoRR Eva Yezerets, En Yang, Misha B. Ahrens, Adam S. Charles
2026	J	`jnl`	Fast and accessible morphology-free functional fluorescence imaging analysis. PLoS Comput. Biol. Alejandro Estrada Berlanga, Gabrielle Y. Kang, Amanda Kwok, Thomas Broggini, Jennifer Lawlor, Kishore V. Kuchibhotla, David Kleinfeld, Gal Mishne, Adam S. Charles
2026	J	`jnl`	Multi-Integration of Labels across Categories for Component Identification (MILCCI). CoRR Noga Mudrik, Yuxi Chen, Gal Mishne, Adam S. Charles
2025	A	`conf`	Data mining the functional architecture of the brain's circuitry. SDM Adam S. Charles
2024	—	`conf`	Cellular-Resolution Image-Guided Localization in the Primate Brain. EMBC Jacob Stefanowicz, John S. Choi, Katie Wingel, Jarl Haggerty, Adam S. Charles, Bijan Pesaran
2024	J	`jnl`	Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. J. Mach. Learn. Res. Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles
2024	J	`jnl`	Deep and shallow data science for multi-scale optical neuroscience. CoRR Gal Mishne, Adam S. Charles
2024	J	`jnl`	LINOCS: Lookahead Inference of Networked Operators for Continuous Stability. CoRR Noga Mudrik, Eva Yezerets, Yenho Chen, Christopher Rozell, Adam S. Charles
2024	J	`jnl`	Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training. CoRR Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik, Gal Mishne, Adam S. Charles
2024	A*	`conf`	Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. NeurIPS Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher Rozell
2024	J	`jnl`	Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. CoRR Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher J. Rozell
2024	A*	`conf`	SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. ICML Noga Mudrik, Gal Mishne, Adam S. Charles
2024	J	`jnl`	Sparse-Coding Variational Autoencoders. Neural Comput. Victor Geadah, Gabriel Barello, C. Daniel Greenidge, Adam S. Charles, Jonathan W. Pillow
2024	J	`jnl`	Unsupervised discovery of the shared and private geometry in multi-view data. CoRR Sai Koukuntla, Joshua B. Julian, Jesse C. Kaminsky, Manuel Schottdorf, David W. Tank, Carlos D. Brody, Adam S. Charles
2024	A*	`conf`	realSEUDO for real-time calcium imaging analysis. NeurIPS Iuliia Dmitrieva, Sergey Babkin, Adam S. Charles
2023	—	`conf`	Prospective Learning: Principled Extrapolation to the Future. CoLLAs Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael L. Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam S. Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
2023	J	`jnl`	SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. CoRR Noga Mudrik, Gal Mishne, Adam S. Charles
2022	J	`jnl`	Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. CoRR Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles
2022	J	`jnl`	GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging. IEEE Trans. Image Process. Adam S. Charles, Nathan Cermak, Rifqi O. Affan, Benjamin B. Scott, Jackie Schiller, Gal Mishne
2022	J	`jnl`	Multi-Lingual DALL-E Storytime. CoRR Noga Mudrik, Adam S. Charles
2022	J	`jnl`	Prospective Learning: Back to the Future. CoRR Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam S. Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang
2021	J	`jnl`	An Efficient and Flexible Spike Train Model Via Empirical Bayes. IEEE Trans. Signal Process. Qi She, Xiaoli Wu, Beth Jelfs, Adam S. Charles, Rosa H. M. Chan
2020	J	`jnl`	Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer. IEEE Signal Process. Lett. Nicholas P. Bertrand, Adam S. Charles, John Lee, Pavel Dunn, Christopher J. Rozell
2020	A*	`conf`	Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. NeurIPS John S. Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran
2019	Misc	`conf`	Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. ICASSP Gal Mishne, Adam S. Charles
2019	A*	`conf`	Visualizing the PHATE of Neural Networks. NeurIPS Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne
2019	J	`jnl`	Visualizing the PHATE of Neural Networks. CoRR Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne
2018	J	`jnl`	Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow
2018	J	`jnl`	Interpreting Deep Learning: The Machine Learning Rorschach Test? CoRR Adam S. Charles
2018	Misc	`conf`	Sparse Dynamic Filtering via Earth Mover's Distance Regularization. ICASSP Nicholas P. Bertrand, John Lee, Adam S. Charles, Pavel Dunn, Christopher J. Rozell
2017	J	`jnl`	Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks. J. Mach. Learn. Res. Adam S. Charles, Dong Yin, Christopher J. Rozell
2017	—	`conf`	Earth-Mover's distance as a tracking regularizer. CAMSAP Adam S. Charles, Nicholas P. Bertrand, John Lee, Christopher J. Rozell
2017	Misc	`conf`	Stochastic filtering of two-photon imaging using reweighted ℓ1. ICASSP Adam S. Charles, Alexander Song, Sue Ann Koay, David W. Tank, Jonathan W. Pillow
2016	J	`jnl`	Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks. CoRR Adam S. Charles, Dong Yin, Christopher J. Rozell
2016	J	`jnl`	Dynamic Filtering of Time-Varying Sparse Signals via ℓ IEEE Trans. Signal Process. Adam S. Charles, Aurele Balavoine, Christopher J. Rozell
2015	—	`—`	Dynamics and correlations in sparse signal acquisition. Adam S. Charles
2014	—	`conf`	Can random linear networks store multiple long input streams? GlobalSIP Adam S. Charles, Dong Yin, Christopher J. Rozell
2014	—	`conf`	Convergence of basis pursuit de-noising with dynamic filtering. GlobalSIP Adam S. Charles, Christopher J. Rozell
2014	J	`jnl`	Short-Term Memory Capacity in Networks via the Restricted Isometry Property. Neural Comput. Adam S. Charles, Han Lun Yap, Christopher J. Rozell
2014	J	`jnl`	Spectral Superresolution of Hyperspectral Imagery Using Reweighted ℓ IEEE Geosci. Remote. Sens. Lett. Adam S. Charles, Christopher J. Rozell
2013	Misc	`conf`	Dynamic filtering of sparse signals using reweighted ℓ1. ICASSP Adam S. Charles, Christopher J. Rozell
2013	J	`jnl`	Short Term Memory Capacity in Networks via the Restricted Isometry Property. CoRR Adam S. Charles, Han Lun Yap, Christopher J. Rozell
2012	J	`jnl`	A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems. Neural Comput. Adam S. Charles, Pierre Garrigues, Christopher J. Rozell
2012	J	`jnl`	Low Power Sparse Approximation on Reconfigurable Analog Hardware. IEEE J. Emerg. Sel. Topics Circuits Syst. Samuel A. Shapero, Adam S. Charles, Christopher J. Rozell, Paul E. Hasler
2012	—	`conf`	The Restricted Isometry Property for Echo State Networks with applications to sequence memory capacity. SSP Han Lun Yap, Adam S. Charles, Christopher J. Rozell
2011	Misc	`conf`	Estimation and dynamic updating of time-varying signals with sparse variations. ICASSP Muhammad Salman Asif, Adam S. Charles, Justin K. Romberg, Christopher J. Rozell
2011	J	`jnl`	Learning Sparse Codes for Hyperspectral Imagery. IEEE J. Sel. Top. Signal Process. Adam S. Charles, Bruno A. Olshausen, Christopher J. Rozell
2011	Misc	`conf`	Sparsity penalties in dynamical system estimation. CISS Adam S. Charles, Muhammad Salman Asif, Justin K. Romberg, Christopher J. Rozell

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.