جست‌وجوی استاد و پژوهشگر

Jason McDermottمشاهده پروفایل

دانشیار

• Systems biology
• Network inference
• Data integration
• +۱۰ مورد دیگر

Jason McDermott is a senior research scientist and Team Lead for Systems Biology at Pacific Northwest National Laboratory (PNNL), with an Affiliate Associate Professor appointment in the Department of Molecular Microbiology and Immunology at Oregon Health & Science University (OHSU). His work bridges computational and experimental biology, focusing on data integration, network analysis, and systems-level understanding of biological processes. McDermott earned his BA in Biology from Reed College in 1993, PhD in Structural Virology from OHSU in 2000, and completed post-doctoral training in Bioinformatics at the University of Washington in 2006. His research spans cancer biology, host-pathogen interactions, microbiome science, and computational prediction of protein functions. His work integrates high-throughput omics data to develop systems biology models, with particular emphasis on biomarker discovery, network inference, and pathway analysis. Recent publications demonstrate his leadership in applying multi-omics approaches to understand viral infections, soil microbial communities, cancer mechanisms, and metabolic diseases. His research portfolio shows consistent focus on developing computational methods for biological data integration and analysis, with applications across diverse domains from infectious disease to cancer biology and environmental microbiology. 1997 Sears Fellowship Award (OHSU) 1998 Tartar Fellowship Award (OHSU) 2008 Analytics Challenge Winner (Supercomputing 2008) McDermott actively collaborates with experimental biologists, statisticians, and mass spectrometrists to develop and apply computational methods. He has made significant contributions to the Clinical Proteomic Tumor Analysis Consortium (CPTAC), particularly in ovarian cancer research, where he serves as chair of both the biology working group and data analysis working group. His work often involves interdisciplinary teams focused on translating computational insights into biological understanding.

Ryan Renslowمشاهده پروفایل

دانشیار

• Metabolomics
• Chemical Biology
• Exposure Science
• +۴ مورد دیگر

Ryan Renslow serves as a Chemical Engineer at Pacific Northwest National Laboratory (PNNL) and holds a Research Associate Professor position at Washington State University's Gene and Linda Voiland School of Chemical Engineering and Bioengineering. His interdisciplinary work bridges computational modeling, advanced imaging, and experimental biology to address complex challenges in metabolomics and microbial systems. Education BS in Chemical Engineering, Washington State University MS in Chemical Engineering, Washington State University PhD in Chemical Engineering, Washington State University Linus Pauling Distinguished Postdoctoral Fellowship, Pacific Northwest National Laboratory Renslow's research centers on identifying novel metabolites in complex biological samples, deciphering microbial community structure-function relationships, and understanding emergent properties in multispecies systems. He employs computational mathematics, machine learning, and high-resolution imaging techniques including mass spectrometry and nuclear magnetic resonance. His work spans diverse applications from human health diagnostics and bioenergy production to ecological monitoring and national defense solutions, with particular emphasis on biofilm dynamics and metabolite characterization in challenging environments. Analysis of his recent publications reveals a strong trend toward integrating ion mobility spectrometry with computational modeling for metabolite identification, developing in silico libraries for small molecule annotation, and applying machine vision to biological systems. His work consistently demonstrates cross-cutting applications across energy, environmental science, and biomedical research through sophisticated data analysis frameworks. Scientific Awards: No awards listed in available documentation. Renslow's collaborative research program involves extensive partnerships across PNNL's Environmental Molecular Sciences Laboratory and Washington State University. His work receives institutional support through DOE-funded initiatives focused on chemical biology and exposure science, with emphasis on developing advanced analytical capabilities for complex sample analysis. While specific grant details aren't provided, his publication record indicates consistent funding for interdisciplinary projects combining experimental and computational approaches. At PNNL, Renslow contributes to the Chemical Biology and Exposure Science group within the Biological Sciences division. He leverages specialized facilities including high-field mass spectrometers, nuclear magnetic resonance microimaging systems, and biofilm reactors to investigate microbial community dynamics. His research team integrates expertise from chemical engineering, microbiology, and computational science to develop novel approaches for characterizing complex biological systems at multiple scales.

Oscar Corchoمشاهده پروفایل

استاد

• Ontology-based Data Integration
• Semantics in Open Science
• Semantic Web
• +۶ مورد دیگر

Oscar Corcho is a Full Professor at the Department of Artificial Intelligence within the School of Computer Science at the Technical University of Madrid. He is a prominent member of the Ontology Engineering Group and has been serving as a Professor since May 2007, with his current position as Full Professor reflecting his significant contributions to the field. Professor Corcho's research primarily focuses on Ontology-based Data Integration and the application of Semantics in Open Science. His expertise extends across the broader domains of Semantic Web, Linked Data, Knowledge Graphs, and Ontological Engineering. His work bridges theoretical research with practical applications, particularly in data integration systems and knowledge representation frameworks. With over 460 publications and more than 12,000 citations, he has established himself as a leading researcher in semantic technologies. His recent publications show a strong emphasis on virtual knowledge graph architectures, RDF-star generation, semantic labeling techniques for tabular data, and applications of semantic technologies in scientific literature analysis. His research demonstrates a clear trajectory toward solving real-world data integration challenges across heterogeneous sources while advancing the theoretical foundations of knowledge representation. Third Spanish Award on Computer Science (2001) Beyond his academic role, Professor Corcho is a co-founder of LocaliData Spain, demonstrating his commitment to translating research into practical applications. He has supervised numerous research projects and has been instrumental in developing tools like Morph-KGC for knowledge graph construction. His work has significant implications for public procurement systems, transportation data integration, citizen science initiatives, and scientific research infrastructure. As leader of the Ontology Engineering Group, he oversees a research program that develops innovative methodologies for ontology engineering and semantic data integration across diverse domains. His group's work has influenced both academic research and industry practices in knowledge representation and semantic technologies.

Philipp Bruneمشاهده پروفایل

استاد

• Artificial Intelligence
• Facial Emotion Recognition
• Cybersecurity
• +۵ مورد دیگر

Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.

Michael Pradelمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Bug Detection
• +۵ مورد دیگر

Michael Pradel is a full professor in the Computer Science Department at the University of Stuttgart and faculty member at CISPA Helmholtz Center for Information Security (effective September 2025), where he leads the Software Lab. He is also affiliated with the International Max Planck Research School for Intelligent Systems and the Stuttgart ELLIS Unit, reflecting his interdisciplinary research approach. His research interests focus on software engineering, particularly program analysis, bug detection, and the application of machine learning to developer tools. Pradel's recent work increasingly explores LLM-based approaches for program repair, code analysis, and automated software development, as evidenced by projects like RepairAgent and ExecutionAgent. Pradel's publication record shows a clear trend toward integrating AI techniques with traditional software engineering methods, with recent papers focusing on LLM applications for program repair, change validation, and quantum software analysis. His work bridges theoretical foundations with practical tool development, as seen in frameworks like DyLin for Python analysis and LintQ for quantum programs. Ernst-Denert Software Engineering Award Emmy Noether grant (1.3 million Euro) by the DFG ERC Starting Grant (1.5 million Euro) Multiple ACM SIGSOFT Distinguished Paper Awards ACM Distinguished Member recognition Pradel actively mentors PhD students, with recent graduates including Matteo (specializing in quantum software) and Luca (focusing on software evolution). His group has received significant funding and maintains strong industry connections, including past sabbaticals at Facebook. He serves in leadership roles for major conferences, including PC co-chair for FSE 2027, demonstrating his standing in the software engineering community. The Software Lab maintains active collaborations with institutions worldwide, including CMU, Google, KAIST, and several European universities.

Lozana Rossenovaمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Data Curation
• Linked Open Data
• +۵ مورد دیگر

Lozana Rossenova is a Post-doctoral researcher at the German National Library of Science and Technology (TIB) where she works at the Open Science Lab under Prof. Dr. Ina Blümel. She serves as team lead for the Wikibase4Research service and related open source tool ecosystem developed within NFDI4Culture, the German national consortium for research data on material and immaterial cultural heritage. Her work bridges the fields of digital humanities, cultural heritage preservation, and open knowledge infrastructure development. Dr. Rossenova earned her PhD from the Centre for the Study of the Networked Image at London South Bank University through a Collaborative Doctoral Partnership project with Rhizome, a leading international born-digital art organization. Her doctoral research focused on the redesign of Rhizome's ArtBase archive, examining user agency in born-digital archives. Prior to her PhD, she completed an MA in Book Design at the University of Reading with Distinction and a BFA in Studio Art and Graphic Design from Adelphi University, graduating Summa Cum Laude. Her research interests center on user interface design for cultural heritage systems, research data management, data curation practices, and semantic web applications in digital humanities contexts. She has particular expertise in developing open source tools for knowledge graph infrastructure, with significant contributions to Wikibase implementations for cultural heritage institutions. Her work emphasizes community-oriented and ethical approaches to open source software development, especially as it relates to preserving born-digital cultural artifacts. Analysis of her recent publications reveals a strong focus on practical applications of linked data technologies for cultural heritage institutions, with particular attention to 3D model annotation, semantic enrichment workflows, and collaborative data curation approaches. Her work consistently addresses the intersection of technical infrastructure development and user-centered design principles, creating tools that serve both researchers and cultural heritage practitioners. MA Distinction from Reading University (2012) BFA Summa Cum Laude from Adelphi University (2011) Collaborative Doctoral Partnership Award from Arts and Humanities Research Council UK (2016-2020) Dr. Rossenova actively mentors students and emerging researchers, having supervised master's theses at Aalto University and taught semester-long modules at Hochschule Hannover's Faculty for Media, Information and Design since 2023. She has received research funding from multiple prestigious sources including Deutsche Forschungsgemeinschaft (NFDI4Culture project 2020-2025), Swiss National Science Foundation (2023-2027), Wikimedia Foundation (2021-2022), and National Endowment for the Humanities (2020-2021). She leads development of the Wikibase4Research service ecosystem and is deeply involved with the Wikibase Stakeholder Group. Her work connects multiple communities including the Linked Open Data Working Group in NFDI4Culture, the Knowledge Graphs Working Group in NFDI, and the LD4 Art & Design Affinity Group. Through these roles, she fosters collaboration between cultural heritage institutions, academic researchers, and open source software communities.

Andrea Rappمشاهده پروفایل

استاد

• Digital Humanities
• Computational Philology
• Medieval Literature
• +۳ مورد دیگر

Prof. Dr. Andrea Rapp is a Professor at the Institute of Linguistics and Literature , Technical University of Darmstadt , specializing in Digital Humanities , Computational Philology , and Medieval Studies . She serves as Managing Director of the Institute and Vice President for Scientific Infrastructure (2017–2019), with extensive roles in academic administration, including Senate Representative for Appointment Procedures and member of the Graduate School Topology of Technology. Research Focus: Digital philology, medieval text corpora, digital editions, and lexicography. Teaching: Courses in Digital Humanities, Middle High German, and medieval literature since 2011. Projects: Leadership in initiatives like TextGrid and the Darmstadt Model Edition. Publications span 20+ years, emphasizing digital methodologies, manuscript studies, and interdisciplinary applications of XML encoding, textual criticism, and historical linguistics. Her work bridges traditional philology with computational tools, contributing to sustainable digital research infrastructures. Academic Leadership: She chairs the Scientific Advisory Board of the Marbach-Weimar-Wolfenbüttel Research Association and co-founded the TextGrid Association, while serving on DFG committees and the Academy of Sciences and Literature in Mainz.

Sebastian Hoffmannمشاهده پروفایل

استاد

• Corpus Linguistics
• Usage-based Linguistics
• Syntactic Change
• +۷ مورد دیگر

Sebastian Hoffmann is a distinguished academic affiliated with the University of Trier , where he served as Dean of the Faculty of Languages, Literatures and Media Studies from 2017 to 2023. He has held significant roles, including Visiting Professor at the University of Tampere (2014–2017) and Lecturer in English Linguistics at Lancaster University (2006–2009). His expertise lies in Corpus Linguistics , with a focus on World Englishes , Syntactic Change , and Corpus Methodology . He co-developed BNCweb , a widely used web interface for the British National Corpus. PhD from the University of Zurich His research explores usage-based approaches to language study, including syntactic evolution , phonological patterns (e.g., intrusive /r/), tag questions , and verb complementation . Recent work highlights the role of Internet-derived data in corpus linguistics and the fixedness of collocations . His publications span Corpus Methodology , Historical Linguistics , and Phraseology , with key works such as Corpora and Lexis (2018) and Grammaticalization and English Complex Prepositions (2005). He has contributed extensively to journals like the International Journal of Corpus Linguistics and Journal of English Linguistics . Hoffmann has been recognized for leadership in professional organizations, including Chair of ICAME (2017–2023) and Vice-President for the Profession of the International Society for the Linguistics of English (2018–2023). He teaches courses such as The History of the English Language and Transcribing and Analyzing Spoken Language .

Jan Piotr Buchmannمشاهده پروفایل

پژوهشگر

• Plant Genomics
• Bioinformatics
• Evolutionary Genomics
• +۲ مورد دیگر

Dr. Jan Piotr Buchmann is a researcher at the Institute for Biological Data Science, Heinrich Heine University Düsseldorf, contributing to the Cluster of Excellence on Plant Sciences (CEPLAS) under Research Area 4: Theoretical Plant Sciences and Data Sciences. His core research involves plant gene family analysis across dicotyledonous and eudicotyledonous species, with emphasis on: Genomic classification via Mercator4/MapMan frameworks Evolutionary studies using protein sub-family information Full genome comparisons without prior gene calling Metabolic network adaptation in plant-microbe systems Buchmann's methodology enables rapid genomic analysis by bypassing traditional gene-calling steps, directly leveraging protein sub-family data to identify gene sub-families in raw genomes. This approach supports CEPLAS's broader goals in optimizing plant performance through metabolism-development interfaces and synthetic biology applications.

Antonio Giraldezمشاهده پروفایل

استاد

• Developmental Biology
• Gene Expression Regulation
• Genomics
• +۹ مورد دیگر

Antonio Giraldez is the Fergus F. Wallace Professor of Genetics at Yale School of Medicine, where he has led his research laboratory since 2007. He served as Director of Graduate Studies (2012-2016) and Chair of the Genetics Department (2017-2023). His academic appointments span multiple departments and centers including Biochemistry, Quantitative Biology, Biophysics and Structural Biology (BQBS), the Center for RNA Science and Medicine, Yale Cancer Center, Yale Center for Genomic Health, Yale Combined Program in the Biological and Biomedical Sciences (BBS), and the Yale Stem Cell Center. Professor Giraldez's research focuses on understanding the regulatory code that governs gene expression during vertebrate development after fertilization. His lab investigates four interconnected areas: genome activation during the maternal-to-zygotic transition, post-transcriptional regulation of gene expression, modeling of therapeutic mRNA design, and modeling of gene regulatory networks. Using zebrafish as a primary model organism combined with advanced genomic, computational, and imaging approaches, his lab has made significant contributions to understanding how transcription factors activate the embryonic genome and how RNA regulatory elements control mRNA stability and translation. Analysis of Professor Giraldez's recent publications reveals a strong focus on the molecular mechanisms of embryonic development, particularly the maternal-to-zygotic transition where control of development passes from maternal to embryonic factors. His work increasingly integrates high-throughput genomic approaches with computational modeling to decode regulatory elements in RNA that determine stability, translation efficiency, and tissue-specific expression - knowledge directly applicable to therapeutic mRNA design for gene therapy and vaccines. Scientific Awards and Honors: HHMI Faculty Scholar (2016) Blavatnik Award for Young Scientists National Finalist (2016) Vilcek Prize for Creative Promise in Biomedical Science (2014) Pew Scholar in Biomedical Sciences (2008) NYAS Blavatnik Young Investigator Award Finalist (2007) Professor Giraldez has established a productive research program with consistent funding and publication in top-tier journals. His lab maintains active collaborations with researchers across Yale and beyond, as evidenced by his frequent co-authorship with scientists from multiple departments. He has trained numerous students and postdocs who have gone on to independent research careers. The Giraldez Lab maintains a diverse team of researchers working at the intersection of developmental biology and genomics. The lab provides extensive resources including published datasets, computational tools like CRISPRscan, and detailed genomic annotations available through their data repository. Professor Giraldez's research program continues to advance our understanding of fundamental gene regulatory mechanisms with implications for both basic science and therapeutic applications.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

• Cytogenetics
• Chromosome Aberrations
• Genomic Analysis
• +۳ مورد دیگر

Dr. Peining Li is a Professor of Genetics at Yale School of Medicine, where she serves as Director of the Cytogenetics Laboratory and Co-Director of the Fellowship in Laboratory Genetics and Genomics. Her primary affiliations include the Department of Genetics, Yale Center for Genomic Health, and Yale Ventures. She leads a research program focused on structural and functional characterization of human chromosome abnormalities using advanced molecular techniques. Her research interests span: Cytogenetics and chromosome aberration analysis Genomic diagnostics using microarray and sequencing technologies Cancer genomics with focus on hepatocellular carcinoma Reproductive genetics including pregnancy loss mechanisms Recent publications demonstrate her work in pediatric genetic diagnostics, NGS concordance studies, stem cell applications, and ring chromosome characterization. Her lab provides clinical services for diverse specimen types including blood, amniotic fluid, and tumor samples.

• Breast Cancer
• Translational Research
• Biomarker Development
• +۲ مورد دیگر

Dr. Mariya Rozenblit is an Assistant Professor of Medicine at Yale School of Medicine specializing in Medical Oncology. She holds appointments at Yale Cancer Center and the Center for Breast Cancer, where she leads translational research initiatives. Her clinical practice focuses exclusively on breast cancer management, from ductal carcinoma in situ to metastatic disease. She completed her medical education at Icahn School of Medicine (MD 2015), internal medicine residency at NYU Langone Medical Center (2018), and medical oncology fellowship at Yale. Her research program investigates: Biomarker-driven clinical trial design Genomic alterations preceding breast cancer development Oligometastatic disease biology Age-specific molecular differences in breast cancer Immunotherapy biomarkers in early-stage disease Recent publications (2022-2025) demonstrate consistent focus on breast cancer genomics and precision oncology. Over 80% of her 15 most recent articles examine molecular biomarkers, with particular emphasis on: Homologous recombination deficiency signatures HER2-low characterization ctDNA analysis techniques Germline mutation patterns in young patients Epigenetic aging markers Honors include: ASCO Conquer Cancer Young Investigator Award (2020) Susan G. Komen Career Catalyst Grant (2022) She currently serves as sub-investigator on multiple clinical trials evaluating novel therapeutic approaches for breast cancer. Her research group collaborates extensively with Yale's Genomics, Genetics, and Epigenetics Program.

Juri Rappsilberمشاهده پروفایل

استاد

• Proteomics
• Mass Spectrometry
• Protein Structure
• +۴ مورد دیگر

Prof. Juri Rappsilber is a Professor at the Technical University of Berlin's Institute of Biotechnology and Group Leader of Research Unit E in the UniSysCat Cluster of Excellence. His affiliations include Visiting Professorship at Kyoto University (2017, JSPS Fellow) and Senior Research Fellowships at the Wellcome Trust (2009-2019). He leads the Rappsilber Laboratory, focused on developing mass spectrometry technologies to study protein structure and interactions. Research interests center on proteomics , crosslinking mass spectrometry , and protein-network analysis . Key areas include: Functional protein networks through co-regulation studies Chromatin dynamics and mitotic chromosome composition In-cell structural proteomics integrating deep learning Development of bioorthogonal catalysis for medical applications His publication record demonstrates consistent output in high-impact journals, with recent work emphasizing: Structural mechanisms of protein complexes (γ-tubulin, condensin) Crosslinking MS methodology innovation and error control Integration of AI with experimental structural biology Catalytic processes in bioinorganic systems Awards and honors: EMBO Membership (2021) BSPR Lectureship (2014) Einstein Professorship (2011) Wellcome Trust Senior Research Fellow (2009-2019) Marie Curie Excellence Fellow (2005-2010) He actively advises doctoral students, including DAAD Hilde-Domin Program recipients, and secures substantial funding through ERC Synergy Grants, Wellcome Trust awards, and national initiatives. The lab focuses on developing anti-cancer therapeutics and sustainable chemistry solutions while pursuing Green Lab Certification.

Olga Kalininaمشاهده پروفایل

استاد

• Bioinformatics
• Drug Discovery
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Olga Kalinina leads the Drug Bioinformatics research group at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). She holds an M.Sc. in Mathematics from Moscow State University (2003) and a Ph.D. in Bioinformatics from the Russian Academy of Sciences (2007). Her academic journey includes postdoctoral positions at the European Molecular Biology Laboratory (EMBL) and University of Heidelberg, followed by establishing her independent research group at the Max Planck Institute for Informatics (2012-2018). Her research program integrates cutting-edge bioinformatics and computational approaches to address challenges in drug discovery and resistance mechanisms. Primary research domains include: Development of machine learning tools for predicting functional consequences of genetic variants and drug resistance phenotypes Structural bioinformatics approaches for protein-drug interaction analysis Graph theory-based investigation of drug-binding pockets Protein language models enhanced with 3D structural information Explainable AI for drug-target interaction prediction Her group maintains active research projects on: Predicting molecular mechanisms of genetic variants in cardiac/renal diseases (BMBF-funded) Investigating off-target drug binding mechanisms (BMBF-funded) Antimicrobial resistance discovery using explainable AI (HelmholtzAI-funded) Protein structure-informed drug affinity prediction Prof. Kalinina has received the EMBO Long Term Fellowship and currently supervises seven doctoral students. Her laboratory develops computational resources including novel methods for solvent accessibility estimation (SphereCon), variant annotation in structural context (StructMAn), and protein interaction analysis (DIGGER).