Member of the Board of Directors at the International Biomedical Research Alliance - https://biomedalliance.org/

◦ Project leadership and management both in a variety of internal projects, as well as in collaborative work with labs at Stanford and Karolinska Institute to research autoimmune diseases.◦ Development of an in-house ML pipeline for benchmarked parallel execution of various ML methodologies, involving Python + scikit-learn, R, Keras/Tensorflow/PyTorch, and Stan.◦ Integration of RNA-seq, microarray, FACS, protein expression, and other high-dimensional patient-level data du jour.◦ Application of state of the art machine learning / deep learning for discovery of disease-related and therapy-related targets, biomarkers, and fingerprints.◦ Pathway/network expansion of the results of high-dimensional data analysis for exploration of their biological context.◦ Preparation of an interactive data visualization framework and constituent apps, including the backend + database, as well as the frontend; enabling scientists in the department to make data driven decisions using this platform.◦ Analysis and integration of EMR/EHR data, discovery of co-morbidities.

Providing mentorship to undergraduate students at the University of Oxford.

DrugRev was a platform for analysis of adverse drug reaction reports and prediction of drug side effects. The underlying research was developed during my postdoc, and later transformed into a SaaS product.

◦ Application of machine learning methods to refine the drug discovery/hit expansion pipelines, with focus on reinforcement learning; optimization of the applicability domain of activity models.◦ Deep analysis of postmarketing data to define adverse phenotypes.◦ Studying drug - adverse drug reaction - target networks to determine novel off-target - adverse drug reaction associations.◦ Integration of data (including pharmacokinetic data) to model adverse drug reaction thresholds for off-targets.◦ Development of "open-source" high-throughput screening fingerprints (HTSFPs).◦ Studying target-target similarities based on the similarity of their ligands, associated diseases, and metabolites.

◦ Development and technical design of the DocDx product. Implementation of database and data analysis, and the back-end architecture.

◦ Derivation of models for enrichment of molecular diversity using bioactivity data.◦ Determining dynamics (including interdomain flexibility) of domain pairs from the middle section of the immune protein factor H, as well as their mutant variants, using NMR relaxation datasets.◦ Finalizing assignments of the NMR spectra of the three N-terminus domain portion of the CR1 protein; calculating its structure.◦ Carrying out molecular dynamics simulations of small therapeutic peptides (compstatin analogues) and theoretical calculations of chemical shifts of resulting conformational ensemble.◦ Selecting ensembles that best reproduce chemical shifts in NMR experiments collected for these molecules.

◦ PhD in Computational Biophysics.◦ Thesis title: Structure and dynamics of proteins that inhibit complement activation.◦ Advisors: Dr. Nico Tjandra (National Heart, Lung and Blood Institute, NIH) and Prof. Paul Barlow (School of Chemistry, University of Edinburgh).Research:◦ De novo design of Brownian interdomain dynamics simulations. Statistical learning and determination of characteristics of motion in multi-domain proteins. Simulation of dynamic trajectories of multi-domain proteins with domains coupled under repulsive potentials and prediction of measurable properties of their interdomain motion.◦ Integration of various data types from a number of innate immune system proteins, such as factor H, including residual dipolar couplings, NOEs, relaxation data, SAXS measurements; ◦ Development of ensemble strategies for calculating the structure and dynamic behavior of macromolecules◦ Using the integrated data and ensemble-optimized simulated annealing methods to parameterize the interdomain dynamics of macromolecules (e.g. factor H, CR1).◦ Derivation of structures of Compstatin analogues from NMR data using data-guided simulated annealing methods. Modeling dynamics of these peptide molecules using ensemble methods.

The aim of this work was to expand on the results of my BSc project and investigate drug delivery using polystyrene microspheres equipped with cleavable disulfide linkers. This work was conducted under the supervision of Dr. Rosario Sanchez-Martin with funding provided by the Royal Society of Chemistry.

Laboratory of Prof. Mark Bradley (under the supervision of Dr. Rosario Sanchez-Martin), School of Chemistry, University of Edinburgh:◦ Development of drug delivery system consisting of functionalized polystyrene microspheres attached to drug molecules via physiologically cleavable linkers.◦ Use of synthesized system for drug delivery to fixed cell lines (HeLa and B16F10).

Laboratory of Prof. Karen Kavanagh, Department of Physics:◦ Identification of pitfalls of transmission electron microscope (TEM) sample preparation using ion millers.◦ Development of “soft” methods for preparation of semi-conductor samples for TEM.◦ Technical maintenance of transmission and scanning electron microscopes.

Laboratory of Prof. Marian Turek, Department of Chemistry, Silesian University of Technology:◦ Research on nanofiltration and use of reverse electrodialysis to generate electrical energy.

Triochem, Ltd. - Bytom, Poland (Summers 2002, 2003, 2004, 2005):◦ Design, development, and quality control of polymer products used in textile industry, mining, and civil engineering.◦ Optimization of cost-effectiveness and properties of polymer products.