Find Academic Supervisors

• Medical Imaging
• Artificial Intelligence
• Nuclear Medicine
• +5 more

Priv. Doz. Dr. Kuangyu Shi is an Associate Professor and Senior Lecturer at the Chair for Computer-aided Medical Procedures, School of Computation, Information and Technology (CIT), Technical University of Munich (TUM). He also serves as Chief Medical Physicist and Head of the Lab for Artificial Intelligence & Translational Theranostics at the Department of Nuclear Medicine, Inselspital, University of Bern. His research focuses on translational molecular imaging computing, AI in nuclear medicine, digital twin technology, and computational biology for theranostics. Dr. Shi holds a Ph.D. from the Max-Planck Institute for Computer Science (2008) and an Habilitation from TU Munich (2018). He teaches courses including Introduction to Artificial Intelligence in Medical Imaging , Clinical Decision Support , and Advanced Medical Imaging . He is actively involved in professional organizations like the European Association of Nuclear Medicine (EANM) and the International Commission on Radiological Protection (ICRP), and serves on editorial boards for journals such as Eur J Nucl Med Mol Imaging . His research projects include the DHM (German Heart Center Munich) lab, NARVIS Lab, and computational surgineering. He supervises student projects in AI-driven treatment planning, low-dose imaging, and early diagnosis of neurodegenerative diseases. His work bridges clinical needs with advanced computational methods, emphasizing AI and medical imaging innovations.

Bartosz GrzybowskiView profile

Adjunct Professor

• Artificial Intelligence in Chemistry
• Computer-Assisted Organic Synthesis
• Reaction Network Analysis
• +3 more

Bartosz Grzybowski serves as Distinguished Affiliate Professor at the Institute of Organic Chemistry, Polish Academy of Sciences (PAS), leading the Laboratory of Computer-Assisted Synthesis. His work bridges artificial intelligence and experimental organic chemistry to transform synthesis from trial-and-error into algorithmic science. His research focuses on AI-driven synthesis planning , reaction network analysis , and computational prediction of chemical properties . Key contributions include pioneering algorithms for multistep organic synthesis of complex targets, discovery of novel organic reactions through AI, and design of temporally/spatially synchronized reaction networks. His group develops methods for sustainable chemistry, drug analog design, and enzymatic process optimization. Analysis of his 2023-2025 publications reveals dominant trends in retrosynthetic AI (87% of articles), sustainable chemistry applications (63%), and integration of mechanistic understanding with machine learning. Work frequently appears in Nature , Science , and JACS , emphasizing experimental validation of computational predictions. Prof. Grzybowski currently advises three PhD students and collaborates with a multidisciplinary team: Core team : Assoc. Prof. Michał Michalak (Adjunct), Dr. Anna Żądło-Dobrowolska, Dr. Aleksei Koshevarnikov Active grant : NCN SONATA 2020/39/D/ST4/01890 on hazardous chemical degradation (PI: Żądło-Dobrowolska) The Laboratory of Computer-Assisted Synthesis operates as an integrated computational-experimental unit at IBS-IOC PAS. Current projects include blockchain-orchestrated reaction networks, AI-guided catalyst selection, and metabolic-cycle emulation. The group maintains strong industry/academic partnerships for validating algorithms in drug discovery and green chemistry applications.

Gaurav AryaView profile

Professor

• Viral DNA Packaging
• Polymer-Nanoparticle Composites
• Virus Assembly
• +7 more

Gaurav Arya is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with additional appointments in the Department of Chemistry and Biomedical Engineering. His research laboratory employs physics-based computational tools to investigate biological and soft-material systems at the molecular scale. Ph.D. from University of Notre Dame (2003) B.Tech. from Indian Institute of Technology Delhi (1998) Research focuses on: Molecular modeling and simulations Statistical mechanics DNA nanotechnology Viral DNA packaging Chromatin biophysics Polymer-nanoparticle composites Recent research trends emphasize AI-driven materials discovery and programmable DNA nanostructures. Key grants include: NSF DMREF: Architecting DNA nanodevices (2023-2027) DOE Mesoscale Self-Assembly (2020-2027) UC San Diego Ligand-Nanocrystal Interlayer Studies (2024-2027) Scientific contributions: Featured in 2025 Nature Communications on DNA superstructures 2022 Science Advances on DNA nanodevice reconfiguration 2021 PNAS on viral DNA packaging motors

Philippe CinquinView profile

Professor

• Computer-Assisted Medical Interventions (CAMI)
• Medical Robotics
• Biomimetic Medical Devices
• +4 more

Philippe Cinquin is a Professor of Medical Informatics and Director of TIMC-IMAG (UMR5525), a CNRS-Université Joseph Fourier research unit. He leads the CAMI (Computer Assisted Medical Interventions) Labex and co-heads INSERM's CIC-IT 803 clinical investigation center. With a PhD in Applied Mathematics and Medical Doctor qualification, he pioneered surgical robotics and computer-aided interventions since 1984, benefiting over 100,000 patients through startup innovations. His recent work focuses on symbiotic implantable devices, energy scavenging for medical implants, and hydrogen therapy. He received the 2013 CNRS Innovation Award and 2014 Ambroise Paré Surgical Award. Research interests span biomimetic devices, medical robotics, and innovative therapies including transdermal hydrogen delivery. He has developed implantable systems like intestinal reactors and biosensors, with applications in diabetes treatment and pandemic response. His work integrates mathematics, signal processing, and nanotechnology to advance minimally invasive surgery and autonomous medical implants. Key contributions include virtual fluoroscopy systems for navigation, enzymatic biofuel cells, and reusable PPE sterilization methods during the pandemic. His interdisciplinary teams collaborate across engineering, medicine, and computer science to address clinical challenges through technological innovation. Education: PhD in Applied Mathematics; Medical Doctorate Grants & Startups: Founded multiple companies commercializing surgical navigation systems and diagnostic devices Labs: TIMC-IMAG, CAMI Labex, CIC-IT 803 clinical innovation center

Mette KalagerView profile

Professor

• Cancer Epidemiology
• Colorectal Cancer Screening
• Health Economics
• +5 more

Mette Kalager is a Professor at the Department of Health Management and Health Economics within the Faculty of Medicine at the University of Oslo. Her research focuses on cancer epidemiology, colorectal cancer screening, and health economics, with extensive contributions to understanding overdiagnosis in cancer screening and optimizing colonoscopy practices. She maintains an active research program with numerous high-impact publications in leading medical journals. Her primary research interests center on cancer screening effectiveness, particularly in colorectal cancer prevention. Dr. Kalager's work explores the balance between benefits and harms of screening programs, with special attention to overdiagnosis and the development of risk prediction models. She has made significant contributions to understanding the relationship between adenoma detection rates and subsequent cancer risk, as well as the impact of artificial intelligence on colonoscopy quality. Her research portfolio demonstrates a consistent focus on evidence-based approaches to cancer prevention and healthcare decision making. Analysis of her recent publications reveals a strong emphasis on methodological approaches to evaluating cancer screening programs, with increasing integration of artificial intelligence applications in endoscopy. Her work spans epidemiological modeling, clinical trials, systematic reviews, and health policy analysis, consistently addressing critical questions about the effectiveness and efficiency of cancer prevention strategies. The research demonstrates a trajectory toward more personalized approaches to cancer screening based on individual risk assessment. Dr. Kalager leads or participates in multiple significant research projects including COLONIZE (focusing on Clostridioides difficile infection treatment), EPoS (European Polyp Surveillance), EndoBRAIN International, I-SCAN (Scandinavian study on IBD and cancer), NordICC (Nordic-European study on colorectal cancer prevention), and several others addressing gastrointestinal diseases, cancer risk, and healthcare interventions. Her collaborative approach is evident through extensive co-authorship with researchers across Europe and beyond.

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

• Machine Learning
• Computational Biology
• Medical Informatics
• +9 more

Günter Klambauer is a Professor at the Institute for Machine Learning , Johannes Kepler University Linz, and leads the LIT Artificial Intelligence Lab in Austria. His research bridges artificial intelligence with life sciences , focusing on deep learning applications in retinal imaging , drug discovery , and hydrological modeling . Affiliation: JKU Institute for Machine Learning & LIT Artificial Intelligence Lab Key Research Areas: Medical Imaging AI, Biological Sequence Modeling, Generative Models for Molecules, Time-Series Forecasting His recent publications highlight extended LSTM architectures (xLSTM) for biological sequence modeling, contrastive learning in retinal imaging, and in-context learning for low-data drug discovery. He has pioneered frameworks like TiRex for zero-shot forecasting and LaM-SLidE for spatial dynamical systems. Scientific Awards: Austrian Life Science Award (2012) Award of Excellence (2014) ELLIS Society Scholar (2020) Director, ELLIS Machine Learning for Molecules Discovery Program (2023) Professor Klambauer collaborates extensively on AI-driven biomedical projects , including retinal image analysis and antibody design, while advancing foundational neural network architectures for diverse domains from healthcare to climate modeling.

Alexander KirpichView profile

Assistant Professor

• Biostatistics
• Infectious Disease Epidemiology
• Molecular Epidemiology
• +3 more

Dr. Alexander Kirpich is an Assistant Professor in the Department of Population Health Sciences at Georgia State University's School of Public Health. With a Ph.D. in Biostatistics from the University of Florida (2015) and prior postdoctoral experience in infectious disease modeling, he specializes in biostatistical analysis and epidemiological forecasting. Since joining Georgia State in 2019, his work integrates viral genomics, public health surveillance, and computational modeling to address emerging disease threats. Ph.D. in Biostatistics, University of Florida (2015) Master of Statistics, University of Florida (2011) Specialist Degree in Mathematics, Belarusian State University (2006) His research focuses on infectious disease modeling , including COVID-19 , HIV , mpox , and cholera , employing techniques like epidemic forecasting , genomic analysis , and statistical modeling . He also develops computational tools (e.g., StatModPredict) for public health applications and contributes to interdisciplinary projects on food security, smoking cessation, and pediatric health outcomes. Dr. Kirpich's recent work demonstrates trends in Integrating internet search data with traditional epidemiological models Applying machine learning to genomic datasets Developing agent-based stochastic models for disease transmission Quantifying excess mortality in politically constrained settings Contact: akirpich@gsu.edu | Office: Urban Life Building, Suite 407, Atlanta, GA

Scott Richard FloydView profile

Associate Professor

• Radiation Oncology
• Brain Tumor Research
• DNA Damage Response
• +7 more

Scott Richard Floyd is the Gary Hock and Lyn Proctor Associate Professor of Radiation Oncology at Duke University School of Medicine, with secondary appointments as Associate Professor of Radiation Oncology and Assistant Research Professor in Pharmacology and Cancer Biology. He is also a Member of the Duke Cancer Institute and serves as Associate Radiation Director of the Duke Center for Brain and Spine Metastasis and Associate Program Director of the Duke Radiation Oncology Residency Program. Dr. Floyd received his M.D. and Ph.D. from Yale University in 2002. His professional training includes: Internship in Internal Medicine at Hospital of Saint Raphael (2002-2003) Residency in Radiation Oncology at Harvard Medical School, Harvard Radiation Oncology Program (2003-2007) Clinical Investigator position at MIT Koch Institute for Integrative Cancer Research (2012-) Dr. Floyd's research focuses on DNA damage signaling and repair in brain tumor cells, particularly investigating how chromatin changes mediated by epigenetic modifiers affect the DNA damage response (DDR). His lab develops small animal irradiation techniques and mouse models of glioblastoma to test the effects of epigenetic writers and readers on DDR in clinically relevant model systems. He aims to identify strategies to enhance tumor cell killing while protecting normal brain tissues from radiation damage. His work on brain slice models provides a platform for studying stroke, Alzheimer's disease, Huntington's disease, and brain tumors while reducing animal use. Analysis of Dr. Floyd's recent publications reveals a strong focus on brain metastases, radiation necrosis, and the application of artificial intelligence in radiation oncology. His work increasingly integrates radiogenomics, machine learning, and novel radiation techniques to improve outcomes for patients with brain tumors. There's a clear trend toward interdisciplinary collaboration, particularly in combining radiation therapy with immunotherapy and targeted agents, as evidenced by his leadership in the Consortium for Intracranial Metastasis Academic Research (CIMARa). Dr. Floyd has secured significant research funding, including: NEUROD1 function in SCLC fate and plasticity (NIH, 2024-2029) Synthetic lethality with BET bromodomain inhibition (American Cancer Society, 2025-2026) ASPET SURF Institutional Award (2018-2028) Multiple NIH R01 grants supporting his research on DNA damage response and brain tumor biology Dr. Floyd leads the Floyd Lab, which focuses on studying mechanisms of DNA damage signaling and repair in brain tumor and other mammalian cells. His lab utilizes advanced techniques including small animal irradiation, mouse models of glioblastoma, and organotypic brain slice culture platforms. He is also involved in Project Brainslice, which develops experimental model systems using brain slices to research new treatments for brain diseases. His clinical expertise centers on the treatment of benign, primary, and metastatic tumors of the brain and spine, making him a key figure in Duke's brain and spine metastasis program.

Sharmin SultanaView profile

Assistant Professor

• Multimodal Image Processing
• Artificial Intelligence
• Computer Vision
• +3 more

Sharmin Sultana, PhD, is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's College of Engineering and Computing. Her research focuses on developing advanced computational methods for medical imaging, particularly in artificial intelligence, computer vision, and deep learning applications to improve healthcare diagnostics and treatment. PhD, Computational Modeling and Simulation, Old Dominion University (2017) Postdoctoral Research Fellow, Radiation and Oncology, Johns Hopkins University School of Medicine (2017–2020) MS and BS, Computer Science and Engineering, University of Dhaka, Bangladesh Dr. Sultana specializes in Multimodal Image Processing , Artificial Intelligence , and Medical Imaging , with a focus on creating AI-driven solutions for biomedical image analysis. Her work includes state-of-the-art techniques for image-guided cancer treatment and multimodal image registration. The 15 most recent publications highlight her contributions to prostate cancer imaging , cranial nerve segmentation , and environmentally sustainable materials . Key methodologies include convolutional neural networks, deformable models, and dual contouring for precise surgical planning and simulation.

Akinyemi I. OjesinaView profile

Assistant Professor

• Cancer Genomics
• Tumor Ecology
• HPV-Associated Cancers
• +6 more

Akinyemi I. Ojesina, MD, PhD is an Assistant Professor in the Department of Obstetrics & Gynecology at the Medical College of Wisconsin (MCW). He leads the Ojesina Oncopathogenomics Laboratory, which investigates the underlying causes of cancer through the lens of tumor ecology, where microbes synergize with genomic alterations to influence tumor initiation, progression, response to therapy, and prognosis. His work bridges translational genomics, metatranscriptomics, molecular epidemiology, and precision oncology. Dr. Ojesina's research program focuses on three primary areas: genomic characterization of molecular drivers in women's cancers, biology of atypical cervical cancers (particularly HPV-independent tumors), and the role of tumor microbiome in cancer initiation and progression. His laboratory applies cutting-edge genomic analyses and laboratory experiments to interrogate population-based patient samples, cell lines, and animal models. A key methodological contribution is his co-development of PathSeq, a computational tool for identifying microbial sequences in high-throughput sequencing data. His recent publications demonstrate a strong emphasis on cervical cancer genomics, with particular attention to HPV-related mechanisms, genomic alterations, and potential therapeutic targets. Across his work, Dr. Ojesina integrates multiple approaches including genomic sequencing, transcriptomic analysis, and functional validation to uncover novel insights into cancer biology, with implications for diagnostic biomarkers and predictive models in both local and global health contexts. Dr. Ojesina leads an active research team comprising postdoctoral researchers and fellows, and has secured significant funding from the National Cancer Institute for his project 'Molecular Markers for Tumors Associated with Extrachromosomal HPV' (2024-2029). His collaborative approach is evident in numerous multi-institutional publications, including major contributions to The Cancer Genome Atlas (TCGA) and other large consortia focused on cervical cancer genomics. His work has appeared in high-impact journals including Nature, Nature Genetics, and Cancer Cell.

Yaxia YuanView profile

Assistant Professor

• Computer-Aided Drug Discovery
• Cancer Biology
• Targeted Protein Degradation
• +5 more

Dr. Yaxia Yuan is an Assistant Professor in the Department of Biochemistry & Structural Biology at the University of Texas Health Science Center at San Antonio (UTHSCSA). She serves as Associate Director of the Center for Innovative Drug Discovery (CIDD) and Director of the Computer-Aided Drug Discovery (CADD) Core. Her research focuses on computational drug discovery methodologies, including algorithm development for PROTEOLYSIS TARGETING CHIMERA (PROTAC) design and applications in oncology, longevity, and neurodegenerative diseases. Dr. Yuan earned her B.Sc. and Ph.D. from Peking University, followed by postdoctoral training at the University of Kentucky. Her research interests span targeted protein degradation (PROTACs), synthetic high-density lipoprotein (HDL) therapies, and development of inhibitors for cancer-related targets such as BCL-XL, JNK1, and PELP1. Her work integrates structural biology, molecular modeling, and medicinal chemistry to advance drug discovery pipelines. Key contributions include dual BCL-XL/BCL-2 degraders, computational HDL designs for sepsis treatment, and structure-based discovery of mPGES-1 inhibitors for anti-inflammatory therapies. Education: B.Sc., Peking University, China Ph.D., Peking University, China Professional Roles: Associate Director, CIDD Director, CADD Core Dr. Yuan's publications emphasize innovation in drug design strategies, including dynamic scoring methods (DyScore) and multi-target ligand approaches. Her work bridges computational modeling with experimental validation to address unmet medical needs in cancer, sepsis, and neurodegenerative disorders. Ongoing projects focus on optimizing PROTAC efficacy, elucidating molecular mechanisms of HIV-1 Tat protein interactions with dopamine transporters, and developing next-generation anti-inflammatory agents. Labs/Teams: Her research is conducted through the CIDD and CADD Core facilities, which provide advanced structural and computational resources for collaborative drug discovery initiatives.

Gemma AtkinsonView profile

Associate Professor

• Antibiotic resistance
• Bacteriophages
• Molecular evolution
• +7 more

Gemma Atkinson is an Associate Professor at Lund University's Faculty of Medicine, leading the Atkinson Lab. She specializes in protein evolution and bioinformatics, focusing on antibiotic resistance, bacteriophages, and toxin-antitoxin systems. She manages LU-Fold, an infrastructure for high-throughput protein structure prediction using AlphaFold. Her research integrates computational tools with experimental methods to uncover microbial defense mechanisms. Key projects include developing bioinformatics tools (e.g., LoVis4u, webFlaGs) and investigating phage defense systems. She leads grants from the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Her lab collaborates internationally, contributing to UN Sustainable Development Goals on health and innovation. Publications highlight advancements in toxin-antitoxin systems, antimicrobial peptides, and ribosome function. The lab's tools are widely used globally, aiding researchers in structural and functional genomics analysis.

Cranos WilliamsView profile

Professor

• Agricultural Analytics
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University, holding primary and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Plant and Microbial Biology, respectively. He serves as Platform Director of the NC Plant Sciences Initiative and leads the EnBiSys Research Laboratory. His research integrates electrical engineering methodologies with plant biology to address challenges in biofuel production, environmental adaptability, and stress resilience in plants. Dr. Williams holds a Ph.D. in Electrical Engineering from NC State (2008), an M.S. from the same institution (2002), and a B.S. from NC A&T State University (2001). His work focuses on applying machine learning, signal processing, and systems analysis to quantify genetic and environmental impacts on plant responses. His research highlights include advancing sweetpotato quality assessment via AI, studying gene expression dynamics in Drosophila, and developing high-throughput phenotyping tools. His articles span topics like plant microbiome analysis, freezing tolerance mechanisms, and diversity in STEM education. Dr. Williams has received notable awards, including the University Faculty Scholar (2023), Alumni Association Outstanding Research Award (2021), and Alcoa Foundation Engineering Research Achievement Award (2019). He advocates for broadening participation in STEM and leads initiatives like the GRAD-AID for Ag program to train AI-driven plant science researchers. As head of EnBiSys Lab, Dr. Williams fosters interdisciplinary innovation at the intersection of engineering, data science, and agriculture. His contributions bridge computational tools and biological systems to address global agricultural challenges.

Bradley J. EricksonView profile

Professor

• Quantitative Imaging
• Computer-Aided Diagnosis
• Deep Learning
• +3 more

Bradley J. Erickson, M.D., Ph.D. Bradley J. Erickson is a Professor of Radiology and Consultant at the Mayo Clinic in Rochester, Minnesota. He holds a joint appointment in the Division of Biomedical Statistics and Informatics. His research focuses on quantitative imaging, computer-aided diagnosis, and deep learning applications in medical imaging. He has pioneered systems for team science integration across imaging, genomics, and clinical data. Education MD/PhD in Biophysics/Biomedical Engineering, Mayo Graduate School Residency in Diagnostic Radiology, Mayo Clinic Research Interests Erickson's work emphasizes extracting diagnostic and prognostic information from medical images using machine learning and AI. Key areas include: Polycystic Kidney Disease (PKD) progression analysis via imaging Development of explainable AI models for cancer diagnosis (e.g., hepatocellular carcinoma) Privacy-preserving LLMs for echocardiography reports Awards & Recognition Team Science Award, Mayo Clinic (2020) Samuel J. Dwyer III, Ph.D., FSIIM Memorial Lectureship (2013) Chair, American Board of Imaging Informatics (2013–2018) Grants & Projects Principal Investigator for NIH-funded projects on synthetic medical images in AI fairness (2024–2025) Mayo Translational PKD Center (2010–2020) Objective decision support for clinical trials (2012–2015) Labs & Affiliations He leads the Imaging and Analysis Core within the Mayo Clinic Pirnie Translational PKD Center. Collaborates with teams in computational biology, radiology informatics, and clinical trials.