Moshe Sipper (מֹשֶׁה זִיפֶּר)

It’s not the years, honey, it’s the mileage.

Briefly

• Alien Intelligence 🎥
• AIPaL: AI as Part of Life 📃
• ​Artificial Intelligence Under Fire 🎥
• Tips for Approaching a Prospective Academic Grad Advisor 📃

Samples
recent and highly cited (♡) publications

• Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks, 2025
• On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective, 2024
• Deep Learning-Based Operators for Evolutionary Algorithms, 2024
• Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors, 2024
• XAI-Based Detection of Adversarial Attacks on Deepfake Detectors, 2024
• Task and Explanation Network, 2024
• What’s in an AI’s Mind’s Eye? We Must Know, 2024
• A Melting Pot of Evolution and Learning, 2024
• Open Sesame! Universal Black Box Jailbreaking of Large Language Models, 2023 ♡
• Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State, 2023
• I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models, 2023
• Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors, 2023
• Classy Ensemble: A Novel Ensemble Algorithm for Classification, 2023
• Foiling Explanations in Deep Neural Networks, 2023
• EC-KitY: Evolutionary Computation Tool Kit in Python, 2023
• Combining Deep Learning with Good Old-Fashioned Machine Learning, 2023
• Artificial General Intelligence: Pressure Cooker or Crucible? 2022
• High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms, 2022 ♡
• Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances…, 2022 ♡
• Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 2022
• AddGBoost: A Gradient Boosting-Style Algorithm Based on Strong Learners, 2022 ♡
• Evolution of Activation Functions for Deep Learning-Based Image Classification, 2022
• From Requirements to Source Code: Evolution of Behavioral Programs, 2022
• Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2022
• Neural Networks with À La Carte Selection of Activation Functions, 2021
• Symbolic-Regression Boosting, 2021
• Conservation Machine Learning: A Case Study of Random Forests, 2021 ♡
• Investigating the parameter space of evolutionary algorithms, 2018 ♡
• Flight of the FINCH through the Java wilderness, 2011 ♡
• Machine nature: the coming age of bio-inspired computing, 2002 ♡
• Fuzzy CoCo: A cooperative-coevolutionary approach to fuzzy modeling, 2001 ♡
• On the generation of high-quality random numbers by two-dimensional cellular automata, 2000 ♡
• Toward Robust Integrated Circuits: The Embryonics Approach, 2000 ♡
• The emergence of cellular computing, 1999 ♡
• A fuzzy-genetic approach to breast cancer diagnosis, 1999 ♡
• Design, observation, surprise! A test of emergence, 1999 ♡
• Evolution of parallel cellular machines: the cellular programming approach, 1997 ♡
• A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems, 1997 ♡
• Toward a viable, self-reproducing universal computer, 1996 ♡
• Co-evolving non-uniform cellular automata to perform computations, 1996 ♡

Biography Moshe Sipper is a professor of artificial intelligence at Ben-Gurion University of the Negev (Israel), who holds degrees in computer science from the Technion (B.A.) and Tel Aviv University (M.Sc., Ph.D.). He previously served as a senior researcher at EPFL (Switzerland) and as a visiting professor at the University of Pennsylvania (USA).

His research spans deep learning, machine learning, AI, and evolutionary computation, with earlier work in bio-inspired computing, cellular computing, artificial life, robotics, and more. He has authored over 220 scientific publications and several books while supervising nearly 40 graduate students.

Dr. Sipper has served as an associate editor for several journals, organized various conferences, participated in nearly 150 conference program committees, and reviewed for close to 50 journals and funding agencies. A noted figure in AI, he has won multiple awards — including an IEEE Outstanding Paper Award and the EPFL Latsis Prize — and is recognized among the top 2% of scientists globally.

Beyond academia, Dr. Sipper is also a fiction author, cartoonist, and occasional singer.​

A little perspective. That's it. I'd like some fresh, clear, well-seasoned perspective. Can you suggest a good wine to go with that?
​— Anton Ego, Ratatouille

All animals are under stringent selection pressure to be as stupid as they can get away with.
​— ​Peter Watts, Echopraxia​