Dynamic Combinatorial Libraries Professors and Supervisors | ApplySharif

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Antonio MontesView profile

Professor

Polytechnic University of CataloniaSpain

Antonio Montes is a faculty member in the Departament de Matemàtica Aplicada at Universitat Politècnica de Catalunya (UPC), with affiliations to both the Facultat de Matemàtiques i Estadística and the Facultat d'Informàtica de Barcelona. He has taught a wide range of courses from 1981 to 2013, including Computer Algebra, Calculus, Symbolic Computation, and foundational mathematics for computer science and mathematics students. Research Focus: Computer Algebra, Symbolic Computation, Parametric Polynomial Systems, Gröbner Bases, Automatic Geometric Theorem Proving, Locus and Envelope Computation. Key Contribution: Development of the Gröbner Cover method and the grobcov.lib library in Singular, enabling canonical discussion of parametric polynomial systems. Major Publication: Author of the book The Gröbner Cover (Springer, 2018), which serves as a comprehensive theoretical and practical guide to the method. The recent articles highlight a strong and consistent research trajectory centered on the Gröbner Cover and its applications in symbolic computation and geometry. His work bridges theoretical algebra with practical software implementation, particularly in dynamic geometry environments like GeoGebra. The publications span algorithmic improvements, canonical representations, and applications to geometric theorem discovery, demonstrating both depth and interdisciplinary impact. The grobcov.lib library is a major software contribution, widely used in Singular for parametric system analysis. His research has enabled exact algebraic solutions in dynamic geometry, providing taxonomies to distinguish true loci from extraneous components. Collaborations with Michael Wibmer, Tomás Recio, and Montserrat Manubens have advanced the field of comprehensive Gröbner systems. Antonio Montes has made significant contributions to the theory and application of symbolic computation, particularly through the development and dissemination of the Gröbner Cover method. His work combines deep theoretical insight with practical software engineering, influencing both computer algebra and automated geometry. He has advised or collaborated with researchers in related areas, though formal advisees are not listed. His innovations continue to be applied in mathematical software and geometric reasoning systems.

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Frank KammerView profile

Researcher

Technical University of MittelhessenGermany

Frank Kammer is a researcher at the Technical University of Central Hesse , actively engaged in teaching and research projects. He co-leads the RegioTainment platform with Biebertal municipality and contributes to the SEA Project , an open C++ library for space-efficient algorithms. Teaching : Database Systems (CS1020), Software Engineering (CS1023), Efficient Algorithms (CS2353), Artificial Intelligence (CS2364/WK1613), etc. Research : Focuses on space-efficient graph algorithms, temporal graph exploration, and algorithm engineering. His work addresses computational challenges in huge datasets and memory-constrained devices , with applications in planar graph encoding, vertex separators, and student feedback systems. Advising : Supervised theses on topics including data-driven analytics platforms, low-code integration, cloud orchestration, and AI-optimized marketing campaigns. Collaboration : Works with students like Max Stephan, Timon Pellekoorne, and Johannes Meintrup on algorithm implementation and educational tools. Projects like RegioTainment emphasize digital community networking to preserve rural culture while embracing modernity.

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Marius LindauerView profile

Professor

Leibniz University HannoverGermany

Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute

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Kit S. LamView profile

Professor

University of California, DavisUnited States

Kit S. Lam is a Distinguished Professor and Chairman Emeritus in the Department of Biochemistry and Molecular Medicine at the University of California Davis School of Medicine. He also serves as Professor of Hematology & Oncology and holds the Sue Jane Leung Presidential Chair in Cancer Research. Dr. Lam is a board-certified medical oncologist and laboratory investigator who pioneered the field of combinatorial chemistry. B.A. in Microbiology (1975) - University of Texas at Austin Ph.D. in Oncology (1980) - McArdle Laboratory for Cancer Research, University of Wisconsin M.D. (1984) - Stanford University School of Medicine Internal Medicine residency and Medical Oncology Fellowship - University of Arizona Dr. Lam's research spans combinatorial chemistry, chemical biology, drug development, molecular imaging, and nanotheranostics. He is renowned for inventing the 'one-bead-one-compound' (OBOC) combinatorial library method published in Nature in 1991, which has been cited over 2,500 times. His laboratory focuses on developing revolutionary methods for disease treatment at the nano-scale, including cancer targeting ligands for imaging and therapy, nanotheranostic agents, and exosome diagnostics. Recent work includes developing tumor-targeting nanoparticles, membrane-active peptides, and novel platforms for cancer immunotherapy. Analysis of Dr. Lam's recent publications (2021-2023) reveals a strong focus on nanomedicine applications in cancer therapy, particularly in targeted drug delivery, immunotherapy enhancement, and diagnostic imaging. His work consistently integrates combinatorial chemistry approaches with nanotechnology to address challenges in cancer treatment, including drug resistance, tumor microenvironment modulation, and precision targeting. Key themes include bispecific nano-immunoengagers, tumor-associated macrophage imaging, and metabolic pathways in cancer radioresistance. Dr. Lam has received numerous prestigious awards throughout his career: Sue Jane Leung Presidential Chair in Cancer Research (2018) American Peptide Society's Vincent du Vigneaud Award (2013) Harold Albin Johnson Endowed Chair in Biomedical Research (2010-2021) Award of the Society of Combinatorial Sciences (2007) UC Davis Dean's Award for Excellence in Mentoring (2007) Leukemia Scholar of the Leukemia and Lymphoma Society As co-director of the Cancer Therapeutic Program at the UC Davis NCI-designated Comprehensive Cancer Center, Dr. Lam has secured significant grant funding for his research. He served on the NCI Board of Scientific Counsellor from 2016-2022 and serves on the External Advisory Boards of multiple cancer centers. His laboratory has developed numerous patented technologies including the OBOC combinatorial library method, with over 37 issued US patents to his name. The Lam Lab is a multidisciplinary research team comprising chemists, biologists, engineers, and clinicians working collaboratively on nanomedicine and combinatorial chemistry projects. The lab maintains strong collaborations with the UC Davis Veterinary School of Medicine for translational research in canine and feline patients. Their state-of-the-art facilities include capabilities for combinatorial library synthesis, nanoparticle formulation, in vitro and in vivo cancer models, and advanced imaging techniques.

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Daniel AndersonView profile

Teaching Professor

McGill UniversityCanada

Daniel Anderson is an Assistant Teaching Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He teaches Algorithm Design and Analysis (15-451) and previously taught at Monash University in Australia. His academic journey includes a PhD from CMU under Professor Guy Blelloch and a Bachelor of Science (Honours) in Applied Mathematics from Monash University. His research spans multiple areas of computer science with a strong focus on parallel computing. Anderson develops practical libraries like ParlayLib to make parallel programming more accessible, works on concurrent memory management techniques including "smarter atomic smart pointers," and explores parallel graph algorithms. His work bridges theoretical algorithm design with practical implementation challenges, particularly in making high-performance parallel computing more approachable for programmers. Anderson's publications reveal a strong trajectory in parallel algorithms and memory management, with significant contributions to wait-free algorithms, reference counting techniques, and batch-dynamic data structures. His research consistently appears in top venues like PLDI, SPAA, and EuroPar, demonstrating both theoretical rigor and practical relevance. Recent work focuses on making reference counting as efficient as manual memory reclamation while maintaining usability. Scientific Awards: Herbert A. Simon Award for Teaching Excellence in Computer Science (2024) Alan J. Perlis Graduate Student Teaching Award (2022) While Anderson currently focuses primarily on teaching, he remains active in research supervision, particularly for undergraduate and master's projects related to parallel computing. His work with the ParlayLib research group continues to advance the state of parallel programming tools. Anderson also has experience coaching the Monash University Programming Team for the International Collegiate Programming Contest (ICPC) and serving as a judge for South Pacific Programming Contests.

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Jörg ScheuermannView profile

Senior Lecturer

ETH ZurichSwitzerland

Jörg Scheuermann is a Senior Lecturer and Principal Investigator at the ETH Zürich in the Department of Chemistry and Applied Biosciences. His research focuses on DNA-encoded chemical library (DEL) technology and its applications in drug discovery, including PROTAC development and small-molecule drug conjugates (SMDCs). Broad research areas: DEL technology, medicinal chemistry, bioconjugate chemistry His work involves library construction, screening methodologies, hit validation, and translational applications against tumor-associated targets. Recent publications highlight innovations in dual-display DELs, tyrosinase inhibition, and ex vivo mass spectrometry analysis. Key contributions: Enzymatic DEL construction (SNF grant CRSII5_198673/1) 2020: Group leader at ETH Zürich Habilitation in DEL technology (2018)

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Monica S. GuoView profile

Assistant Professor

University of WashingtonUnited States

Monica S. Guo is an Assistant Professor in the Department of Microbiology at the University of Washington, where she leads research on genome organization and essential DNA processes in bacteria. Her work bridges molecular mechanisms with genomic-scale consequences using interdisciplinary approaches. Education: Ph.D. in Biochemistry and Molecular Biology, University of California, San Francisco (Advisor: Dr. Carol Gross) Postdoctoral Research, Massachusetts Institute of Technology (Advisor: Dr. Michael Laub) Research Focus: The Guo Laboratory investigates how DNA structuring proteins regulate molecular machines involved in genome maintenance and the organization of local DNA structure. Using genetics, biochemistry, single-molecule experiments, and high-throughput sequencing , her team explores fundamental questions in chromosome dynamics, DNA topology, and bacterial cell biology . This work provides critical insights into genome stability and molecular mechanisms driving essential cellular processes. Publication Trends: Recent work demonstrates consistent focus on bacterial chromosome architecture, with key contributions in DNA supercoiling mapping, protein-DNA interactions, and development of genetic tools. Publications span high-impact journals including Cell and eLife , revealing evolutionary principles in enzyme expression and molecular mechanisms of bacterial competition. Scientific Awards: No awards listed in provided text Advising and Lab Structure: Dr. Guo mentors two graduate students (Lucy Kwiatkowski, Sarina Kao) and leads a multidisciplinary team including a lab manager, postdoctoral researcher, research associate, and undergraduate trainees. The Guo Laboratory actively accepts graduate students year-round and maintains a collaborative research environment focused on training next-generation scientists. Research Infrastructure: The laboratory operates as a cohesive unit utilizing diverse molecular and genomic techniques, with ongoing projects examining chromosome organization in model bacterial systems. Current work extends to developing novel approaches for studying real-time molecular behaviors in living cells.

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Tuck Seng WongView profile

Professor

University of SheffieldUnited Kingdom

Professor Tuck Seng Wong is a leading academic in the School of Chemical, Materials and Biological Engineering at the University of Sheffield , holding the position of Professor of Biomanufacturing and serving as Director of the UK-South East Asia Vaccine Manufacturing Research Hub (a £7.6M initiative funded by DHSC and EPSRC). He maintains adjunct positions with Thailand's National Centre for Genetic Engineering and Biotechnology (BIOTEC) and Indonesia's Bandung Institute of Technology (ITB). His educational background includes a BEng(Hons) , MSc , and PhD (Special distinction) , with professional recognition as AMIChemE . Wong's research spans over two decades in protein engineering and engineering biology, with significant contributions to biopharmaceuticals, enzyme biocatalysis, and alternative food systems. Wong's research program focuses on evolutionary concepts for biological system optimization , miniaturized device development for biomanufacturing , and virus-like particle technology for vaccines . His work bridges fundamental science with industrial application, evidenced by his co-founding of biotech spinouts Evolutor Ltd (2022, advanced microbes for biomanufacturing) and ReNewFood Ltd (2024, protein-based flavoring ingredients). His textbook A Practical Guide to Protein Engineering has become a standard reference in the field. His publication record shows consistent output in high-impact journals, with recent work emphasizing sustainable biomanufacturing , vaccine development , and alternative protein systems . The research demonstrates strong regional collaboration with Southeast Asia and a clear trajectory toward commercial implementation of academic discoveries. RAEng | The Leverhulme Trust Senior Research Fellowship (2019) Royal Academy of Engineering Industrial Fellowship (2016) Medical Research Council Career Development Fellowship (2007) JSPS Invitational Fellowship (2023, declined due to hub award) Wong actively translates research into commercial ventures through Evolutor and ReNewFood, while directing a major international vaccine hub. His mentorship includes training under Nobel Laureate Frances H. Arnold and other distinguished scientists. The laboratory maintains strong industry partnerships and focuses on developing scalable biomanufacturing platforms for global health and sustainability challenges.

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Maxim LikhachevView profile

Professor

Carnegie Mellon UniversityQatar

Maxim Likhachev is a Professor at the Robotics Institute and National Robotics Engineering Center , both within the School of Computer Science at Carnegie Mellon University (CMU). His research focuses on Artificial Intelligence and Robotics , particularly in real-time graph search algorithms , planning under uncertainty , and high-dimensional mobile manipulation . He co-founded TravelWits.com and RobotWits (acquired by Waymo), and his lab develops the Search-based Planning Library (SBPL), integrated into ROS. Research Themes : Graph search-based planning for deterministic and probabilistic domains Decomposition of hard planning problems into sequential graph searches Incremental algorithms for dynamic environments Experience-driven heuristic optimization Scientific Awards : Gold Edison Award (2013) for industrial mobile manipulator project DARPA Urban Challenge Winner (2007) for autonomous SUV navigation Students : Supervises current PhD students including Itamar Mishani , Muhammad Suhail Saleem , and Rishi Veerapaneni , alongside Masters students like Gabriel Olin . Past advisees include Dhruv Saxena (now at Uber), Michael Phillips (now at Uber), and Shivam Vats (now at Waymo). Lab & Projects : Leads the Search-based Planning Laboratory at CMU, working on systems like K-MAX helicopter , PR2 robots , and industrial mobile manipulators for tasks including autonomous paint stripping and aerial navigation.

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Veronica Jimenez OrtizView profile

Associate Professor

California State University, FullertonUnited States

Veronica Jimenez Ortiz is an Associate Professor in the Department of Biological Sciences at California State University, Fullerton (CSUF). She serves as Program Coordinator for the Undergraduate Research Training Initiative for Student Enhancement (U-RISE) Program from 2023-2028. Her research focuses on ion channel function in protozoan parasites, particularly Trypanosoma cruzi, exploring mechanisms of environmental sensing and adaptation including mechanosensation, osmoregulation, and electrophysiological processes. Education: PhD, University of Chile, Santiago, Chile MS, Juan Agustin Maza University, Mendoza, Argentina BS, Juan Agustin Maza University, Mendoza, Argentina Her research interests include understanding the role of ion channels in parasite survival, particularly mechanosensitive channels regulating osmoregulation and infectivity. She investigates how Trypanosoma cruzi adapts to host environments through membrane potential control and pH regulation. Her work also explores Acinetobacter baumannii virulence mechanisms in host fluids like CSF and pleural fluid. Publications Trends: Recent work emphasizes novel mechanosensitive channels, antibiotic resistance modulation via zinc complexes, and host-pathogen metabolic interactions. Key areas include drug discovery targeting parasitic ion channels and enzymatic resistance pathways. Labs & Collaboration: Maintains an active lab at CSUF (website: Jimenez Lab ). Collaborates with institutions like the University of Chile and the University of California San Diego on parasite physiology and antimicrobial research.

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Hannah MorganView profile

Assistant Professor

University of ChicagoUnited States

Hannah Morgan is an Assistant Instructional Professor in the Department of Computer Science at the University of Chicago and also serves as a Lecturer in the Masters Program in Computer Science. She previously held a postdoctoral position in numerical methods at Argonne National Laboratory. Her work focuses on high performance computing, numerical models, and algorithms, particularly in the context of exascale computing and parallel implementations. Her research emphasizes developing performance models for high-performance computing algorithms, including those for solving large sparse systems and finite element methods for fluid models. At Argonne, she contributed to the Exascale Computing Project by studying computational kernels on heterogeneous CPU-GPU architectures. Her publications span topics like PETSc library enhancements for exascale systems, performance analysis of Krylov solvers, and finite element methods for fluid dynamics. These reflect her expertise in parallel computing, numerical linear algebra, and scalable simulation techniques. Morgan’s work bridges theoretical numerical methods with practical high-performance computing challenges, aiming to advance computational tools for scientific discovery at extreme scales.

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Amelia FullerView profile

Professor

Santa Clara UniversityUnited States

Amelia Fuller is Professor of Chemistry & Biochemistry at Santa Clara University, based at the Sobrato Campus for Discovery and Innovation (Room 2117B). Her research bridges plant evolutionary biochemistry and synthetic polymer design. Research focuses on two interconnected domains: Floral Biochemistry : Mechanisms of color polymorphisms in Lysimachia species, including anthocyanin pathway evolution and pollinator-driven selection Peptoid Engineering : Design of sequence-specific N-substituted glycine oligomers for molecular recognition in aqueous environments, with applications in drug delivery and environmental sensing Recent publications (2019-2025) demonstrate dual expertise: biochemical studies of flower color transitions using multiscale approaches, and peptoid development for nanoparticle stabilization, membrane interactions, and contaminant detection through fluorescence and X-ray techniques. Key awards: Ralph F. Hirschmann Award in Peptide Chemistry (2012) NSF CAREER Award (2011) Secured multiple NSF grants including RUI awards (2019, 2023) supporting undergraduate research integration. Developed course-based research experiences (CUREs) that replace traditional organic chemistry labs with combinatorial synthesis modules. Mentors students in multidisciplinary projects spanning biochemistry, nanotechnology, and chemical ecology. Leads a collaborative research group utilizing fluorescence spectroscopy, surface X-ray scattering, and computational modeling. Partners with institutions across the US and Cuba on antimicrobial discovery and chemistry education initiatives.

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James C. Adrian Jr.View profile

Professor

Union CollegeUnited States

James C. Adrian Jr. is a Professor of Chemistry at Union College, located in the Integrated Science and Engineering Complex (ISEC). His academic journey includes a B.S. from the University of Maryland, a Ph.D. from the University of Pittsburgh, and postdoctoral research at Yale University. His primary research focuses on organic synthesis, synthetic methods development, and NMR interpretation. Notably, he also explores interdisciplinary topics like the military costume of the 14th century, showcasing a unique blend of scientific and historical interests. Teaching responsibilities include core organic chemistry courses (CHM 231/232) and advanced synthetic methods (CHM 332). His research group has contributed to combinatorial library synthesis, stereochemistry in amino derivatives, and the functional significance of alarm pheromones in fish. Over 20 years of publications highlight his work in journals like Journal of Organic Chemistry , Tetrahedron , and Chemoecology . Key research themes include multicomponent reactions, nickel-catalyzed processes, and evolutionary conserved signaling mechanisms. His methodological innovations in β-amino carbonyl derivatives have applications in drug discovery and materials science. Though no awards are explicitly listed, his sustained publication record reflects consistent scholarly contribution. Adrian advises students in synthetic organic chemistry projects and integrates research into laboratory pedagogy through data-pooling approaches. His work bridges traditional organic synthesis with ecological applications, particularly in understanding chemical communication systems in aquatic species.

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Aydin BuluçView profile

Adjunct Associate Professor

University of California, BerkeleyUnited States

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

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Luca Castelli AleardiView profile

Assistant Professor

IMDEA Software InstituteSpain

Luca Castelli Aleardi is an Assistant Professor at the Laboratoire d'Informatique (LIX) of École Polytechnique, France, since September 2008. His research focuses on theoretical computer science, computational geometry, and discrete mathematics, particularly on Schnyder woods , graph drawing , and compact data structures for planar and surface-embedded graphs. PhD in Computer Science from École Polytechnique (2006) DEA in Algorithmique from Université Paris 6 (2003) Laurea in Applied Mathematics from University of Milan (2002) Erasmus exchange at Université de Versailles-St. Quentin (1999) His work spans graph encoding , mesh compression , and higher genus triangulations , with applications to data visualization, geometric algorithms, and network analysis. Recent publications in SoCG and GD emphasize practical implementations of theoretical constructs, such as toroidal Schnyder woods and SCARST data structures . He has supervised 15+ research internships (2008–2024) on topics including planar graph drawing and dynamic Laplacian systems . Software tools developed by his team include Jcg (Java library for computational geometry) and SCARST implementations.

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