جست‌وجوی استاد و پژوهشگر

• Drug Delivery
• Nanomedicine
• Biopharmaceutics
• +۷ مورد دیگر

Dr. Shyh-Dar Li is a Professor and Tong Louie Chair in Pharmaceutical Sciences at the University of British Columbia's Faculty of Pharmaceutical Sciences, where he also serves as Chair of Nanomedicine and Chemical Biology. With a BSc in pharmacy from National Taiwan University (1998) and PhD in pharmaceutical sciences from UNC Chapel Hill (2008), followed by postdoctoral training at UC San Diego's Moores Cancer Center (2009), Dr. Li has established himself as a leading researcher in advanced drug delivery systems. His research focuses on developing innovative nanomedicine platforms for targeted delivery of biological therapeutics including peptides, proteins, antibodies, and nucleic acids. Dr. Li's laboratory has pioneered several novel drug delivery approaches, particularly in lipid-based nanoparticles, phospholipid-free vesicles, and polymer systems for cancer immunotherapy, pain management, and protein delivery. His work bridges fundamental nanotechnology with translational applications for difficult-to-treat diseases. Analysis of his recent publications reveals a strong emphasis on tumor microenvironment modulation, endosomal escape mechanisms for nucleic acid delivery, and non-invasive delivery routes for protein therapeutics. His research demonstrates increasing sophistication in nanocarrier engineering, with recent work incorporating machine learning approaches to optimize nanoparticle design and expanding into immunomodulatory therapies that harness the body's immune system to fight cancer. Scientific Recognition: 2014 AFPC New Investigator Award 2013 AAPS New Investigator Award in Pharmaceutics and Pharmaceutical Technologies 2013 CIHR New Investigator Award 2013 CSPS Early Career Award 2012 Prostate Cancer Foundation Young Investigator Award Dr. Li's research program has been consistently supported by major Canadian funding agencies including CIHR, NSERC, and MITACS. He actively collaborates across disciplines and accepts graduate students into his research program, focusing on cutting-edge approaches to overcome biological barriers in drug delivery. His laboratory, the Laboratory of Targeted Drug Delivery and Nanomedicine, serves as a hub for innovation in pharmaceutical nanotechnology.

Vikramaditya G. Yadavمشاهده پروفایل

دانشیار

• Biocatalysis
• Bioremediation
• Bioprocess engineering
• +۱۵ مورد دیگر

Vikramaditya G. Yadav is an Associate Professor at the University of British Columbia (UBC) in the Department of Chemical and Biological Engineering, Faculty of Applied Science. He directs the Master of Engineering Leadership (MEL) Program in Sustainable Process Engineering and leads the BioFoundry research group. Education: B.A.Sc., University of Waterloo (2007) Ph.D., Massachusetts Institute of Technology (2013) Postdoctoral Associate, Harvard University (2014) His research spans sustainable chemical manufacturing, metabolic engineering, and biotechnology. Key areas include: Designing biosynthetic enzymes for biomass valorization Developing bioremediation strategies for industrial water quality Creating innovative drug delivery systems and tissue engineering solutions Advancing synthetic biology for pharmaceutical and bioenergy applications His recent work focuses on ocular drug delivery, cannabinoid biosynthesis in E. coli, lignin-based nanoparticles for cancer therapy, and computational analysis of plant secondary metabolites. Collaborations with start-ups, industry, and medical labs drive innovation in Canada's bioeconomy. Professional Leadership: Chair, Biotechnology Division of the Chemical Institute of Canada Associate Editor, The Canadian Journal of Chemical Engineering He is affiliated with UBC's BioProducts Institute and contributes to project-based learning pedagogy.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Aberham Hailu Feyissaمشاهده پروفایل

دانشیار

• Food Process Engineering
• Heat and Mass Transfer
• Transport Phenomena in Porous Media
• +۷ مورد دیگر

Aberham Hailu Feyissa is an Associate Professor at the National Food Institute of the Technical University of Denmark , specializing in process modeling and sustainable food engineering. His research bridges complex transport phenomena with practical food manufacturing challenges. Ph.D. in Food Process Engineering, Technical University of Denmark MSc in Food Science, K.U.Leuven and Universiteit Gent BSc in Chemical Engineering, Bahir Dar University His work focuses on coupled mass and heat transfer during solid food processing, aiming to develop robust first-principles models for predictive process optimization. Key applications include Ohmic heating , digital twin technology , and bioactive ingredient extraction . Recent publications highlight his contributions to sustainable food processes , including insect-based feed modeling , seaweed bread kinetics , and clean-label cheese formulation . His methodological innovations span FTIR spectroscopy , computational fluid dynamics , and kinetic modeling . Supervisor for PhD students S. S. Turgut , M. E. Jabali , and Dahal S. Principal Investigator for projects like Modelling and Digitalisation of Food Processes and Sustainable Extraction of Bioactive Insect Fractions Active in conference presentations and peer review , he drives advancements in food process understanding through mechanistic modeling and mathematical simulation .

• Computational Physics
• Machine Learning
• Applied Mathematics
• +۷ مورد دیگر

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

Thomas Michaelsمشاهده پروفایل

استادیار

• Soft Matter Physics
• Living Matter Physics
• Protein Aggregation
• +۵ مورد دیگر

Thomas Michaels is an Assistant Professor at the Department of Biology, ETH Zürich, leading the Michaels Group . His research focuses on theoretical models of biomolecular condensates and protein aggregation in biological systems. Research Themes : Protein aggregation, liquid-liquid phase separation, membrane biophysics, and the role of condensates in neurodegenerative diseases like Alzheimer’s and Parkinson’s. Collaborative Approach : Integrates theoretical physics, control theory, and computational biology with experimental validation to design therapeutic strategies. Recent Publications highlight his work on amyloid formation mechanisms, lipid interactions, and phase-separated compartments as biochemical reactors. His group trains PhD students in systems biology and biocondensate physics.

Wengong Jinمشاهده پروفایل

استادیار

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +۵ مورد دیگر

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

Raul Astudillo Marbanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Decision-Making Systems
• +۵ مورد دیگر

Raul Astudillo Marban is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences at Caltech, hosted by Professor Yisong Yue. He will join MBZUAI as a tenure-track Assistant Professor in August 2025. His research focuses on adaptive learning and decision-making in complex, data-intensive environments, with applications in personalized healthcare, engineering design, and scientific discovery. He earned his Ph.D. in Operations Research and Information Engineering from Cornell University under Professor Peter Frazier and holds an undergraduate degree in Mathematics from the University of Guanajuato and the Center for Research in Mathematics. His work integrates Bayesian optimization and machine learning to address real-world challenges such as protein engineering, plant breeding, and computational biology. Key contributions include steering generative models with experimental data, preferential multi-objective optimization, and cost-aware Bayesian strategies. He has received recognition as a Rising Star in Management Science and Engineering (Stanford) and a Rising Star in Data Science (University of Chicago/UCSD). Recent research highlights include optimizing protein fitness through generative models, active learning in directed evolution, and Bayesian optimization for budget allocation in agriculture. His publications span top venues like NeurIPS, Nature Communications, and TMLR. He actively recruits students/researchers for projects in machine learning and optimization.

• Natural Products Chemistry
• Medicinal Chemistry
• Biosynthesis
• +۴ مورد دیگر

David H. Sherman is the Hans W. Vahlteich Professor of Medicinal Chemistry at the University of Michigan, holding joint appointments in the College of Pharmacy (Department of Medicinal Chemistry), Medical School (Microbiology & Immunology), and College of Literature, Science, and the Arts (Chemistry). He leads the Sherman Lab at the Life Sciences Institute and co-founded the Natural Products Discovery Core. His research focuses on natural product discovery, biosynthetic pathways, and drug development for infectious diseases, cancer, and neurological disorders. Education: PhD in Synthetic Organic Chemistry from Columbia University (1981), BA in Chemistry from UC Santa Cruz (1978). Postdoctoral research at MIT (1984). Research interests include microbial secondary metabolites, enzymatic catalysis (e.g., C-H functionalization, polyketide assembly), and high-throughput drug screening. He pioneered a microbial natural product library with over 50,000 samples. Current projects emphasize developing macrolide antibiotics and advancing compounds toward clinical trials through the Natural Products Biosciences Initiative. Collaborations span global institutions, with a focus on biodiversity conservation and capacity-building in low-income nations. He has mentored 67 PhD students, 60 postdocs, and 85+ undergraduates, fostering interdisciplinary training in chemical biology and microbial biochemistry. Labs/Teams: Sherman Lab (Life Sciences Institute), Center Member at Samuel and Jean Frankel Cardiovascular Center, Center for Computational Medicine and Bioinformatics, Rogel Cancer Center.

Rina Dechterمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۵ مورد دیگر

Rina Dechter is a Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences (ICS). She specializes in automated reasoning, probabilistic and constraint-based graphical models, and causal inference. Dechter has held leadership roles, including Co-Editor-in-Chief of Artificial Intelligence since 2011 and editorial board memberships in journals such as the Constraint Journal and Journal of Machine Learning Research . Education : Ph.D., Computer Science, University of California, Los Angeles (UCLA) M.S., Applied Mathematics, Weizmann Institute B.S., Mathematics and Statistics, Hebrew University of Jerusalem Research Interests : Dechter’s work focuses on computational aspects of automated reasoning, constraint processing, probabilistic reasoning, and causal inference. She develops efficient algorithms for graphical models, emphasizing tractable reasoning tasks and anytime search strategies. Her recent projects include causal decision-making frameworks funded by a $5M NSF grant. Awards : Presidential Young Investigator Award (1991) AAAI Fellow (1994) ACP Research Excellence Award (2007) ACM Fellow (2013) Elected to the American Academy of Arts & Sciences (2025) Grants & Collaborations : She leads a multi-institutional NSF-funded project on causal foundations of AI decision-making. Her work emphasizes trustworthiness in AI through causal models, with applications in robotics and public health.

Jörg F. Löfflerمشاهده پروفایل

استاد

• Bulk Metallic Glasses
• Metallic Biomaterials
• Magnetic Materials
• +۴ مورد دیگر

Jörg F. Löffler is a Full Professor of Metal Physics and Technology at the Department of Materials, ETH Zürich , where he has been since 2003. He previously served as Chairman of the Department (2010–2013) and holds Adjunct Professor positions at Tohoku University (Japan) and a Visiting Faculty role at Caltech. His research focuses on bulk metallic glasses , metallic biomaterials , and magnetic materials , combining synthesis, characterization, and applications in biomedical and structural contexts. Educations : Studied Physics and Materials Science at Saarland University (Germany), earned his doctorate at ETH Zurich and Paul Scherrer Institute on magnetism and neutron scattering (1997). Research Trends : Recent work explores additive manufacturing (e.g., laser powder bed fusion), biodegradable magnesium alloys for implants, and magneto-structural coupling in metallic glasses. Collaborative efforts integrate in situ analysis with computational modeling. Scientific Awards : International Magnesium Science and Technology Award (2023) MRS Fellow (2021) DGM 'Breakthrough' Prize (2016) Masing Memorial Prize (2005) ETH Zurich Medal (1998) Alexander von Humboldt Fellowship (1998-2001) Löffler advises the Department of Materials Science and Engineering at UC Davis and serves on editorial boards. His group at ETH Zürich investigates advanced metallic materials using synchrotron radiation and neutron scattering facilities.

Rebecca Schulmanمشاهده پروفایل

دانشیار

• DNA Self-Assembly
• Smart Biomaterials
• Synthetic Biology
• +۷ مورد دیگر

Rebecca Schulman is an Associate Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School of Engineering, Johns Hopkins University. She holds secondary appointments in Chemistry and Computer Science and is affiliated with multiple interdisciplinary institutes, including the Institute for NanoBioTechnology, the Hopkins Extreme Materials Institute, the Chemistry-Biology Interface Program, the Center for Cell Dynamics, and the Laboratory for Computational Sensing and Robotics. She currently co-directs the Passport to Future Technology Leadership program for PhD students. Research Interests: Schulman's research lies at the intersection of DNA nanotechnology, synthetic biology, and smart materials. Her group develops intelligent, adaptive biomolecular materials and nanostructures by integrating concepts from materials science, biochemistry, circuit design, and soft matter physics. The team focuses on engineering dynamic self-assembly processes using DNA to create reconfigurable materials, molecular circuits, and autonomous soft micro-robots. Key themes include self-healing nanostructures, feedback-regulated crystallization, programmable hydrogels, and synthetic genetic networks for materials control. Publication Trends: Her recent publications demonstrate a consistent focus on using DNA-based chemical reaction networks to program spatial and temporal behavior in materials. The work spans from fundamental mechanisms like catalytic polymerization and crystal growth regulation to applications in soft robotics, self-wiring circuits, and synthetic pattern formation. The research is highly interdisciplinary, combining synthetic biology with materials engineering to achieve life-like functionalities in non-living systems. Scientific Awards: AIMBE Fellowship Award Vannevar Bush Faculty Fellowship Award Hartwell Individual Biomolecular Research Award President’s Early Career Award in Science and Engineering (PECASE) DARPA Young Faculty Award DARPA Directors Fellowship NSF CAREER Award Turing Scholar Award DOE Early Career Award Advising and Grants: Schulman mentors graduate students and leads a vibrant research group focused on next-generation biomolecular engineering. Her work is supported by major federal grants, including the NSF CAREER, DOE Early Career, DARPA, and the Vannevar Bush Fellowship—a prestigious Department of Defense award for basic research. She is actively involved in training future leaders through programs like the Passport to Future Technology Leadership. Labs and Teams: The Schulman Lab at Johns Hopkins is a multidisciplinary team working on DNA-powered materials and molecular programming. The lab is embedded within several collaborative centers, enabling strong cross-departmental and cross-institutional research. Their work combines experimental biochemistry with theoretical modeling to design and implement complex molecular systems.

Britt Adamsonمشاهده پروفایل

دانشیار

• Stress Response Networks
• DNA Damage Response
• Genome Editing
• +۳ مورد دیگر

Britt Adamson is an Associate Professor in the Department of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where she serves as Director of the Undergraduate Program in Quantitative and Computational Biology. Her lab investigates molecular networks in human cells with focus on stress response mechanisms and genome editing technologies. She received her B.S. in Biology from the Massachusetts Institute of Technology (2005) and Ph.D. in Genetics and Genomics from Harvard University (2012), followed by postdoctoral training at UCSF under Jonathan Weissman supported by a Damon Runyon Cancer Research Foundation Fellowship. Adamson's research centers on how cells organize stress response networks during DNA damage and endoplasmic reticulum stress, developing CRISPR-based functional genomics and single-cell sequencing tools to map molecular behaviors. Her work bridges fundamental cell biology with therapeutic applications in genome editing. Analysis of her 15 most recent publications reveals dominant themes in precision genome editing (prime/base editing optimization) and systematic dissection of DNA repair pathways through combinatorial CRISPR screening. Her lab consistently integrates computational approaches with high-resolution experimental techniques to uncover context-dependent cellular behaviors. Her scientific recognitions include: Damon Runyon Cancer Research Foundation Postdoctoral Fellowship Princeton IP Accelerator Award (2025) STAT Who to Know: 10 Scientists leading a new generation of gene editors (2024) Adamson actively mentors eight graduate students (including alumni Ann Cirincione and Jun Hussmann) and two postdocs, with research funded through institutional awards and collaborative grants. Her lab's technological developments have enabled projects spanning virology, immunology, and developmental biology. The Adamson Lab operates within Princeton's Lewis-Sigler Institute for Integrative Genomics, fostering an interdisciplinary environment that merges cell biology, genomics, and computational science. Current projects focus on improving prime editing efficiency and understanding stress response adaptation in disease contexts.

Andrew Spakowitzمشاهده پروفایل

استاد

• Chromosomal organization and dynamics
• Protein self-assembly
• Polymer membranes
• +۷ مورد دیگر

Andrew Spakowitz is a Professor of Chemical Engineering, Materials Science and Engineering, and by courtesy, Applied Physics and Chemistry at Stanford University. He currently serves as the Senior Associate Dean for Research and Faculty Affairs and holds the Tang Family Foundation Chair of the Department of Chemical Engineering. His academic career at Stanford spans from Assistant Professor (2006-2014) to Associate Professor (2014-2020) and now Professor since 2020. Dr. Spakowitz earned his PhD in 2004, MS in 2001 from the California Institute of Technology, and his BS in Chemical Engineering from the University of Wisconsin, Madison in 1999. He completed postdoctoral training in Molecular and Cell Biology and Biophysics at UC Berkeley from 2004-2006. His research focuses on theoretical and computational approaches to understanding biological processes and complex materials. The Spakowitz lab addresses fundamental chemical and physical phenomena through four main research themes: chromosomal organization and dynamics, protein self-assembly, polymer membranes, and charge transport in conducting polymers. His group employs diverse theoretical and computational methods including analytical theory of semiflexible polymers, polymer field theory, continuum elastic mechanics, Brownian dynamics simulation, equilibrium and dynamic Monte Carlo simulations, and reaction-diffusion modeling. Analysis of his recent publications reveals a strong emphasis on epigenetics and chromatin dynamics, with significant work on DNA methylation patterns, nucleosome clustering, and chromosome organization. His research also extends to polymer physics applications in biological systems, particularly in respiratory diseases, water purification membranes, and bacterial phage interactions with human mucus. Tang Family Foundation Chair of the Department of Chemical Engineering Professor Spakowitz mentors several graduate students and postdoctoral scholars in the Chemical Engineering and Materials Science departments. His lab members work on diverse projects spanning from chromatin dynamics to polymer membranes for water purification. He teaches multiple courses including CHEMENG 120B (Energy and Mass Transport), CHEMENG 340 (Molecular Thermodynamics), CHEMENG 466 (Polymer Physics), and CHEMENG 467 (Physics of Biomacromolecules). The Spakowitz lab operates from Clark S295 at Stanford University, conducting theoretical and computational research that bridges chemistry, physics, biology, and engineering disciplines to address complex problems across multiple length and time scales.

Weiping Tangمشاهده پروفایل

استاد

• Targeted Protein Degradation
• PROTACs
• Medicinal Chemistry
• +۵ مورد دیگر

Weiping Tang is a Professor of Pharmaceutical Sciences and Chemistry at the University of Wisconsin-Madison, holding the Janis Apinis Professorship in the School of Pharmacy and the Vilas Distinguished Achievement Professorship. He also serves as Director of the Medicinal Chemistry Center at the School of Pharmacy and maintains a faculty appointment with the Department of Chemistry in the College of Letters and Science. Janis Apinis Professor of Pharmaceutical Sciences Vilas Distinguished Achievement Professor Director of Medicinal Chemistry Center Faculty Appointment with Department of Chemistry Dr. Tang received his B.S. in Chemistry from Peking University in 1997, M.S. in Chemistry from New York University in 1999, Ph.D. in Organic Chemistry from Stanford University in 2005, and completed a postdoctoral fellowship in Medicinal Chemistry, Chemical Biology and Drug Discovery at Harvard University in 2007. Dr. Tang's research program focuses on drug discovery for cancer, infectious diseases, and neurodegenerative disorders through three interconnected areas: Organic Synthesis (advancing glycoscience through novel carbohydrate synthesis technologies), Medicinal Chemistry (developing small molecules that selectively remove disease-associated proteins), and Chemical Biology (dissecting biological pathways using novel small molecule probes). His group operates as an interdisciplinary team where chemists and biologists collaborate closely on drug discovery projects, with particular emphasis on developing novel degraders for disease-causing proteins. Analysis of Dr. Tang's publication record reveals a significant shift toward targeted protein degradation technologies, particularly PROTACs and molecular glues, while maintaining strong foundations in carbohydrate chemistry. His most impactful recent work includes developing degraders for extracellular and membrane proteins (previously considered 'undruggable'), creating rapid synthesis platforms like Rapid-TAC and Rapid-Glue, and advancing understanding of ternary complex formation for novel PROTAC design. His research spans both chemical methodology development and therapeutic applications across multiple disease areas. Vilas Distinguished Achievement Professorship Janis Apinis Professorship Numerous high-impact publications in leading chemistry and pharmacology journals Editor's pick and hot paper designations for significant contributions Dr. Tang mentors a diverse team of graduate students, postdoctoral fellows, and staff scientists with expertise spanning synthetic chemistry, medicinal chemistry, carbohydrate chemistry, computational chemistry, biochemistry, and cell biology. His group has developed innovative platforms for the rapid synthesis of protein degraders and has made significant contributions to understanding the mechanisms of action for these novel therapeutics. Current research includes developing selective degraders for cancer targets like RIPK1, BRD4, and CARM1, as well as advancing delivery systems for clinical translation. The Tang Research Group maintains state-of-the-art facilities within the School of Pharmacy at UW-Madison, equipped for comprehensive chemical synthesis, compound characterization, and biological evaluation. The group actively collaborates with researchers across campus and with industry partners to advance discoveries toward clinical applications, with particular focus on cancer therapeutics and protein degradation technologies.