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Erik RosolowskyView profile

Professor

• Astrophysics
• Star Formation
• Interstellar Medium
• +3 more

Erik Rosolowsky is a Professor of Physics and Associate Chair in the Faculty of Science at the University of Alberta. He holds a PhD from the University of California, Berkeley (2005), MA from UC Berkeley (2001), and BA from Swarthmore College (1998). His research focuses on understanding the recycling of stellar matter in galaxies, using radio and submillimeter telescopes alongside algorithmic methods. Key interests include star formation, interstellar medium dynamics, and molecular gas properties in nearby galaxies. He teaches graduate and undergraduate courses in astronomy and physics, emphasizing flipped classrooms and problem-solving skills. Notable courses include PHYS 144 (Newtonian Mechanics) and PHYS 574 (Data Analysis in Physics). Rosolowsky is a core member of the PHANGS collaboration, leveraging JWST and ALMA for high-resolution galaxy studies. He also leads efforts in data science for astrophysics, developing tools like CARTA and CIRADA. Recent work includes analyzing molecular gas in NGC 6822, modeling starburst galaxies, and processing JWST data. His research bridges observational techniques with computational methods to explore galaxy evolution and stellar lifecycle connections. Despite no listed awards, his contributions to astrophysical data pipelines and collaborative surveys highlight his academic impact.

Susanna RöblitzView profile

Professor

• Systems Biology
• Computational Pharmacology
• Mathematical Modeling
• +4 more

Susanna Röblitz is a Professor at the Department of Informatics within the Computational Biology Unit at the University of Bergen. Her research focuses on mathematical modeling of high-dimensional dynamical systems in life sciences, including molecular conformation dynamics, systems biology, and pharmacology. She specializes in rare event sampling, meshfree methods, clustering algorithms, and uncertainty quantification, with a particular emphasis on oscillatory systems such as hormonal rhythms. Her teaching includes courses such as BINF100 Introduction to Bioinformatics and BINF305 Systems Biology . Her research has been supported by grants from the Research Council of Norway and Nordforsk, including projects like Markov State Models for Cellular Phenotype Switching and RAS-TOOLS . Key research areas include hormonal regulation (e.g., menstrual cycle modeling), gene regulatory networks, and applications in pharmacokinetics and aquaculture. Recent work highlights include the development of Markov state models for gene networks and spectral clustering techniques for analyzing complex biological systems. She has supervised students such as Anna Kristina Fredheim Grandma, whose master’s thesis focused on pharmacokinetic modeling in personalized dosing. Her interdisciplinary collaborations span computational biology, veterinary medicine, and clinical pharmacology.

Ricardo Grau-CrespoView profile

Associate Professor

• Materials Science
• Computational Chemistry
• Energy Materials
• +3 more

Dr. Ricardo Grau-Crespo is an Associate Professor of Materials Theory and Lead of the Chemical Sciences Research Division at the University of Reading. He is affiliated with the School of Chemistry, Food, and Pharmacy within the Department of Chemistry, focusing on computational materials science for clean energy applications. His research explores molecular-level simulations to investigate materials for energy storage and environmental technologies. Key projects include studies on spinel ferrites, thermal conductivity in pyrochlores, and nanofluids for thermal energy systems. He leads the GCMT Group (https://gcmt-group.github.io/) and is active in advancing computational methods for materials discovery, including machine learning applications. His work bridges theory and experiment, with notable contributions to photocatalysis, thermoelectrics, and interfacial phenomena in nanomaterials. Research Themes: Environment, Energy Materials, and Computational Materials Science. Collaborations include experimental partners for validation of computational models. Active in high-throughput screening and AI-driven material design, emphasizing sustainability and energy efficiency.

David Ryan KoesView profile

Associate Professor

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +3 more

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

Christian TetzlaffView profile

Professor

• Computational Neuroscience
• Synaptic Physiology
• Neuromorphic Engineering
• +4 more

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

Zhiwei FengView profile

Assistant Professor

• Drug Discovery
• Computational Chemistry
• Machine Learning in Biology
• +6 more

Zhiwei Feng is an Assistant Professor at the School of Pharmacy, University of Pittsburgh , serving as Assistant Director of the Computational Chemical Genomics Screening Center (CCGS). He holds a PhD in Computational Chemistry/Materials from China and completed postdoctoral training at the University of Pittsburgh. With over 20 years of experience in drug discovery tools, his expertise includes computer-aided drug design (13 years), antibody drug discovery (6 years), and virtual reality apps for scientific visualization (7 years). Education: PhD in Computational Chemistry/Materials (2013), China MS and BS in Computer Sciences (2010 and 2007, respectively) Research Focus: AI-driven drug design (antibodies, small molecules) ML/DL-based chemogenomics platforms (e.g., Pain-CKB, Virus-CKB) Targeted therapies for pain, cancer, and stem cell expansion Systems pharmacology and PK/PD modeling His work emphasizes computational tools for drug repurposing, GPCR modulation, and anti-SARS-CoV-2 drug discovery. Key Achievements: Developed IsAb computational antibody design protocol Co-inventor of U.S. Patent 10,899,702 (TRPV1-based pain treatment) Over 65 publications in top journals like Cell , Nature Communications , and ACS Chemical Neuroscience . Lab & Collaborations: CCGS integrates computational and experimental methods for drug discovery Active collaborations in structural biology, systems pharmacology, and clinical data analysis

Guoqing LuView profile

Professor

• Bioinformatics
• Genomics
• Viral Evolution
• +5 more

Guoqing Lu is an Isaacson Professor of Bioinformatics and Genomics at the University of Nebraska at Omaha (UNO), affiliated with the Department of Biology and School of Interdisciplinary Informatics. He holds a Ph.D. in Biology from Laval University, with additional master's degrees in Biology and Computer Science, and completed postdoctoral research at McGill University. His research focuses on computational methods for genomic data analysis, viral evolution, and applications in aquaculture and conservation biology. Dr. Lu has directed the Bioinformatics Research Core Facility at UNL and received major grants from NIH, NSF, and USDA. Awards include UNO’s Excellence in Research Award and Distinguished Research Award. He teaches courses such as Bioinformatics Algorithms and Comparative Genomics, and chairs committees including Bioinformatics UPC and Biology GPC. His lab explores topics like fish microbiomes, pathogen-host interactions, and CRISPR applications in aquaculture. Education: Ph.D. Biology (Laval University), M.Sc. Biology & Computer Science, Postdoc (McGill University). Research Interests: Bioinformatics tools development, genomic data mining, viral evolution modeling, fish pigmentation genetics, and eDNA-based ecological monitoring. His work bridges computational methods with biological problems like invasive species control and disease dynamics. Grants & Awards: Multi-million grants from NIH/NSF/USDA; awards for research excellence in UNO’s College of Arts & Sciences. Serves on editorial boards and reviews for agencies like NSF-China and UK MRC. Advising & Service: Advisor to students, leader in curriculum development for BIOT programs. Consults on sustainability and bioinformatics initiatives. Labs & Teams: Directs Dr. Lu’s Lab, focusing on bioinformatics applications in ecology, aquaculture, and conservation genetics. Collaborates with institutions globally on genomic projects.

Michael ShelleyView profile

Professor

• Fluid Dynamics
• Active Matter
• Biophysics
• +3 more

Michael Shelley is the Lilian and George Lyttle Professor of Applied Mathematics at New York University's Courant Institute of Mathematical Sciences. He holds additional roles as Professor of Mathematics, Neural Science, and Mechanical Engineering. His research focuses on fluid dynamics, active matter, and biophysical systems, with notable contributions to microswimmer dynamics, cytoplasmic flows, and fluid-structure interactions. Shelley leads the Applied Mathematics Laboratory at Courant and directs the Center for Computational Biology at the Flatiron Institute. His work bridges theoretical, computational, and experimental approaches to understand complex biological and physical phenomena. Research interests include nonlinear dynamics of fluids, collective behavior in active matter systems, and biomechanical processes such as mitosis and cellular transport. Recent studies involve modeling microtubule networks, cytoplasmic stirring, and spindle positioning in cells. His publications span topics like fluid-structure interactions, viscoelastic flows, and the mechanics of swimming organisms. Key projects include the dynamics of erodible bodies in fluid flows, optimization of microswimmer designs, and the rheology of active suspensions. Shelley collaborates across disciplines, integrating applied mathematics with biology, physics, and engineering. His work has advanced understanding of self-organization in living systems and fluid-driven morphological changes.

Jean Adelina KingView profile

Professor

• Neuroscience
• Chronic Pain Management
• Mental Health
• +7 more

Dr. Jean Adelina King is the Peterson Family Dean of Arts & Sciences and Professor of Biology & Biotechnology at Worcester Polytechnic Institute (WPI). Previously, she served as Vice Provost of Biomedical Research and tenured Professor at UMass Medical School (1994–present). She leads interdisciplinary initiatives in Neuroscience, AI, and Learning Science at WPI while maintaining research on stress-brain interactions. Education: PhD (New York University, 1988), MS (City University of New York, 1982), BS (St. Francis College, 1979) Leadership Roles: NIH Advisory Boards, Co-chair of World Women in Neuroscience, Co-Director Marine Biological Lab-MBL Summer Program Her research focuses on stress impacts on brain/body health, chronic pain, and youth mental health. Notable projects include NIH-funded HEAL Initiative work using AI to optimize mindfulness for pain management. Over 100 peer-reviewed papers and 10 book chapters in neurophysiology underscore her scholarly contributions. Awards: GK50 Healthcare Influencer, Massachusetts Mentorship Citation, Katherine Erskine Award As a diversity advocate, she mentors underrepresented scientists and developed the micro-communities mentorship model. Featured in Nature for her leadership in STEM equity.

• Theoretical Computer Science
• Algorithmic Information Theory
• Computational Complexity
• +3 more

Neil Lutz is a Visiting Assistant Professor at Swarthmore College and an Affiliate Assistant Professor at Iowa State University, specializing in Theoretical Foundations. His research focuses on algorithmic information theory, computational complexity, and fractal geometry. Key contributions include work on algorithmic dimensions, point-to-set principles, and fairness in facility location. Lutz has published extensively in top venues such as STACS, TOCT, and SODA. His work bridges theoretical computer science with applications in molecular programming and distributed computing.

Andrew GordusView profile

Assistant Professor

• Cellular Neuroscience
• Molecular Neuroscience
• Neural Circuits
• +13 more

Andrew Gordus is an Assistant Professor in the Department of Biology at Johns Hopkins University, with affiliations in the Neuroscience Training Program and the Cellular, Molecular, Developmental and Biophysics (CMDB) Graduate Program. He leads an active research laboratory focused on the neural and genetic basis of behavior using model organisms such as C. elegans and the spider Uloborus diversus . His research centers on understanding how neural circuits generate both novel and innate behaviors in response to environmental variability. Using genetic, imaging, and behavioral tools, his lab investigates decision-making, motor control, and sensory integration in these organisms. Notably, they study the precise movement sequences in spider web-building and stochastic foraging behaviors in worms. The lab's recent publications reveal a strong emphasis on neural circuit dynamics , behavioral algorithms , genetic tools development , and systems neuroscience , often combining molecular genetics with quantitative behavioral analysis. Their work appears in high-impact journals such as Current Biology , eLife , and PNAS . NSF Division of Physics grant (2023) NIGMS R35 grant renewal (2023) JHU Catalyst Award (2022) Buck Institute Nathan Shock Center grant (2022) NSF Postdoctoral Fellowship (mentored awardee) Gordus mentors a diverse group of graduate students, postdocs, and undergraduates, and supports their professional development. His lab culture emphasizes diversity, equity, and inclusion, welcoming scientists from immigrant, LGBTQ+, Black, Jewish, Latino, and Asian backgrounds. The lab is supported by multiple competitive grants and actively contributes to advancing the understanding of how brains generate complex behaviors.

Geoffrey BartonView profile

Professor

• Bioinformatics
• Computational Biology
• Protein Structure Prediction
• +4 more

Professor Geoffrey Barton is the Chair of Bioinformatics at the Computational Biology department within the School of Life Sciences at the University of Dundee . With a career spanning decades, he has been pivotal in advancing bioinformatics tools and methodologies for analyzing biological data, particularly in protein structure prediction and RNA sequencing. Core Research Interests : Protein structure and function prediction, computational methods for large biological datasets, small RNA analysis, and integration with wet-lab collaborations across plants, model organisms, and human disease. Awards : Fellow of the Royal Society of Edinburgh (2019) Fellow of the Royal Society of Biology (2011) Brian Cox Prize for Excellence in Public Engagement with Research (2023) Publications and Software : He leads the development of widely used tools like JPred (25,000+ predictions/month) and Jalview (55,000+ installations). His group's work spans proteomics, transcriptomics, and structural genomics, addressing questions in basic science and clinical applications. Media and Collaboration : Available for media commentary on bioinformatics and computational biology, he collaborates extensively with international institutions, including the University of Cambridge and Oxford.

Susann MüllerView profile

Associate Professor

• Microbial Ecology
• Flow Cytometry
• Environmental Biotechnology
• +5 more

Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.

Attila KumanovicsView profile

Clinical Professor

• Molecular genetic pathology
• Immunology
• Primary immunodeficiency diseases
• +5 more

Attila Kumanovics, M.D., is a Clinical Professor in the Department of Laboratory Medicine and Pathology at Mayo Clinic in Rochester, Minnesota, USA. A board-certified clinical pathologist (American Board of Pathology, 2011) and molecular genetic pathologist (American Board of Pathology, 2012), he focuses on molecular and cellular immunology testing and the diagnosis of inborn errors of immunity. Education & Certifications Board Certification – Molecular Genetic Pathology, American Board of Pathology, 2012 Board Certification – Clinical Pathology, American Board of Pathology, 2011 M.D. (institution & year not specified in text) Research Focus Dr. Kumanovics’ research integrates cutting-edge molecular genetic techniques with clinical immunology to uncover the genetic underpinnings of primary immunodeficiency diseases. His work spans: Molecular Genetic Pathology: Development and validation of next-generation sequencing assays for HLA typing and immunodeficiency gene panels. Primary Immunodeficiencies: Identification and characterization of novel monogenic defects affecting immune cell development and function. Diagnostic Immunology: Optimization of flow-cytometric and functional assays for immune cell subset analysis and autoantibody detection. Publication Impact With 61 peer-reviewed publications from 1995 to 2025, Dr. Kumanovics has produced an influential body of work that advances both basic understanding and clinical practice. Landmark papers include the 2024 Science Advances study on ITPR3 variants, multi-author reviews on primary immunodeficiency diagnostic guidelines, and technical articles on next-generation HLA typing and flow cytometry. His research trajectory illustrates a steady evolution from foundational immunology and mouse models toward high-resolution genomic diagnostics and international consensus guidelines. Scientific Awards & Recognition Board Certification – Molecular Genetic Pathology, American Board of Pathology (2012) Board Certification – Clinical Pathology, American Board of Pathology (2011) Active contributor to American Academy of Allergy, Asthma & Immunology working groups Collaborations & Teams Dr. Kumanovics collaborates extensively within Mayo Clinic’s Department of Laboratory Medicine and Pathology, serving as a key resource for molecular genetic pathology consultations. He participates in multi-institutional consortia such as the Primary Immunodeficiency Diseases Committee of the American Academy of Allergy, Asthma & Immunology, co-authoring diagnostic frameworks that are used worldwide. Details of specific laboratories or grant funding were not disclosed in the provided text.

Taofic MounajjedView profile

Consultant Lecturer

• Liver Pathology
• Hepatic Adenomas
• Focal Nodular Hyperplasia
• +5 more

Taofic Mounajjed, M.D. is a Consultant Pathologist specializing in liver and gastrointestinal pathology at the Mayo Clinic in Rochester, Minnesota. He serves as Co-Director of the Molecular Anatomic Pathology Laboratory and Co-Section Head within the Department of Laboratory Medicine and Pathology. Dr. Mounajjed is actively involved in multiple professional committees focused on digital pathology and pathology informatics. MD from University of Damascus Medical School (2004) BS in Basic Medical Sciences from University of Damascus (1999) Residency in Anatomic and Clinical Pathology at University of Cincinnati (2009) Residency in Surgical & Medical Pathology at Mayo Clinic (2010) Fellowship in Surgical Pathology at Mayo Clinic (2010) Mayo Clinic Scholar in Gastrointestinal and Hepatic Pathology (2011) Dr. Mounajjed's research focuses on liver pathology with particular expertise in hepatic adenomas, focal nodular hyperplasia, and gastrointestinal pathology. His work spans diagnostic pathology, molecular characterization of liver diseases, and implementation of digital pathology technologies. He has made significant contributions to understanding the histopathological features of various liver conditions including Wilson disease, eosinophilic esophagitis, and alcohol-related liver disease. His recent publications demonstrate a strong focus on advanced imaging techniques for liver disease assessment, particularly magnetic resonance elastography for evaluating liver fibrosis and steatosis. There is also a notable emphasis on AI applications in pathology, particularly for quantitative histopathological analysis of gastrointestinal conditions. His work bridges traditional pathology with modern computational approaches and molecular diagnostics. Best graduation thesis for the year 1999 from University of Damascus Certified by American Board of Pathology in Anatomic Pathology (2010) Certified by American Board of Pathology in Clinical Informatics (2019) Dr. Mounajjed serves in leadership roles including Chair of the DLMP Digital Pathology Practice Subcommittee and member of multiple digital pathology committees. He co-directs the Molecular Anatomic Pathology Laboratory and participates in various disease-oriented groups focusing on gastrointestinal and hepatic conditions. His collaborative research involves extensive work with multidisciplinary teams across Mayo Clinic, including radiology, gastroenterology, and surgery departments.