Alexander Gorban

Director of the Centre for Artificial Intelligence, Data Analysis and Modelling (AIDAM) and Professor of Applied Mathematics

Tel: +44(0) 116 223 1433
Email: ag153@le.ac.uk

 

A honorary title: Pioneer of Russian Neuroinformatics (2 October 2017).

Nomination for the Research Impact Award

Pattern Recognition in Big Data
Researchers: Professor Alexander Gorban, Professor Jeremy Levesley, Dr Ivan Tyukin, Dr Evgeny Mirkes, Dr Andrey Mudrov, Department of Mathematics
Nomination

LtoR: I. Tyukin, J. Levesley, A. Gorban, E. Mirkes

 

LtoR: I. Tyukin, J. Levesley, A. Gorban, E. Mirkes

 

Websites

  • Google Scholar Profile scholar_results

Teaching: Data mining

Online resources:

Highlights:

Workshop "Hilbert's Sixth Problem'', University of Leicester, May 02-04, 2016. The original Hilbert's formulation (in English translation) was: "6. Mathematical Treatment of the Axioms of Physics. ...(continued here)

Preface to the special issue “Model reduction across disciplines” of Math. Model. Nat. Phenom. (Vol. 10, No. 3, 2015, pp. 1–5) dedicated to 60th birthday of A. Gorban, by Guest Editors: G. Fridman, J. Levesley, I. Tyukin, D. Wunsch

Lifetime Achievement Award

MaCKIE-2015, Mathematics in (bio)Chemical Kinetics and Engineering
Lifetime Achievement Award in recognition of outstanding contributions to the research field of (bio)chemical kinetics.

Media

Press release in AlphaGalileo

Press release in EurecAlert

Press releases in ScienceDaily:

Publications

Selected books

  1. A.N. Gorban and D. Roose (eds.), Coping with Complexity: Model Reduction and Data Analysis,  Lecture Notes in Computational Science and Engineering, 75, Springer: Heidelberg – Dordrecht - London -New York, 2011.
  2. A.N. Gorban, B. Kegl, D. Wunsch, A. Zinovyev  (Eds.), Principal Manifolds for Data Visualisation and Dimension Reduction, Lecture Notes in Computational Science and Engineering, Vol. 58, Springer, Berlin – Heidelberg – New York, 2007. (ISBN 978-3-540-73749-0)
  3. A.N. Gorban, N.  Kazantzis, I.G. Kevrekidis, H.C. Öttinger, C. Theodoropoulos (eds), Model Reduction and Coarse--Graining Approaches for Multiscale Phenomena, Springer, Berlin-Heidelberg-New York, 2006.
  4. A.N. Gorban, B.M. Kaganovich, S.P. Filippov, A.V. Keiko, V.A. Shamansky, I.A. Shirkalin, Thermodynamic Equilibria and Extrema: Analysis of Attainability Regions and Partial Equilibria, Springer, Berlin-Heidelberg-New York, 2006.
  5. A.N. Gorban, I.V. Karlin, Invariant Manifolds for Physical and Chemical Kinetics, Lect. Notes Phys. 660, Springer, Berlin, Heidelberg, 2005. [Review in Bull. London Math. Soc. 38 (2006) (pdf)] [Review in Zentralblatt Math. (2006) (pdf)] [Abstract(txt)] [Preface-Contents-Introduction(pdf)] [Editorial Reviews (htm)][Authors(gif)] Russian web-site with this book
  6. A.N. Gorban, Singularities of transition processes in dynamical systems: Qualitative theory of critical delays, Electron. J. Diff. Eqns. Monograph 5, 2004, 55 p.
  7. G.S.Yablonskii, V.I.Bykov, A.N. Gorban, and V.I.Elokhin,  Kinetic Models of Catalytic Reactions (Comprehensive Chemical Kinetics, V.32, ed. by R.G. Compton), Elsevier, Amsterdam, 1991, 396p. (Reviews on this book: (1) W.H. Weinberg in J. Am. Chem. Soc. 114 (13) (1992), 5484-5485; (2) G. Wedler in Chem.-Ing.-Tech. 64 (1992) (8), 767-768)

Selected papers

1. Grechuk, Bogdan, Alexander N. Gorban, and Ivan Y. Tyukin. "General stochastic separation theorems with optimal bounds." Neural Networks 138 (2021): 33-56. https://doi.org/10.1016/j.neunet.2021.01.034

2. Rybnikova, N., Portnov, B. A., Mirkes, E. M., Zinovyev, A., Brook, A., & Gorban, A. N. (2021). Coloring Panchromatic Nighttime Satellite Images: Comparing the Performance of Several Machine Learning Methods. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3076011

3. Gorban, A. N., Tyukina, T. A., Pokidysheva, L. I., & Smirnova, E. V. Dynamic and thermodynamic models of adaptation. Physics of Life Reviews. 37, (2021), 17-64. https://doi.org/10.1016/j.plrev.2021.03.001

4. Gorban, A. N. Transition states and entangled mass action law. Results in Physics22, (2021), 103922. https://doi.org/10.1016/j.rinp.2021.103922

5. Tyukin, I. Y., Gorban, A. N., McEwan, A. A., Meshkinfamfard, S., & Tang, L. Blessing of dimensionality at the edge and geometry of few-shot learning. Information Sciences, 564, (2021), 124-143. https://doi.org/10.1016/j.ins.2021.01.022

6. Gordleeva, S. Y., Tsybina, Y. A., Krivonosov, M. I., Ivanchenko, M. V., Zaikin, A. A., Kazantsev, V. B., & Gorban, A. N. Modelling working memory in spiking neuron network accompanied by astrocytes. Frontiers in Cellular Neuroscience15, (2021), 86. https://doi.org/10.3389/fncel.2021.631485

7. Roland, D., Suzen, N., Coats, T. J., Levesley, J., Gorban, A. N., & Mirkes, E. M. What can the randomness of missing values tell you about clinical practice in large data sets of children’s vital signs?. Pediatric research89(1), (2021), 16-21. https://doi.org/10.1038/s41390-020-0861-2

8. Golovenkin, S. E., Bac, J., Chervov, A., Mirkes, E. M., Orlova, Y. V., Barillot, E., Gorban, A.N. & Zinovyev, A. (2020). Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data. GigaScience9(11), giaa128. https://doi.org/10.1093/gigascience/giaa128

9. Mirkes EM, Allohibi J, Gorban A. Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality. Entropy. 2020; 22(10):1105. https://doi.org/10.3390/e22101105

10. Chen, Z., Wang, B., & Gorban, A. N. Multivariate Gaussian and Student-t process regression for multi-output prediction. Neural Computing and Applications32(8), (2020), 3005-3028. https://doi.org/10.1007/s00521-019-04687-8

11. Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. Automatic short answer grading and feedback using text mining methods. Procedia Computer Science169, (2020), 726-743. https://doi.org/10.1016/j.procs.2020.02.171

12. Gorban, A. N., Makarov, V. A., & Tyukin, I. Y. High-dimensional brain in a high-dimensional world: Blessing of dimensionality. Entropy22(1), (2020), 82. https://doi.org/10.3390/e22010082

13. Gorban, A. N., Mirkes, E. M., & Tyukin, I. Y. How deep should be the depth of convolutional neural networks: a backyard dog case study. Cognitive Computation12(2), 388-397. (2020). https://doi.org/10.1007/s12559-019-09667-7

14. Gorban, A. N. (2019). Universal Lyapunov functions for non-linear reaction networks. Communications in Nonlinear Science and Numerical Simulation79, (2019), 104910. https://doi.org/10.1016/j.cnsns.2019.104910

15. Tyukin, I., Gorban, A. N., Calvo, C., Makarova, J., & Makarov, V. A. High-dimensional brain: A tool for encoding and rapid learning of memories by single neurons. Bulletin of mathematical biology81(11), (2019), 4856-4888. https://doi.org/10.1007/s11538-018-0415-5

16. Gorban, A. N., Makarov, V. A., & Tyukin, I. Y. The unreasonable effectiveness of small neural ensembles in high-dimensional brain. Physics of Life Reviews29, (2019), 55-88. https://doi.org/10.1016/j.plrev.2018.09.005

17. AN Gorban, R Burton, I Romanenko, IY Tyukin, One-trial correction of legacy AI systems and stochastic separation theorems, Information Sciences 484 (2019) 237–254, https://doi.org/10.1016/j.ins.2019.02.001

18. IY Tyukin, AN Gorban, S Green, D Prokhorov, Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study. Information Sciences 485 (2019), 230-247, https://doi.org/10.1016/j.ins.2018.11.057

19. IY Tyukin, D Iudin, F Iudin, T. Tyukina, V. Kazantsev, I Muhina, AN Gorban, Simple model of complex dynamics of activity patterns in developing networks of neuronal cultures, PLoS One, 2019, https://doi.org/10.1371/journal.pone.0218304

20. I.Y. Tyukin, A.N. Gorban, K.I. Sofeykov, I. Romanenko, I.  Knowledge transfer between artificial intelligence systems. Frontiers in Neurorobotics, 12. 2018, https://doi.org/10.3389/fnbot.2018.00049

21. A.N. Gorban, N Çabukoǧlu. Mobility cost and degenerated diffusion in kinesis models. Ecological complexity, 36, 16-21 (2018). https://doi.org/10.1016/j.ecocom.2018.06.007

22. A.J. Bell, B.H. Foy, M. Richardson,  A. Singapuri, E. Mirkes, E., M. van den Berge, D. Kay,  C. Brightling, A.N. Gorban, C.J. Galban, S. Siddiqui, Functional CT imaging for identification of the spatial determinants of small-airways disease in adults with asthma. Journal of Allergy and Clinical Immunology, Volume 144, Issue 1, July 2019, Pages 83-93. https://doi.org/10.1016/j.jaci.2019.01.014

23. A.N. Gorban, N. Çabukoǧlu, Basic model of purposeful kinesis, Ecological Complexity, 33, 2018, 75-83. https://doi.org/10.1016/j.ecocom.2018.01.002

24. AN Gorban, Hilbert's Sixth Problem: the endless road to rigour, Phil. Trans. R. Soc. A 376 (2118), 20170238 (2018). https://doi.org/10.1098/rsta.2017.0238

25. AN Gorban, Model reduction in chemical dynamics: slow invariant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph, Current Opinion in Chemical Engineering 21 (2018), 48-59, https://doi.org/10.1016/j.coche.2018.02.009.

26. AN Gorban, IY Tyukin, Blessing of dimensionality: mathematical foundations of the statistical physics of data, Phil. Trans. R. Soc. A 376 (2118), 20170237 (2018). https://doi.org/10.1098/rsta.2017.0237

27. Grechuk, Bogdan, Alexander N. Gorban, and Ivan Y. Tyukin. "General stochastic separation theorems with optimal bounds." Neural Networks 138 (2021): 33-56. https://doi.org/10.1016/j.neunet.2021.01.034

28. Rybnikova, N., Portnov, B. A., Mirkes, E. M., Zinovyev, A., Brook, A., & Gorban, A. N. (2021). Coloring Panchromatic Nighttime Satellite Images: Comparing the Performance of Several Machine Learning Methods. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3076011

29. Gorban, A. N., Tyukina, T. A., Pokidysheva, L. I., & Smirnova, E. V. Dynamic and thermodynamic models of adaptation. Physics of Life Reviews. 37, (2021), 17-64. https://doi.org/10.1016/j.plrev.2021.03.001

30. Gorban, A. N. Transition states and entangled mass action law. Results in Physics, 22, (2021), 103922. https://doi.org/10.1016/j.rinp.2021.103922

31. Tyukin, I. Y., Gorban, A. N., McEwan, A. A., Meshkinfamfard, S., & Tang, L. Blessing of dimensionality at the edge and geometry of few-shot learning. Information Sciences, 564, (2021), 124-143. https://doi.org/10.1016/j.ins.2021.01.022

32. Gordleeva, S. Y., Tsybina, Y. A., Krivonosov, M. I., Ivanchenko, M. V., Zaikin, A. A., Kazantsev, V. B., & Gorban, A. N. Modelling working memory in spiking neuron network accompanied by astrocytes. Frontiers in Cellular Neuroscience, 15, (2021), 86. https://doi.org/10.3389/fncel.2021.631485

33. Roland, D., Suzen, N., Coats, T. J., Levesley, J., Gorban, A. N., & Mirkes, E. M. What can the randomness of missing values tell you about clinical practice in large data sets of children’s vital signs?. Pediatric research, 89(1), (2021), 16-21. https://doi.org/10.1038/s41390-020-0861-2

34. Golovenkin, S. E., Bac, J., Chervov, A., Mirkes, E. M., Orlova, Y. V., Barillot, E., Gorban, A.N. & Zinovyev, A. (2020). Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data. GigaScience, 9(11), giaa128. https://doi.org/10.1093/gigascience/giaa128

35. Mirkes EM, Allohibi J, Gorban A. Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality. Entropy. 2020; 22(10):1105. https://doi.org/10.3390/e22101105

36. Chen, Z., Wang, B., & Gorban, A. N. Multivariate Gaussian and Student-t process regression for multi-output prediction. Neural Computing and Applications, 32(8), (2020), 3005-3028. https://doi.org/10.1007/s00521-019-04687-8

37. Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. Automatic short answer grading and feedback using text mining methods. Procedia Computer Science, 169, (2020), 726-743. https://doi.org/10.1016/j.procs.2020.02.171

38. Gorban, A. N., Makarov, V. A., & Tyukin, I. Y. High-dimensional brain in a high-dimensional world: Blessing of dimensionality. Entropy, 22(1), (2020), 82. https://doi.org/10.3390/e22010082

39. Gorban, A. N., Mirkes, E. M., & Tyukin, I. Y. How deep should be the depth of convolutional neural networks: a backyard dog case study. Cognitive Computation, 12(2), 388-397. (2020). https://doi.org/10.1007/s12559-019-09667-7

40. Gorban, A. N. (2019). Universal Lyapunov functions for non-linear reaction networks. Communications in Nonlinear Science and Numerical Simulation, 79, (2019), 104910. https://doi.org/10.1016/j.cnsns.2019.104910

41. Tyukin, I., Gorban, A. N., Calvo, C., Makarova, J., & Makarov, V. A. High-dimensional brain: A tool for encoding and rapid learning of memories by single neurons. Bulletin of mathematical biology, 81(11), (2019), 4856-4888. https://doi.org/10.1007/s11538-018-0415-5

42. Gorban, A. N., Makarov, V. A., & Tyukin, I. Y. The unreasonable effectiveness of small neural ensembles in high-dimensional brain. Physics of Life Reviews, 29, (2019), 55-88.  https://doi.org/10.1016/j.plrev.2018.09.005

43. AN Gorban, R Burton, I Romanenko, IY Tyukin, One-trial correction of legacy AI systems and stochastic separation theorems, Information Sciences 484 (2019) 237–254, https://doi.org/10.1016/j.ins.2019.02.001

44. IY Tyukin, AN Gorban, S Green, D Prokhorov, Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study. Information Sciences 485 (2019), 230-247, https://doi.org/10.1016/j.ins.2018.11.057

45. IY Tyukin, D Iudin, F Iudin, T. Tyukina, V. Kazantsev, I Muhina, AN Gorban, Simple model of complex dynamics of activity patterns in developing networks of neuronal cultures, PLoS One, 2019, https://doi.org/10.1371/journal.pone.0218304

46. I.Y. Tyukin, A.N. Gorban, K.I. Sofeykov, I. Romanenko, I.  Knowledge transfer between artificial intelligence systems. Frontiers in Neurorobotics, 12. 2018, https://doi.org/10.3389/fnbot.2018.00049

47. A.N. Gorban, N Çabukoǧlu. Mobility cost and degenerated diffusion in kinesis models. Ecological complexity, 36, 16-21 (2018). https://doi.org/10.1016/j.ecocom.2018.06.007

48. A.J. Bell, B.H. Foy, M. Richardson,  A. Singapuri, E. Mirkes, E., M. van den Berge, D. Kay,  C. Brightling, A.N. Gorban, C.J. Galban, S. Siddiqui, Functional CT imaging for identification of the spatial determinants of small-airways disease in adults with asthma. Journal of Allergy and Clinical Immunology, Volume 144, Issue 1, July 2019, Pages 83-93. https://doi.org/10.1016/j.jaci.2019.01.014

49. A.N. Gorban, N. Çabukoǧlu, Basic model of purposeful kinesis, Ecological Complexity, 33, 2018, 75-83. https://doi.org/10.1016/j.ecocom.2018.01.002

50. AN Gorban, Hilbert's Sixth Problem: the endless road to rigour, Phil. Trans. R. Soc. A 376 (2118), 20170238 (2018). https://doi.org/10.1098/rsta.2017.0238

51. AN Gorban, Model reduction in chemical dynamics: slow invariant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph, Current Opinion in Chemical Engineering 21 (2018), 48-59, https://doi.org/10.1016/j.coche.2018.02.009.

52. AN Gorban, IY Tyukin, Blessing of dimensionality: mathematical foundations of the statistical physics of data, Phil. Trans. R. Soc. A 376 (2118), 20170237 (2018). https://doi.org/10.1098/rsta.2017.0237

53. Inventors: Ilya Romanenko, Ivan Tyukin, Alexander Gorban, Konstantin Sofeikov;
Assignee: Apical Ltd; Priority date: 2015-12-23. Method of image processingUnited States Patent, Patent No.: US 10,062,013 B2; Date of Patent: Aug. 28, 2018, published

54. A.N. Gorban, A. Golubkov, B. Grechuk, E.M. Mirkes, I.Y. Tyukin,
Correction of AI systems by linear discriminants: Probabilistic foundationsInformation Sciences 466 (2018), 303-322.

55. I. Tyukin, K. Sofeikov, J. Levesley, A.N. Gorban, P. Allison, N.J. Cooper, Exploring Automated Pottery Identification [Arch-I-Scan], Internet Archaeology 50 (2018). https://doi.org/10.11141/ia.50.11.

56. I.Y. Tyukin, A.N. Gorban, K.I. Sofeykov, I. Romanenko, Knowledge transfer between artificial intelligence systems, Frontiers in Neurorobotics 12 (2018), https://doi.org/10.3389/fnbot.2018.00049

57. A.N. Gorban, N. Çabukoǧlu, Mobility cost and degenerated diffusion in kinesis modelsEcological Complexity 36 (2018), 16-21.

58. A.N. Gorban, E.M. Mirkes, A. Zinovyev, Data analysis with arbitrary error measures approximated by piece-wise quadratic PQSQ functionsProceedings of IJCNN 2018, paper #18525.

59. I.Y. Tyukin, A.N. Gorban, D. Prokhorov, S. Green, Efficiency of Shallow Cascades for Improving Deep Learning AI SystemsProceedings of IJCNN 2018, paper #18433.

60. A.N. Gorban. Model reduction in chemical dynamics: slow invariant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph. Current Opinion in Chemical Engineering, Volume 21, September 2018, 48-59.

61. I. Tyukin, A.N. Gorban, C. Calvo, J. Makarova, V.A. Makarov. High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons, Bull Math Biol, 2018, https://doi.org/10.1007/s11538-018-0415-5

62. A.N. Gorban. Hilbert's Sixth Problem: the endless road to rigour. Philosophical Transactions of The Royal Society A 376(2118), 20170238, 2018.

63. A.N. Gorban, I.Y. Tyukin. Blessing of dimensionality: mathematical foundations of the statistical physics of data. Philosophical Transactions of The Royal Society A 376(2118), 20170237, 2018.

64. A.N. Gorban, N. ÇabukoǧluBasic model of purposeful kinesis Ecological Complexity, 33, 2018, 75-83.

65. E.M. Mirkes, A.N. Gorban, J. Levesley, P.A.S. Elkington, J.A. Whetton, Pseudo-outcrop Visualization of Borehole Images and Core Scans , Mathematical Geosciences, November 2017, 49 (8), 947–964.

66. Fehrman E., Muhammad A.K., Mirkes E.M., Egan V., Gorban A.N.
The Five Factor Model of Personality and Evaluation of Drug Consumption Risk. In: Palumbo F., Montanari A., Vichi M. (eds) Data Science. Studies in Classification, Data Analysis, and Knowledge Organization. Springer (2017), pp 231-242.

67. Gorban A.N., Tyukin I.Y. Stochastic Separation Theorems, Neural Networks, 94, October 2017, 255-259

68. A.N. Gorban, I.V. Karlin, Beyond Navier–Stokes equations: capillarity of ideal gasContemporary Physics 58(1) (2017), 70-90, DOI:10.1080/00107514.2016.1256123. arXiv e-print

69. E. Moczko, E.M. Mirkes, C. Ceceres, A.N. Gorban, S. Piletsky, Fluorescence-based assay as a new screening tool for toxic chemicalsScientific Reports 6, Article number: 33922 (2016).

70. A.N. Gorban, E.M. Mirkes, A. Zinovyev, Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning, Neural Networks 84 (2016), 28-38

71. E.M. Mirkes, T.J. Coats, J. Levesley, A.N. Gorban, Handling missing data in large healthcare dataset: a case study of unknown trauma outcomesComputers in Biology and Medicine 75 (2016), 203-216.

72. A.N. Gorban, T.A. Tyukina, E.V. Smirnova, L.I. Pokidysheva, Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating deathJournal of Theoretical Biology 405 (2016), 127-139.

73. A.N. Gorban, I.Yu. Tyukin, D.V. Prokhorov, K.I. Sofeikov, Approximation with random bases: Pro et ContraInformation Sciences 364-365, (2016), 129-145.

74. A. N. Gorban,·A. Zinovyev, Fast and user-friendly non-linear principal manifold learning by method of elastic maps, in Proceedings DSAA 2015 -- IEEE International Conference on Data Science and Advanced Analytics, Paris; 10/2015.

75. A.N. Gorban, V.N. Kolokoltsov, Generalized Mass Action Law and Thermodynamics of Nonlinear Markov ProcessesMath. Model. Nat. Phenom. Vol. 10, No. 5, 2015, pp. 16–46.

76. A.N. Gorban, I.Yu. Tyukin, H. Nijmeijer, Further Results on Lyapunov-Like Conditions of Forward Invariance and Boundedness for a Class of Unstable SystemsProceedings of 53rd IEEE Conference on Decision and Control, December 15-17, 2014. Los Angeles, California, USA, IEEE, 2014, pp. 1557-1562.

77. A.S. Manso, M.H. Chai, J.M. Atack, L. Furi, M. De Ste Croix, R. Haigh, C. Trappetti, A.D. Ogunniyi, L.K. Shewell, M. Boitano, T.A. Clark, J. Korlach, M. Blades, E. Mirkes, A.N. Gorban, J.C. Paton, M.P. Jennings, M.R. Oggioni, A random six-phase switch regulates pneumococcal virulence via global epigenetic changesNature Communications 5 (2014), Article number: 5055. Supplementary Information

78. A.N. Gorban, I. Karlin, Hilbert's 6th Problem: exact and approximate hydrodynamic manifolds for kinetic equations, Bulletin of the American Mathematical Society,  Vol. 51, Issue 2, 2014, 186-246. arXiv:1310.0406 [math-ph]

79. E.M. Mirkes, I. Alexandrakis, K. Slater, R. Tuli, A.N. Gorban, Computational diagnosis and risk evaluation for canine lymphoma, Computers in Biology and Medicine, Volume 53, 1 October 2014, 279-290. arXiv:1305.4942 [q-bio.QM].

80. A.N. Gorban, D.J. Packwood, Enhancement of the stability of lattice Boltzmann methods by dissipation control, Physica A 414 (2014) 285–299.

81. A.N. Gorban, I. Tyukin, E. Steur, and H. Nijmeijer, Lyapunov-like conditions of forward invariance and boundedness for a class of unstable systemsSIAM J. Control Optim., Vol. 51, No. 3, 2013, pp. 2306-2334.

82. A.N. Gorban, Maxallent: Maximizers of all entropies and uncertainty of uncertaintyComputers & Mathematics with Applications, Volume 65, Issue 10, May 2013, 1438-1456.

83. A.N. Gorban, G.S. Yablonsky, Grasping Complexity, Computers & Mathematics with Applications, Volume 65, Issue 10, May 2013, 1421-1426.

84. R.A. Brownlee, J. Levesley, D. Packwood, A.N. Gorban, Add-ons for Lattice Boltzmann Methods: Regularization, Filtering and LimitersProgress in Computational Physics, 2013, vol. 3, 31-52.

85. A.N. Gorban, Thermodynamic Tree: The Space of Admissible Paths, SIAM J. Applied Dynamical Systems, Vol. 12, No. 1 (2013), pp. 246-278. DOI: 10.1137/120866919

86. A.N. Gorban, Local equivalence of reversible and general Markov kinetics, Physica A 392 (2013) 1111–1121.

87. A.N. Gorban, E.M. Mirkes, G.S. Yablonsky, Thermodynamics in the limit of irreversible reactions, Physica A 392 (2013) 1318–1335.

88. Zinovyev, N. Morozova, A.N. Gorban, and A. Harel-Belan, Mathematical Modeling of microRNA-Mediated Mechanisms of Translation Repression, in U. Schmitz et al. (eds.), MicroRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools, Advances in Experimental Medicine and Biology Vol. 774, Springer, 2013, pp. 189-224.

89. A.N. Gorban and D. Packwood, Allowed and forbidden regimes of entropy balance in lattice Boltzmann collisions, Physical Review E 86, 025701(R) (2012).

90. N. Morozova, A. Zinovyev, N. Nonne, L.-L. Pritchard, A.N. Gorban, and A. Harel-Bellan, Kinetic signatures of microRNA modes of action, RNA, Vol. 18, No. 9 (2012), 1635-1655.

91. A.N. Gorban, G.S.Yablonsky, Extended detailed balance for systems with irreversible reactions, Chem. Eng. Sci. 66 (2011) 5388–5399.

92. A.N. Gorban, L.I. Pokidysheva,·E,V. Smirnova, T.A. Tyukina, Law of the Minimum Paradoxes, Bull Math Biol 73(9) (2011), 2013-2044.

93. A.N. Gorban, E.V. Smirnova, T.A. Tyukina, Correlations, risk and crisis: From physiology to finance, Physica A, Vol. 389, Issue 16, 2010, 3193-3217.

94. A.N. Gorban, A. Zinovyev, Principal manifolds and graphs in practice: from molecular biology to dynamical systems, International Journal of Neural Systems, Vol. 20, No. 3 (2010) 219–232.

95. A.N. Gorban, O. Radulescu, A. Y. Zinovyev, Asymptotology of chemical reaction networks, Chem. Eng. Sci. 65 (2010) 2310–2324.

96. A.N. Gorban, A. Y. Zinovyev, Principal Graphs and Manifolds, Chapter 2 in: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, Emilio Soria Olivas et al. (eds), IGI Global, Hershey, PA, USA, 2009, pp. 28-59.

97. R.A. Brownlee, A. N. Gorban, and J. Levesley, Nonequilibrium entropy limiters in lattice Boltzmann methods, Physica A, 387, (2-3) (2008), 385-406.
A.N. Gorban, Selection Theorem for Systems with Inheritance, Math. Model. Nat. Phenom., Vol. 2, No. 4, 2007, pp. 1-45.

98. A.N. Gorban, N.R. Sumner, and A.Y. Zinovyev, Topological grammars for data approximationApplied Mathematics Letters, 20 (4)  (2007),  382-386.

99. A.N. Gorban,  T.G.Popova, A.Yu. Zinovyev, Codon usage trajectories and 7-cluster structure of 143 complete bacterial genomic sequences Physica A 353C (2005), 365-387.

100. A.N. Gorban, I.V. Karlin, A.Y Zinovyev, Constructive methods of invariant manifolds for kinetic problems, Phys. Rep., 396, 2004, 197-403.

 

101. A.N. Gorban, I.V. Karlin, Method of invariant manifold for chemical kinetics, Chem. Eng. Sci. 58, 2003, 4751-4768.

 

 

Scientific achievements

Kinetics

I developed a family of methods for model reduction and coarse-graining: method of invariant manifold, method of natural projector, relaxation methods. I have solved problems in gas kinetics, polymer dynamics, chemical reaction kinetics and biological kinetics. For this series of work I received the I. Prigogine prize and medal. I have been Clay Scholar (Cambridge, USA, 2000).

Solution of Hilbert problem

IV Karlin and me received the recognition of the scientific community for solving an important part of the Hilbert Sixth problem about the irreducibility of continuum mechanics to physical kinetics that remained unsolved almost for 100 years.

Stable Lattice Boltzmann methods

New family of numerical methods is developed. They are based on the ideas of lattice Boltzmann models (LBM) in combination with methods of invariant manifold and specific entropic stabilisers. Standard test examples demonstrate that the new methods erase spurious oscillations without blurring of shocks, and do not affect smooth solutions.

Bioinformatics

The methods of genome analysis based on frequency dictionaries are elaborated and applied to various biological problems (genome redundancy, mosaic structure of genome, genetic species signature, etc.). For example, existence of a universal 7-cluster structure in all available bacterial genomes is proved.

Data mining and rules extraction

A general neural networks based technology of extraction of explicit knowledge from data was developed. This technology was implemented in a series of software libraries and allowed us to create dozens of knowledge-based expert systems in medical and technical diagnosis, ecology and other fields. For example, new tools were developed for differential diagnosis of allergic and pseudoallergic reactions, for anticipation of myocardial infarction complications, and for evaluation of the accumulated radiation dose based on parameters of human blood.

Revealing and visualisation of hidden structure of complex systems

A system of methods is developed to reveal the hidden intelligible models in complex systems: complex datasets and complex reaction networks. First of all, this is revealing of hidden geometry.

New special tools have been proposed and elaborated, the grammars of elementary transformations which allow us to create the intelligible models of complex systems by the chains of simple steps and dominant systems that represent the complex networks by the simple networks, which dynamics can be studied analytically. Several biological and medical centres now use these methods and algorithms, for example, Institute Curie (France).

MicroRNAs kinetic signatures

MicroRNAs are key regulators of all important biological processes, including development, differentiation and cancer. Although remarkable progress has been made in deciphering the mechanisms used by miRNAs to regulate translation, many contradictory findings have been published that stimulate active debate in this field. I, with with co-authors, have developed computational tools for discriminating among different possible individual mechanisms of miRNA action based on translation kinetics data that can be experimentally measured (kinetic signatures). They have found sensitive parameters of the translation process for various conditions.

The crises anticipation

I invented a new method to measure the stress caused by environmental factors. In particular, this is a possibility to measure the health of the groups of healthy people. This method is based on a universal effect discovered by me in my study of human adaptation. This effect is supported by hundreds of experiments and observations and extended to systems of different nature. Now, this method is used for monitoring of Far North populations, for analysis of crises in national financial systems and in companies. It becomes a part of the modern approach to crises anticipation.

Research interests

  • Architecture of neurocomputers and training algorithms for neural networks
  • Dynamics of systems of physical, chemical and biological kinetics
  • Human adaptation to hard living conditions
  • Methods and technologies of collective thinking

Grants and awards

· CoI, An analysis of diabetes mortality and morbidity risk, IFoA (Institute and Faculty of Actuaries, UK), £60,000, 2021-2022.

· PI, Reliable and logically transparent artificial intelligence: technology, verification and application in socially significant and infectious diseases, Russian Federal Ministry of Science and Higher Education, agreement number 075-15-2020-808, RUB 300M (£3.1M), 2020-2022.

· CoI, Arch-I Scan: Image recognition (Automated recording and machine learning for collating Roman ceramic tablewares and investigating eating and drinking practices),   Arts and Humanities Research Council 2020-2023, £999,906.

· CoI, Predictive modelling of Major Trauma PROMs using machine learning, Insight Research Programme, The Health Foundation, 2020-2023 £444,333.

· The honorary title “Pioneer of Russian Neuroinformatics” for “extraordinary contribution into theory and applications of artificial neural networks.” The award was made at the XIX International Conference "Neuroinformatics-2017", Moscow, 2 October 2017.

· PI “Scalable Artificial Intelligence Networks for Data Analysis in Growing Dimensions”, Russian Federal Ministry of Science and Education Megagrant, (Project No. 14.Y26.31.0022 ), 2018-2020, RUB 90M (£1.1M)

· Lead academic in KTP 010522 between Visual Management Systems Ltd and University of Leicester (Visual Intelligence), 2016-2019 (approved in Aug 2016), £189K

· Lead academic in KTP 009890 between Apical Ltd and the University of Leicester (Visual Intelligence), 2015-2018 (approved in Dec 2014), £179K

· International Research Workshop: Hilbert’s Sixth Problem, LMS, 2016;

· International Research Workshop: Hilbert’s Sixth Problem, EPSRC, 2016;

· Lifetime Achievement Award in recognition of outstanding contributions to the research field of (bio)chemical kinetics, MaCKIE-2015, Mathematics in (bio)Chemical Kinetics and Engineering, Ghent, Belgium, 2015.

· Lattice Boltzmann methods for analysis of permeability in geophysics, Weatherford Contract Research,  07.2013  06.2015;

· Data Mining for Geological Information, NERC (Natural Environment Research Council), 07.2013  10.2013;

· Data Mining for Lymphoma Differential Diagnosis, European Regional Development Fund, 2012;

· Isaac Newton Institute for Mathematical Sciences Fellowship (Cambridge, UK), 2010

· Mathematical Modelling of Adaptation and Decision-Making in  Neural Systems, The Royal Society, UK: International Joint UK-Japan Project;

· Modularity, Abstraction and Robustness of Network Models in Molecular Biology, Alliance : Franco-British Research Partnership Programme;

· EPSRC and LMS grants for the International Workshop “Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena,” Leicester, UK August 24-26 2005;

· Prigogine Award and Medal (2003);

· Clay Scholar, (Clay Mathematics Institute, Cambridge, USA, 2000);

· Russian Federal Grant of the “Integration” program, 4 times (1998-2003);

· Grant of Russian Federal subprogram “New Information Processing Technology” (1999);

· Russian Federal Fellowship for outstanding scientists, twice (6 years);

· Grant of Russian Foundation of Basic Research (1996-1998);

· 1994-1996 American Mathematical Society Fellowship.

 

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