Speaker Biographies

Green is joining the Institute for Protein Design and the Department of Biochemistry at the University of Washington as an assistant professor in January 2027. Green received her PhD at Stanford University under the mentorship of Prof. Carolyn Bertozzi, where she co-developed lysosome targeting chimeras (LYTACs), a first-in-class platform for degrading extracellular and membrane proteins. She then completed her postdoctoral training in Prof. David Baker's laboratory, where she co-developed de novo endocytosis-triggering proteins that expanded the scope of targeted extracellular degradation and demonstrated in vivo efficacy in tumor models. She also led the development of computational approaches for rationally encoding pH sensitivity into designed proteins, producing proteins that act selectively in acidic vesicles and the tumor microenvironment. Green is the recipient of the 2026 Burroughs Wellcome Fund's Career Awards at the Scientific Interface (CASI) award, which supports her independent research program.

Surge is a co-founder and the CEO at Nabla Bio, an antibody design company. Surge received his PhD from George Church’s lab. There, he and his colleagues pioneered the development of protein language modeling, a technology that has been central to state-of-the-art protein structure prediction and machine learning-guided protein design. Outside of Nabla, Surge enjoys hanging out with his wife and Nabla co-founder, Frances, and their two dogs, Archie and Riley.

Lasse Møller Blaabjerg works as a Data Scientist in the Discovery Data Science department in Genmab, Copenhagen. At Genmab, Lasse’s work focuses on the use of machine learning for improved antibody engineering. Lasse holds a PhD from the University of Copenhagen with a focus on machine learning methods for understanding protein stability and function.

Richard Bonneau is a computational biologist and data scientist who now leads Computational Drug Discovery in the Computational Sciences division at Genentech. This effort centers on pioneering new methods for combining machine learning and molecular modeling to power drug discovery. His past research spans multiple levels of biological structure and includes: learning biological networks, designing protein, biomimetic chemical biology and exploring social networks. He received his PhD at the University of Washington, Seattle, studying under Dr. David Baker, where he pioneered new methods to predict biomolecular structures. His postdoctoral training was completed at the Institute for Systems Biology, working with Leroy Hood. Dr. Bonneau has been a professor at NYU jointly appointed in the Computer Science and Biology departments. He was a founding member or co-founder of the RosettaCommons, The Center for Data Science at NYU, the Simons Center for Data Analysis (which evolved into the Flatiron institute) and recently co-founded Prescient Design (acquired by Genentech). Dr. Bonneau is excited to be part of Genentech’s effort to revolutionize molecular design and drug discovery with computational advances.

Dr. Phil Bradley is a computational biologist who studies the 3-D structures of proteins, the nano-sized molecular machines that power nearly all of life’s processes. He develops software to visualize and predict how one kind of protein will interact with another protein or with molecules such as RNA and DNA, which carry genetic information. He is a leading figure in the new field of de novo protein design, in which scientists envision and build proteins unlike anything found in nature. He and colleagues at Fred Hutch and the Institute for Protein Design at the University of Washington have designed proteins that close in on themselves, forming rings that resemble tiny donuts. Their elegant, symmetrical structure may serve a host of medical applications. Dr. Bradley and colleague Dr. Barry Stoddard are working with Hutch immunotherapy researcher Dr. Stanley Riddell and vaccine expert Dr. Larry Corey to explore the use of these donut proteins as molecular backbones for therapeutics. Their experiments will test whether the modular structure of donut proteins can support and deliver multiple copies of the active biochemicals that make drugs or vaccines work, improving their performance. He continues research he began as a postdoctoral fellow at UW developing Rosetta, a world-leading software tool for designing protein structures and predicting protein interactions. Dr. Bradley is currently working on computer programs to predict how selectively and precisely proteins will bind with DNA and with protein snippets called peptides. These molecular interactions are essential for life and occur continually inside every one of the trillions of cells in our bodies.

Vanessa has spent her academic and business career exclusively in life sciences & healthcare. She has worked at large pharma/biotech/tech companies such as Sanofi, Abbott, and NVIDIA and start-ups such as Ingenuity Systems (acquired by Qiagen) and Fabric Genomics (acquired by GeneDx). Vanessa is currently Senior Director at Eli Lilly focused on the Lilly TuneLab AI/ML Platform for accelerating drug discovery. Vanessa began her career in academics and clinical research in molecular and cell biology and public health at UC Berkeley and UCSF and then transitioned to roles building new products, strategic alliances, commercial ecosystem growth in AI and life sciences.

Andrew Buchanan is SVP and Head of Discovery at a stealth-mode biotechnology company. He has extensive expertise in large molecule drug discovery and development, spanning target selection through first-in-human (FiH) studies. Andrew has led pipeline projects and multifunctional teams that have delivered over 20 FiH drug candidates in oncology, inflammation, and cardiovascular therapy areas, resulting in three marketed medicines to date. A key highlight of his career has been fostering interdisciplinary collaborations with academic and industry partners in AI/ML, PKPD, and translational biology. These efforts have led to new partnerships and over 50 original publications and patents. As a science leader, Andrew is passionate about mentoring early-career scientists and advancing the development of tomorrow’s medicines.

Leonardo Castorina is a Senior ML Engineer at Apheris, where he works on AI for antibodies, with a focus on antibody/antigen co-folding. He completed his PhD in Machine Learning for Protein Design at the University of Edinburgh, where he developed deep learning methods to make protein design faster and more interpretable, including TIMED-Design, for protein sequence design, and Tessera, a fragment-based representation that accelerates structural search and guides functional backbone generation. At AstraZeneca, he worked on AI for antibody design, multispecifics pairing prediction, and fast surface comparison and analysis. Leo has also worked extensively on machine learning for immune recognition, studying how HLA alleles shape T-cell receptor repertoires at Microsoft Research and developing models for TCR-pMHC binding prediction at NEC Laboratories Europe.

I am a passionate drug developer, with wide range experience in protein chemistry, structural biology, protein engineering, computational biology & biophysics. I lead team combining data science and experimentation to accelerate biologics discovery and development for unmet medical needs.

Pranam Chatterjee is an Assistant Professor of Bioengineering and Computer and Information Science and the Africk-Lesley Distinguished Scholar of Innovation at the University of Pennsylvania. Having earned his SB, SM, and PhD from MIT, Professor Chatterjee has received the MIRA Award, Hartwell Individual Biomedical Research Award, and multiple NIH and foundation grants for his work. He has also co-founded Gameto, Inc., UbiquiTx, Inc., AtomBioworks, Inc., and Recognition Bio, Inc., which translates his research into fertility solutions, RNA medicines, and cancer therapeutics, respectively.

Bowen Dai, PhD, is an Advisor in Computational Biology within BioTDR at Eli Lilly, where he develops foundation models and pipelines spanning de novo design and multi-objective optimization for antibody design and lead optimization. Before joining Lilly, he was a Senior Scientist in Machine Learning at Bristol Myers Squibb, applying protein language models to antibody engineering. Bowen holds a PhD in Computer Science from Dartmouth College, with research in geometric deep learning for computational biology. His work bridges cutting-edge machine learning research with the practical demands of biologic drug discovery.

Dr. Ittai Dayan is the CEO of Rhino Federated Computing, a company transforming the way data is used for creating and deploying Artificial Intelligence solutions in regulated industries. A pioneer of bringing privacy preservation technologies into regulated industries, Dr. Dayan previously served as the Executive Director at Mass General Brigham’s Center for Data Science and held managerial roles at Boston Consulting Group. Dr. Dayan graduated from the Johns Hopkins Bloomberg School of Public Health and earned his MD and his Bachelor of Science degrees from The Hebrew University of Jerusalem. He also serves on the Editorial Board of Nature Digital Medicine.

Kris is the Director of Molecular AI in AI and Digital Innovation at Novo Nordisk. She is leading a global area focused on the development and implementation of AI/ML methods for therapeutic design and optimization across modalities. Molecular AI is actively developing Novo Nordisk–tailored foundation models to enhance molecular representation, predict biophysical properties linked to developability and potency, and develop advanced generative AI methods in molecular design and optimization. Before joining Novo Nordisk, Kris trained as an organic chemist and earned her Ph.D. in Organic Chemistry from Northwestern University. She pivoted her research focus to computational molecular design during her postdoctoral fellowship with David Baker, where she specialized in designing peptidomimetic GPCR agonists. During this time, she developed innovative computational methods for designing macrocyclic peptides that incorporate non-canonical amino acids. Kris is not only passionate about developing innovative AI methods but is even more driven by the pursuit of discovering impactful therapeutics.

Following a PhD in theoretical physics at CERN, I worked as a scientist at MIT and the University of Oxford pursuing research at the interface between machine learning and high energy physics, before joining a language understanding team at Meta AI. I then worked at Exscientia, where I led a team of AI researchers on a range of projects across small molecules and biologics, before joining Genentech and Prescient Design in 2024.

Hunter is an interdisciplinarian with a decade of experience developing machine learning tools to solve biomedical research problems. Moving from biophysics into computer vision during his PhD research at The Scripps Research Institute and Harvard Medical School, he then founded and led the Image and Data Analysis Core at Harvard Medical School. He was an early member of PathAI where he helped build the machine learning tech, platform, and team and led ML research. At BigHat he has developed novel predictive and generative methods for antibody engineering before recently taking on leadership of ML.

M. Frank Erasmus is the head of bioinformatics at Specifica, Inc. where he specializes in the use of next-generation sequencing technologies and software development to aid in the design of and selection from therapeutic antibody libraries. Formerly, Frank was awarded a national fellowship from the National Cancer Institute for his translational research associated with B cell precursor acute lymphoblastic leukemia conducted at the Spatiotemporal Modeling Center and Los Alamos National Labs. He brings over 13 years of experience in both biotechnology and academic settings in the development and characterization of therapeutic antibodies using theoretical modeling, bioinformatics, and experimental approaches.

Monica Fernández-Quintero studied Theoretical Chemistry at the University of Innsbruck. During her PhD she demonstrated how molecular dynamics simulations can improve the structure prediction of proteins, i.e., antibodies and ion channels. Already since her Bachelor thesis 2015 Monica is working on the dynamics of antibodies and graduated in 2020. She already authored several papers, gave talks, and presented posters on international conferences featuring various aspects of antibody and T-cell receptor dynamics. Since 2023 she joined the lab of Prof. Andrew Ward, combining structural biology with physics-based machine learning approaches to characterise protein-protein binding interfaces, facilitating the design of antibodies and de-novo proteins.

Professor Franca Fraternali is Chair of Integrative Computational Biology and Head of the Department of Structural and Molecular Biology at University College London (UCL). She is an internationally recognized leader in computational biology, structural bioinformatics, and molecular simulations, whose research integrates protein structure analysis, protein interaction networks, machine learning, and systems immunology to understand biomolecular function in health and disease. Her work has pioneered computational approaches for analyzing protein dynamics, allosteric communication, antibody repertoires, and immune-state transitions, leading to widely used software tools and methodological advances in computational immunology. Her current research combines molecular simulations, single-cell genomics, immune repertoire sequencing, and foundation models to advance next-generation antibody engineering and predictive biomolecular modeling.

Upon finishing his studies in Pharmaceutical Sciences, Dr. Furtmann pursued his interdisciplinary Ph.D. in Computational Life Sciences and Pharmaceutical Chemistry at the University of Bonn's B-IT (Bonn-Aachen International Center for Information Technology), a leading center for AI and computational research. Since joining Sanofi in 2016, he has held various roles focused on computational and AI-based biologics design. Currently, Dr. Furtmann heads the global Biologics AI & Design teams within Sanofi's Computational and AI Strategy organization, spanning next-generation foundational models, virtual screening and property prediction, and de novo design for next-generation biotherapeutic modalities.

I am passionate about the development and application of novel technologies in the biotechnology sector. As the Vice President of Technology at Alchemab Therapeutics I am realising this passion through the development of a novel platform which harnesses the natural power of the immune system to discover therapeutics. Previously I was the Bioinformatics lead at Kymab Ltd, where my group developed the IntelliSelect® antibody discovery software platform. I am one of the pioneers of immune receptor repertoire sequencing, serving as a member of the AIRR community, and authoring numerous publications in the area. I have worked at The Oxford Vaccine Group, GSK Vaccines, and University of Zürich, applying immune repertoire sequencing to further understand basic immunology, as well as in support of vaccine development, diagnostics, and antibody drug discovery. I hold a BA in Biological Sciences (First Class) and a PhD in Medical Sciences, both from the University of Oxford.

Dr. Xiaojing Gao is an Assistant Professor of Chemical Engineering from Stanford University. He received a BS in Biology from Peking University and a PhD in Biology from Stanford University. He received his postdoctoral training from Biology and Biological Engineering at Caltech. Some of his recent recognitions include ACS Synthetic Biology Early Career Innovator Award, BioInnovation Institute & Science Prize for Innovation, and NIH’s New Innovator Award (DP2).

Dr. Victor Greiff is Associate Professor for Computational and Systems Immunology at the University of Oslo. His work focuses specifically on the development of machine learning, computational and experimental tools for the analysis, prediction and engineering of adaptive immune receptor repertoires.

Quanquan Gu is the Founder and CEO of Geodesic Intelligence and an Associate Professor of Computer Science at UCLA. His research spans artificial intelligence, including large language models, reinforcement learning, deep learning, and optimization, with a particular focus on foundation models for protein therapeutics, AI-driven drug discovery, and AI for science. He received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign in 2014. He is the recipient of the Sloan Research Fellowship, the NSF CAREER Award, the Simons-Berkeley Research Fellowship, and several industry research awards.

Athena leads AI/ML initiatives for Biotherapeutics and Genetic Medicine at AbbVie, driven by a personal mission to bring better medicines to patients, faster. Her team have portfolio-level impact by driving AI/ML integration into discovery and development workflows. Prior to joining AbbVie, Athena directed program-embedded, cross-functional drug discovery teams at Recursion Pharmaceuticals, scaling the impact of multi-modal data in accelerating drug discovery timelines. Athena’s curiosity and drive to innovate through cross-pollination have often led her to chart new paths, like leading ML model development teams in finance, where model risk is rigorously managed under federal regulations. Athena holds a PhD in Human Genetics and Genomics from the University of Miami, Miller School of Medicine, and completed her postdoctoral training at Yale.

Simon is the Founder and CEO of Latent Labs. Formerly, he was Senior Research Scientist at Google DeepMind in London, where he worked on AlphaFold2 and problems in structural biology. Before joining DeepMind, he has obtained a PhD at the German Cancer Research Center in Heidelberg, Germany, where he developed generative models for biomedical image segmentation. Broadly, Simon is interested in developing generative deep neural networks for natural science and real-world applications. His research is often concerned with uncertainty quantification and the generation of multi-modal outputs in problems that allow multiple solutions.

Dr. Jake Kraft is Co-Founder and CEO of Lila Bio, a Seattle-based biotech using AI to design radiopharmaceuticals for solid tumors. He earned his PhD in Pharmaceutical Sciences from the University of Washington and completed postdoctoral training in Dr. Neil King’s lab at the Institute for Protein Design in Seattle.

Yuan Lin is the Biologics Business Partner and Solution Lead in Pfizer Digital. He manages, develops, and supports Pfizer Biologics R&D informatics platforms. He has 20+ years of experience in bioinformatics, business system analysis, and software development. Before joining Pfizer in 2017, Yuan was a Senior Principle Business Analyst and Lead Software Engineer at Novartis, focusing on the in-house E2E biologics screening platform and leading the support of a global biologics registration platform. He led the implementation of an industry-first clinical NGS genetic testing data workflow system at GeneDX and built a comparative Genome Browser and Sequence Analysis platform that enabled the sequencing and analyzing of the first individual human genome at J Craig Venter Institute.

Ryan Mahling completed his PhD at the University of Iowa in the Department of Biochemistry and is currently a postdoctoral fellow at Columbia University. Ryan is a structural biochemist by training and his work is focused on understanding the mechanisms that underlie ion channel dysfunction in various diseases. He aims to combine these insights with computational protein design to engineer novel synthetic actuators of ion channel function.

Alex Martinko is a scientist, entrepreneur, and leader in antibody engineering and protein sciences, with over a decade of experience developing therapeutic antibody technologies. As the Senior Director of Antibody Engineering and Design at Cartography Biosciences, Alex leads a multidisciplinary team specializing in antibody discovery, computational antibody engineering, and protein science to develop highly specific cancer therapies using bi- and multi-specific antibodies. Alex earned his PhD in Chemistry & Chemical Biology from UCSF under the mentorship of Dr. Jim Wells. His research included the discovery of novel antibody targets for RAS-driven cancers, which led to a licensing agreement with a major biopharmaceutical company. Additionally, he developed an innovative antibody technology for small molecule regulation of antibody function, which became the foundation for Soteria Biotherapeutics, a venture-backed startup co-founded by Alex to further advance this platform. Prior to joining Cartography, Alex was Senior Director of Antibody Engineering at Soteria Biotherapeutics, where he led a team of scientists in advancing the T-LITEâ„¢ platform. This bispecific T-cell engager technology enables small-molecule-controlled activation, offering a safer and more effective dosing strategy for T-cell engagers. With expertise spanning protein engineering, chemical biology, and cancer immunotherapy, Alex has authored over 20 peer-reviewed publications and patents. He remains dedicated to translating innovative science into transformative therapies that improve patient outcomes.

David is an expert in protein engineering and computational design, with extensive experience applying AI-driven modeling tools in an industry setting. He serves as Vice President of the Rosetta Commons Foundation and Industry Chair on the Rosetta Commons board, helping bridge academic advancements with industry applications. As Managing Member of Rosetta Design Group, he collaborates with companies of all sizes to tackle complex challenges in biologics design. David's deep expertise in leveraging cutting-edge tools like Rosetta, AlphaFold, and diffusion-based models for protein design make him an invaluable guide for participants looking to apply AI-driven biologics design in real-world settings.

Sergey Ovchinnikov received his B.S. in Micro/Molecular Biology from Portland State University and a Ph.D in Molecular and Cellular Biology from the University of Washington in Seattle. In the lab of Dr. David Baker, Sergey worked on algorithms for protein structure determination using evolutionary information. Currently, Sergey is a John Harvard Distinguished Science Fellow at Harvard University. The Ovchinnikov Lab is interesting in developing a unified statistical model of protein evolution to better understand phylogenetics, protein folding, origins of life/multicellularity, and to mine metagenomic “dark matter” sequences to discover new protein families, functions, and protein-protein interactions.

Robin Röhm is co-founder and CEO of Apheris, enabling governed, private, and secure access to life science data for ML. Robin is passionate about helping organizations safeguard their data assets and IP while ensuring it can be leveraged for AI. Having experienced first-hand the challenges of distributed data and regulatory constraints, he understands the need to overcome these to unleash the true value of life science data that’s currently sitting unused in organizations today. It’s this data that will ultimately help us transform drug discovery and development. Prior to Apheris, Robin founded a start-up in the Genomics space, worked in the financial industry, and has degrees in medicine, philosophy, and mathematics.

Jorge is the Head of the Biologics de novo Design Group at Sanofi. His work focuses on the design of next-generation biologics, integrating computational and experimental approaches to accelerate translation into the clinic. Jorge holds a PhD in Computational Structural Biology from Utrecht University (NL) and was an EMBO postdoctoral fellow in de novo protein design.

Laila is a Senior Research Scientist in large-molecule biophysics within early-stage research at Novo Nordisk, Denmark. She obtained a PhD in Biochemistry in 2019 from Lund University, Sweden, and moved to Austria to join Novartis as a Senior Scientist in Global Drug Development. In 2020, she returned to academia as a Postdoctoral Research Associate at the University of Cambridge, UK. Her research interests include experimental and computational work within antibody developability, and study of protein-protein/molecular interactions.

Dr. Lonneke Scheffer is a computational immunologist developing methods for adaptive immune receptor repertoire analysis. She has experience benchmarking predictive tools for immune receptor specificity, and her recent work centers on the standardization and cross-platform integration of immune receptor data in the Immune Epitope Database.

Dr. Franziska Seeger is a protein biophysicist who uses computational modeling to address challenges in biomedicine. Her doctoral research at the University of Maryland Baltimore County and Lawrence Berkeley National Laboratory helped explain the activation mechanism of a key drug target for cardiovascular diseases. She then pursued postdoctoral research with David Baker at the Institute for Protein Design, where she engineered new inhibitors against autoimmune disease targets. Dr. Seeger has consulted on protein biophysics and design, and has worked in protein engineering and machine learning at organizations like Amazon and Novo Nordisk. Currently, Dr. Seeger leads a strategic group focused on applying advanced machine learning techniques to Roche’s antibody portfolio within the Artificial Intelligence for Drug Discovery Department at Genentech. Outside of work, Dr. Seeger enjoys outdoor activities such as running, mountain biking, (backcountry) skiing, horseback riding, and hiking/backpacking. Indoors, she practices yoga and meditation, and enjoys cooking and reading. https://www.linkedin.com/in/fseeger/ https://www.youtube.com/watch?v=SIdS8bUZT7c&ab_channel=LadyScientistPodcast https://www.bakerlab.org/index.php/2019/03/27/franziska-seeger-profile/

Dr. Yi Shi is an Associate Professor and Director of the Center for Protein Engineering and Therapeutics at the Icahn School of Medicine at Mount Sinai. His lab develops cutting-edge technologies in protein and antibody engineering, with a focus on camelid VHH antibodies, commonly known as nanobodies. Dr. Shi’s team has pioneered proteomics and machine learning–based platforms for the high-throughput discovery of nanobody repertoires. These approaches enable the identification of thousands of high-affinity nanobodies targeting diverse epitopes on disease-associated antigens. His lab applies these technologies to tackle key challenges in cancer biology, virology, and diabetes, with the goal of advancing both fundamental understanding and the development of next-generation therapeutics. Dr. Shi received his PhD from Baylor College of Medicine in 2011.

Dr. Rohit Singh is an Assistant Professor in the Departments of Biostatistics & Bioinformatics and Cell Biology at Duke University. His research interests are broadly in computational biology, ranging from protein design and small-molecule drug discovery to causal regulatory inference, with a particular interest in probing biological foundation models to better understand the biology they capture. He is the recipient of Duke's Whitehead Scholar Award, the Test of Time Award at RECOMB, MIT's George M. Sprowls Award for his PhD thesis in Computer Science, and Stanford's Christopher Stephenson Memorial Award for Masters Research. In addition to academia, he has experience in quantitative finance.

Dr. Arvind Sivasubramanian is a Director, Computational Biology and Platform Technologies, at Adimab LLC. In this role, he oversees a group involved in hybrid computational-experimental activities for the discovery and optimization of antibody and TCR biologics. His expertise includes the design of synthetic IgG and TCR libraries, the structure-based design of bispecific antibodies, and in silico predictions of antibody-antigen interactions. He received his Ph.D. in Chemical Engineering from Drexel University, Philadelphia, PA, after which he completed a post-doc in antibody:antigen docking with Prof. Jeff Gray at Johns Hopkins University, Baltimore, MD.

Pietro Sormanni is an Associate Professor and Royal Society University Research Fellow at Imperial College London. His group develops computational and experimental methods for antibody and biomolecule design, integrating model development with the measurements needed to train, validate and apply these tools. Their work focuses on computational antibody-design technologies to transform antibody discovery and engineering, with industrial and academic collaborations demonstrating faster and more cost-effective routes than traditional approaches. Before joining Imperial, Pietro led a research group at the University of Cambridge, having previously been a Borysiewicz Biomedical Sciences Postdoctoral Fellow. He holds a PhD in Chemistry and an MSc in Theoretical Physics.

Roberto Spreafico is Senior Director, Biologics AI Innovation at AstraZeneca. After earning an MSc in Biotechnology and a PhD in Immunology from the University of Milano-Bicocca in Italy, Roberto spent 10 years in the USA. There, he worked for institutions such as the University of California Los Angeles, Synthetic Genomics, and Vir Biotechnology, where he was the lead computational biologist on the team that developed Sotrovimab during the COVID-19 pandemic. Since returning to Europe, he has held managerial roles at GlaxoSmithKline, Absci and Genmab before joining AstraZeneca in February 2026. His expertise encompasses immunology, genomics, bioinformatics, protein engineering and artificial intelligence.

I am interested in foundational machine learning research with applications that promote human flourishing, particularly the life sciences. I wish to understand the best way to build self-sustaining intelligent systems that automatically collect and incorporate the necessary information to solve difficult optimization problems, such as black-box optimization and adaptive control problems. Applications of my work include lab-in-the-loop systems for antibody engineering and therapeutic pipeline portfolio strategy.

Peter Tessier is the Albert M. Mattocks (Endowed) Professor in the Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, and a member of the Biointerfaces Institute at the University of Michigan in Ann Arbor, MI. He received his Ph.D. in Chemical Engineering from the University of Delaware (2003, NASA Graduate Fellow) and performed his postdoctoral studies at the Whitehead Institute for Biomedical Research at MIT (2003-2007, American Cancer Society Fellow). Tessier started his independent career as an assistant professor in the Department of Chemical & Biological Engineering at Rensselaer Polytechnic Institute in 2007, and he was an endowed full professor at Rensselaer prior to moving to the University of Michigan in 2017. Tessier’s research focuses on designing, optimizing, characterizing and formulating a class of large therapeutic proteins (antibodies) that hold great potential for detecting and treating human disorders ranging from cancer to Alzheimer’s disease. He has received a number of awards and fellowships in recognition of his pioneering work: Pew Scholar Award in Biomedical Sciences (2010-2014), Humboldt Fellowship for Experienced Researchers (2014-2015), Young Scientist Award from the World Economic Forum (2014), Young Investigator Award from the American Chemical Society (2015) and NSF CAREER Award (2010-2015).

Anna is Director of ML/AI for Large Molecule Drug Discovery at Roche/Genentech in Basel (Switzerland). In Roche, she leads an interface focused on developing and applying machine learning methods for therapeutic antibody design and discovery. Previously, she was scientist and project leader at Roche Innovation Center Munich (Germany). She specialized in computational structure biology, machine learning approaches for protein design, protein-protein interactions, discovery and engineering of therapeutic antibodies. Anna obtained her PhD from University of Salerno (Italy), with a visiting PhD at the University of Oxford (UK). She was also Marie Sklodowska-Curie postdoctoral fellow at University of Utrecht (NL) and was invited scientist at leading institutions, including IRB Barcelona (Spain) and KAUST (Saudi Arabia). She maintains strong connections with academic institutions, working on joint projects, serving as an external lecturer and industry supervisor for students.

Dr. Vlysidis obtained his PhD in Chemical Engineering at the University of Minnesota, Twin Cities, studying and modeling the stochasticity of biochemical reaction networks. With over 6 years of experience in the industry, he has made significant contributions to the fields of scientific software development and engineering. Currently serving as a team leader at AbbVie, Dr. Vlysidis' primary focus is on supporting the biologics organization in capturing and analyzing experimental data. He possesses a deep understanding of protein properties and leverages innovative protein language models to further enhance research in this area. Prior to joining AbbVie, he worked at Intel, where his expertise was instrumental in supporting R&D research on semiconductors and cutting-edge technology. With a strong academic background and industry experience, Dr. Vlysidis is dedicated to driving advancements at the intersection of chemical engineering, software development, and AI/ML models.

Ye Wang is a Principal Scientist at Biogen, where he leads a machine learning group focused on accelerating drug discovery and design by generative modeling. He spans deep generative models for both small molecule and antibody design, as well as reinforcement learning for multi-objective molecular/antibody optimization. He received his Ph.D. in computer science from Auburn University.

Yulei is a computational scientist focused on antibody discovery and engineering. She holds a BS in Chemical Engineering from Beijing University of Chemical Technology and Rutgers University, and an MS and PhD in Chemical Engineering from the University of Michigan, where she studied under Prof. Peter M. Tessier, predicting antibody biophysical properties to improve drug development. At Eli Lilly she works on the in silico discovery of binders from immune repertoires against challenging targets such as GPCRs.