جست‌وجوی استاد و پژوهشگر

• Small Molecule Libraries
• Protein-Tyrosine Kinases
• Adaptor Proteins, Signal Transducing
• +۷ مورد دیگر

Kheewoong Baek, Ph.D. is a Research Fellow in the Department of Biological Chemistry and Molecular Pharmacology (BCMP) at Dana-Farber Cancer Institute in Boston, Massachusetts. His research focuses on protein degradation mechanisms, molecular glues, and targeted therapeutics for cancer treatment. Dr. Baek's research spans several interconnected areas in chemical biology and cancer pharmacology. He specializes in targeted protein degradation technologies, particularly focusing on molecular glues that harness the CRBN E3 ubiquitin ligase system. His work involves chemoproteomics to identify novel protein-protein interactions, structural studies of ubiquitin ligases, and the development of combinatorial libraries for discovering new molecular glue degraders. His research demonstrates expertise in Small Molecule Libraries, Protein-Tyrosine Kinases targeting, Adaptor Proteins signaling, Cell Cycle Proteins regulation, and Combinatorial Chemistry Techniques. Dr. Baek has published multiple high-impact papers between 2023-2025, with significant contributions to understanding CRBN-dependent degradation pathways and ubiquitin signaling mechanisms. His work shows strong interdisciplinary integration of structural biology, chemical biology, and cancer pharmacology approaches. Dr. Baek collaborates extensively with researchers in the Biological Chemistry and Molecular Pharmacology department, particularly with Dr. Eric Fischer's laboratory. His research is likely supported by institutional funding from Dana-Farber Cancer Institute and potentially by grants from the National Institutes of Health or other research foundations focused on cancer biology and chemical biology. Dr. Baek is part of a vibrant research community at Dana-Farber working on targeted protein degradation, with colleagues including Rebecca Metivier, Shourya Roy Burman, and Hong Yue. His laboratory work focuses on understanding the molecular mechanisms of E3 ubiquitin ligases and developing novel therapeutic approaches based on these insights for cancer treatment.

• Protein Engineering
• Medical Diagnostics
• Cancer Therapy
• +۳ مورد دیگر

John Löfblom is a Professor at KTH Royal Institute of Technology specializing in Combinatorial Protein Technology. His research focuses on engineering proteins for medical applications, particularly in diagnostics and therapies for cancer and neurodegenerative diseases. He employs advanced techniques like directed evolution and combinatorial libraries to develop affinity proteins such as affibodies. His work includes creating protein-based drugs, prodrugs activated by cancer-associated proteases, and diagnostic tools like PET imaging agents targeting HER3, PDGFRβ, and CD69. Löfblom’s methods involve bacterial display systems (E. coli, staphylococci) for protein selection and optimization of radiolabeled affibody molecules for in vivo imaging. Key projects include: Development of HER3-targeted therapeutics and imaging probes Design of sequestrins to inhibit amyloid aggregation in Alzheimer’s Pioneering PET imaging solutions for early disease detection (e.g., fibrogenesis, inflammatory arthritis) Engineered proteins for BBB-crossing drug delivery systems His research bridges biotechnology and clinical translation, emphasizing preclinical efficacy and molecular imaging contrast optimization.

• Biological Engineering
• Data Science and Digital Agriculture
• Enzymatic Reaction Chemistry
• +۴ مورد دیگر

Karthik Sankaranarayanan is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University. His research focuses on applying data science to enzymology, developing computational models for enzyme reaction chemistry to address manufacturing challenges in pharmaceuticals, agrochemicals, and food flavoring. He leads the KSankar Group, which emphasizes experimentally testable predictions for complex synthetic molecules. His work bridges computational methods with biological engineering. He holds a position in the College of Engineering at Purdue, with affiliations to the ADM Agricultural Innovation Center and the LORRE Integrative Center for Biotechnology & Engineering. His research interests include enzymatic synthesis optimization, computational modeling of biochemical pathways, and sustainable biomanufacturing processes. He collaborates with institutions like the Alfred Mann Institute for Biomedical Development through Discovery Park. Key areas of exploration include similarity-based retrosynthesis algorithms, tunable enzymatic production of immunomodulatory lipids, and the use of machine learning for predicting enzymatic reaction outcomes. His lab develops tools for efficient biomanufacturing and analyzes structure-activity relationships in biomolecules. Dr. Sankaranarayanan’s research is supported by facilities like the Maha Fluid Power Research Center and the Bindley Bioscience Center. His work aims to enhance manufacturing efficiency while addressing societal needs in healthcare and agriculture through interdisciplinary engineering approaches.

Laurent Charlinمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۳ مورد دیگر

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

• Algorithmic Game Theory
• Mechanism Design
• Approximation Algorithms
• +۶ مورد دیگر

Itai Feigenbaum is an Associate Professor in the Department of Computer Science at Lehman College and the Computer Science Program at the Graduate Center, both part of the City University of New York (CUNY) system. His academic career focuses on bridging theoretical computer science with practical applications in economic and social systems. Dr. Feigenbaum's educational background includes: Ph.D. in Operations Research from Columbia University (2016) M.Sc. in Operations Research from Columbia University (2012) B.Sc. in Mathematics from Rutgers University-New Brunswick (2011), with minors in Computer Science and Operations Research His research spans multiple interconnected domains at the intersection of computer science, economics, and operations research. Dr. Feigenbaum specializes in algorithmic game theory and mechanism design , developing theoretically sound yet practically applicable solutions to complex allocation problems. His work in causal inference has produced significant contributions to understanding the theoretical foundations of causal discovery algorithms. In combinatorial optimization , he has tackled challenging problems in kidney exchange and school choice systems, creating algorithms that balance efficiency, fairness, and strategic considerations. His recent work increasingly integrates machine learning techniques with traditional optimization approaches, particularly in the causal inference space. Analysis of Dr. Feigenbaum's publication record reveals a consistent focus on strategic decision-making in constrained environments. His work demonstrates a progression from theoretical foundations in mechanism design to increasingly complex real-world applications, particularly in healthcare (kidney exchange) and education (school choice). The recent emphasis on causal inference represents a natural extension of his expertise in algorithmic decision-making under uncertainty. His publications appear in top venues across computer science, operations research, and economics, reflecting the interdisciplinary nature of his contributions. Dr. Feigenbaum has established productive collaborations with researchers across multiple institutions, particularly with colleagues at Columbia University (where he completed his PhD under Jay Sethuraman) and various AI research labs. His work on causal inference has involved collaborations with researchers from major tech companies, as evidenced by publications like the Salesforce CausalAI Library framework.

Sijbren Ottoمشاهده پروفایل

استاد

• Systems Chemistry
• Origin of Life
• Synthetic Biology
• +۵ مورد دیگر

Sijbren Otto is a Professor of Systems Chemistry at the Faculty of Science and Engineering , University of Groningen. His research bridges chemistry and biology through pioneering work on chemical evolution and self-replicating molecular systems . Key contributions include demonstrating how synthetic molecules can evolve Darwinian traits and developing life-emulating systems with replication, metabolism, and compartmentalization. Education: PhD in Chemistry (1998) from the University of Groningen, Royal Society University Research Fellow (2001–2009) Research Interests: Focus on systems chemistry , self-replication , foldamers , and dynamic combinatorial chemistry , aiming to synthesize life de novo . Article Trends: Recent work explores light-driven molecular systems , error-correcting replicators , and DNA-based programmable networks , integrating dissipative processes and stochastic emergence . Awards: James Flack Norris Award (2021) ERC Synergy Grant (2023) KNAW Membership (2020) Vici Grant (2013) Royal Society of Chemistry Fellowship Grants: Recipient of multiple ERC grants (2011, 2017, 2023) and NWO funding for Functional Molecular Systems (2013–2023). Labs: Leads the Otto Research Group at the Stratingh Institute, focusing on artificial life and evolutionary molecular networks .

Daniel Sussmanمشاهده پروفایل

استادیار

• Probability and Statistics
• Network Analysis
• Graph Theory
• +۳ مورد دیگر

Dr. Daniel Sussman is an Assistant Professor at Boston University, affiliated with the Probability and Statistics research group. He also serves as the Associate Director of the Program in Statistics. His work focuses on statistical methodologies for network analysis, including graph matching, latent variable models, and causal inference under network interference. Dr. Sussman develops computational tools for analyzing complex networks, such as the iGraphMatch R package for graph matching analysis. His research bridges theoretical foundations with practical applications in neuroscience, epidemiology, and computational social science. Key research interests include network dynamics, stochastic processes on graphs, and statistical estimation in noisy network data. He explores geometric and algebraic properties of random graphs, as well as scalable algorithms for large network datasets. His contributions span theoretical results (e.g., asymptotic consistency of embeddings) to applied methods (e.g., unbiased estimation in exposure models). Dr. Sussman’s publications highlight advancements in graph matching techniques, including matched filter approaches and multi-scale heat diffusion methods. His work emphasizes robustness to noise and scalability for real-world networks. While no explicit awards are listed, his contributions reflect significant impact in statistical network analysis. His professional role as Associate Director involves overseeing statistical training programs and curricula. Though no advising details are provided, his research group likely supports graduate students focused on network science and computational statistics.

William Petterssonمشاهده پروفایل

پژوهشگر

• Operations Research
• Integer Programming
• Graph Theory
• +۶ مورد دیگر

Dr. William Pettersson is a Researcher at the School of Computing Science, University of Glasgow, focusing on optimization and algorithmic research. He leads projects like the EPSRC-funded IP-MATCH initiative, addressing resource allocation challenges such as kidney exchange and student-doctor hospital placement systems. His work bridges theoretical contributions (e.g., integer programming, graph decomposition) and practical software development, including the Regina topology tool and kep_solver for kidney exchange analysis. Prior to Glasgow, he specialized in parallel algorithms and combinatorial topology in Australia. Key Projects: IP-MATCH (EPSRC), Regina software, kidney exchange optimization Research Themes: Combinatorial optimization, parameterized complexity, high performance computing Publications span graph theory, operations research, and algorithm design, with notable contributions to kidney exchange program modeling and spatial correlation analysis. Active collaboration with Prof. David Manlove and international teams in healthcare and computational topology.

Peter Andreas Kastمشاهده پروفایل

مدرس

• Enzymology
• Protein Engineering
• Directed Evolution
• +۳ مورد دیگر

Prof. Peter Andreas Kast is a Lecturer in the Department of Chemistry and Applied Biosciences at ETH Zurich. His research focuses on enzymology, protein engineering, and directed evolution, particularly investigating enzymes from pathogens such as Mycobacterium tuberculosis. He holds an honorary professorship at ETH Zurich (2011) and has received ETH medals for his diploma (1986) and doctoral theses (1991). Kast earned his Diploma in Natural Sciences (Dipl. Natw. ETH) from ETH Zurich in 1986, followed by a PhD in Microbiology. After postdoctoral work at ETH Zurich and the Scripps Research Institute, he joined ETH Zurich in 1997. His lab employs directed evolution to engineer enzymes and studies inter-enzyme allostery in metabolic pathways. Key research projects include optimizing chorismate mutase activity and elucidating structural mechanisms. His work spans enzyme kinetics, X-ray crystallography, and computational modeling. Awards include ETH medals and grants from the Swiss National Science Foundation. Teaching includes courses in Biological Chemistry. Collaborations involve structural biologists like Ute Krengel and Donald Hilvert. The Kast Lab is part of the Laboratory of Organic Chemistry at ETH Zurich, addressing challenges in biocatalysis and microbial metabolism.

• Fuzzy Logic
• Swarm Intelligence
• Computational Finance
• +۷ مورد دیگر

Dr. Hubert Zarzycki is a researcher at the Department of Computer Science and Systems Engineering within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on computational intelligence, fuzzy systems, and algorithm design for optimization problems. Research Interests: Dr. Zarzycki specializes in Ordered Fuzzy Numbers and their arithmetic operations Swarm Intelligence algorithms (e.g., bacterial foraging, cuckoo search, firefly) Applications in financial modeling, sensor placement, and routing optimization Integration of blockchain technology for supply chain transparency Publication Trends: His research spans multiple domains including fuzzy logic, computational finance, and industrial IoT. Recent work emphasizes directional representation in fuzzy systems, robust risk management frameworks, and modular control systems for time-sensitive applications. Contact: Available at hubert.zarzycki@pwr.edu.pl

• Plant Physiology
• Molecular Biology of Ethylene
• Hormonal Regulation in Plants
• +۷ مورد دیگر

Maria Del Carmen Rodriguez Gacio is a Contracted Professor and Doctor in the Department of Functional Biology at the Faculty of Biology, University of Santiago de Compostela. Her academic work spans teaching and research in plant physiology, particularly focusing on the molecular biology of ethylene and hormonal regulation in plants. She teaches core courses in Plant Physiology for Pharmacy and Biology degrees, as well as Experimental Sciences for the Master’s in Teaching. PhD in Plant Physiology, University of Santiago de Compostela, 1999 Thesis: "Alterations in the oxidation of ACC during embryogenesis and seed germination of Brassica rapa subsp. pabularia" Supervised by Dr. Angel Jesús Matilla Carro Her research interests include plant physiology, ethylene signaling, seed germination, hormonal regulation, plant stress responses, and developmental biology. She is a member of the research group "Molecular biology of ethylene" and has a long publication record in plant and environmental sciences. Her recent publications (2022–2025) reflect a strong engagement in plant molecular responses to environmental stress, species delimitation in algae, and ecological data sharing. These works span disciplines such as plant science, genetics, molecular biology, ecology, and biotechnology, indicating a multidisciplinary research profile with focus on plant adaptation and environmental interactions. Maria Del Carmen Rodriguez Gacio has contributed to over 400 publications, including articles, book chapters, and conference presentations. Her work is widely disseminated through open-access repositories like PANGAEA, Zenodo, and figshare. She is actively involved in research data management and scientific collaboration. Research group: Molecular biology of ethylene Area: Plant Physiology Department: Functional Biology School: Faculty of Biology University: University of Santiago de Compostela

Katherine Hollywoodمشاهده پروفایل

پژوهشگر

• Metabolomics
• Mass Spectrometry
• Bioanalytical Techniques
• +۵ مورد دیگر

Katherine Hollywood serves as the Senior Technical Specialist for Mass Spectrometry & Separations at the Manchester Institute of Biotechnology and the School of Chemistry, University of Manchester. She holds overall responsibility for facilities supporting research across the Faculty of Science and Engineering (FSE), the wider university, and external collaborations. With a PhD earned in 2010 under Prof. Roy Goodacre, her career spans post-doctoral research, SYNBIOCHEM involvement, and current leadership in analytical techniques. Her educational background includes a PhD from the University of Manchester (2010) focusing on bioanalytical techniques for skin diseases with emphasis on untargeted metabolomics. Her doctoral research, The Development of Bio-Analytical Techniques for the Treatment of Psoriasis and Related Skin Disorders , established her expertise in applying advanced analytical methods to clinical problems. Dr. Hollywood's research interests center on metabolomics, mass spectrometry applications, and bioanalytical technique development. She specializes in untargeted metabolomics approaches for clinical diagnosis and biomarker discovery, with significant contributions to Parkinson's disease research, aging studies, and synthetic biology applications. Her work intersects with multiple UN Sustainable Development Goals, particularly those addressing health and wellbeing through advanced diagnostics and biotechnology. Analysis of her 15 most recent publications reveals a strong focus on translational research bridging analytical chemistry with clinical applications. Her work spans neurodegenerative disease biomarkers (particularly Parkinson's), aging research incorporating ethnic diversity considerations, microbial engineering for compound production, analytical method validation for biosurfactants, and pandemic response diagnostics. The predominant methodologies involve advanced mass spectrometry techniques, particularly gas chromatography-mass spectrometry and LC-MS/MS approaches. Her research has been recognized through significant project funding, including leadership as Principal Investigator on the High Resolution Metabolomics project (2023-2024) and the 21ALERT initiative (2022-2023), along with contributions as Co-Investigator on multiple collaborative grants addressing microbiome engineering, protein dynamics, and synthetic biology applications. Dr. Hollywood actively participates in knowledge dissemination through the British Science Week Schools' Fair and media engagement on metabolomics applications. Her work on the Parkinsons Super Smeller project demonstrates commitment to health impact through innovative diagnostic approaches, while her research on meat adulteration detection shows application of metabolomics to food safety challenges.

• NF-κB signaling
• Oncology
• Immunology
• +۷ مورد دیگر

Professor Guido Franzoso holds the Chair of Signal Transduction and Inflammation at Imperial College London's Faculty of Medicine. He is an Honorary Consultant at Imperial College Healthcare NHS Trust and co-Lead of the Immuno-Pathology Network, collaborating with institutions like the Institute of Cancer Research and the Francis Crick Institute. His research focuses on NF-κB pathway regulation, cancer metabolism, and immunotherapy, with translational projects in clinical trials for multiple myeloma and lymphoma. He leads the Centre for Molecular Immunology and Inflammation (CMII) and has held academic roles since 1997, including professorships at the University of Chicago. Education: M.D. (Summa Cum Laude), University of Padua, 1987 Ph.D. in Microbiological Sciences, University of Padua, 1993 Key Research Themes: Therapeutic targeting of NF-κB in oncology Cancer metabolism and precision medicine Immunosuppressive tumor microenvironment Clinical development of GADD45β/MKK7 inhibitors (DTP3) Awards: 7 patents and multiple research awards for contributions to NF-κB biology and translational oncology. Current Projects: Phase I/II clinical trials for multiple myeloma Companion biomarker discovery in CRC and other cancers Anti-cancer immuno-therapy development Affiliations: CRUK Convergence Science Centre, Membrane Receptor Network, and the Centre for Drug Discovery Science (CDDS).

Benjamin C. Gorskeمشاهده پروفایل

دانشیار

• Peptoid Chemistry
• Synthetic Biomimetics
• Organic Synthesis
• +۴ مورد دیگر

Benjamin C. Gorske is an Associate Professor of Chemistry and Biochemistry at Bowdoin College, currently on leave for the 2025–2026 academic year. His research lab focuses on developing synthetic mimics of biomolecules to engineer molecular machines, advanced materials, and therapeutic interventions for diseases like cancer and Alzheimer's. Education: PhD, Organic Chemistry, University of Wisconsin-Madison (2007) BA, Chemistry, Lawrence University (1995) BM, Piano Performance, Lawrence University (1995) Research Interests: The Gorske Lab employs molecular synthesis, chemical analysis, and biological activity assays to engineer peptoids (synthetic peptide analogues). Key areas include controlling peptoid secondary structures, leveraging noncovalent interactions for biomimetic functions, and applying these systems to disrupt disease mechanisms. Research spans organic chemistry, chemical biology, and materials science, with emphasis on stereoselective synthesis and structure-function relationships. Publication Trends: Gorske's 10 most recent articles (2005–2017) focus on peptoid conformation, catalytic synthesis, and biomolecular interactions. Dominant themes include helical stabilization via electronic interactions, gold-catalyzed regioselective reactions, and peptidomimetic strategies against bacterial pathogens. His work consistently bridges fundamental organic chemistry with biomedical applications. Awards: Faculty of 1000 Biology Highlight (2007) for work on tunable amide isomerism Faculty of 1000 Biology Tech Advance (2006) for pentafluoroaromatic peptoid design Lab: The Gorske Lab at Bowdoin College welcomes researchers of all experience levels to study peptoid chemistry and biomimetic systems.

Neng-Fa Zhouمشاهده پروفایل

استاد

• Programming Languages
• Constraint Programming
• Logic Programming
• +۶ مورد دیگر

Neng-Fa Zhou is a Professor of Computer and Information Science at Brooklyn College and the Graduate Center of the City University of New York (CUNY). He holds a BS from Nanjing University (1984), and MS and PhD from Kyushu University (1988, 1991). Before joining CUNY, he served as an Associate Professor at Kyushu Institute of Technology (1991-1999) and held visiting positions at Yale, Alberta, Tokyo Tech, and Melbourne. Specializes in programming languages, constraint logic programming, and compiler design Developed Picat and B-Prolog languages with constraint-based graphics libraries Contributed to SAT encodings, multi-agent pathfinding, and declarative programming Scientific Awards: Most Practical Paper Award at PADL 2017 Award in ASP Solver Competition for BPSolver (2011)