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# Constructor Labs: The Research That Drives Progress

## Sitemaps
[XML Sitemap](https://constructorlabs.org/sitemap_index.xml): Includes all crawlable and indexable pages.

## Posts
- [The road to A2RL Imola is officially underway.](https://constructorlabs.org/news/the-road-to-a2rl-imola-is-officially-underway/): This week marks the start of Testing Window 1 as Constructor Racing begins preparations for the European stage of the Abu Dhabi Autonomous Racing League (A2RL).
- [Last week, Constructor Knowledge Labs had the pleasure of contributing to the Constructor University Alumni Future Skills Academy](https://constructorlabs.org/news/last-week-constructor-knowledge-labs-had-the-pleasure-of-contributing-to-the-constructor-university-alumni-future-skills-academy/): On June 25, as part of the program, Constructor Knowledge Labs hosted a session on the future of AI and applied research.
- [1st Prize Award at the Poster Presentation Competition of the AI4X Conference 2026](https://constructorlabs.org/news/1st-prize-award-at-the-poster-presentation-competition-of-the-ai4x-conference-2026/): A great day with the BRAINS Lab BRAINS | Center for Brain-Inspired Computing at the University of Twente, Advanced Research + Invention Agency (ARIA), and the Constructor Knowledge Labs team, reviewing progress on MIND-MATTER — our ARIA-funded AI Scientist project investigating charge-transport mechanisms in self-learning neuromorphic materials (RNPUs)
- [Last week in Enschede — MIND-MATTER working session.](https://constructorlabs.org/news/last-week-in-enschede-mind-matter-working-session/): A great day with the BRAINS Lab BRAINS | Center for Brain-Inspired Computing at the University of Twente, Advanced Research + Invention Agency (ARIA), and the Constructor Knowledge Labs team, reviewing progress on MIND-MATTER — our ARIA-funded AI Scientist project investigating charge-transport mechanisms in self-learning neuromorphic materials (RNPUs)
- [Inside Constructor Talks: Alexander Makarov on the Future of Mass Spectrometry](https://constructorlabs.org/news/inside-constructor-talks-alexander-makarov-on-the-future-of-mass-spectrometry/): On May 20, we hosted another session of Constructor Talks, bringing together more than 50 participants on-site and online for a conversation with Prof. Dr. Alexander Makarov – Senior Director of Research, Life Science Mass Spectrometry, Thermo Fisher Scientific, Fellow of the Royal Society in the UK, and inventor of the Orbitrap mass spectrometer.
- [Selected by MSCA for a new generation of Al researchers](https://constructorlabs.org/news/selected-by-msca-for-a-new-generation-of-al-researchers/): PRIME proposal (Postdoctoral Research in Innovation and Multidisciplinary Excellence) submitted by CKL has been evaluated positively and has been invited to start grant preparation under the MSCA Choose Europe for Science 2025 call.
- [On April 22, we brought together 90 participants — on-site and online — for the 3rd session of our monthly series, this time under the Constructor Talks format.](https://constructorlabs.org/news/on-april-22-we-brought-together-90-participants-on-site-and-online-for-the-3rd-session-of-our-monthly-series-this-time-under-the-constructor-talks-format/): On April 22, we brought together 90 participants — on-site and online — for the 3rd session of our monthly series, this time under the Constructor Talks format.
- [Constructor GenAI Hackathon 2026](https://constructorlabs.org/news/constructor-genai-hackathon-2026/): Over 150 registrations for the participation certificate and 69 submitted projects across BMW, CKL, and Constructor Tech tracks during Constructor GenAI Hackathon 2026
- [CKL Continues Its Monthly Series with Constructor Talk: Agentic Flows for Scientific Research: A Practical Perspective (22 April)](https://constructorlabs.org/news/constructor-knowledge-labs-continues-its-seminar-series-3d-session-on-agentic-ai-in-scientific-research-22-april/): Constructor Knowledge Labs (CKL), in collaboration with the School of Computer Science and Engineering (CSE), continues its successful talk series with its third session, to be held on 22 April, 13:00–14:00 CET.
This upcoming seminar will focus on the transformative role of agentic AI systems in scientific research workflows, with particular attention to how such systems reshape the economics, structure, and pace of research.
- [CKL/CSE Seminar Series Session 2: Ideas That Spark Discussion](https://constructorlabs.org/news/ckl-cse-seminar-series-session-2-ideas-that-spark-discussion/): This Wednesday, we brought together 50+ participants for the 2nd session of the CKL/CSE Seminar Series and the discussion turned out to be genuinely thought-provoking.
- [Autonomous Racing × Constructor ecosystem](https://constructorlabs.org/news/autonomous-racing-x-constructor-ecosystem/): Constructor Autonomous Racing is now fully integrated into the Constructor ecosystem. This is a natural step for us, but also an important one. It turns what used to be separate efforts into a single, continuous loop between learning, discovery and hands-on engineering.
- [Following the successful opening session of the CKL/CSE Seminar Series, Constructor Knowledge Labs (CKL) continues the series with its 2nd seminar, scheduled for 18 March](https://constructorlabs.org/news/following-the-successful-opening-session-of-the-ckl-cse-seminar-series-constructor-knowledge-labs-ckl-continues-the-series-with-its-2nd-seminar-scheduled-for-18-march/): The CKL/CSE Seminar Series brings together researchers and practitioners to discuss advances in robotics, intelligent systems, and computational sciences. The upcoming session will focus on machine perception, world modeling, and cognitive architectures, highlighting how fundamental research in robotics and intelligent systems contributes to the development of advanced autonomous technologies capable of adaptive reasoning and decision-making.
- [Constructor Knowledge Labs (CKL) will participate in the TOPAS Autonomous Systems Meetup at Constructor University in Bremen](https://constructorlabs.org/news/constructor-knowledge-labs-ckl-will-participate-in-the-topas-autonomous-systems-meetup-at-constructor-university-in-bremen/): On March 16, Constructor Knowledge Labs (CKL) will participate in the TOPAS Autonomous Systems Meetup at Constructor University in Bremen.
- [We’re excited to be part of the Constructor GenAI Hackathon 2026 – Multi-Agent & Decision Systems this March](https://constructorlabs.org/news/were-excited-to-be-part-of-the-constructor-genai-hackathon-2026-multi-agent-decision-systems-this-march/): For 3 days, students and alumni will come together to design and build real GenAI solutions in collaboration with BMW Group, Lovable, Constructor Tech, and Constructor Knowledge Labs, co-hosted by Constructor University and the Beyond the Pond Alumni Association.At Constructor Knowledge Labs, we’ll be leading one of the three hackathon tracks:
- [CKL and CSE launch new Seminar Series at Constructor University](https://constructorlabs.org/news/ckl-and-cse-launch-new-seminar-series-at-constructor-university/): Yesterday, 25 February, Constructor Knowledge Labs (CKL), in collaboration with the School of Computer Science and Engineering (CSE), hosted the opening session of the new CKL/CSE Seminar Series at Constructor University venue, which gathered 38 participants and sparked an engaging discussion. This new initiative is meant to bring together researchers from CKL, CSE, and the wider Constructor University community for meaningful scholarly dialogue, and long-term interdisciplinary collaboration across research and practice.
- [We’re excited to share that our academic research project PhantomOS was presented at FOSDEM 2026 in Brussels – Europe’s largest open-source software conference, bringing together a broad international community of developers, researchers, and industry practitioners.](https://constructorlabs.org/news/were-excited-to-share-that-our-academic-research-project-phantomos-was-presented-at-fosdem-2026-in-brussels-europes-largest-open-source-software-co/): Held in Brussels, FOSDEM is a long-established meeting point for the open-source community, known for its rigorously technical tracks covering operating systems, programming languages, developer tooling, infrastructure, security, and experimental systems research. It provides a setting where theoretical work is examined through the lens of real system constraints.
- [Constructor Knowledge Labs at Constructor Career Fair 2026](https://constructorlabs.org/news/constructor-knowledge-labs-at-constructor-career-fair-2026/): Constructor Knowledge Labs took part in Constructor Career Fair 2026, meeting students and early-career researchers interested in applied research, advanced AI systems, and academic career paths within the Constructor ecosystem.
- [MIND-MATTER: AI-Driven Discovery of Self-Learning Materials](https://constructorlabs.org/news/mind-matter-ai-driven-discovery-of-self-learning-materials/): We are pleased to highlight our colleagues' participation in MIND-MATTER, a research project focused on the Al-guided study of materials with computational functionality.
- [Constructor University and Constructor Knowledge Labs (CKL) are engaged in SCIANCE, a Horizon Europe Coordination and Support Action supporting the development of the Resource for AI Science in Europe (RAISE).](https://constructorlabs.org/news/constructor-university-and-constructor-knowledge-labs-ckl-are-engaged-in-sciance-a-horizon-europe-coordination-and-support-action-supporting-the-development-of-the-resource-for-ai-scie/): SCIANCE is designed to strengthen Europe’s capacity for AI enabled scientific research by bringing together a consortium of European research organisations, infrastructures and networks to align approaches to data, computing, skills and governance across scientific domains.
- [Constructor Knowledge Labs, in collaboration with Constructor Tech, concluded the Constructor Hackathon Fall 2025, held from 21–26 November – six days of focused work, experimentation, and cross-disciplinary collaboration.](https://constructorlabs.org/news/constructor-knowledge-labs-in-collaboration-with-constructor-tech-concluded-the-constructor-hackathon-fall-2025/): More than 50 participants formed 19 teams and worked across 6 challenge tracks:
- [CKL + CU Talk with Dr. Nikolay Malkin: Exploring the Future of Structured Inference and Neurosymbolic AI](https://constructorlabs.org/news/ckl-cu-talk-with-dr-nikolay-malkin-exploring-the-future-of-structured-inference-and-neurosymbolic-ai/): On November 13, Constructor Knowledge Labs and Constructor University hosted the second event in our “TED-style” CKL Talk series, welcoming Dr. Nikolay Malkin – a Chancellor’s Fellow in Informatics at the University of Edinburgh and a Fellow of CIFAR’s Learning in Machines and Brains program.
- [We’re proud to share new research from Constructor Knowledge Labs – a group led by our Principal Investigator Petr Popov has developed OrgNet, a new artificial intelligence model that overcomes long-standing challenges in predicting protein stability. ](https://constructorlabs.org/news/were-proud-to-share-new-research-from-constructor-knowledge-labs-a-group-led-by-our-principal-investigator-petr-popov-has-developed-orgnet-a-new-artificial-intelligence-model-that-overcomes/): Accurately predicting how single-point mutations affect protein stability is vital for understanding genetic diseases and designing new proteins for biotechnology and medicine. Traditional computational methods have struggled with accuracy and robustness, thus, novel approaches are in great need. 3D convolutional neural networks proved to be powerful, but are not robust with respect to the input orientations.
- [We’re proud to share new research from Constructor Knowledge Labs by our Principal Investigator Petr Popov’s group, and now published in ”Quarterly Reviews of Biophysics”.](https://constructorlabs.org/news/were-proud-to-share-new-research-from-constructor-knowledge-labs-by-our-principal-investigator-petr-popovs-group-and-now-published-in-quarterly-reviews-of-biophysics/): Titled “Computational Methods for Binding Site Prediction on Macromolecules”, a new study presents a comprehensive review of state-of-the-art computational approaches for predicting binding sites on macromolecules—an essential step for drug discovery, functional annotation, and understanding molecular mechanisms.
- [Code – Advancing Intelligent Software Integration at Constructor Knowledge Labs](https://constructorlabs.org/news/code-advancing-intelligent-software-integration-at-constructor-knowledge-labs/): Constructor Knowledge Labs (CKL) is proud to announce the launch of Code, a three-year research initiative led by Prof. Dr. Giancarlo Succi, Interim Dean of the School of Computer Science & Engineering at Constructor University, and Prof. Alexander Tormasov, Director of Academic Affairs at Constructor Knowledge Labs. Rooted in CKL’s mission to advance knowledge creation and processing through applied AI and software engineering, Code will address a long-standing challenge in modern software development: enabling intelligent, automated integration of open-source components within highly complex, heterogeneous software ecosystems.
- [We’re delighted to welcome Professor Giancarlo Succi to the research activities of Constructor Knowledge Labs (CKL)](https://constructorlabs.org/news/were-delighted-to-welcome-professor-giancarlo-succi-to-the-research-activities-of-constructor-knowledge-labs-ckl/): A Full Professor at the University of Bologna and Interim Dean of the School of Computer Science & Engineering at Constructor University, Prof. Succi is an internationally recognized leader in software engineering with an academic career spanning Europe and North America.
- [Constructor Knowledge Labs and Constructor TECH Launch the First Constructor Datathon](https://constructorlabs.org/news/constructor-knowledge-labs-and-constructor-tech-launch-the-first-constructor-datathon/): From 7 to 10 August 2025, bright minds from different backgrounds came together for an intense, fast-paced deep dive into advanced research methods. The first Constructor Datathon, organised by Constructor Knowledge Labs and Constructor TECH, supported by Agen, brought focus, curiosity and ambition into one shared experience.
- [Constructor Knowledge Labs hosts Nobel Prize-winning research team member Dr. Michael Figurnov for his lecture “Highly Accurate Protein Structure Prediction with AlphaFold.” ](https://constructorlabs.org/news/constructor-knowledge-labs-hosts-nobel-prize-winning-research-team-member-dr-michael-figurnov/): On June 5, 2025, Constructor Knowledge Labs was honored to welcome Dr. Michael Figurnov, who was a member of the team that received a 2024 Nobel Prize in Chemistry and who is a leading researcher at Google DeepMind, for a groundbreaking lecture titled “Highly Accurate Protein Structure Prediction with AlphaFold.” The talk explored AlphaFold 2, the revolutionary deep learning system that solved the 50-year challenge of predicting protein structures from their primary sequences with unprecedented accuracy.
- [Congratulations to Andrey Ustyuzhanin and Maxim Borisyak on winning the 2025 Breakthrough Prize in Fundamental Physics as part of the LHCb Collaboration!](https://constructorlabs.org/news/congratulations-to-andrey-ustyuzhanin-and-maxim-borisyak-on-winning-the-2025-breakthrough-prize/): We are proud to celebrate this outstanding achievement by two brilliant minds affiliated with Constructor University and Constructor Knowledge Labs.

## Pages
- [Roborace](https://constructorlabs.org/roborace/): Roborace was a global competition featuring autonomous, electric-powered vehicles that drive themselves.
- [Publications](https://constructorlabs.org/publications/): Publications 2026 2025 PreprintA. Ustyuzhanin, M. LazarevSymbolic regression for defect interactions in 2D materialsIn: Materials & Design, Volume 264Date of publication: 23 February, 2026DOI: https://doi.org/10.1016/j.matdes.2026.115706 ArticleMaevskiy A, Kapitan V, and Ustyuzhanin AArtificial Intelligence for Multiscale Modeling in Solid-State Physics and Chemistry: A Comprehensive ReviewIn: Advanced Intelligent SystemsDate of publication: 11 March, 2026DOI: https://doi.org/10.1002/aisy.202501219 Conference paperKuzmin, D., Snigireva, M., Tulkunova, N., Tormasov, A. and Zuev, E.A Preliminary Analysis of the Presence of Wicked and Tamed Projects in Software EngineeringIn: Proceedings of the 2025 9th International Conference on Software and e-Business (pp. 79-85)Date of publication: 17 March, 2026DOI: 10.1145/3789037.3789052 ArticleYaroslav V. Solovev, Nikita N. Kostin, Yuri A. Prokopenko, Patrick Masson, Ivan V. Smirnov, Hongkai Zhang, Wei Zheng, Igor A. Yaroshevich, Alexey V. Stepanov, Petr A. Popov, and Alexander G. GabibovChemical neighborhood exploration for substrate discovery in biocatalysisIn: Proceedings of the National Academy of Sciences, Vol. 123 | No. 24, 123 (24) e2535430123Date of publication: 8 June, 2026DOI: https://www.pnas.org/doi/10.1073/pnas.2535430123 ArticleAnastasia Sarycheva, Aleksandr Shumilov, and Petr PopovOrgNet+: towards robust protein stability prediction with convolutional neural networksIn: Bioinformatics, Vol. 42 | Supplement 1, btag258Date of publication: 7 July 2026DOI: https://doi.org/10.1093/bioinformatics/btag258 ArticleIgor Kozlovskii, Petr PopovMultivalent ion binding site identification with structure-based deep learningIn: Communications Biology, Vol. 9, Article 1039Date of publication: 27 July 2026DOI: https://www.nature.com/articles/s42003-026-10659-1 ArticleXiao, J., Bozkurt, A., Nichols, M., Pazurek, A., Stracke, C.M., Bai, J.Y.H., Farrow, R., Mulligan, D., Nerantzi, C., Sharma, R.C., Singh, L., Frumin, I., Swindell, A., Honeychurch, S., Bond, M., Dron, J., Moore, S., Leng, J., Slagter van Tryon, P.J., Garcia, M., Terentev, E., Tlili, A., Chiu, T.K.F., Hodges, C.B., Jandrić, P., Sidorkin, A., Crompton, H., Hrastinski, S., Koutropoulos, A., Cukurova, M., Shea, P., Watson, S., Zhang, K., Lee, K., Costello, E., Sharples, M., Vorochkov, A., Alexander, B., Bali, M., Moore, R.L., Zawacki-Richter, O., Asino, T.I., Huijser, H., Zheng, C., Sani-Bozkurt, S., Duart, J.M. and Themeli, C.Venturing into the unknown: Critical insights into grey areas and pioneering future directions in educational generative AI researchIn: TechTrends, Volume 69, pages 582–597.Date of publication: 19 February, 2025DOI: https://doi.org/10.1007/s11528-025-01060-6 ArticleKozlovskii, I. and Popov, P.Computational methods for binding site prediction on macromolecules.In: Quarterly Reviews of Biophysics, 58, p.e12.Date of publication: 12 March, 2025DOI: https://doi.org/10.1017/S003358352500006X ArticleDlamini, G., Huraira, A., Kholmatova, Z., Mikriukov, A., Safiullina, G., Succi, G. and Tormasov, A.A systematic literature review on measuring brain activity while reviewing code and paintingsIn: IEEE Access, Volume 13Date of publication: 17 April, 2025DOI: 10.1109/ACCESS.2025.3562001 ArticleMaevskiy A., Carvalho A., Sataev E., Turchyna V., Noori K., Rodin A., Castro Neto A. H., and Ustyuzhanin A.Predicting ionic conductivity in solids from the machine-learned potential energy landscapeIn: Physical Review Research 7.2 (2025): 023167.Date of publication: 19 May, 2025DOI: https://doi.org/10.1103/PhysRevResearch.7.023167 Conference paperAnbar, F., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A. and Trofimova, E.Towards ordinal data in LLM evaluation meta-analysis: A non-parametric perspectiveIn: 2025 IEEE 6th International Conference on Pattern Recognition and Machine Learning (PRML) (pp. 335-339). IEEE.Date of publication: 13 June, 2025DOI: 10.1109/PRML66062.2025.11159734 ArticleZhang, P., Wang, Q., Zhang, Y., Lin, M., Zhou, X., David, A., Ustyuzhanin, A., Chen, M., Katsnelson, M.I., Trubyanov, M., and NovoselovStrain-induced crumpling of graphene oxide lamellas to achieve fast and selective transport of H2 and CO2In: Nature Nanotechnology, pp.1-8.Date of publication: 14 July, 2025DOI: https://doi.org/10.1038/s41565-025-01971-8 ArticleBuyanov, I., Sarycheva, A., and Popov, P.OrgNet: Orientation-gnostic protein stability assessment using convolutional neural networks.In: Bioinformatics, Volume 41, Issue Supplement_1, July 2025, Pages i458–i465Date of publication: 15 July, 2025DOI: https://doi.org/10.1093/bioinformatics/btaf252 ArticleRodin, A., Olsen, B. A., Ustyuzhanin, A., & Maevskiy, A.Time-local stochastic equation of motion for solid ionic electrolytesIn: Physical Review Research 7.3 (2025): 033120.Date of publication: 4 August, 2025DOI: https://doi.org/10.1103/jnzr-q953 Conference paperMikriukov, A., Senokosov, A., Succi, G., Tormasov, A., Plaksin, Y., Trofimova, E. and Sitnikov, V.AI Tools for Automating Systematic Literature ReviewsIn: Proceedings of the 2025 International Conference on Software Engineering and Computer Applications (pp. 25-30).Date of publication: 27 August, 2025DOI: https://dl.acm.org/doi/10.1145/3747912.3747962 ArticleFrumin, I. and Kalgin, A.Digital Transformation and Growth in Germany’s Private Higher EducationIn: International Higher Education, Issue 22Date of publication: 3 September, 2025DOI: https://doi.org/10.6017/895b9e0d.3e80046c Conference paperFerrario, G., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A., & Trofimova, E.Evaluating cost-effectiveness and coherence of LLMs for supplement recommendations using routing techniquesIn: 2025 10th International Conference on Machine Learning Technologies (ICMLT) (pp. 350-354). IEEE.Date of publication: 13 October, 2025DOI: 10.1109/ICMLT65785.2025.11193346 Conference paperMikriukov, A., Plaksin, Y., Ravveduto, A., Succi, G., Tormasov, A. and Trofimova, E.Auto-Configuration of the Constructor Research PlatformIn: Proceedings of the Future Technologies Conference (pp. 616-621). Cham: Springer Nature Switzerland.Date of publication: 16 October, 2025DOI: https://doi.org/10.1007/978-3-032-07989-3_40 Conference paperCiancarini, P., Farina, M., Mikriukov, A., Succi, G., Tulkunova, N., Tormasov, A., Thapaliya, A. and Zuev, EA Systemic Perspective on Software EngineeringIn: Proceedings of the 2025 18th International Conference on Computer Science and Information Technology (pp. 91-97), Bilbao, Spain.Date of publication: 27 October, 2025DOI: 10.1145/3783862.3783875 Conference paperAnbar, F., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A. and Trofimova, E.Toward an understanding of the self-coherence and the cross-coherence of LLMs — An empirical investigationIn: International Conference on Computer and Communication Engineering (pp. 127-137).Date of publication: 10 November, 2025DOI: https://doi.org/10.1007/978-3-032-06757-9_12 ArticleFrumin, I., Vorochkov, A., Kiryushina, M., Platonova, D., & Terentiev, E.Mapping the Generative AI Research in Higher EducationIn: Higher Education Quarterly, 80(1), p.e70075.Date of publication: 14 November, 2025DOI: https://doi.org/10.1111/hequ.70075 ArticleM. Y. Lukianov, A. Maevskiy, A. Ustyuzhanin et al.Inverse design of broadband antennas for terahertz devices based on two-dimensional materialsIn: APS, Physical Review Applied, 24(5), 054079.Date of publication: 25 November, 2025DOI: https://doi.org/10.1103/gr2z-3qjp ArticleRehders, M., Alekseitseva, K., Gissoni, J., Doğru, A.G., Popov, P., Boiarov, A. and Brix, K.CU Cilia – An Application for Image Analysis by Machine Learning – Reveals Significance of Cysteine Cathepsin K Activity for Primary Cilia of Human Thyroid Epithelial CellsIn: Frontiers in Endocrinology, Sec. Thyroid Endocrinology, Volume 16, 2025Date of publication: 27 November, 2025DOI: https://doi.org/10.3389/fendo.2025.1588394 Conference paperMikriukov, A., Plaksin, Y., Ravveduto, A., Snigireva, M., Succi, G., Tormasov, A., & Trofimova, E.A preliminary analysis of the current limitation and future directions of AI applied to the legal domain based on a SLRIn: 2025 International Conference on Data Science and Intelligent Systems (DSIS) (pp. 1-10). IEEE.Date of publication: 28 November, 2025DOI: 10.1109/DSIS67228.2025.11390564 ArticleBozkurt, A., Xiao, J., Farrow, R., Bai, J.Y.H., Nerantzi, C., Moore, S., Dron, J., Stracke, C.M., Singh, L., Crompton, H., Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A.M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., Hodges, C.B., Sharples, M., Swindell, A., Frumin, I., Tlili, A., Slagter van Tryon, P.J., Bond, M., Bali, M., Leng, J., Zhang, K., Cukurova, M., Chiu, T.K.F., Lee, K., Hrastinski, S., Garcia, M.B., Sharma, R.C., Alexander, B., Zawacki-Richter, O., Huijser, H., Jandrić, P., Zheng, C., Shea, P., Duart, J.M., Themelis, C., Vorochkov, A., Sani-Bozkurt, S., Moore, R.L. and Asino, T.I.The manifesto for teaching and learning in a time of generative AI: A critical collective stance to better navigate the futureIn: Open Praxis, 16(4), pp. 487–513Date of publication: 29 November, 2025DOI: 10.55982/openpraxis.16.4.777 Conference paperAdashchik, A., Huraira, A., Kholmatova, Z., Mikriukov, A., Ravveduto, A., Snigireva, M., Succi, G., Tormasov, A. and Trofimova, E.Agentic LLM Pipelines for Reproducible Scientific Software: Opportunities and ChallengesIn: Proceedings of the 2025 9th International Conference on Computer Science and Artificial Intelligence (pp. 38-46)Date of publication: 12 December, 2025DOI: https://doi.org/10.1145/3788149.378822 PreprintA. Ustyuzhanin, M. LazarevSymbolic regression for defect interactions in 2D materialsIn: Materials & Design, Volume 264Date of publication: 23 February, 2026DOI: https://doi.org/10.1016/j.matdes.2026.115706 ArticleMaevskiy A, Kapitan V, and Ustyuzhanin AArtificial Intelligence for Multiscale Modeling in Solid-State Physics and Chemistry: A Comprehensive ReviewIn: Advanced Intelligent SystemsDate of publication: 11 March, 2026DOI: https://doi.org/10.1002/aisy.202501219 Conference paperKuzmin, D., Snigireva, M., Tulkunova, N., Tormasov, A. and Zuev, E.A Preliminary Analysis of the Presence of Wicked and Tamed Projects in Software EngineeringIn: Proceedings of the 2025 9th International Conference on Software and e-Business (pp. 79-85)Date of publication: 17 March, 2026DOI: 10.1145/3789037.3789052 ArticleYaroslav V. Solovev, Nikita N. Kostin, Yuri A. Prokopenko, Patrick Masson, Ivan V. Smirnov, Hongkai Zhang, Wei Zheng, Igor A. Yaroshevich, Alexey V. Stepanov, Petr A. Popov, and Alexander G. GabibovChemical neighborhood exploration for substrate discovery in biocatalysisIn: Proceedings of the National Academy of Sciences, Vol. 123 | No. 24, 123 (24) e2535430123Date of publication: 8 June, 2026DOI: https://www.pnas.org/doi/10.1073/pnas.2535430123 ArticleAnastasia Sarycheva, Aleksandr Shumilov, and Petr PopovOrgNet+: towards robust protein stability prediction with convolutional neural networksIn: Bioinformatics, Vol. 42 | Supplement 1, btag258Date of publication: 7 July 2026DOI: https://doi.org/10.1093/bioinformatics/btag258 ArticleIgor Kozlovskii, Petr PopovMultivalent ion binding site identification with structure-based deep learningIn: Communications Biology, Vol. 9, Article 1039Date of publication: 27 July 2026DOI: https://www.nature.com/articles/s42003-026-10659-1ArticleXiao, J., Bozkurt, A., Nichols, M., Pazurek, A., Stracke, C.M., Bai, J.Y.H., Farrow, R., Mulligan, D., Nerantzi, C., Sharma, R.C., Singh, L., Frumin, I., Swindell, A., Honeychurch, S., Bond, M., Dron, J., Moore, S., Leng, J., Slagter van Tryon, P.J., Garcia, M., Terentev, E., Tlili, A., Chiu, T.K.F., Hodges, C.B., Jandrić, P., Sidorkin, A., Crompton, H., Hrastinski, S., Koutropoulos, A., Cukurova, M., Shea, P., Watson, S., Zhang, K., Lee, K., Costello, E., Sharples, M., Vorochkov, A., Alexander, B., Bali, M., Moore, R.L., Zawacki-Richter, O., Asino, T.I., Huijser, H., Zheng, C., Sani-Bozkurt, S., Duart, J.M. and Themeli, C.Venturing into the unknown: Critical insights into grey areas and pioneering future directions in educational generative AI researchIn: TechTrends, Volume 69, pages 582–597.Date of publication: 19 February, 2025DOI: https://doi.org/10.1007/s11528-025-01060-6 ArticleKozlovskii, I. and Popov, P.Computational methods for binding site prediction on macromolecules.In: Quarterly Reviews of Biophysics, 58, p.e12.Date of publication: 12 March, 2025DOI: https://doi.org/10.1017/S003358352500006X ArticleDlamini, G., Huraira, A., Kholmatova, Z., Mikriukov, A., Safiullina, G., Succi, G. and Tormasov, A.A systematic literature review on measuring brain activity while reviewing code and paintingsIn: IEEE Access, Volume 13Date of publication: 17 April, 2025DOI: 10.1109/ACCESS.2025.3562001 ArticleMaevskiy A., Carvalho A., Sataev E., Turchyna V., Noori K., Rodin A., Castro Neto A. H., and Ustyuzhanin A.Predicting ionic conductivity in solids from the machine-learned potential energy landscapeIn: Physical Review Research 7.2 (2025): 023167.Date of publication: 19 May, 2025DOI: https://doi.org/10.1103/PhysRevResearch.7.023167 Conference paperAnbar, F., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A. and Trofimova, E.Towards ordinal data in LLM evaluation meta-analysis: A non-parametric perspectiveIn: 2025 IEEE 6th International Conference on Pattern Recognition and Machine Learning (PRML) (pp. 335-339). IEEE.Date of publication: 13 June, 2025DOI: 10.1109/PRML66062.2025.11159734 ArticleZhang, P., Wang, Q., Zhang, Y., Lin, M., Zhou, X., David, A., Ustyuzhanin, A., Chen, M., Katsnelson, M.I., Trubyanov, M., and NovoselovStrain-induced crumpling of graphene oxide lamellas to achieve fast and selective transport of H2 and CO2In: Nature Nanotechnology, pp.1-8.Date of publication: 14 July, 2025DOI: https://doi.org/10.1038/s41565-025-01971-8 ArticleBuyanov, I., Sarycheva, A., and Popov, P.OrgNet: Orientation-gnostic protein stability assessment using convolutional neural networks.In: Bioinformatics, Volume 41, Issue Supplement_1, July 2025, Pages i458–i465Date of publication: 15 July, 2025DOI: https://doi.org/10.1093/bioinformatics/btaf252 ArticleRodin, A., Olsen, B. A., Ustyuzhanin, A., & Maevskiy, A.Time-local stochastic equation of motion for solid ionic electrolytesIn: Physical Review Research 7.3 (2025): 033120.Date of publication: 4 August, 2025DOI: https://doi.org/10.1103/jnzr-q953 Conference paperMikriukov, A., Senokosov, A., Succi, G., Tormasov, A., Plaksin, Y., Trofimova, E. and Sitnikov, V.AI Tools for Automating Systematic Literature ReviewsIn: Proceedings of the 2025 International Conference on Software Engineering and Computer Applications (pp. 25-30).Date of publication: 27 August, 2025DOI: https://dl.acm.org/doi/10.1145/3747912.3747962 ArticleFrumin, I. and Kalgin, A.Digital Transformation and Growth in Germany’s Private Higher EducationIn: International Higher Education, Issue 22Date of publication: 3 September, 2025DOI: https://doi.org/10.6017/895b9e0d.3e80046c Conference paperFerrario, G., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A., & Trofimova, E.Evaluating cost-effectiveness and coherence of LLMs for supplement recommendations using routing techniquesIn: 2025 10th International Conference on Machine Learning Technologies (ICMLT) (pp. 350-354). IEEE.Date of publication: 13 October, 2025DOI: 10.1109/ICMLT65785.2025.11193346 Conference paperMikriukov, A., Plaksin, Y., Ravveduto, A., Succi, G., Tormasov, A. and Trofimova, E.Auto-Configuration of the Constructor Research PlatformIn: Proceedings of the Future Technologies Conference (pp. 616-621). Cham: Springer Nature Switzerland.Date of publication: 16 October, 2025DOI: https://doi.org/10.1007/978-3-032-07989-3_40 Conference paperCiancarini, P., Farina, M., Mikriukov, A., Succi, G., Tulkunova, N., Tormasov, A., Thapaliya, A. and Zuev, EA Systemic Perspective on Software EngineeringIn: Proceedings of the 2025 18th International Conference on Computer Science and Information Technology (pp. 91-97), Bilbao, Spain.Date of publication: 27 October, 2025DOI: 10.1145/3783862.3783875 Conference paperAnbar, F., Mikriukov, A., Plaksin, Y., Sitnikov, V., Succi, G., Tormasov, A. and Trofimova, E.Toward an understanding of the self-coherence and the cross-coherence of LLMs -- An empirical investigationIn: International Conference on Computer and Communication Engineering (pp. 127-137).Date of publication: 10 November, 2025DOI: https://doi.org/10.1007/978-3-032-06757-9_12 ArticleFrumin, I., Vorochkov, A., Kiryushina, M., Platonova, D., & Terentiev, E.Mapping the Generative AI Research in Higher EducationIn: Higher Education Quarterly, 80(1), p.e70075.Date of publication: 14 November, 2025DOI: https://doi.org/10.1111/hequ.70075 ArticleM. Y. Lukianov, A. Maevskiy, A. Ustyuzhanin et al.Inverse design of broadband antennas for terahertz devices based on two-dimensional materialsIn: APS, Physical Review Applied, 24(5), 054079.Date of publication: 25 November, 2025DOI: https://doi.org/10.1103/gr2z-3qjp ArticleRehders, M., Alekseitseva, K., Gissoni, J., Doğru, A.G., Popov, P., Boiarov, A. and Brix, K.CU Cilia – An Application for Image Analysis by Machine Learning – Reveals Significance of Cysteine Cathepsin K Activity for Primary Cilia of Human Thyroid Epithelial CellsIn: Frontiers in Endocrinology, Sec. Thyroid Endocrinology, Volume 16, 2025Date of publication: 27 November, 2025DOI: https://doi.org/10.3389/fendo.2025.1588394 Conference paperMikriukov, A., Plaksin, Y., Ravveduto, A., Snigireva, M., Succi, G., Tormasov, A., & Trofimova, E.A preliminary analysis of the current limitation and future directions of AI applied to the legal domain based on a SLRIn: 2025 International Conference on Data Science and Intelligent Systems (DSIS) (pp. 1-10). IEEE.Date of publication: 28 November, 2025DOI: 10.1109/DSIS67228.2025.11390564 ArticleBozkurt, A., Xiao, J., Farrow, R., Bai, J.Y.H., Nerantzi, C., Moore, S., Dron, J., Stracke, C.M., Singh, L., Crompton, H., Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A.M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., Hodges, C.B., Sharples, M., Swindell, A., Frumin, I., Tlili, A., Slagter van Tryon, P.J., Bond, M., Bali, M., Leng, J., Zhang, K., Cukurova, M., Chiu, T.K.F., Lee, K., Hrastinski, S., Garcia, M.B., Sharma, R.C., Alexander, B., Zawacki-Richter, O., Huijser, H., Jandrić, P., Zheng, C., Shea, P., Duart, J.M., Themelis, C., Vorochkov, A., Sani-Bozkurt, S., Moore, R.L. and Asino, T.I.The manifesto for teaching and learning in a time of generative AI: A critical collective stance to better navigate the futureIn: Open Praxis, 16(4), pp. 487–513Date of publication: 29 November, 2025DOI: 10.55982/openpraxis.16.4.777 Conference paperAdashchik, A., Huraira, A., Kholmatova, Z., Mikriukov, A., Ravveduto, A., Snigireva, M., Succi, G., Tormasov, A. and Trofimova, E.Agentic LLM Pipelines for Reproducible Scientific Software: Opportunities and ChallengesIn: Proceedings of the 2025 9th International Conference on Computer Science and Artificial Intelligence (pp. 38-46)Date of publication: 12 December, 2025DOI: https://doi.org/10.1145/3788149.378822
- [Gender Equality Plan (GEP) 2025-2027](https://constructorlabs.org/gender-equality-plan-gep-2025-2027/): Constructor Labs gGmbH (CL)Publication Date: December, 2025
- [For Scientists and Researchers](https://constructorlabs.org/for-scientists-and-researchers/): Join a collaborative ecosystem where researchers and PhDs pursue interdisciplinary projects that bridge theoretical innovation with real-world applications - supported by mentorship, resources, and industry partnerships.
- [For Industry](https://constructorlabs.org/for-industry/): We blend groundbreaking AI with 100+ years of educational expertise and Nobel-level scientists to solve the challenges holding your business back.
- [Partnerships](https://constructorlabs.org/partnerships/)
- [Leadership](https://constructorlabs.org/leadership/): Constructor Labs is led by experienced researchers and innovators shaping the future of applied intelligence. Their leadership ensures scientific rigor, real-world impact, and responsible innovation.
- [Our story](https://constructorlabs.org/our-story/): Your research, teaching, and learning deserve better. Constructor Labs build the technologies and methodologies that enable scientists to accelerate discovery, educators to transform learning outcomes, and students to master complex challenges—so you can focus on what truly matters: creating breakthrough knowledge that advances your field and shapes the future.
- [Constructor Talks](https://constructorlabs.org/constructor-talks/): Join Constructor Labs, in collaboration with Constructor University, for a series of talks that explore the frontiers of artificial intelligence, machine learning, and computational science. Each session brings together leading researchers and curious minds to share ideas, challenge assumptions, and illuminate how today’s science drives tomorrow’s discoveries.
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- [Home](https://constructorlabs.org/): Our mission is to solve humanity’s problems by bridging elite research and leveraging computing, software and robotics to make every scientist, teacher, and student more efficient and effective.
- [Careers​](https://constructorlabs.org/careers/): Our teams bring together leading scientists, postdocs, PhD candidates, and university students to create a dynamic environment where expertise and fresh perspectives converge.
- [Projects](https://constructorlabs.org/projects/): CKL’s research strategy provides a clear roadmap for our stakeholders, outlining key priorities and innovative approaches to critical challenges. Each project bridges multiple research disciplines to foster interdisciplinary collaboration and generate impactful solutions.
- [Contact Us](https://constructorlabs.org/contact/): If you have questions about our research or want to collaborate, we invite you to reach out. Our team is dedicated to supporting educational and scientific endeavors. We look forward to connecting with you and discussing how we can work together.

## Research Directions
- [Learning science and cognitive psychology](https://constructorlabs.org/research-directions/learning-science-and-cognitive-psychology/): Analyzing the cognitive and social mechanisms of professional collaboration to design evidence-based interventions and scaffolding strategies.
- [Robotics and autonomous machines](https://constructorlabs.org/research-directions/robotics-and-autonomous-machines/): Integrating human–robot collaboration and automated laboratories to accelerate productivity through physical experimentation and remote research access.
- [Organization, decision and operations research​](https://constructorlabs.org/research-directions/organization-decision-and-operations-research/): Developing adaptive governance and operational frameworks that leverage digital twins and communication analytics to maximize organizational efficiency.
- [Neuroscience and neuro psychology](https://constructorlabs.org/research-directions/neuroscience-and-neuro-psychology/): Leveraging neural foundations of learning and memory to engineer assistive technologies and bio-inspired algorithms that enhance cognitive performance.
- [Metapresence, virtual and augmented reality](https://constructorlabs.org/research-directions/metapresence-virtual-and-augmented-reality/): Designing immersive communication environments that integrate autonomous digital twins and AI-driven avatars to formalize, accelerate, and unify knowledge exchange between humans and machines.
- [Knowledge generation and processing​](https://constructorlabs.org/research-directions/knowledge-generation-and-processing-education-and-science/): Developing next-generation AI systems that combine symbolic reasoning, statistical learning, and simulation to formalize, connect, and evolve scientific knowledge across domains.
- [Applied AI and software engineering​​](https://constructorlabs.org/research-directions/applied-ai-software-engineering/): Supporting scientists is the primary objective, and computational methods serve as both a testbed and an example of large-scale scientific activity to better understand scientific needs and to adapt knowledge models and research platform features accordingly.
- [Computational methods, including Quantum](https://constructorlabs.org/research-directions/computational-methods-including-quantum/): Supporting scientists is the primary objective, and computational methods serve as both a testbed and an example of large-scale scientific activity to better understand scientific needs and to adapt knowledge models and research platform features accordingly.

## Projects
- [Quantum Spin Chain ML (SDRG-GNN)](https://constructorlabs.org/projects/quantum-spin-chain-ml-sdrg-gnn/): Core innovations:
- [MiAD Crystal Generation (Mirage Atoms)](https://constructorlabs.org/projects/miad-crystal-generation-mirage-atoms/): Core innovations:
- [Systematics Audit (DL advocatus)](https://constructorlabs.org/projects/systematics-audit-dl-advocatus/): Core innovations:
- [AI Antibiotics (Gram-negative AMR)](https://constructorlabs.org/projects/ai-antibiotics-gram-negative-amr/): Core innovations:
- [CERN Detector Optimization (Co-design)](https://constructorlabs.org/projects/cern-detector-optimization-co-design/): Core innovations:
- [Code](https://constructorlabs.org/projects/code/): The integration of open-source components in complex software systems remains largely manual and difficult to scale. Teams face persistent challenges in discovering compatible components, aligning interfaces and protocols, and validating integrations across heterogeneous environments. These constraints slow down deployment, limit reproducibility, and increase the risk of failure in production and regulated settings.
- [Emerging Demands and Innovative Practices in Use of AI ​in Higher Education and Science](https://constructorlabs.org/projects/emerging-demands-and-innovative-practices-in-use-of-ai-in-higher-education-and-science/): EdTech companies face challenges in anticipating industry trends amid rapidly evolving AI technologies and educational practices.
- [Using Wearables for “Construct” for Bi-directional Collaboration](https://constructorlabs.org/projects/using-wearables-for-construct-for-bi-directional-collaboration/): Goal:
- [Knowledge Discovery](https://constructorlabs.org/projects/knowledge-discovery/): Scientific knowledge is largely unstructured and static, while existing LLM- and RAG-based approaches do not represent how concepts and expertise evolve over time. This limits systematic analysis, reproducibility, and the identification of knowledge gaps across domains.
- [Semantic Flow AI](https://constructorlabs.org/projects/semantic-flow-ai/): Core innovations:
- [AI-Scientist (MIND-MATTER)](https://constructorlabs.org/projects/ai-scientist-mind-matter/): A 9-month, CKL × U Twente (BRAINS Lab) initiative funded by ARIA’s AI-Scientist Programme.
- [AI-agents for Life Science Industry](https://constructorlabs.org/projects/ai-agents-for-life-science-industry/): Current workflows lack a continuous feedback loop between:
- [AI Garbage Sorting – Robotic Cell Based on an Industrial Robotic Arm for Sorting Randomly Located Objects Using CV and AI](https://constructorlabs.org/projects/ai-garbage-sorting-robotic-cell-based-on-an-industrial-robotic-arm-for-sorting-randomly-located-objects-using-cv-and-ai/): Manual sorting of unordered parts or materials in harsh, high-mix production settings is error-prone and limits throughput.
- [Autonomous Cable Robot](https://constructorlabs.org/projects/autonomous-cable-robot/): Traditional large-scale construction and manufacturing rely on rigid gantry systems or heavy machinery, which are costly, energy-intensive, and limited in scalability.
- [Smart Shirt](https://constructorlabs.org/projects/smart-shirt/): Experimental implementation of a new class of wearable devices powered by advanced AI algorithms.
- [Control and Motion System of Robotic Dog and Delivery Robots](https://constructorlabs.org/projects/control-and-motion-system-of-robotic-dog-and-delivery-robots/): Robotic mobility in uneven terrain remains a major bottleneck. Current platforms struggle with transparency, adaptability, and cost efficiency. A core challenge lies in mapping, situational analysis, and decision making – fundamental for all moving robots.
- [3D Printer for Additive Manufacturing of Large Dimensions](https://constructorlabs.org/projects/3d-printer-for-additive-manufacturing-of-large-dimensions/): Conventional additive manufacturing systems are constrained by:
- [Predictive Analytics Platform for Demand Forecasting Using Artificial Intelligence Technologies](https://constructorlabs.org/projects/predictive-analytics-platform-for-demand-forecasting-using-artificial-intelligence-technologies/): A machine-learning-powered analytics platform was developed to bring data-driven forecasting into operational workflows:
- [Predictive Data Analytics Service for Industrial Enterprises](https://constructorlabs.org/projects/predictive-data-analytics-service-for-industrial-enterprises/): Enterprises struggle to turn vast historical data into actionable forecasts:
- [Digital 4D Model of the Region](https://constructorlabs.org/projects/digital-4d-model-of-the-region/): These challenges delay time-sensitive insights and create inefficiencies in monitoring natural resources, infrastructure, and regional operations.
- [Using Brain Signals to Increase The Quality of IDEs](https://constructorlabs.org/projects/using-brain-signals-to-increase-the-quality-of-ides/): The IDE dynamically adjusts its interface and assistance features according to these detected states, such as:
- [Autonomous mobility](https://constructorlabs.org/projects/autonomous-mobility/): Constructor’s MI-powered racing vehicles are meticulously crafted for unparalleled speed, agility, and precise control, even at extreme velocities. Equipped with cutting-edge sensor fusion technology, these vehicles seamlessly detect and respond to dynamic racing conditions in real-time.
- [Mixed and Augmented Control System of Industrial Robotized Environment](https://constructorlabs.org/projects/mixed-and-augmented-control-system-of-industrial-robotized-environment/): This leads to escalating operational costs and growing inefficiencies in traditional support models.
- [Platform for Collecting and Analyzing Web Texts for Social Science and Business Research](https://constructorlabs.org/projects/platform-for-collecting-and-analysing-web-texts-for-social-science-and-business-research/): Designed for research institutes, corporate R&D divisions, think tanks, NGOs, international organizations, market researchers, and strategic advisory units requiring scalable, accessible data analysis capabilities.
- [Digital Platform for Search, Analysis, and Management of Scientific and Technical Information](https://constructorlabs.org/projects/digital-platform-for-search-analysis-and-management-of-scientific-and-technical-information/): Designed for research institutes, corporate R&D divisions, innovation centers, and strategic advisory units seeking to accelerate technology intelligence and decision-making.
- [From Task to Code: Automating ML Pipeline Generation with Linguacodus](https://constructorlabs.org/projects/from-task-to-code-automating-ml-pipeline-generation-with-linguacodus/): Translating natural language ML task descriptions into executable, high-quality code remains a major bottleneck for rapid ML development.
- [Remotely Controlled Unmanned Underwater Vehicle](https://constructorlabs.org/projects/remotely-controlled-unmanned-underwater-vehicle/): Manual inspection always requires divers, making exploration and monitoring beneath the waterline one of the most complex engineering tasks. Visibility is limited, pressure and chemistry vary sharply with depth, and access is often restricted or unsafe for divers.
- [Fan Blade Defect Monitoring Service for Aircraft Engines](https://constructorlabs.org/projects/fan-blade-defect-monitoring-service-for-aircraft-engines/): According to turbine and pipeline regulations, defectoscopy is mandatory and has traditionally been carried out manually.
- [Next-Gen Material Discovery with Mirage Atom Diffusion  ](https://constructorlabs.org/projects/next-gen-material-discovery-with-mirage-atom-diffusion/): Existing crystal material generative models face two major bottlenecks:
- [Accelerating Advanced Device Design with Generative Optimization](https://constructorlabs.org/projects/accelerating-advanced-device-design-with-generative-optimization/): Introduce Local Generative Surrogate Optimization (L-GSO):
- [FastTrack SSB: AI Screening for Next-Gen Electrolytes](https://constructorlabs.org/projects/fasttrack-ssb-ai-screening-for-next-gen-electrolytes/): Discovery of new superionic conductors is critical for next-generation solid-state batteries (SSBs), which offer higher energy density and safety.
- [Robotic Tool Kit (RTK) for milling based on industrial robotic arm](https://constructorlabs.org/projects/robotic-tool-kit-rtk-for-milling-based-on-industrial-robotic-arm/): Traditional Computer Numerical Control (CNC) milling systems face key limitations:
- [Collaborative Robotics – Force Localization via Artificial Skin Sensors](https://constructorlabs.org/projects/collaborative-robotics-force-localization-via-artificial-skin-sensors/): In environments where humans and robots work side by side, precision and safety are non-negotiable. Yet fundamental limitations in tactile awareness continue to constrain robotic systems:
- [Intelligent Transport Systems (ML + Machine vision)](https://constructorlabs.org/projects/intelligent-transport-systems-ml-machine-vision/): Most autonomous driving solutions today are difficult to scale:

## Solutions
- [EdTech](https://constructorlabs.org/solutions/edtech/): We accelerate the growth of educational companies by solving their hardest challenges through groundbreaking research that blends AI with over 100 years of educational expertise and Nobel-level scientists.

## Team Members
- [Ilya Shimchik](https://constructorlabs.org/team-members/ilya-shimchik/): Ilya Shimchik’s expertise sits squarely within Constructor Labs’ Robotics & Autonomous Machines direction. As Team Principal of Constructor Racing he owns the complete self-driving pipeline — multi-modal perception across LiDAR, radar, camera, IMU, GNSS and event-based sensors; robust state estimation and localisation; multi-agent motion planning in dense traffic; and control at the physical limits of grip and aerodynamic load. Validating this stack in A2RL, where cars race wheel-to-wheel at up to 300 km/h, forces a standard of safety, determinism and real-time reliability that few autonomy programmes ever encounter.
- [Dr. Sari Saba-Sadiya](https://constructorlabs.org/team-members/dr-sari-saba-sadiya/): Dr. Sari Saba-Sadiya’s work leverages state-of-the-art machine learning techniques for biological signal analysis. His toolbox includes model–brain alignment, neural architecture search, transfer-learning (such as feature imitating and prior fitted networks), and interpretability techniques.
- [Prof. Dr. Giancarlo Succi](https://constructorlabs.org/team-members/prof-dr-giancarlo-succi/): Prof. Dr. Giancarlo Succi is a leading researcher in software engineering whose work at Constructor Labs combines artificial intelligence, empirical software engineering, human factors, and software process innovation. His research focuses on AI-assisted software development, software quality, agile and DevOps methodologies, software architecture, and the application of data-driven and empirical methods to improve how software is designed, developed, and maintained. A distinctive aspect of his work is the integration of behavioral sciences into software engineering, using physiological signals such as EEG, eye tracking, and other biometric measurements to understand developers’ cognitive processes, collaboration, stress, and decision-making. This research provides the scientific foundation for next-generation AI systems that enhance both software development and human productivity.
- [Prof. Dr. Petr Popov](https://constructorlabs.org/team-members/prof-dr-petr-popov/): Prof. Dr. Petr Popov’s core expertise lies in developing physics-based machine-learning methods for molecular science and computational biology. Over the past fifteen years, he has built numerical approaches that accelerate scientific discovery across structural biology, drug discovery, protein engineering, and computational chemistry. His work spans the complete computational discovery pipeline: geometric and graph neural networks for molecular representation learning, deep learning for protein structure and binding-site prediction, AI-driven virtual screening, large language models for biological sequence analysis, and optimization methods for molecular design. Alongside methodological advances, he has translated these approaches into practical scientific software and patented technologies that are used to identify druggable targets, predict protein stability, design receptor mutations, and support structure-based drug discovery. Petr leads interdisciplinary collaborations spanning machine learning, structural biology, chemistry, and medicine while producing influential publications in leading journals.
- [Prof. Dr. Isak Frumin](https://constructorlabs.org/team-members/prof-dr-isak-frumin/): Prof. Dr. Isak Frumin’s work at Constructor Labs connects research on education and learning, applied AI, large-scale knowledge processing, and organizational analysis. He leads two Constructor Labs projects in this area: “Emerging Demands and Innovative Practices in Use of AI in Higher Education and Science”, focused on AI adoption, early adopters, weak signals, and future demand in universities and research organizations; and “Platform for Collecting and Analyzing Web Texts for Social Science and Business Research”, focused on scalable web-data collection and text-analysis infrastructure for research and applied analytics.
- [Prof. Dr. Andrey Ustyuzhanin](https://constructorlabs.org/team-members/prof-dr-andrey-ustyuzhanin/): Prof. Ustyuzhanin’s core expertise lies in developing machine-learning methods that accelerate scientific discovery, which maps directly onto the Autonomous Science and AI Applications directions at Constructor Labs. For over two decades he has built AI systems that formulate and test hypotheses under the demanding constraints of real experimental science — from high-energy physics at CERN (the LHCb and SHiP experiments) to materials design, catalysis, quantum materials, and antibiotics discovery. This work spans the full autonomous-science loop: generative models that propose candidate structures, differentiable and surrogate-based optimization of experiments and detectors, anomaly detection for new-physics searches, and reproducible ML pipelines that make results auditable across large collaborations. These directions are backed by concrete outcomes rather than method work alone: as a member of the LHCb Collaboration he shares in the 2025 Breakthrough Prize in Fundamental Physics, his group’s methods have been adopted in experimental-physics workflows and open-source tooling, and he continues to lead international, cross-disciplinary collaborations — including current work as a Visiting Research Professor at the National University of Singapore (I-FIM) and as coordinator of the Materials Science Working Group in the EU Horizon Europe project SCIANCE (Strategic Coordination of AI-enabled Science in Europe).
- [Prof. Dr. Alexander Tormasov](https://constructorlabs.org/team-members/prof-dr-alexander-tormasov/): Prof. Dr. Alexander Tormasov’s work at Constructor Labs connects research on virtualization and distributed systems, applied AI and software engineering, and secure, large-scale computing infrastructure. His research at Constructor labs is focused on using LLM-based agents to automate code generation, deployment configuration, and DevOps workflows; addressing the hosting of large language models in shared environments with minimal overhead and strong security guarantees; and exploring a universal virtual-machine paradigm that aggregates heterogeneous compute to execute AI and cryptographic workloads reliably.

## Events
- [Nikolay Malkin: “Learning to Construct: Advances in Structured Inference and Bayesian Neurosymbolic AI”](https://constructorlabs.org/constructor-talks/nikolay-malkin-learning-to-construct-advances-in-structured-inference-and-bayesian-neurosymbolic-ai/): On November 13, we hosted Dr. Nikolay Malkin, Chancellor's Fellow in Informatics at the University of Edinburgh and fellow of CIFAR's Learning in Machines and Brains programme, for an engaging hybrid session exploring recent advances in structured probabilistic inference.
- [Prof. Dr. Andrey Ustyuzhanin: “Using AI to Find Better Battery Materials”](https://constructorlabs.org/constructor-talks/prof-dr-andrey-ustyuzhanin-using-ai-to-find-better-battery-materials/)
- [Dr. Denis Federyakin: “Psychology-informed and User-Centric Review of Prompt Engineering Techniques”](https://constructorlabs.org/constructor-talks/dr-denis-federyakin-psychology-informed-and-user-centric-review-of-prompt-engineering-techniques/): On April 22, we brought together 90 participants — on-site and online — for the 3rd session of our monthly series, this time under the Constructor Talks format.
- [Dr. Michael Figurnov: “Highly Accurate Protein Structure Prediction with AlphaFold”](https://constructorlabs.org/constructor-talks/dr-michael-figurnov-highly-accurate-protein-structure-prediction-with-alphafold/)
- [Prof. Dr. Andrey Ustyuzhanin: “Agentic Flows for Scientific Research: A Practical Perspective”](https://constructorlabs.org/constructor-talks/prof-dr-andrey-ustyuzhanin-agentic-flows-for-scientific-research-a-practical-perspective/): On April 22, we brought together 90 participants — on-site and online — for the 3rd session of our monthly series, this time under the Constructor Talks format.
- [Prof. Dr. Alexander Makarov: “Bremen: The World Capital of Mass Spectrometry”](https://constructorlabs.org/constructor-talks/prof-dr-alexander-makarov-bremen-the-world-capital-of-mass-spectrometry/): On May 20, we hosted another session of Constructor Talks, bringing together more than 50 participants on-site and online for a conversation with Prof. Dr. Alexander Makarov – Senior Director of Research, Life Science Mass Spectrometry, Thermo Fisher Scientific, Fellow of the Royal Society in the UK, and inventor of the Orbitrap mass spectrometer.