Speaker Biographies

Omar Abudayyeh, Ph.D., is a McGovern Institute Fellow at MIT where he runs a small research group on exploring microbial diversity for new biotechnological tools related to genome editing and gene delivery. He completed his training at Harvard Medical School and the Harvard-MIT Health Sciences and Technology program as a graduate student in Feng Zhang’s lab. His graduate research centered on uncovering novel CRISPR enzymes beyond Cas9 for applications in genome editing, therapeutics, and diagnostics. He co-led the discovery and characterization of multiple landmark pieces of work, including the characterization of Cpf1/Cas12 for novel genome editing applications and the first single-protein RNA-guided RNA-targeting enzyme C2c2/Cas13. His follow-up work on C2c2/Cas13 biology led to the development of the SHERLOCK technology for CRISPR diagnostics, and a new set of tools for precise RNA editing for gene therapy. Dr. Abudayyeh is a co-founder of Sherlock Biosciences and an advisor for Beam Therapeutics. In recognition of his technology developments, Dr. Abudayyeh was recognized as 2018 Forbes 30 under 30 in Science and Health Care and Business Insider 30 under 30. Dr. Abudayyeh graduated from MIT in 2012 with a B.S. in mechanical engineering and biological engineering, where he was a Henry Ford II Scholar and a Barry M. Goldwater Scholar.

Alain Ajamian joined CCG in January 2011, bringing more than 10 years of proven experience in pharmaceutical research and development. Prior to joining Chemical Computing Group, Alain served as Managing Director at BioChemia where he played an integral role in establishing the North American operational business. Before this, Alain was a medicinal chemist at MethylGene, now Mirati Therapeutics, where he made significant contributions to the development of HDAC, HAT and Sirtuin inhibitors and is an inventor on several related patents.

Dr. Nicola Bonzanni co-founded ENPICOM to bridge the gap between life sciences and technology, empowering researchers with advanced computational tools. His expertise spans computational biology, NGS data analysis, and molecular biology, driving the creation of state-of-the-art software solutions that enhance biologics discovery and AI integration.

Andrew Bradbury is Chief Scientific Officer of Specifica. He trained in medicine at the universities of Oxford and London and received his PhD from the university of Cambridge at the MRC Laboratory of Molecular Biology under the guidance of Nobel Laureate, Cesar Milstein. He has worked in the fields of phage and yeast display, library generation, antibody engineering and Next Generation Sequencing for over thirty years. He was a Group Leader at Los Alamos National Laboratory before founding Specifica. Specifica's mission is to enable companies developing therapeutic antibodies with the world’s best antibody discovery platform.

Maria Calderon Vaca holds a Bachelor’s degree in Chemical Engineering and a Master’s in Business and Science. She is a PhD student at the University of Miami in the Department of Chemical, Environmental, and Materials Engineering, working in the Soft Matter Product Design Lab. Her research aims to transform product development into a data-driven process by uncovering how protein interactions, from the molecular to the bulk scale, influence formulation performance, using advanced characterization techniques. Maria brings six years of R&D experience in the personal care industry, applying formulation insights from that sector to biopharmaceutical challenges, and is passionate about science communication and mentoring the next generation of researchers.

Dr. Jenna Caldwell is an Associate Principal Scientist in the Early Stage Formulations Sciences group within Biopharmaceutical Development at AstraZeneca (Gaithersburg, MD). She leads the In Silico Developability team as part of the MLAB program, which seeks opportunities to accelerate and improve the antibody discovery and development process using machine learning and artificial intelligence. She is passionate about developing technologies that require cross-disciplinary perspectives to tackle unsolved problems. She earned her SB in Biology from MIT and her PhD in Biochemistry from Stanford, where she developed a mass spectrometric technology to make quantitative, high-resolution measurements of protein conformational change within living cells.

I'm a PhD student in the Oxford Protein Informatics Group, supervised by Professor Charlotte Deane. My research is focused on machine learning tools for the prediction of antibody developability.

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.

Dr. Lieza Danan is a scientist, two-time biotech entrepreneur, and the founder and CEO of LiVeritas Biosciences, a full-stack AI company reinventing analytical testing for biologics and regulated therapeutics. With a PhD in Biological Chemistry and over a decade of experience leading mass spectrometry, analytical development, and quality control functions across startups and global labs, she brings deep technical expertise and operational insight to regulated method development. Under her leadership, LiVeritas has contributed to over 10 IND and BLA submissions by deploying GxP-ready, AI-native infrastructure that integrates assay design, validation, and regulatory automation. Her work spans advanced structural characterization, post-translational modification analysis, and automated workflows for high-throughput labs. A champion of regenerative leadership and system integrity, Dr. Danan is building a new standard for compliance-first innovation in biotherapeutics.

Aline Oliveira started as an oncology expert with a Master's and PhD from INCA. With over 16 years in biopharmaceutical development, she has held key roles as R&D Project Manager and Portfolio Manager. Joining the Competitive Intelligence Office (Aicom) at Bio-Manguinhos, Aline leverages her deep industry knowledge to conduct strategic foresight studies and gather critical information that informs leadership decision-making.

I am a PhD student co-advised by Torben Schiffner and Jens Meiler. I work on Computational and experimental High-Throughput Characterization of Protein-Protein Interactions.

Charlotte Deane, MBE, is a Professor in the Department of Statistics at the University of Oxford, Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC) and Co-Founder of Dalton Tx. During the COVID-19 pandemic, she served on SAGE, the UK Government’s Scientific Advisory Group for Emergencies, and acted as UK Research and Innovation’s COVID-19 Response Director. In 2025, Charlotte was elected as a Fellow of the International Society for Computational Biology (ISCB). At Oxford, Charlotte leads the Oxford Protein Informatics Group (OPIG), which works on diverse problems across immunoinformatics, protein structure and small molecule drug discovery using statistics, AI and computation to generate biological and medical insight. Charlotte’s research focuses on the development of novel algorithms, tools and databases which are openly available to the community. They are widely used in both academia and industry and embedded in pharmaceutical drug discovery pipelines. She is a member of several advisory boards and has consulted extensively with industry, having also established a consulting arm within her research group as a way of promoting industrial interaction and use of the group’s software tools. Charlotte is part of the team leading OpenBind, a £8 million government-backed consortium aiming to create the world’s largest open dataset of drug-protein interactions to accelerate AI-driven drug discovery. She also serves as one of five experts advising the UK Government’s new AI for Science strategy, which aims to boost AI adoption across research and accelerate scientific discovery.

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.

Gina is a PhD candidate in Biophysics at Stanford University. Her doctoral research focuses on developing deep learning methods for de novo protein design toward enzymatic function and allostery. She is more broadly interested in modeling the dynamic nature of proteins for new-to-nature mechanisms. Gina received her bachelors from Johns Hopkins University in computer science, applied mathematics & statistics, and biophysics and is currently an NSF Graduate Research Fellow.

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.

CEO & Cofounder of Adaptyv Bio. Protein designers should be designing, not pipetting. We build automated wet labs to allow you to synthesize and test any protein you design!

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.

Dr. Jiangyan Feng earned her Ph.D. in Computational Biology from the University of Illinois at Urbana-Champaign, where she specialized in molecular dynamics simulations to investigate protein conformational dynamics and applied bioinformatics for machine learning model development. She currently works at Eli Lilly, where her research focuses on antibody engineering and the application of in silico methods to accelerate biologics discovery and optimization.

James is a Postdoctoral Researcher in Dr. Andrew Ward's laboratory at The Scripps Research Institute in La Jolla, California. Building upon expertise in protein dynamics developed during doctoral studies at Scripps, James's research focuses on rational vaccine design and antibody engineering, with particular emphasis on harnessing protein design tools to engineer more effective antigens. James's work centers on applying electron microscopy (EM) to advance understanding of antigen-antibody interactions. A key component of this research involves Electron Microscopy Polyclonal Epitope Mapping (EMPEM) to characterize antibody responses following vaccination and infection. This structural approach is complemented by computational methods including molecular dynamics simulations and cutting-edge AI tools integrated with bioinformatics pipelines to identify high-affinity antibody binders. Through this multidisciplinary approach combining structural biology, computational modeling, and immunology, James aims to accelerate the development of next-generation vaccines and therapeutic antibodies.

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.

Dr. Yves Fomekong Nanfack is the Head of AI/ML – Research at Takeda, where he leads the integration and application of artificial intelligence and machine learning across drug discovery. Before joining Takeda, he served as Head of AI End-to-End Foundations at Sanofi, where he launched the BioAIM program to transform biologics research through data and AI. Earlier in his career at Merck/EMD Serono, Yves led teams in computational chemistry and computational biology, advancing digital and data-driven approaches to discovery. He holds a PhD in Computer Science from the University of Amsterdam and brings more than 15 years of experience at the intersection of AI, data science, and pharmaceutical research.

Upon finishing his studies in Pharmaceutical Sciences, Dr. Furtmann pursued his interdisciplinary Ph.D. thesis in Computational Life Sciences and Pharmaceutical Chemistry at the University of Bonn, focusing on computer-aided design, synthesis, and biological evaluation of protease inhibitors. After starting his professional career at Merck KGaA as Principal Scientist, he joined Sanofi in 2016 as Lab Head for Bioinformatics within the Biologics Research department. Currently, Dr. Furtmann is heading the Data Science & Computational Design group to support the discovery of next-generation protein therapeutics.

Michelle Gaylord is an experienced scientist with over 13 years in biotherapeutics, specializing in process improvement, platform development, and upstream workflows from cloning through large-scale protein expression. Her expertise spans automation, streamlining inefficiencies, and optimizing production to accelerate timelines and increase output across small- to large-scale, manual, and high-throughput platforms. Beyond her scientific work, Michelle is a dedicated nonprofit leader, actively serving with Soroptimist International, National Charity League, and Project Mercy Baja. She is passionate about empowering women, strengthening families, and supporting underserved communities through fundraising, advocacy, and hands-on service projects. At the intersection of science and service, Michelle brings a proven ability to solve complex problems, lead cross-functional teams, and create sustainable, purpose-driven impact.

Sebastian Giehring earned his PhD in analytical chemistry from the University of Hamburg. He subsequently worked in a scientific role at a spectrometry company before transitioning to a venture capital firm as an investment manager for life sciences. In 2006, he chose to change to the startup sector, taking on various management roles in life science tool companies. In 2014, he invented the PAIA microplate technology and founded PAIA Biotech GmbH, where he has served as CEO ever since.

Jonathan Gootenberg, Ph.D. draws from fundamental microbiology to engineer new molecular tools. These tools, including the popular genome editing system CRISPR, allow for unprecedented manipulation and profiling of cellular states in the body, and have multiple applications in basic science, diagnostics, and therapeutics. Dr. Gootenberg uses gene editing, gene delivery, and cellular profiling methods to understand the changes that occur in the brain and other organs during aging, with the goal of generating new therapies for degenerative disease. Dr. Gootenberg earned his bachelor’s degree in mathematics and biological engineering at MIT and received his PhD in Systems Biology from Harvard University, during which he conducted research with Aviv Regev and Feng Zhang at the McGovern Institute and Broad Institute of MIT and Harvard. During his graduate work, Gootenberg focused on the development of molecular technologies for treating and sensing disease states, crossing disciplines by utilizing novel computational techniques, microbiology, biochemistry, and molecular biology to uncover new CRISPR tools, including Cas12 and Cas13. He and his co-authors developed Cas13 into a toolbox with uses in fundamental research, therapeutics, and diagnostics. These applications include RNA knockdown, imaging, the base editing platform REPAIR, and the sensitive, specific, and portable diagnostic platform SHERLOCK. He is one of the first members of the McGovern Institute Fellows program, which supports the transition to independent research for exceptional recent PhD graduates.

Peyton Greenside is the co-founder and CSO of BigHat Biosciences, an early-stage Bay Area startup developing an AI-first experimental platform to radically reduce the difficulty of designing antibodies and other therapeutic proteins. Before BigHat, Peyton was an inaugural Schmidt Science Fellow, a computational biologist at the Broad Institute, a scientific founder of Valis, and holds a PhD from Stanford University, an MPhil in Computational Biology from Cambridge University, and a BA in Applied Math from Harvard.

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.

Abhinav Gupta joined Sanofi in August 2022 and is currently a Principal Machine Learning Scientist, in the AI Innovation team for Large Molecules Research. Abhinav received his PhD in Mechanical Engineering and Computation from MIT where he developed state-of-the-art algorithms and methodologies for uncertainty quantification, Bayesian learning, and deep learning for dynamical systems. At Sanofi, Abhinav is working on developing foundational ML/AI solutions to facilitate in silico Ab/Nb engineering and design, as well as supporting pipeline projects by application of advanced ML/AI methods.

Winston is the VP Computational Sciences and Engineering at LabGenius, where he leads a team of experts in data science, machine learning, and software development to expand LabGenius’ ML-driven discovery platform capabilities. He has extensive experience leading the development and application of computational tools to advance both antibody therapeutics (BigHat Biosciences) and diagnostics (Serimmune). Winston holds a PhD in Biomedical Informatics from Stanford University, where he was a NSF GRFP fellow.

Dr. Possu Huang received his PhD from Caltech with the first demonstration of a computationally designed novel protein-protein interface. He subsequently conducted postdoctoral research at the University of Washington before starting his group at Stanford. His research focuses on advancing the understanding of proteins for the engineering of novel therapeutics and other protein-based nanotechnology. He has contributed to a large number of de novo designed proteins, most notably to the unlocking of the design principles behind the TIM barrel fold. His group uses computational modeling, structural biology and experimental library optimization to continue the expansion of protein-based molecular platforms, as well as creating new design tools with modern neural network architectures.

Deniz is the co-Founder and CEO of Tamarind Bio, the software platform to use the leading molecular design tools, through a web interface, programmatic API and AI assistant. Users, including tens of thousands of scientists, can run tools like AlphaFold, RFdiffusion, BoltzGen and 200+ more at scale with up to hundreds of thousands of inputs running in paralel.

Hubert Kettenberger holds a PhD in structural biology from the University of Munich. After a PostDoc at the Max-Planck-Institute for Biochemistry he joined the Large Molecule Research unit of Roche in Penzberg/Germany in the year 2006. Since 2020, he has acted as the Head of Computational Protein Engineering, a department which contributes in silico methods to Roche's therapeutic protein portfolio projects.

Kylie Konrath completed her PhD at University of Pennsylvania in the Cell and Molecular Biology department and is a postdoctoral fellow at the Scripps Research Institute. She is structural vaccinologist focused on mapping and understanding structural features of broadly reactive antibodies against antigenically diverse viruses. She then aims to leverage protein engineering to induce similar broadly reactive antibodies by vaccination.

Annie Kwon is a protein engineer that has developed and patented several technologies to enhance the developability and productivity of antibody therapeutic molecules at Amgen. With a hybrid computational and wet lab background in protein biochemistry, her focus is leveraging data, state of the art protein design tools, and novel antibody engineering ideas to design smart molecule panels with high probabilities of success and that can be interrogated at speed with small-scale, high-throughput experiments. Before Amgen, Annie earned her PhD in Computational Biology from the University of Georgia, completed postdoctoral training at the University of California San Francisco, and provided protein engineering services to several biotech startups as a consultant.

Adrian Lange is the Director of Machine Learning Research at A-Alpha Bio, where he leads a research team developing computational models for protein-protein interactions. Adrian hails from an academic background in physical chemistry as a PhD researcher at the Ohio State University. After a postdoc at Argonne National Laboratory, Adrian sought a career in software engineering at Apple. Later, his career shifted focus toward data science and machine learning, reviving his scientific research roots at biotech companies including Tempus, Evozyne, and currently A-Alpha Bio.

Mimi brings over 25 years of experience producing industry-leading conferences across multiple continents. She joined CHI in 2013 producing biologics and bioprocessing programs and has since been an integral part of CHI's flagship events such as PEGS Boston, PEGS Europe, PepTalk, and the Bioprocessing Summit. She also spearheaded the early international expansions of PEGS to Shanghai and Seoul prior to COVID-19. Earlier in her career, Mimi produced Drug Discovery Technology (DDT) and Bioprocess International with IBC/Informa in the U.S. and Asia. Before relocating to the U.S., she was based in Singapore, where she developed and launched telecom events throughout Asia including Singapore, Japan, China, and India. Prior to producing conferences, Mimi worked at Nippon Yusen Kaisha (NYK) in Singapore and Japan, coordinating marketing strategies along the US-Asia trade routes. Mimi holds a BA from National University of Singapore and an MBA from Washington University in St. Louis.

Christina has spent the last 25+ years creating more than 300 events hosted by CHI. She is the creator and driving force behind the PEGS Summit, now in its 18th year, and the Molecular Medicine Tri-conference, now in its 29th year, and has identified and developed emerging topics including bispecific antibodies, genomics, molecular diagnostics, phage display, point-of-care diagnostics, bioinformatics and many more. Christina emphasizes the importance of bringing together the academic and industrial sectors to create environments where innovation is fostered and commercial applications are advanced.

As a computational chemist, I specialize in applying molecular modeling techniques to drug discovery. My work, both in academic settings like The Scripps Research Institute and in industry roles at pharmaceutical companies, has focused on protein and antibody design. I'm particularly interested in understanding the fundamental biophysical drivers of molecular recognition. My goal is to develop computational methods that provide deeper insights into complex biological systems, with the hope of helping to accelerate the design of new therapeutics.

Leigh Manley is a Scientist in the Machine Learning group at Seismic Therapeutic. Her work focuses on developing computational tools to create better biotherapeutics for autoimmune disease. She received her PhD in Biophysics from UT Southwestern where she elucidated entropically and temporally mediated allosteric networks in the Ras family.

Dr. Marks is Professor and Vice-Chairman of the Department of Anesthesia and Perioperative Care at the University of California, San Francisco (UCSF) and Chief of Performance Excellence at Zuckerberg San Francisco General Hospital and Trauma Center (ZSFG). Dr. Marks received his medical degree from UCSF where he also completed residencies in Internal Medicine and Anesthesia and a fellowship in Critical Care Medicine. He received his Ph.D. in molecular biology from the Medical Research Council Laboratory of Molecular Biology in Cambridge, England. Dr. Marks is an internationally recognized pioneer in the field of antibody engineering, has had constant federal funding for 27 years and has authored more than 200 publications and 100 patents. In recognition of these scholarly achievements, he was elected to the National Academy of Medicine. As an entrepreneur, he has co-founded four biotechnology companies and currently serves on three biotechnology corporate boards.

PhD, Co-Founder, Chai Discovery

Josh Moller is a Senior Biological Engineer specializing in AI and Machine Learning at Ginkgo Bioworks. As part of the Datapoints Antibody Developability team, they focus on the generative design of proteins and biologics, leveraging advanced computational approaches and high-throughput data generation to push the boundaries of antibody and biologic developability. Josh applies cutting-edge AI/ML models to enable predictive protein engineering and collaborates within Ginkgo’s robust R&D ecosystem to accelerate innovation in therapeutic development.

Deborah joined ProFound Tx in 2024 as the VP of Protein Sciences to lead protein and antibody development campaigns. Prior to ProFound Tx, Deborah was with Abcam for 5 years, leading the Protein Development and Sequencing Platforms, responsible for protein and sequencing needs for Abcam. Prior to Abcam, she spent 16 years working in both the reagent and therapeutic spaces. For many years she led Antibody Production at Cell Signaling Technology. From there she joined Merck Research Laboratories, where she led the team responsible for antigen & antibody generation within Biologics Discovery.

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.

Dr. Andrew Nixon is currently Senior Vice President, Global Head of Biotherapeutics Discovery Research at Boehringer Ingelheim. In this role, Andy leads the organization responsible for all biologic drug discovery efforts. Immediately prior to joining Boehringer Ingelheim in 2017, Andy was Vice President of Biotherapeutics Discovery at Magenta Therapeutics. At Magenta he established and led the team responsible for generating antibody drug conjugates that could serve as an alternative to traditional myeloablative conditioning regimes. Prior to joining Magenta in 2016, Andy held roles of increasing responsibility at Dyax Corp., including serving as Vice President of Discovery Research. In this capacity he led the DX-2930 program, a fully human antibody inhibitor of plasma kallikrein, from discovery to development and built Dyax’s internal research pipeline that resulted in the acquisition of Dyax by Shire in January 2016. DX-2930 was subsequently approved in 2018 and is marketed as TAKHZYRO. Additionally, Andy has been responsible for over 100 antibody discovery programs resulting in numerous clinical candidates and approved biologics. Andy completed a post-doctoral fellowship in the laboratory of Prof. S.J. Benkovic, in the Department of Chemistry at Pennsylvania State University, where he was involved in the development of techniques to facilitate enzyme engineering. Andy earned his PhD from the University of London for studies completed at the MRC’s National Institute for Medical Research.

Frank has a Bachelor degree in Electrical Engineering, a Master degree in Computer Science and a PhD from University of Heidelberg (Germany) in Computer Science and Biophysics. For 10 years he has been professor for AI for Science at Freie Universität Berlin and since 2022 he is Partner Research Manager at Microsoft Research AI for Science, leading the lab in Berlin. Frank is still honorary professor at Freie Universität Berlin and adjunct professor at Rice University Houston. Frank's research focuses on the development of AI methods to advance the molecular sciences, in particular addressing fundamental challenges such as the electronic structure problem in quantum Chemsitry, the many-body sampling problem in statistical Mechanics and the modeling and simulation of biomolecular structure, dynamics and function. His research was awarded with two grants of the European Research Council (ERC), Frank is an ISI highly cited fellow, a member of Berlin-Brandenburg Academy of Sciences, a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) and a fellow of the American Physical Society (APS).

Dr. Papoian received his PhD at Cornell University under guidance of Dr. Roald Hoffmann, a Nobel Laureate. He continued with postdoctoral work with Dr. Michael Klein (National Academy of Sciences member) and Dr. Peter Wolynes (National Academy of Sciences member), studying quantum and protein physics. He has held faculty positions at the University of North Carolina at Chapel Hill and subsequently at the University of Maryland, College Park. He is currently the first Monroe Martin Professor at the University of Maryland, in the Department of Chemistry and Biochemistry and Institute for Physical Science and Technology. He received numerous awards, including Beckman Young Investigator, Camille Dreyfus Teacher-Scholar and National Science Foundation CAREER Award. He uses computational chemistry, physics, and machine learning to study biological processes at multiple scales, from protein dynamics and epigenetics to cellular-level processes, such as immune cell activation and neuronal dynamics. He is also the co-founder and CSO of DeepOrigin Inc., a startup company that accelerates drug discovery through pioneering combination of physics-informed AI, quantum methods, and molecular dynamics.

Ji Won is a Principal Scientist at Prescient Design, Genentech. Her research focuses on uncertainty quantification for Bayesian experimental design, unifying ideas from optimization, calibrated inference, and sampling to guide adaptive decision-making in high-dimensional settings. More recently, she has extended these ideas to large language models, exploring how they can be adapted into reliable tools for drug discovery. She received her Ph.D. in Physics from Stanford University, where she worked on hierarchical Bayesian methods for cosmology. During her studies, she interned at NASA Ames and the Center for Computational Astrophysics at the Flatiron Institute. She holds a BS in Mathematics and a BS in Physics from Duke University.

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.

Joost Schymkowitz is a Belgian structural biologist and biophysicist, Vice-Director of the VIB–KU Leuven Center for Neuroscience. He obtained his PhD in Protein Engineering from the University of Cambridge and completed a postdoctoral fellowship in computational biology at EMBL Heidelberg. Joost runs the Switch Laboratory together with his long-term collaborator Frederic Rousseau, and they are co-founders of the Leuven Protein Aggregation conference series. Their research combines computational modeling, biophysics, chemical and cell biology, and patient-derived tissue analysis to investigate the mechanisms of protein misfolding and aggregation. In parallel, Joost develops widely used computational tools such as TANGO, FoldX, and Waltz. He is also actively involved in Be.Amycon, the national Belgian expert network on systemic amyloidosis. Joost has authored over 230 peer-reviewed publications in journals including Nature, Cell, and Science, and is co-inventor on more than 20 patents.

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/

I am Director of Data Strategy & Digital Transformation at Eli Lilly. Throughout my career I have worked on development of different technology platforms for biologics discovery such as implementation of immune in vitro phage display antibody libraries, droplet microfluidics for high-throughput sequencing of antibodies at single cell level, and Next-Generation-Sequencing for immune repertoire profiling. I have a BSc in Bioengineering from the Jacobs School of Bioengineering at University of California San Diego and a MSc in Biotechnology from Joseph Fourier University in Grenoble. During my PhD at the Enzymatic and Cellular Engineering Laboratory of Sorbonne University, I studied the presence and implication of catalytic antibodies in health and disease using mouse models and high-throughput in vitro display technologies. In my current role, I lead a team responsible for the establishment and execution of our Data Strategy at BioTDR, in line with our aspirations for Digital Transformation toward an AI-enabled biologics discovery process.

Reshef is the Director of AI at Biolojic Design. He has 20+ years of experience in computer science research and software engineering, with 10+ years in management and technical leadership. His academic background is in Natural Language Processing (NLP) in the field of Machine Translation (Hebrew-Arabic), as a part of the interdisciplinary program for outstanding students in Tel Aviv University. During his studies he worked as a part-time researcher and software engineer in Thomson Reuters. After his studies, he spent several years doing research at various NLP start-ups and as an NLP consultant. The department he built at Ginger Software, focusing on Automatic Speech Recognition and Natural Language Understanding, was acquired by Intel in 2014, and he moved to the Silicon Valley shortly after. He spent 2 years at Intel working on virtual personal assistants and managing a research team. Later, he moved to Facebook, where he spent 6 years, working mostly on automatic detection of various Trust & Safety violations, such as Hate Speech, Misinformation, Suicide Prevention, Graphical Violence, and others. In 2022 he returned to Israel with his family, and moved to Biologic Design, with the goal of leveraging AI and data science for designing drugs for cancer.

I am a machine learning scientist at Seismic Therapeutic, where we are developing immunology therapeutics through our platform combining machine learning, protein engineering, structural biology, and translational immunology. While leading one of our early-stage drug discovery programs, I apply deep learning models of protein sequence and structure for functional design of developable and non-immunogenic biologics. I received my doctorate in Debora Marks' lab at Harvard Medical School, where I developed and tested deep learning models for protein mutation effect prediction and protein design.

Fabian Spoendlin is a PhD student at the Oxford Protein Informatics Group, University of Oxford. His research focuses on antibody drug design, with an emphasis on modeling conformational flexibility.

Roberto Spreafico is Vice President, Head of Discovery Data Science at Genmab. 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 and Absci before joining Genmab in September 2022. His expertise encompasses immunology, genomics, bioinformatics, and artificial intelligence.

Timothy J. Stasevich is an Associate Professor of Biochemistry and Molecular Biology at Colorado State University (CSU). His lab uses a combination of advanced fluorescence microscopy, genetic engineering, and computational modeling to study the dynamics of gene regulation in living mammalian cells. His lab helped pioneer the imaging of real-time single-mRNA translation dynamics in living cells (1,2). Dr. Stasevich received his B.S. in Physics and Mathematics from the University of Michigan, Dearborn, and his Ph. D. in Physics from the University of Maryland, College Park. He transitioned into experimental biophysics as a post-doctoral research fellow in the laboratory of Dr. James G. McNally at the National Cancer Institute. During this time, he developed technology based on fluorescence microscopy to help establish gold-standard measurements of live-cell protein dynamics. Dr. Stasevich next moved to Osaka University, where he worked with Dr. Hiroshi Kimura as a Japan Society for the Promotion of Science Foreign Postdoctoral Research Fellow. While there, he helped create technology to image endogenous proteins and their post-translation modifications in vivo. This allowed him to image the live-cell dynamics of epigenetic histone modifications during gene activation for the first time (3). Dr. Stasevich has received numerous awards throughout his career: he is a Boettcher Investigator, a CSU Monfort Professor and, most recently, the first Dean and Pingping Tsao Professor of Biochemistry. 1. Morisaki, T. et al. Real-time quantification of single RNA translation dynamics in living cells. Science 352, 1425–1429 (2016). 2. Morisaki, T. & Stasevich, T. J. Quantifying Single mRNA Translation Kinetics in Living Cells. Cold Spring Harb. Perspect. Biol. 10, a032078 (2018). 3. Stasevich, T. J. et al. Regulation of RNA polymerase II activation by histone acetylation in single living cells. Nature 516, 272–275 (2014).

Frank holds a Ph.D. in Computational Biology from the University of North Carolina, where he developed a requirement-driven protein design algorithm used to create the first fully rationally designed miniprotein libraries. As a founding team member and current Head of Computational Sciences at AI Proteins, he leads the development of both classical and AI-driven algorithms supporting protein design and optimization. His work spans generative models for novel protein scaffolds as well as predictive tools for protein expression, stability, immunogenicity, and developability, as well as the infrastructure required to deploy them across the organization. His experience sits at the intersection of AI strategy and experimental design, with a focus on building practical, scalable tools that accelerate therapeutic protein development.

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).

Warren Thompson is a senior scientist leading the Fast Forward Fragments programme at Diamond Light Source, where he works to lower the barriers to fragment-based drug discovery. His work focuses on developing fragment progression technologies, including: the digitized chemistry platform Chemist Assisted Robotics (CAR), MSCheck for automated reaction quality control, Syndirella for compound elaboration and chemical space exploration, and Fragalysis, a web application for structural data dissemination and annotation. Warren combines experimental expertise and computational tools to address challenges in fragment-based drug discovery. He has experience in industry, where he worked on processes for the scaled-up synthesis of active pharmaceutical ingredients (APIs), and academia, where he has interests in fragment-based drug discovery technologies and digitising experimental approaches.

Stephanie Truhlar, Ph.D., is a Vice President in Biotechnology Discovery Research at Eli Lilly and Company. She leads the Biotherapeutic Enabling Technologies team, which drives innovation at the intersection of biotechnology and machine learning (ML)/artificial intelligence (AI) to accelerate the discovery of next generation biotherapeutics. Stephanie began her scientific journey exploring protein folding and biophysics, earning her Ph.D. from the University of California, San Francisco, followed by postdoctoral training at UC, San Diego. Over her 17-year career at Lilly, she has played a pivotal role in antibody discovery, engineering numerous clinical candidates, co-developing a proprietary bispecific antibody platform, and advancing digital transformation and computational capabilities for biotherapeutic discovery. Stephanie is a co-inventor on seven patents and has delivered numerous invited talks and peer-reviewed publications. Her work has earned her multiple awards and continues to shape the future of therapeutic discovery through bold scientific rigor, technological innovation, and strategic vision.

Elahe Vedadi is a Research Scientist at Google DeepMind, where she works on foundation models. She holds a PhD in Electrical Engineering from the University of Illinois Chicago, where her research focused on adaptive and secure coded computation for distributed learning. Her expertise includes machine learning, information theory, and Bayesian statistics. Elahe has published in top-tier journals and conferences, including Nature and IEEE Transactions on Information Forensics and Security, and holds patents in personalized federated learning and medical query processing.

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.

Jingzhou Wang is an Associate Principal Scientist within Discovery Chemistry, Modeling and Informatics at Merck Research Laboratories. In this role, he provides computational chemistry support to both pipeline and scientific projects across various therapeutic modalities. Jingzhou Wang obtained a BS in Biochemistry from UCLA and a PhD in Biochemistry and Molecular Biophysics from Caltech, working on computational protein design and engineering.

Amy is a computational researcher focused on accelerating drug development by integrating biological data with scalable machine learning. As a Senior ML Scientist at Prescient Design (Genentech), she develops platforms supporting multiple discovery programs and works closely with experimental teams. She earned her PhD at Stanford studying cell adhesion proteins and collaborating on protein modeling and simulation, following earlier research in protein–polymer systems at MIT. Her combined expertise in AI/ML and biophysics drives innovation and adoption of new methods in biotech.

Jeremy is a recent PhD graduate from MIT, advised by Regina Barzilay. Jeremy's research focuses on biomolecuar modeling and applications in immunology. Jeremy is one of the authors of Boltz-1 and Boltz-2, leading open-source models for structure and binding affinity prediction.

Daniel Yoo is a Scientific Associate Director in Amgen's Large Molecule Discovery group, where he leads teams that develop promising protein therapeutic candidates and design innovative automation solutions. His areas of expertise include protein purification and analytics, high-throughput automation and informatics, and protein folding & modifications. Daniel Yoo received his Bachelor’s degree in Biology at the University of Rochester in 2003.