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BBQ Independent Research InitiativeIndependent research initiative · not an accredited institution · Warsaw, Poland
Operating Form
Online initiative
Domain
bbq.edu.pl
Eligibility
Not established
PROPOSED RESEARCH AGENDA

Laboratory for Machine Cognition & Neural Systems (MCNC Lab)

Investigating deep learning theory, foundational representations, neuro-symbolic reasoning, and verifiable neural architectures on high-performance compute clusters.

Laboratory Overview

The Laboratory for Machine Cognition & Neural Systems (MCNC Lab) is the flagship artificial intelligence research group within the BBQ Institute of Advanced Science & Technology. Headed by Prof. dr hab. inż. Janusz Kowalczyk, the laboratory investigates fundamental questions in machine learning theory, representation learning, and verified artificial intelligence.

Our research group combines deep theoretical analysis with large-scale empirical experiments. We are particularly interested in developing foundation models that possess provable properties—such as guaranteed adherence to logical constraints, robust generalization under distribution shift, and auditable reasoning pathways.

Key Research Directions

1. Neuro-Symbolic Reasoning & Theorem Proving

Bridging the gap between statistical gradient-based learning and exact symbolic logic. We build hybrid architectures where neural models propose mathematical conjectures and proof steps, while formal proof engines (Lean 4, Isabelle/HOL) verify validity in real time.

2. Deep Learning Generalization Theory

Analyzing why modern overparameterized deep networks generalize well despite having capacity to memorize data. We study optimization trajectories, implicit regularization, and curvature of empirical loss surfaces.

3. Distributed Verifiable Neural Execution

Developing lightweight zero-knowledge and cryptographic provenance protocols that verify a remote neural network executed the exact weights and inputs claimed, preventing data tampering in scientific pipelines.

Key Research Questions & Hypotheses

  1. What theoretical principles govern generalization and representation stability in multi-billion parameter foundation models?
  2. How can differentiable neural representations be soundly coupled with discrete symbolic reasoning engines and interactive theorem provers?
  3. What mathematical protocols enable cryptographic and deterministic verification of distributed model inference?

Methodologies & Technical Approaches

  1. Development of non-asymptotic generalization bounds and spectral analyses for transformer and state-space architectures.
  2. Creation of differentiable proof-search algorithms integrated with Lean 4 and Coq interactive theorem provers.
  3. Benchmarking neural verification frameworks on certified safety-critical control and mathematical reasoning domains.

Facilities, Compute & Equipment

  • Dedicated allocation of 64x NVIDIA H100 GPU compute nodes hosted in the CeNT datacenter.
  • Open-source publication of code repositories, model weights, training logs, and verifiable execution proofs under MIT/Apache licenses.

Lab Seminars & Open Positions

  • Laboratory seminars held every Tuesday at 11:00 CET in CeNT Seminar Room 3.12.
  • Currently recruiting 2 Postdoctoral Fellows and 3 Doctoral Candidates.

Key Scientific Publications & Foundation Works

Selected foundation literature and methodology references utilized by the laboratory team.

External scholarly reference · 2016Third-party work

The FAIR Guiding Principles for Scientific Data Management and Stewardship

Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. 't Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons

Scientific Data Vol. 3, pp. 160018(2016). DOI: 10.1038/sdata.2016.18

Defines the principles that digital research objects should be Findable, Accessible, Interoperable, and Reusable for people and machines.

External scholarly reference · 2017Third-party work

A Manifesto for Reproducible Science

Marcus R. Munafò, Brian A. Nosek, Dorothy V. M. Bishop, Katherine S. Button, Christopher D. Chambers, Nathalie Percie du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J. Ware, John P. A. Ioannidis

Nature Human Behaviour Vol. 1, pp. 0021(2017). DOI: 10.1038/s41562-016-0021

Sets out practical measures to improve the reliability and efficiency of scientific research through stronger methods, transparency, and incentives.

External scholarly reference · 2019Third-party work

Model Cards for Model Reporting

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru

Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220-229(2019). DOI: 10.1145/3287560.3287596

Proposes standardized model documentation covering intended use, performance characteristics, evaluation conditions, and limitations.