Advisors

István Hodász, PharmD

Istvan Hodasz is a seasoned pharmaceutical expert with over 30 years of industry experience. As the former CEO of Egis Pharmaceuticals, he led the 4,500-employee, a vertically integrated company, for 13 years, overseeing its entire value chain in 18 countries while expanding its global reach to 40 more markets through partnerships. He enhanced Egis’ R&D capacities and capabilities, modernized production facilities, initiated digital transformation, adopted agile management practices, and fostered innovation with start-up-like initiatives. Istvan began his career with prominent roles at Glaxo and Sanofi, later serving as President and Vice-President of the Association of Hungarian Pharmaceutical Manufacturers from 2010 to 2022. A qualified pharmacist with an MBA, Istvan combines deep expertise in pharmaceuticals with a progressive approach to leadership and innovation.

György M. Keserű, PhD

György M. Keserű obtained his Ph.D. in Budapest, Hungary, and joined Sanofi, heading a chemistry research lab. In 1999, he moved to Gedeon Richter, where he was appointed as the Head of Discovery Chemistry in 2007. He contributed to the discovery of the antipsychotic Vraylar® (cariprazine), which has been approved and marketed since 2016 in the US and EU. Since 2015, he has been heading the Medicinal Chemistry Research Group at the Research Centre for Natural Sciences, and more recently, he has been appointed as the Head of the National Drug Discovery and Development Laboratory. György published over 340 papers and more than 10 books and book chapters. He was awarded the prestigious Overton and Meyer Award by the European Federation of Medicinal Chemistry. He was elected as a Fellow of the Royal Society of Chemistry, a Corresponding member of the Hungarian Academy of Sciences, and a Member of Academia Europaea.

Bálint Antal, Phd

Balint Antal holds a PhD in Computer Science and spent most of his career researching at top-tier academic (Harvard, MIT, University of Cambridge) and industry institutes (Flagship Pioneering). Dr. Antal spent his career solving life science problems utilizing Machine Learning. He led an IARPA-funded biosurveillance project to detect signs of genetic engineering in pathogens' DNA sequences. Dr. Antal also made a significant contribution to the field of immunology by creating novel antigens using Artificial Intelligence.

Radouane Oudrhiri, PhD

Radouane Oudrhiri is a seasoned Tech-Entrepreneur combining venture capital, commercial, strategic and hands-on deep tech. He is convenor of the ISO standardisation workgroups on: Six Sigma, Design of Experiments and Big Data Analytics. Also a liaison officer with the ISO/JTC1/SC42 committee on Artificial Intelligence and a member of the Royal Statistical Society. Radouane is a lecturer on system design & engineering and innovation at: Kingston University, Telecom-Paris, ESSEC, HEC, La Sorbonne and UM6P/ABS. Radouane has a PhD from ESSEC Business School, a Doctorate from Universite d'Aix-Marseille III, and a Master's from both ESSEC and ENSAE Paris. He is passionate about mentoring startups in medtech and providing guidance and support to the next generation of innovators.

Prof. Alberto Paccanaro

Alberto Paccanaro is a full professor at the School of Applied Mathematics (EMAp) at FGV in Rio de Janeiro since 2020. He earned his PhD in Computer Science in 2002 from the University of Toronto, specializing in Machine Learning under the guidance of Geoffrey Hinton. He pursued postdoctoral studies in Computational Biology from 2002 to 2006, first at Queen Mary University of London in the laboratory of Mansoor Saqi, and then at Yale University in the laboratory of Mark Gerstein. In 2006, he became a PI at Royal Holloway University of London and established his own laboratory. He was also visiting professor at Cornell, Yale, and the University of Venice.  His machine learning algorithms have been published in reputable journals such as Nature, Nature Methods, Nature Communications, Cell, and PNAS and he is actively engaged in international collaborations in the field of Machine Learning applied to Biology and Medicine.