Advancements at the Intersection of Biomedical Sciences and Engineering
To benefit from this discounted rate, guests are encouraged to make their reservations using the provided reservation link by no later than March 19, 2024.
The 2024 MIT Health Science Technology Conference will highlight broad and transformative trends in biology and healthcare, including early-stage discovery, immune cell responses, diagnostics, clinical capabilities, process development, rapid and continuous manufacturing, robotics and automation, and sustainability, all enhanced by artificial intelligence and machine learning technologies. Leading MIT researchers and MIT-connected startups will showcase these advances and discuss their implications for improving human health.
Dr. Gayathri Srinivasan became Executive Director of MIT Corporate Relations beginning February 15, 2024.
As Executive Director, Dr. Srinivasan leads the growth of the ILP and Startup Exchange, building on a roster of over 200+ member companies, and forging impactful connections between global business leaders and MIT faculty.
Dr. Srinivasan is a distinguished scientist, who received her PhD in Microbiology from The Ohio State University in 2004, where she contributed to the discovery of the 22nd amino acid, Pyrrolysine (2002). She first came to MIT as an NIH Postdoctoral Fellow in Prof. Tom Rajbhandary’s lab, where her research focused on understanding protein synthesis mechanism in Archaea.
Dr. Srinivasan subsequently moved into the business development and technology licensing space, serving in MIT’s Technology Licensing Office where she helped commercialize technologies in medical devices and alternative energies. She then moved to UMass Medical School’s Office of Technology Management in 2009 and to Emory University in Atlanta in 2014, as the Director of Public and Private Partnerships for the Woodruff Health Sciences Center. In 2019, Dr. Srinivasan joined Emory’s Office of Corporate Relations as Executive Director, and, in 2021, she led the Office of Corporate and Foundation Relations.
Sheryl Greenberg initiates and promotes the interactions and development of relationships between academic and industrial entities to facilitate the transfer of new ideas and technologies between MIT and companies, and has created numerous successful partnerships. By understanding the business, technology, and commercial problems within a company, and understanding the technologies and expertise of MIT researchers, Greenberg identifies appropriate resources and expertise to foster new technology applications and collaborative opportunities.
Prior to MIT, Greenberg created and directed the Office of Technology Transfer at Brandeis University. In the process of managing intellectual property protection, marketing, and licensing, she has promoted the successful commercialization of technologies as diverse as new chemicals and manufacturing, biotechnology, food compositions, software, and medical devices. She facilitated the founding and funding of new companies, as well as creating a profitable technology transfer program. She also facilitated the patenting, marketing, and licensing of Massachusetts General Hospital technologies. In addition to her cellular, biochemical, and genetic research experience in academic and corporate environments, she has also created intellectual property for medical uses. Greenberg has been an independent intellectual property and business development consultant, is a U.S. Patent Agent, and has previously served the Juvenile Diabetes Research Foundation as Co-Chair of the Islet Research Program Advisory Committee and grant reviewer. She currently also mentors startup companies and facilitates partnering them with large life science and healthcare companies.
Caroline Uhler joined the MIT faculty in 2015 and is currently a full professor in EECS (Electrical Engineering & Computer Science) and IDSS (Institute for Data, Systems and Society). Professor Uhler is also a core member of the Broad Institute, where she co-directs the Eric and Wendy Schmidt Center. and is a member of LIDS (Laboratory for Information and Decision Systems), the Center for Statistics, Machine Learning at MIT, and the ORC (Operations Research Center).
Professor Uhler holds an MSc in mathematics, a BSc in biology, an MEd in mathematics education from the University of Zurich, and a PhD in statistics from UC Berkeley. Before joining MIT, she spent a semester in the "Big Data" program at the Simons Institute at UC Berkeley, postdoctoral positions at the IMA and at ETH Zurich, and three years as an assistant professor at IST Austria.
Professor Uhler is a SIAM Fellow, an elected member of the International Statistical Institute , and a recipient of an NIH Director's New Innovator Award, a Simons Investigator Award, a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation.
Professor Uhler's research focuses on machine learning, statistics, and computational biology, in particular on causal inference, generative modeling, and applications to genomics, for example linking the spatial organization of the DNA with gene regulation.
Machine learning and the biomedical sciences have both experienced revolutions in the 21st century. We will discuss how these two fields are coming together and, in particular, also how today’s biomedical questions are motivating new developments in machine learning to help find answers.
Wengong Jin is a postdoctoral fellow in the Eric and Wendy Schmidt Center at Broad Institute. Previously, he obtained his PhD at MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. His research focuses on machine learning for drug discovery. He is particularly interested in developing geometric deep learning and generative AI models for virtual drug screening, de novo drug design, antibody design, and protein-ligand/protein binding. His work was published in leading AI conferences and biology journals like ICML, NeurIPS, ICLR, Nature, Science, Cell, and PNAS. His research received extensive media coverage including Guardian, BBC News, CBS Boston, and Financial Times. He is the recipient of the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award.
Modeling antibody-antigen binding is pivotal to drug discovery. Geometric deep learning is a promising paradigm for binding energy prediction, but its accuracy is limited by the size of training data, as high-throughput binding assays are expensive. Herein, we propose an unsupervised binding energy prediction framework named DSMBind, which does not need experimental binding data for training. DSMBind is an energy-based model that estimates the likelihood of a protein complex via SE(3) denoising score matching (DSM). This objective, applied at both backbone and side-chain levels, builds on a novel equivariant rotation prediction network derived from Euler's Rotation Equations. We find that the learned log-likelihood of protein complexes is highly correlated with experimental binding energy across multiple antibody-antigen binding prediction benchmarks. We further demonstrate DSMBind's zero-shot binder design capability through a PD-L1 nanobody design task, where we randomize all three complementarity-determining regions (CDRs) and select the best CDR sequences based on DSMBind score. We experimentally tested the designed nanobodies with ELISA binding assay and successfully discovered a novel PD-L1 binder. In summary, DSMBind offers a versatile framework for binding energy prediction and binder design.
Tavor Baharav is a first year postdoctoral fellow at the Eric and Wendy Schmidt Center at the Broad Institute, working with Professor Rafael Irizarry. His recent work has focused on developing computationally efficient and statistically valid methods for analyzing raw sequencing data without a reference genome. He has also worked on adaptive algorithms for data science problems using multi-armed bandits. His research borrows tools from optimization, information theory, and probability theory to design practically efficient and theoretically grounded algorithms for problems in computational genomics. Tavor obtained his Ph.D. in Electrical Engineering at Stanford University in 2023, where he was advised by Professors David Tse and Julia Salzman. At Stanford, he was a recipient of the NSF Graduate Research Fellowship and the Stanford Graduate Fellowship. Previously, he obtained his bachelor’s degree at UC Berkeley, where he worked with Professor Kannan Ramchandran on coding theory and its applications to distributed computing.
Computational genomics pipelines often rely heavily on alignment of sequencing data to a reference genome; however, this use of a reference genome can bias downstream inference and limit discovery of novel biology. In this talk I will discuss a unifying paradigm for genomic inference, SPLASH, which performs inference directly on raw sequencing data. We demonstrate SPLASH’s power for unbiased discovery by identifying viral strain mutations, cell-type-specific isoforms, and Ig and TCR diversity, in addition to tissue-specific transcripts in octopus and geographic and seasonal variation and diatom association in eelgrass.
Jiaqi Zhang is a 4th year Ph.D. student in MIT EECS, advised by Caroline Uhler. Her research focuses on establishing statistical and algorithmic foundations for decision-making within systems created by underlying causal rules. In particular, she develops algorithms and tools to understand causal relationships in data, extrapolate to predict the effects of unseen interventions, and select informative interventions for experimental design with applications in cell biology. Her research is supported by the Eric and Wendy Schmidt Center PhD Fellowship and the Apple AI/ML Scholarship.
Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains. A predominant example is how to identify optimal genetic perturbations that induce a specific cell state transition. This talk covers our methods for predicting unseen combinatorial perturbational effects and actively selecting the next most-informative experiment for identifying desirable interventions more efficiently.
Dr. Jongyoon Han is currently a professor in the Department of Electrical Engineering and Computer Science and the Department of Biological Engineering, Massachusetts Institute of Technology. He received B.S.(1992) and M.S.(1994) degree in physics from Seoul National University, Seoul, Korea, and Ph.D. degree in applied physics from Cornell University in 2001. He was a research scientist in Sandia National Laboratories (Livermore, CA), until he joined the MIT faculty in 2002. He received NSF CAREER award (2003) and Analytical Chemistry Young Innovator Award (ACS, 2009). His research is mainly focused on applying micro/nanofabrication techniques to a very diverse set of fields and industries, including biosensing, desalination / water purification, biomanufacturing, dentistry, and neuroscience. He is currently the lead PI for MIT’s participation for NIIMBL (The National Institute for Innovation in Manufacturing Biopharmaceuticals).
With the explosive growth in cell and gene therapy during the last decade, manufacturing of high quality gene delivery vectors (AAVs and Lentiviruses) now poses a critical bottleneck for the industry. In this presentation, I will introduce a continuous, intensified HEK293 bioprocessing platforms enabled by high-throughput microfluidic systems. Membrane-free vector harvesting by high-throughput inertial microfluidic system could be used to achieve intensified perfusion culture of host cells, and generated vectors are harvested continuously in order to minimize the loss and degradation. We believe that intensification of upstream vector bioprocessing would lead to a significant increase in productivity and reduction in reagents as well as GMP space needs, eventually resulting in reduction in the cost of gene and cell therapies of the future.
Ariadna Rodenstein is a Program Manager at MIT Startup Exchange. She joined MIT Corporate Relations as an Events Leader in September 2019 and is responsible for designing and executing startup events, including content development, coaching and hosting, and logistics. Ms. Rodenstein works closely with the Industrial Liaison Program (ILP) in promoting collaboration and partnerships between MIT-connected startups and industry, as well as with other areas around the MIT innovation ecosystem and beyond.
Prior to working for MIT Corporate Relations, she worked for over a decade at Credit Suisse Group in New York and London, in a few different roles in event management and as Director of Client Strategy. Ms. Rodenstein has combined her experience in the private sector with work at non-profits as a Consultant and Development Director at New York Immigration Coalition, Immigrant Defense Project, and Americas Society/Council of the Americas. She also served as an Officer on the Board of Directors of the Riverside Clay Tennis Association in New York for several years. Additionally, she earned her B.A. in Political Science and Communications from New York University, with coursework at the Instituto Tecnológico y de Estudios Superiores de Monterrey in Mexico City, and her M.A. in Sociology from the City University of New York.
Jose Amich is the Founder and CEO of Zeta Surgical. He is a graduate of Harvard University, where he majored in molecular biology and studied cancer cell biology under the direction of Professor Joan Brugge. He is a Forbes 30 Under 30 lister and the author of over 20 peer-reviewed articles and patents.
Bettina Hein is a serial software entrepreneur who has built two $100M+ AI-driven technology companies and is now founder and CEO of juli, a chronic condition management platform. Bettina has received numerous awards such as the Massachusetts Immigrant Entrepreneur of the Year Award and the L’Oréal Next Generation Award. She was also named a Young Global Leader at the World Economic Forum. Bettina is a well versed speaker and shares her experience and knowledge on entrepreneurship at various events.
Sean (Shunsuke) Matsuoka brings a wealth of experience in business development across diverse sectors, including notable companies like Mitsubishi Corporation, Takeda Pharmaceutical, and Fujifilm. His ability to foster collaborations, especially within pharmaceuticals and medical devices, is highlighted by his track record of managing deals with industry leaders. With expertise in marketing, consulting, and business development, Sean is well-positioned to lead fundraising efforts and optimize operations post-seed round closure. Holding degrees from Keio University and Harvard Business School, he is a versatile leader poised to drive impactful growth and innovation.
Dr. Uyanga Tsedev is a biomaterials engineer with broad interest in protein assemblies and manipulation of genetically encoded materials. As co-founder and CSO of Gensaic, Dr. Tsedev drives the discovery of functional sequences and the AI-guided evolution of protein structures for therapeutics delivery. She received her PhD in Biological Engineering from MIT, building genetically modified phage-like particles for localized delivery of DNA and small molecules in cancer models at the Koch Institute. Her MS in Mechanical Engineering was also completed at MIT, researching biological scaffolds for nanoscale materials templating. Before coming to MIT, Dr. Tsedev obtained her BS at Harvard University, where she worked on bioresponsive drug delivery and tissue engineering technologies with groups at the Wyss Institute and Beth Israel Deaconess Medical Center.
Dr. Roozbeh Ghaffari obtained his BS and MEng degrees in electrical engineering from the Massachusetts Institute of Technology in 2001 and 2003. He received his PhD degree in biomedical engineering from the Harvard Medical School-MIT Program in Health Sciences and Technology in 2008.
Upon completion of his PhD, Dr. Ghaffari co-founded MC10 Inc. and served as its Chief Technology Officer. In this role, Dr. Ghaffari led the development and launch of the BioStamp® and the My UV Patch wearable products. His contributions in soft bioelectronics, micro/nano-scale systems, and neuroscience have been recognized with the Helen Carr Peake PhD Research Prize (2008), the MIT-100K Grand Prize (2008), the Harvard Business School Social Enterprise Grand Prize (2008), MIT Technology Review Magazine's Top 35 Innovators Under 35 (2013), IEEE Emerging Technology Award (2016), and TEDx Gateway invited speaker (2016).
Dr. Sadegh Riazi is a technology enthusiast, co-founder and CEO of Pyte. He received his PhD in secure computation from UC San Diego where he was awarded the best PhD thesis award. Sadegh founded Project HEAX at Microsoft Research (a new computing platformfor Homomorphic Encryption). Prior to that, he studied Electrical Engineering and designed a new system for Brain-Computer Interfaces. Sadegh is a fellow of the Institute for Global Entrepreneur and has received multiple prestigious awards including Richard Newton Young Fellow Award, Jacobs Graduate Student Award, and Silver Medal in International Student Olympiad.
Dr. Ana Jaklenec, a principal research scientist and principal investigator at the David H. Koch Institute for Integrative Cancer Research at MIT, is a leader in the fields of bioengineering and materials science, focused on controlled delivery and stability of therapeutics for global health. She has over 15 years of experience and is an inventor of several drug delivery technologies that have the potential to enable equitable access to medical care globally. The Jaklenec lab at the Koch Institute is developing new manufacturing techniques for the design of materials at the nano- and micro-scale for self-boosting vaccines, 3D printed on-demand microneedles, heat stable polymer-based carriers for oral delivery of micronutrients and probiotics, and long-term drug delivery systems for cancer immunotherapy. She has published over 100 manuscripts, patents, and patent applications and has founded three companies, Particles for Humanity, VitaKey, and OmniPulse Biosciences.
Engineering translatable technologies to help global populations, and especially in developing countries has been met with various challenges related to infrastructure, patient access, and cold chain. I will present how polymers can be leveraged with engineering design to develop solutions for global health. For example, the development of the SEAL (StampEd Assembly of polymer Layers) technology that allows controlled pulsatile release of biologics days to months after injection, for single-injection self-boosting vaccines and cancer immunotherapy will be discussed. Additionally, the design of a microneedle vaccine printer that enables decentralized manufacturing of thermostable COVID-19 mRNA vaccine will be presented.
Patrick Doyle, a Chemical Engineering Professor at MIT, is renowned for his extensive research interests encompassing fundamental studies of single DNA molecules, nanoemulsions, nanofluidic/microfluidic devices, and innovative technologies for detecting DNA, miRNA, and proteins. Within his research group, a significant thrust involves leveraging microfluidics and soft matter concepts for pharmaceutical formulations. Another key focus is the development of barcoded technologies for sensitive miRNA detection, with impactful applications in cancer research and anti-counterfeiting measures.
Beyond his academic pursuits, Patrick Doyle has left an indelible mark as the co-founder of startup companies Firefly Bioworks (acquired by Abcam in 2015) and Motif Micro (acquired by YPB Systems in 2018). His engagement with numerous companies extends to roles as a consultant, Scientific Advisory Board (SAB) advisor, and participation in sponsored research within his lab. Adding to his diverse portfolio, Patrick serves as the Graduate Officer in the Chemical Engineering Department at MIT.
Specializing in micro- and nano-fluidics, multiplexed sensing, DNA biophysics, nanoemulsions, colloids, rheology, Brownian dynamics simulations, and particle engineering in Pharma, Patrick Doyle continues to be a visionary leader and innovator in the field.
Dr. Qin (Maggie) Qi is the James R. Mares ’24 Career Development Chair Assistant Professor in Chemical Engineering at the Massachusetts Institute of Technology. Her research applies fluid mechanics and transport principles to engineer soft materials for medical applications. She received her Ph.D. in chemical engineering with Prof. Eric Shaqfeh at Stanford University in 2018, where she won the T.S. Lo Fellowship and Stanford Graduate Fellowship. There, she also collaborated with the Royal College of Surgeons and BD Biosciences to develop a diagnostic device for various bleeding disorders. She then conducted postdoctoral research with Prof. Samir Mitragotri at the Wyss Institute of Biologically Inspired Engineering at Harvard University, where she developed a subcutaneous-tissue-on-a-chip model for pharmacokinetic testing (licensed to Sanofi). She was elected to the inaugural class of MIT Rising Stars in Chemical Engineering. She recently received the FY23 MIT research support committee award and was named a Science Influencer Mentor sponsored by the FDA.
Microscopic flows in a biological environment play a remarkable role in regulating human health, from disease causes to driving forces behind diagnostics and therapeutics. Its influence on other living organisms also has far-reaching impact in energy and environment. Such flow-induced dynamic effects, however, are often overlooked in engineering designs due to limitations in existing research toolsets. As a result, conventional biological and medical research face various challenges in accuracy, cost and translational success. In this talk, I will present our group’s work on applying fluid mechanics principles to design biomaterials, cell therapies and pharmacological models. We develop both experimental (in vitro) and computational tools mimicking a dynamic biological flow environment. The combination of these new tools enables us to reduce the use of animal models and shorten the preclinical research timeline while achieving tailor-made design outcomes towards precision medicine.
Owen Leddy is a PhD candidate in the MIT Department of Biological Engineering, co-advised by Professor Bryan Bryson and Professor Forest White. His research uses mass spectrometry analysis of peptides presented on MHCs for recognition by T cells (immunopeptidomics) to reveal mechanisms of antigen processing and presentation in tuberculosis and identify potential vaccine targets. Before coming to MIT, he obtained his B.S. in Biological Chemistry at the University of Chicago, where he conducted research in biophysics and molecular microbiology with Professors Aaron Dinner, Dominique Missiakas, and Olaf Schneewind.
T cell recognition of Mycobacterium tuberculosis (Mtb)-specific peptides presented on major histocompatibility complex class I and II (MHC-I/II) contributes to immunity to tuberculosis (TB), but the principles that govern the presentation of Mtb antigens on MHCs are incompletely understood. We hypothesized that addressing this knowledge gap would accelerate TB vaccine development for use in diverse human populations. We utilized mass spectrometry (MS) analysis to identify the repertoire of peptides presented on MHCs by Mtb-infected primary human phagocytes. We revealed that substrates of Mtb’s type VII secretion systems (T7SS) are overrepresented among Mtb-derived peptides presented on MHC-I. Quantitative, targeted MS showed that Mtb’s ESX-1 secretion system is required for the presentation of Mtb antigens on MHC-I. This system is notably absent in attenuated mycobacterial strains currently used for immunization against TB. We next established a biochemical workflow for the identification of antigens presented on MHC-II. These studies revealed Mtb protein antigens that could be presented on MHC-II by human phagocytes expressing a range of MHC-II alleles. We leveraged these discoveries to develop a workflow to evaluate new vaccine candidates for their capacity to generate peptide-MHC complexes in human antigen-presenting cells that are identical to those generated during Mtb infection, a potentially critical pre-clinical screening step. Our study identifies Mtb antigens presented on MHCs that could serve as targets for TB vaccines and reveals potential explanations for the limited efficacy of existing TB vaccines in human populations.
Dr. Harikesh Wong completed his PhD in Cell Biology at the University of Toronto, studying molecules at the nanoscale to determine how individual immune cells initiate signalling at short timescales. He then pursued his post-doctoral training with Dr. Ronald N. Germain at the National Institutes of Health (NIH), working at the interface of Immunology and Systems Biology. Here, Dr. Wong combined highly multiplexed imaging, cleared organ volume imaging, and computational modelling to study immune cells at single-cell resolution in tissues, with the goal of understanding how multicellular communication regulates the immune response over much greater spatiotemporal scales. Dr. Wong will open his lab in early 2022 as a Core Member of the Ragon Institute and an Assistant Professor in the MIT Department of Biology.
T cells are pivotal in mounting protective responses against pathogens and tumors, yet their activity entails a critical balance to avoid detrimental host tissue damage. This trade-off necessitates mechanisms that continually adjust the magnitude of T-cell responses, both in time and space. In physiological tissue environments, such control involves coordinated communication among multiple cell types, resulting in intercellular regulatory circuits. While this concept has become increasingly appreciated, in most instances, it fails to account for a critical variable: not all tissues are the same. Indeed, tissues differ markedly in their functions, selection pressures, and capacities for regeneration, implying that the trade-offs between effective host defense and the risk of collateral damage may be tissue-specific. We hypothesized that T cell regulatory circuits are uniquely adapted to the specific demands and characteristics of each tissue type, leading to variable immune responses across the body. To explore this concept, we employed high-resolution multiplexed imaging and computational approaches to examine the baseline variations in regulatory circuits across different tissues, such as barrier sites, endocrine organs, and reproductive organs. Our preliminary data has revealed distinct patterns of T cell regulation that correlate with tissue-specific attributes, suggesting a nuanced framework of immune control that is finely adjusted to the local tissue context. These findings challenge the one-size-fits-all view of immune regulation and open new avenues for understanding how tissue-specific immune responses contribute to health and disease.
Xiao Wang is a core institute member of the Broad Institute of MIT and Harvard and an assistant professor in the Department of Chemistry at MIT. She started her lab in 2019 to develop and apply new chemical, biophysical, and genomic tools to better understand tissue function and dysfunction at the molecular level. Xiao conducted postdoctoral research at Stanford University with Prof. Karl Deisseroth. She received her B.S. in Chemistry and Molecular Engineering from Peking University in 2010 and her Ph.D. in Chemistry from the University of Chicago in 2015, mentored by Prof. Chuan He.
Spatially charting molecular cell types at single-cell resolution across the entire three-dimensional (3D) volume of the brain is critical to illustrating the molecular basis of the tissue anatomy and functions. Recent development of spatial transcriptomic methods has enabled scalable profiling of transcriptome-defined spatial cell atlas. Yet, there is still a big gap between spatial cell atlas and tissue function. In this presentation, I will introduce a few experimental and computational advances in the mapping of RNA life cycle in our lab that further enable multi-modality deep profiling of cell types and states in situ, bridging single-cell molecular profiles with single-cell functional status in intact biological tissues and accelerating gene-to-function discoveries in development and diseases.
Anders Sejr Hansen obtained his undergraduate and Master’s degree in Chemistry at Oxford University in 2010. He received his Ph.D. in Chemistry and Chemical Biology from Harvard University in 2015, where he worked with Erin O’Shea and applied systems biology approaches to understand how cells can encode and transmit information in the dynamics of transcription factor activation. For his post-doc at UC Berkeley with Robert Tjian and Xavier Darzacq, Anders developed new imaging approaches for dissecting the dynamics of 3D genome organization with single-molecule resolution in living cells. Anders joined MIT as an Assistant Professor of Biological Engineering in early 2020.
Proper regulation of gene expression is essential for cell function, and dysregulation of gene expression is a primary cause of disease. The primary units of gene control in humans are enhancers. Enhancers can be located far away from the genes they control on the linear genome, raising the question of how the cell ensures that the right enhancer contacts and activates the right gene. We will present new super-resolution live-cell imaging and 3D genomics methods we have developed to begin to answer this question, what we have learned from these studies, and how we are beginning to use these technologies to translate findings from human genetics into a better understanding of how 3D genome misfolding causes disease.
Professor Kiessling received an Sc.B. degree in chemistry at MIT, where she performed undergraduate research in organic synthesis with Professor Bill Roush. She received a Ph.D. degree in chemistry at Yale University for her research with Stuart L. Schreiber. She was an American Cancer Society postdoctoral fellow with Peter B. Dervan at California Institute of Technology. She then joined the faculty at the University of Wisconsin–Madison, where she became the Steenbock Professor of Chemistry, the Laurens Anderson Professor of Biochemistry, and the Director of the Keck Center for Chemical Genomics. In 2017, she returned to MIT as the Novartis Professor of Chemistry.
Professor Kiessling is a member of the American Academy of Arts & Sciences, the American Academy of Microbiology, the American Philosophical Society, and National Academy of Sciences. She is the founding Editor-In-Chief of the journal ACS Chemical Biology . She is an author of over 140 peer-reviewed journal articles, and an inventor on more than 28 US patents. She has advised approximately 100 graduate and postdoctorate students. Alumni from her research group are contributing through their positions as faculty members of distinguished research universities, medical schools, and colleges and as research scientists at innovative start-up companies, leading corporations, and government laboratories.
Blueprints for agents regulating immune responses (vaccines or tolerizing agents) are needed. The design principles have been elusive because many immune system receptors can transmit signals that lead to either immunity or tolerance; therefore, a molecular understanding is needed. Our group is interested in how cell surface glycans of foreign cells (pathogens, cancer cells) influence immune responses and how such information can be co-opted to combat disease. To this end, we are generating small molecules and polymers as chemical probes to elucidate the different combinations of signals that give rise to tolerance or immunity. To this end, we found that glycan conjugates can give rise to potent signals that result in anti-cancer immunity. This seminar will discuss the relevant design features of these conjugates and the mechanisms underlying their activity against tumors.
In the dynamic world of EECS academia, Jason Yim stands out as a distinguished 3rd-year PhD candidate at MIT CSAIL. Guided by the expertise of the esteemed coffee connoisseur Tommi Jaakkola and the avid running enthusiast Regina Barzilay, Jason brings a wealth of experience to his current position.
Before embarking on his doctoral studies, Jason served as a research engineer at DeepMind, a testament to his practical expertise. He earned his foundational knowledge, attaining a B.S. in Computer Science and Applied Mathematics from Johns Hopkins University. The NSF Graduate Research Fellowship (GRFP) generously supports his current research endeavors.
This dedicated researcher is fervently committed to advancing machine learning methods in scientific domains, with a keen focus on applications in biology and chemistry. Jason's passion extends to utilizing these methods in the realms of medicine, environmentalism, and the broader pursuit of scientific knowledge.
Known for his insatiable curiosity, Jason relishes the process of comprehending the intricate science behind each problem. His approach involves developing practical algorithms that faithfully mirror the underlying structure and mechanism. Not constrained by specific techniques, he seeks a solid foundation in mathematics, statistics, and optimization to craft novel machine-learning methods.
His research portfolio showcases a broad spectrum, including applications in medical imaging, protein structure modeling, protein fitness optimization, and de novo protein design. Methodologically, he has delved into geometric deep learning, generative models, discrete optimization, and Riemannian manifolds.
In addition to his academic pursuits, Jason places high value on strong research engineering. He champions efficiency, moving swiftly to tackle challenges and minimize bugs. To delve deeper into his recent contributions, interested parties are encouraged to explore his publications page. For an even more personal insight, one can hear about his work through his CSAIL student spotlight. The narrative of innovation and exploration continues to unfold in Jason Yim's promising academic journey.
Deep learning has enabled considerable progress in protein structure prediction and design. In particular, the same technology underlying ChatGPT and DALL-E is rapidly being integrated into the workflows for de novo protein design. I will describe our work on this recent progress, RoseTTAFold Diffusion (RFdiffusion), that has enabled a generative AI framework for a wide range of protein design challenges. At the same time, generative AI is far surpassing the capabilities of traditional methods. These challenges span designing binder, enzymes, and peptides that have therapeutic applications. I will discuss the innovations, challenges, and future outlook of how generative AI will enable advancements in protein design.