Pietro Sormanni is a University Research Fellow supported by the Royal Society and leads a research group at the University of Cambridge that sits at the interface between computation and in vitro experiments. His research is primarily focused on the development of innovative technologies for computational antibody design, aimed at transforming the ways antibodies are currently discovered and optimised. Through numerous collaborations and industry partnerships, Pietro’s work has demonstrated the potential for computational approaches to complement established procedures and streamline antibody development, offering novel, time- and cost-effective alternatives. Prior to his current position, Pietro was a Borysiewicz Biomedical Sciences Fellow at the University of Cambridge, and holds a PhD in Chemistry and an MSc in Theoretical Physics.
Tzvika Hartman, Senior Vice President Computation at Biolojic Design since 2021, has over 30 years of experience in computer science research and software engineering. He started his career in an Elite intelligence unit in the IDF as a mathematics researcher. He received degrees in mathematics and computer science from Bar-Ilan University, then conducted post-graduate work in the Weizmann Institute. During his studies, which focused on computational complexity and on algorithms for bioinformatics, mainly DNA sequencing and genome rearrangements, he also worked as a part-time software engineer in Orbotech. While at Google Tel-Aviv as a software engineer and researcher for 13 years, Dr. Hartman worked on projects and products such as search engine infrastructure and quality, Google Trends, and routing optimization of Google’s internal data center network. He then initiated, founded, and managed Google Tel-Aviv’s Health group for 5 years, leading various projects such as de-identification of healthcare data, extracting information from medical notes, and predictions based on EHR of ICU patients.
Victor Greiff is an Associate Professor for Systems Immunology at the University of Oslo since 2018. His group develops machine learning, computational and experimental tools for immune-repertoire-based in silico immunodiagnostics and immunotherapeutics discovery and design. His is the president of the Norwegian Society for Immunology and the chair of the AIRR Community. Dr. Greiff received his PhD in Systems Immunology from Humboldt University (Germany, 2012) and performed his postdoctoral training at ETH Zürich (Switzerland, 2013-17).
Sandeep Kumar is a Distinguished Research Fellow (DRF) at the department of Biotherapeutics Discovery in Boehringer-Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA. Sandeep Kumar holds a Ph.D. in Computational Biophysics from Indian Institute of Science and has over 20 years of experience researching protein structure – function relationships using computational means. Dr. Kumar has so far contributed to ~100 research articles, reviews, book chapters, and edited a book entitled “Developability of Biotherapeutics: Computational Approaches”. In 2014, Sandeep Kumar received the Ebert Prize from the American Pharmacist Association (APhA) for the discovery of aggregation – immunogenicity coupling in biotherapeutics. Dr. Kumar has also contributed towards development of several antibody-based biotherapeutics and a vaccine currently on the market. After joining Boehringer-Ingelheim in 2018, Dr. Kumar has been contributing towards discovery of several monoclonal antibodies as well as multi-specific modalities that are currently in development. Using the insights gained from these experiences, Dr. Kumar has been advocating for Biopharmaceutical Informatics, a strategic vision dedicated to synergistic use of computation and experimentation towards a cost effective and more efficient discovery and development of biotherapeutics.
Ben Holland is a co-founder and Chief Technology Officer at Antiverse. Ben has an MEng in Engineering Science from the University of Oxford and experience in mathematical modelling, especially neural networks, and uses the generated data to train the machine learning system.
Jiye Shi is an Associate Vice President and Global Head of Computational Design and Automation Platforms at Eli Lilly and Company. Prior to that, he was a NewMedicines Fellow and Global Head of CADD at UCB Pharma. During the past decades, Jiye and his teams utilized physics, statistics and machine learning/AI based methods to accelerate the discovery of small molecule and antibody therapeutics. He is a co-inventor on 11 patents which led to 2 approved drugs on the market. Jiye received his PhD in Computational Structural Biology with Prof. Sir Tom Blundell at the University of Cambridge and completed his executive MBA training at the University of Rochester. He is an external advisor to the Department of Statistics, University of Oxford. He is passionate about graduate training, and helped establish and manage doctoral training centers in the UK. He holds visiting academic appointments in several countries and has co-authored over 200 scientific publications.
Dr. Tom Diethe is the Head of Machine Learning in the Centre for AI at AstraZeneca. Tom is the ML lead for the AI for Biologics initiative within AstraZeneca’s R&D organisation. Tom is a Machine Learning leader with 20+ years of experience in a mix of industry in academic roles, including Amazon, Microsoft Research, the British Medical Journal, QinetiQ, UCL and the University of Bristol. He has specialized in healthcare applications of ML/AI, including genomics, proteomics, digital health, and neuroscience. He is co-author of the online first book “Model-Based Machine Learning”. Tom is also an Honorary Research Fellow at the University of Bristol, a fellow of the Royal Statistical Society and a member of the IEEE Signal Processing Society.
The panel moderator (and host) is Dr. Konrad Krawczyk.
Andrew Buchanan is a Principal Scientist within Biologics Engineering, early Oncology, AstraZeneca. He is an experienced biologics engineer, contributing to more than 20 antibody-based drugs entering first-time in human clinical studies of which three are marketed products. He is a versatile critical thinker, leading innovative global pipeline projects and platform technologies. With 22 years of experience (CaT, MedImmune and AZ), he has lead teams responsible for wet-lab automation, high throughput data generation, computational structural biology, developability, antibody platform technology and pipeline delivery to candidate drug status. His current focus is on computational design and AI/ML for biologics, quality data curation and innovation for tissue targeted therapy. He was elected Fellow of the Royal Society of Chemistry in 2020 and, with colleagues and postdocs, contributed to over 35 original manuscripts and patents.
Konrad Krawczyk is the founder of NaturalAntibody and holds the post of Assistant Professor in computational precision medicine at SDU in Denmark. He obtained his doctorate from the University of Oxford under the supervision of Prof Charlotte Deane where he was involved in the development of the SAbDab/SAbPred platform. He has more than a decade of experience in developing advanced bioinformatics solutions addressing pertinent issues in therapeutic antibody development such as antibody-antigen binding and developability prediction. Tools developed by his team are used by leading pharmaceutical companies and academic institutions to advance the engineering of therapeutic antibodies.
Janice M. Reichert, Ph.D., is Chief Operating Officer of The Antibody Society, a non-profit trade association that serves individuals and organizations involved in antibody research and development. She is also the founder and Editor-in-Chief of mAbs, a peer-reviewed, PubMed-indexed biomedical journal that focuses on topics relevant to antibody R&D. She has published extensively on development trends for antibody therapeutics, and she has presented her research results as an invited speaker at conferences held worldwide. Dr. Reichert received her PhD in Chemistry from the University of Pennsylvania and did her post-doctoral training at Harvard Medical School.