Sahil Sidheekh

33 papers A* 5A 3B 1C 1Journal 19Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits. CoRR Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2026	J	`jnl`	Geometry-Aware Probabilistic Circuits via Voronoi Tessellations. CoRR Sahil Sidheekh, Sriraam Natarajan
2026	A*	`conf`	Tractable Sharpness-Aware Learning of Probabilistic Circuits. AAAI Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P, Sriraam Natarajan, Narayanan Chatapuram Krishnan
2025	A*	`conf`	A Unified Framework for Human-Allied Learning of Probabilistic Circuits. AAAI Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
2025	A	`conf`	Credibility-Aware Multimodal Fusion Using Probabilistic Circuits. AISTATS Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2025	J	`jnl`	Human-Allied Relational Reinforcement Learning. CoRR Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
2025	A*	`conf`	Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? AAAI Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
2025	—	`conf`	Scalable Knowledge Graph Construction from Unstructured Text: A Case Study on Artisanal and Small-Scale Gold Mining. PAKDD (2) Debashis Gupta, Aditi Golder, Sahil Sidheekh, Sakib Imtiaz, Sarra Alaqahtani, Fan Yang, Gregory D. Larsen, Miles R. Silman, Luis Fernendez, Robert J. Plemmons, Sriraam Natarajan, V. Paul Pauca
2025	J	`jnl`	Tractable Representation Learning with Probabilistic Circuits. CoRR Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting
2025	J	`jnl`	Tractable Representation Learning with Probabilistic Circuits. Trans. Mach. Learn. Res. Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting
2025	J	`jnl`	Tractable Sharpness-Aware Learning of Probabilistic Circuits. CoRR Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P, Sriraam Natarajan, Narayanan C. Krishnan
2024	J	`jnl`	A Unified Framework for Human-Allied Learning of Probabilistic Circuits. CoRR Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	Adaptation: Blessing or Curse for Higher Way Meta-Learning. IEEE Trans. Artif. Intell. Aroof Aimen, Sahil Sidheekh, Bharat Ladrecha, Hansin Ahuja, Narayanan C. Krishnan
2024	A*	`conf`	Building Expressive and Tractable Probabilistic Generative Models: A Review. IJCAI Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	Building Expressive and Tractable Probabilistic Generative Models: A Review. CoRR Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits. CoRR Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2024	—	`conf`	Deep Tractable Probabilistic Models. COMAD/CODS Sahil Sidheekh, Saurabh Mathur, Athresh Karanam, Sriraam Natarajan
2024	J	`jnl`	Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? CoRR Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
2024	C	`conf`	On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits. FUSION Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Sriraam Natarajan
2023	J	`jnl`	Leveraging Task Variability in Meta-learning. SN Comput. Sci. Aroof Aimen, Bharat Ladrecha, Sahil Sidheekh, Narayanan C. Krishnan
2023	A	`conf`	Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. UAI Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
2022	J	`jnl`	VQ-Flows: Vector Quantized Local Normalizing Flows. CoRR Sahil Sidheekh, Chris B. Dock, Tushar Jain, Radu V. Balan, Maneesh Kumar Singh
2022	A	`conf`	VQ-Flows: Vector quantized local normalizing flows. UAI Sahil Sidheekh, Chris B. Dock, Tushar Jain, Radu V. Balan, Maneesh Kumar Singh
2021	J	`jnl`	Attentive Contractive Flow: Improved Contractive Flows with Lipschitz-constrained Self-Attention. CoRR Avideep Mukherjee, Badri Narayana Patro, Sahil Sidheekh, Maneesh Singh, Vinay P. Namboodiri
2021	J	`jnl`	Characterizing GAN Convergence Through Proximal Duality Gap. CoRR Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
2021	J	`jnl`	Learning Neural Networks on SVD Boosted Latent Spaces for Semantic Classification. CoRR Sahil Sidheekh
2021	A*	`conf`	On Characterizing GAN Convergence Through Proximal Duality Gap. ICML Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
2021	B	`conf`	On Duality Gap as a Measure for Monitoring GAN Training. IJCNN Sahil Sidheekh, Aroof Aimen, Vineet Madan, Narayanan C. Krishnan
2021	—	`conf`	Stress Testing of Meta-learning Approaches for Few-shot Learning. MetaDL@AAAI Aroof Aimen, Sahil Sidheekh, Vineet Madan, Narayanan C. Krishnan
2021	J	`jnl`	Stress Testing of Meta-learning Approaches for Few-shot Learning. CoRR Aroof Aimen, Sahil Sidheekh, Vineet Madan, Narayanan C. Krishnan
2021	J	`jnl`	Task Attended Meta-Learning for Few-Shot Learning. CoRR Aroof Aimen, Sahil Sidheekh, Narayanan C. Krishnan
2020	J	`jnl`	On Duality Gap as a Measure for Monitoring GAN Training. CoRR Sahil Sidheekh, Aroof Aimen, Vineet Madan, Narayanan C. Krishnan
2020	—	`conf`	Scale Invariant Fast PHT based Copy-Move Forgery Detection. ICCCNT Aroof Aimen, Amandeep Kaur, Sahil Sidheekh

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.