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Dr. Pavel V. Kolesnichenko

Short Bio

Pavel is an R&D lead with unique international experience, having worked in diverse, multicultural teams and technically complex environments. He has a proven track record of leading interdisciplinary projects across Germany, Sweden, and Australia, fostering international collaborations between cross-functional research groups both within Europe and across the Pacific.

Pavel earned his Bachelor's and Master's degrees at Saint Petersburg State University (Russia) and Lappeenranta University of Technology (Finland), and completed his Ph.D. at Swinburne University of Technology (Australia) as part of FLEET, the Centre for Future Low-Energy Electronics Technologies. There he studied atomically thin semiconductors via a novel self-built optical microscope and machine learning algorithms. He has also developed a novel approach to ultrafast vibrational spectroscopy. Pavel then worked as a postdoctoral researcher at Lund University (Sweden), within the NanoLund center, where he received grants from NanoLund and the Crafoord Foundation, supporting his research on AI-assisted diagnostics of optical pulses, their use as optical interrogators of 2D materials, and capturing material response through a photoemission electron microscope. These enabled the world's first direct, time-domain measurements of dark exciton dynamics in 2D semiconductors, on a sub-100-femtosecond scale. Later, at the Physical Chemistry Institute of Heidelberg University (Germany), amongst multiple other projects, he oversaw the construction of a first-of-its-kind, high-sensitivity 2D electronic spectrometer, accompanied by a published patent.

Alongside his research, Pavel has been deeply engaged in teaching and outreach, leading seminars and tutorials, supervising and mentoring Master's and Ph.D. students, and presenting at international conferences. One of the teaching highlights includes the development of an educational spectroscopy kit “Viking Spectrophotometer” and associated project based learning (PBL) approach to teaching molecular spectroscopy.

A physicist by training, he now applies a blend of AI, quantum physics, and execution-science management skills as well as SCRUM framework at the Digital Health Cluster of the Hasso-Plattner Institute, within the Virus-Host Interplay Lab.

Projects

  1. Prediction of kinase-based activity using Graph Neural Networks (GANs). The project is part of the broader context of kinase-activity-based drug development efforts, and takes advantage of openly available multi-omics knowledge-bases and the experimental data measured via PamGene peptide micro-array technology at Paul-Ehrlich Institute to predict, using GANs, kinase activities triggered by a specific modulation (e.g., HBV virus).
  2. Project “pyKinaXe”.    bioRxiv   GitHub   WebApp
  3. Prediction of significant kinases from PamGene fluorescence images using artificial neural networks. PamGene fluorescence data are highly structured fluorescence patterns, “fingerprints” reflecting kinase activities induced by a certain modulation (such as disease). As such, the data is ideal for training artificial neural networks to infer sets of kinases significantly deregulated by a disease, potentially accelerating  work in clinical and research settings where PamGene technology is used.
  4. Protein folding on an Ising machine. The project aims to solve an epitope-constrained problem of folding proteins via quantum computing, within the broader context of the development of multi-purpose vaccine platforms.

Teaching

Lectures on “Computer Science Meets Molecular Medicine”

Topics covered include: 

  1. Multi-omics: hardware and software aspects;
  2. Special omics: mass-spectrometry;
  3. Tumor imaging: radiology, whole slide imaging;
  4. Virus morphology: 3D reconstruction;
  5. CRISPR;
  6. Quantum computing in medicine.

Master's Projects

  1. Building a Machine Learning Pipeline to Predict Kinase Activation Scores in Cells based on Images.
  2. Quantum simulation of protein folding delivering predefined epitopes.

Selected Publications

  1. pyKinaXe: a fast and robust turnkey kinase activity profiler with high resolution. Wuttke, D., Hildt, E., Kolesnichenko, P.V. bioRxiv (2026). https://doi.org/10.64898/2026.05.12.724658
  2. Enhancing Triplet Excitons Lifetime Through Controlled Intermolecular Interactions. Richter, M., et al. Advanced Optical Materials (2026), 14 (7), e02979. https://doi.org/10.1002/adom.202502979
  3. Dark Excitons and Hot Electrons Modulate Exciton-Photon Strong Coupling in Metal–Organic Optical Microcavities. Kolesnichenko, P.V., et al. ACS Photonics (2025), 12 (7), 3344-3354. https://doi.org/10.1021/acsphotonics.4c01972
  4. Sub-100-fs formation of dark excitons in monolayer WS2. Kolesnichenko, P.V., et al. Nano Letters (2024), 24 (46), 14663–14670. https://doi.org/10.1021/acs.nanolett.4c03807
  5. Two-Dimensional Coherent Electronic Spectrometer with Switchable Multi-Color Configurations. Kefer, O., Kolesnichenko, P.V., Buckup, T. AIP Review of Scientific Instruments (2024), 95, 023003. https://doi.org/10.1063/5.0186915
  6. Viking Spectrophotometer: A Home-Built, Simple, and Cost-Efficient Absorption and Fluorescence Spectrophotometer for Education in Chemistry. Kolesnichenko, P.V., et al.  ACS Journal of Chemical Education (2023), 100 (3), 1128-1137. https://doi.org/10.1021/acs.jchemed.2c00679
  7. Neural-network-powered pulse reconstruction from one-dimensional interferometric correlation traces. Kolesnichenko, P.V., Zigmantas, D. Optics Express (2023), 31 (7), 11806-11819. https://doi.org/10.1364/OE.479638
  8. Multidimensional Analysis of Excitonic Spectra of Monolayers of Tungsten Disulphide: Towards Computer Aided Identification of Structural and Environmental Perturbations of 2D Materials. Kolesnichenko, P.V., et al. Machine Learning: Science and Technology (2021), 2, 025021. https://doi.org/10.1088/2632-2153/abd87c
  9. Disentangling the effects of doping, strain and disorder in monolayer WS2 by optical spectroscopy. Kolesnichenko, P.V., et al. 2D Materials (2020), 7, 025008. https://doi.org/10.1088/2053-1583/ab626a
  10. Background-free time-resolved coherent Raman spectroscopy (CSRS and CARS): Heterodyne detection of low-energy vibrations and identification of excited-state contributions. Kolesnichenko, P.V., Tollerud, J., Davis, J. APL Photonics (2019), 4, 056102 (Editor's choice). https://doi.org/10.1063/1.5090585

Media Appearance

  1. Dark excitons may be suitable for charge transport in future solar cell technologies. NanoLund (2025). Link: NanoLund
  2. Catching up with FLEET alum Pavel Kolesnichenko. FLEET (2022). Link: FLEET
  3. 'Target Identified': Teaching a Machine How to Identify Imperfections in 2D Materials. Materials Australia (2021), 54 (2), 47. Link: Materials Australia Magazine
  4. 'Target Identified': Teaching a Machine How to Identify Imperfections in 2D Materials. Phys.Org (2021). Link: Phys.Org