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Sebastian MaerklView profile

Associate Professor

• Microfluidics
• Cell-Free Synthetic Biology
• Transcription Factor Biophysics
• +4 more

Sebastian Maerkl is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the Laboratory of Biological Network Characterization , part of the School of Engineering (STI) and the Institute of Bioengineering (IBI-STI). He holds a joint appointment in the EDBB-GE PhD Program Committee and leads the SEL-ENS teaching initiative. His academic journey began with dual B.Sc. degrees in Biology and Chemistry from Fairleigh-Dickinson University (2001) before earning a Ph.D. in Biochemistry and Molecular Biophysics from Caltech (2008). Prof. Maerkl's research lies at the intersection of microfluidics , cell-free synthetic biology , transcription factor biophysics , and molecular diagnostics . His lab has pioneered microfluidic platforms for high-throughput characterization of gene regulatory networks, protein-DNA interactions, and cellular processes. Recent work focuses on artificial cell systems using the PURE cell-free translation system, self-regenerating protein networks, and sustainable biomolecular material design via circular economy principles. His 15 most recent articles (2022-2025) reveal consistent innovation in microfluidic reactor design for cell-free systems, with 2025 studies on tRNA synthesis and protein-ligand deep learning tools. He has secured major funding including an ERC Consolidator Grant (2016) and multiple Swiss National Science Foundation grants, supporting work that has yielded over 50 peer-reviewed publications and 6 patents. Honors : 2005: Innovator’s Challenge – 1st Place (Caltech/Stanford/Berkeley) 2008: Demetriades-Tsafka-Kokalis Prize – Best Caltech PhD Thesis in Biotechnology 2012: Prix SSV – Ambition – EPFL Teaching Excellence Award 2016: ERC Consolidator Grant (€2M+) 2019: iGEM Grand Prize Winner (Overgrad Category) – First Swiss Team Victory As founder and advisor of the EPFL iGEM team (2008-2020), he mentored teams that won 8 Gold, 2 Silver, and 1 Bronze medals. His lab hosted international fellows including one Fulbright Scholar and two Whitaker Fellows, while developing microfluidic devices for applications ranging from cancer diagnostics to SARS-CoV-2 antibody detection.

Kiaran KirkView profile

Professor

• Malaria parasite biology
• Membrane transport
• Ion homeostasis
• +5 more

Professor Kiaran Kirk is the Dean of the ANU College of Science and Medicine at the Australian National University, a position he assumed in January 2025. Previously, he served as Dean of the ANU College of Science (2017-2024) and Dean of the ANU College of Medicine, Biology and Environment (2014-2017). He has been a prominent figure at ANU since 1996 when he returned from Oxford University to head the Department of Biochemistry and Molecular Biology in the ANU Faculty of Science, a position he held until June 2009 when he became Director of the newly-created ANU Research School of Biology. His educational background includes: B.Sc. in Biochemistry from the University of Sydney Ph.D. in Biochemistry from the University of Sydney (1985-1988) D.Phil. in Physiology from Oxford University Professor Kirk's research primarily focuses on the biology of malaria parasites, particularly Plasmodium falciparum . His work centers on membrane transport mechanisms, ion homeostasis, and the molecular basis of antimalarial drug action and resistance. He investigates how malaria parasites acquire nutrients from host cells and maintain ionic balance, with special attention to the PfATP4 sodium pump as a key drug target. His research bridges fundamental parasite biology with translational applications for developing new antimalarial therapies that can overcome existing drug resistance mechanisms. Analysis of Professor Kirk's recent publications reveals a strong emphasis on membrane transport in apicomplexan parasites, particularly focusing on nutrient acquisition mechanisms and ion homeostasis in Plasmodium falciparum and Toxoplasma gondii . His work spans molecular characterization of transport proteins, mechanisms of drug resistance, and identification of novel drug targets, with particular attention to the PfATP4 sodium pump and its role in parasite survival. The research integrates biochemical, genetic, and pharmacological approaches to understand how antimalarial compounds disrupt parasite physiology at the molecular level. Professor Kirk has received numerous prestigious awards and honors: Member of the Order of Australia (AM), 2023 Elected Fellow of the Australian Academy of Health and Medical Sciences (FAHMS), 2017 ANU 'Last Lecture' award, 2013 Australian Awards for University Teaching for Outstanding Contribution to Student Learning, 2009 Bancroft-Mackerras Medal of the Australian Society for Parasitology, 2008 Vice Chancellor's Award for Excellence in Education, 2008 Vice-Chancellor's Awards for Excellence in Supervision, 2007 Roche Medal of the Australian Society for Biochemistry and Molecular Biology, 2002 As an educator and mentor, Professor Kirk has made significant contributions to student learning and supervision at ANU. He has supervised numerous PhD and Masters students in parasitology and membrane transport research. His teaching includes BIOL1004 (Molecular and Cell Biology) and BIOL2174 (Cell Physiology in Health and Disease). Professor Kirk has secured substantial research funding for his work on malaria parasite biology, with current projects focusing on Na+-dysregulating antimalarials, nutrient uptake in intracellular parasites, and the National Agricultural and Environment Sciences Precinct. Professor Kirk leads the Kirk Group, which focuses on 'Membrane transport in parasites.' His research team investigates ion homeostasis in malaria parasites, novel nutrient transporters in apicomplexan parasites, and targeting ion transport with new generation antimalarials. The group collaborates extensively with researchers worldwide on malaria and related parasitic diseases, contributing to both fundamental understanding of parasite biology and translational research for new therapeutic approaches.

• Computational Photonics
• Metamaterials
• Inverse Design
• +5 more

Taavi Repän is an Associate Professor of Computational Photonics at the Institute of Physics, Faculty of Science and Technology, University of Tartu. He has held this position since December 2021, with his current appointment running until December 2025. Prior to this, he worked as a Post-Doc at Karlsruhe Institute of Technology from 2019 to 2021. Repän earned his Doctoral Degree in Physics from the Technical University of Denmark (DTU) in 2019, with his dissertation titled 'Dark-field hyperlens: High-contrast subwavelength imaging in optics and acoustics' supervised by Andrei Lavrinenko and Morten Willatzen. He received his Master's Degree in Physics from the University of Tartu in 2014, with a thesis on 'Sub-wavelength imaging with hyperbolic metamaterials' supervised by Siim Pikker and Sergei Zhukovsky. His educational background also includes a Bachelor's Degree in Physics from the University of Tartu (2009-2012). His research focuses on computational photonics, metamaterials, inverse design, and the application of neural networks to optical simulations. He leads the project 'Inverse design methods for integrating nanophotonic structures with gas sensors' (2022-2026) and participates in several other research initiatives related to wood valorization and structural optimization. His work bridges theoretical physics, computational methods, and practical applications in optical sensing and imaging. His publication record shows a consistent trajectory in computational photonics, with recent work heavily emphasizing the integration of machine learning techniques with electromagnetic simulations. The most recent publications demonstrate a strong focus on neural network applications for inverse design problems in nanophotonics, hyperbolic metamaterials, and plasmonic structures, indicating his leadership in applying AI to complex optical design challenges. Repän currently leads or participates in multiple research projects funded by the Estonian Research Council and other institutions, demonstrating his active role in the research community. His work spans fundamental theoretical investigations to applied research with potential industrial applications. His laboratory work appears to focus on computational modeling of photonic structures rather than experimental setups, with emphasis on numerical methods for designing and analyzing optical systems. His collaborations span multiple institutions across Europe, reflecting the international nature of his research.

Kunal RaiView profile

Associate Professor

• Epigenetics
• Cancer Genomics
• Chromatin Structure
• +8 more

Kunal Rai, Ph.D., is an Associate Professor of Genomic Medicine at The University of Texas MD Anderson Cancer Center and an Adjunct Associate Professor in Bioengineering at Rice University. His work bridges epigenetics, cancer genomics, and translational medicine to advance precision oncology. Primary: Associate Professor, MD Anderson Cancer Center Adjunct: Bioengineering, Rice University Scientific Director, MDACC Epigenomics Therapy Initiative (METI) Dr. Rai’s research focuses on chromatin state changes in cancer progression, epigenetic therapy, and single-cell epigenomics. His lab has pioneered high-resolution epigenetic mapping in melanoma, colon cancer, and rare diseases like DSRCT and MPNST, leveraging machine learning and CRISPR-Cas9 editing to identify therapeutic opportunities. His recent work explores epigenomic stratification for patient-specific therapies, single-cell resolution aberrations, and higher-order chromatin structure disruptions. Articles span journals like Nature Communications , Cell Reports , and PNAS , highlighting cross-disciplinary impacts in cancer biology and epigenetic engineering.

Alex TongView profile

Assistant Professor

• Generative Modeling
• Deep Learning
• Optimal Transport
• +4 more

Alex Tong is an incoming Assistant Professor at Duke University (as of 2025) and will join Aithyra in Vienna as a Principal Investigator. Previously, he completed postdoctoral research at Mila under Yoshua Bengio and a visiting postdoc at Oxford with Michael Bronstein. Education : PhD (2021) and MPhil (2020) in Computer Science from Yale University; BS/MS (2017) from Tufts University Research Interests span generative modeling , deep learning , optimal transport , and graph signal processing applied to protein design and single-cell biology . His work bridges mathematical formalism (e.g., SE(3) flows, Wasserstein manifolds) with biological discovery. Publication Trends (2023-2025) focus on diffusion models for protein structure prediction , optimal transport in single-cell analysis , and geometric machine learning for molecular dynamics . Collaborations include labs like Mila, Oxford, and Yale. Awards : Best Paper (Gen Bio @ ICML 2025), Outstanding Paper (DELTA @ ICLR 2025), Best Student Paper (IEEE MLSP 2020)

• Catalysis
• Reaction Engineering
• Propane Dehydrogenation
• +4 more

Dr. David Linke serves as Department Head of Catalyst Development and Reaction Engineering at the Leibniz Institute for Catalysis (LIKAT) in Rostock, Germany. His research focuses on catalytic processes for chemical transformations, with particular emphasis on alkane dehydrogenation, CO 2 hydrogenation, and olefin metathesis. Dr. Linke's research interests span multiple areas of catalysis and reaction engineering: Development of catalysts for propane and isobutane dehydrogenation CO 2 hydrogenation to valuable hydrocarbons Metathesis reactions for olefin production Advanced characterization of catalytic materials Reaction mechanism elucidation Integration of machine learning with catalytic reaction engineering Analysis of Dr. Linke's recent publications (2021-2025) reveals a strong focus on sustainable chemical processes, particularly those involving CO 2 utilization and alkane dehydrogenation. His work combines experimental catalysis with advanced characterization techniques and increasingly incorporates computational approaches. A significant portion of his research addresses the challenge of developing efficient and stable catalysts for propane dehydrogenation, which is crucial for propylene production. His more recent work also shows growing interest in data science applications for catalysis research, including ontology development and machine learning approaches to kinetic modeling. Dr. Linke has been actively involved in developing research data infrastructure for catalysis through the NFDI4Cat initiative, which aims to establish unified data management practices across the catalysis research community.

• Mechanics of Materials
• Impact Engineering
• Multi-Scale Modelling and Characterisation
• +7 more

Professor Luming Shen is a distinguished academic in the School of Civil Engineering at The University of Sydney. With over two decades of experience in mechanical behavior of materials research, he leads cutting-edge investigations at the intersection of civil engineering, materials science, and computational mechanics. His work spans multiple scales from nano to macro, focusing on fundamental understanding that can be applied to real-world engineering challenges in water purification, structural safety, and sustainable infrastructure. Professor Shen's educational background includes: Bachelor's degree in Building Engineering from Tongji University, China Master's degree in Structural Engineering from Tongji University, China PhD in Civil Engineering from the University of Missouri-Columbia, USA Professor Shen's research focuses on the mechanics and behaviors of materials across multiple scales. His primary interest lies in understanding both brittle materials (concrete, rock, glass) and ductile materials (aluminum, titanium, metals). Two major thrusts of his work include nano-mechanics and materials research, particularly developing carbon nanotube membranes for water purification, and studying novel composite materials under impact and extreme loading conditions for applications in blast-resistant structures and vehicle safety. He employs high-performance computing for molecular and macro-level analyses, complemented by physical laboratory testing. Professor Shen's extensive publication record demonstrates a consistent focus on multiscale modeling of materials behavior, with recent work emphasizing granular materials dynamics, carbon nanotube applications, 3D-printed concrete technology, and energy storage systems. His research shows a clear evolution toward increasingly complex multiphysics problems that integrate mechanical, thermal, and fluid dynamics phenomena at multiple scales. The interdisciplinary nature of his work bridges civil engineering, materials science, computational mechanics, and environmental engineering, with applications spanning from fundamental material science to practical civil infrastructure solutions. Professor Shen actively supervises multiple research students, including Yifang Cao working on 3D printing concrete, Jiangshuai Meng studying granular materials under impact loads, and Runda Wang applying machine learning to rock burst prediction. His research is supported by access to advanced computational resources and laboratory facilities at The University of Sydney, particularly through his membership in The University of Sydney Nano Institute. The university has provided specialized space and equipment necessary for conducting physical tests on materials under high-speed impact conditions. Professor Shen maintains active laboratory facilities for conducting physical tests on materials under various loading conditions, particularly high-speed impact testing. His work is supported by computational resources for molecular dynamics and multiscale modeling. As a member of The University of Sydney Nano Institute, he collaborates with interdisciplinary researchers working at the nanoscale, particularly in applications related to water purification technologies using carbon nanotube membranes.

Rahmatullah RocheView profile

Assistant Professor

• Computational Biology
• Applied Machine Learning
• Data Science
• +1 more

Rahmatullah Roche serves as an Assistant Professor in the Department of Computer Science at Columbus State University's TSYS School of Computer Science, joining the tenure-track faculty in Fall 2024. His academic credentials include: Ph.D. in Computer Science and Applications from Virginia Tech (2024) M.S. in Computer Science and Software Engineering from Auburn University (2021) B.S. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2016) Dr. Roche's research spans Computational Biology , Applied Machine Learning , Data Science , and Human-Computer Interaction , with primary focus on macromolecular predictive modeling for intra- and inter-molecular interactions using cutting-edge AI techniques. His work bridges computer science and biology to solve complex structural bioinformatics challenges. Analysis of his 14 publications (2020-2025) reveals a consistent trajectory in applying deep learning—particularly transformer networks, equivariant graph neural networks, and biological language models—to protein-RNA complex prediction, protein-nucleic acid binding, and protein-protein interaction site identification, published in high-impact journals including Cell Systems and Nucleic Acids Research. Key scientific recognitions include: Pratt Fellowship Award (2023-2024) Turner College Research Award (2025) Professional Development Award for Core Course redesign (2025) Core Course Design Institute Professional Development Award (2024) 3rd place Flash Talk award at VT GPSS Symposium (2024) Best Poster Award at ACM-BCB 2020 YSEA Finalist Travel Award at MCBIOS 2024 Dr. Roche actively recruits undergraduate and graduate students for his research lab while securing resources through NSF ACCESS grants. His teaching portfolio includes Computer Science I, Computer Organization, and Graphical User Interface Development, complemented by service as Discipline Coordinator for Web Development and reviewer for top bioinformatics journals. His research group collaborates with Virginia Tech on advancing biomolecular modeling through interdisciplinary AI applications.

• Photonics
• Biosensors
• Nanotechnology
• +3 more

Brian T. Cunningham is a distinguished academic holding multiple professorships and leadership roles at the University of Illinois at Urbana-Champaign. He serves as the Intel Alumni Endowed Chair in Electrical and Computer Engineering (ECE), Program Leader at the Cancer Center at Illinois, and Professor in departments including Bioengineering, Micro and Nanotechnology Lab, Biomedical and Translational Sciences, Nutritional Sciences (Affiliate), Chemistry, and the Beckman Institute for Advanced Science and Technology. He also directs the Center for Genomic Diagnostics at the Carl R. Woese Institute for Genomic Biology. His research focuses on advanced biosensor technologies, photonic crystal engineering, and nanotechnology-driven diagnostics. Key areas include photonic crystal-enhanced fluorescence, label-free detection systems, and portable point-of-care devices. His work integrates photonics, plasmonics, and DNA-based nanostructures for ultrasensitive viral and molecular diagnostics. Notable honors include AAAS Fellow (2016), AIMBE Fellow (2013), and IEEE Fellow (2012). He leads interdisciplinary teams in the Micro and Nanotechnology Lab and collaborates across engineering, biomedical, and genomic fields to advance diagnostic tools for healthcare and biomedical research. Research Themes: Photonic crystals, biosensors, nanotechnology, medical diagnostics, and DNA-based nanodevices. Labs/Initiatives: Micro and Nanotechnology Lab, Center for Genomic Diagnostics, Personalized Nutrition Initiative. Technology Highlights: PRAM (Photonic Resonator Absorption Microscopy), DNA NanoGripper, and smartphone-linked diagnostic platforms.

• Health Economics
• Applied Microeconomics
• Industrial Organization

Assoc Prof Yang Zhang is an Associate Professor in the School of Economics at the National University of Singapore , Department of Economics. He has been affiliated with the university since 2012, progressing from Lecturer (2012-2015) to Senior Lecturer (2016-2021) and currently Associate Professor since 2022. Education: PhD in Managerial Economics and Strategy (Northwestern University, 2011) BA in Economics (Peking University, 2005) Research Interests: Health Economics Applied Microeconomics Industrial Organization Recent Research Trends focus on leveraging deep learning and structural biology tools for protein structure prediction, RNA modeling, and computational drug design. His work integrates advanced algorithms with large-scale metagenomic data to improve accuracy in complex biological system modeling.

Fatemeh VafaeeView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +5 more

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

Leonhard MöcklView profile

Professor

• Nano-optical Imaging
• Super-resolution Microscopy
• Glycocalyx Structure and Function
• +4 more

Leonhard Möckl is a Professor of Nano-optical Imaging at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and an Associated Group Leader at the Max Planck Institute for the Science of Light. He holds a PhD from LMU Munich (2015) and completed postdoctoral research at Stanford University (2016–2020), focusing on glycocalyx dynamics using super-resolution microscopy and deep learning. His research integrates biophysics, nanotechnology, and computational methods to study cell membrane organization and disease mechanisms. Key research areas include glycocalyx architecture, single-molecule imaging, and nano-optical techniques for biomedical applications. He leads the Physical Glycosciences Research Group, developing tools like XLuminA for automated microscopy design and novel drug delivery systems. Notable contributions include studies on RNA-binding proteins in leukemia treatment and glycocalyx-based drug targeting. Publications span high-impact journals like Nature Biotechnology , Cell , and Nature Communications , reflecting his expertise in imaging, glycobiology, and molecular pharmacology. His work bridges fundamental science and translational medicine, addressing challenges in cancer therapy, infectious diseases, and cell engineering.

Manuela ZucknickView profile

Professor

• Statistical learning for translational cancer research
• Personalized cancer therapies
• Multi-omics data integration
• +7 more

Manuela Zucknick is Professor and Director of the Oslo Centre for Biostatistics and Epidemiology at the University of Oslo's Faculty of Medicine, Department of Biostatistics. Her research integrates statistical learning with translational cancer research to advance personalized medicine through multi-omics data integration. PhD Biostatistics, Imperial College London (2008) MSc Bioinformatics, Imperial College London (2004) Diplom Statistik, University of Dortmund (2003) Her research focuses on Bayesian methods for integrating heterogeneous data sources in cancer research, particularly for drug response prediction in pharmacogenomic screens and patient prognosis. She develops structured high-dimensional regression models for 'large p, small n' problems in molecular medicine, with emphasis on incorporating prior biological knowledge into risk prediction frameworks. Her work bridges statistical methodology with clinical applications in personalized cancer therapies. Her recent publications demonstrate consistent contributions to multi-omics integration and survival modeling across diverse clinical contexts including cancer, pregnancy complications, and rheumatoid arthritis. The research shows strong methodological innovation in handling high-dimensional biological data while maintaining clinical relevance. Through the Oslo Centre for Biostatistics and Epidemiology, she leads collaborative projects spanning oncology, obstetrics, and rheumatology. Her work frequently involves designing statistical frameworks for pharmacogenomic screens and developing tools for biomarker discovery in complex disease settings.

• Photonics
• Topological Photonics
• Nanophotonics
• +3 more

Dr. Daria Smirnova is a Professor at the Department of Fundamental & Theoretical Physics, Australian National University (ANU). Her research focuses on topological photonics, nonlinear optics, and dielectric metasurfaces. She leads projects such as 'Topological wave manipulation in hybrid integrated platforms' and 'Ultrafast Infrared Spectroscopy Facility', demonstrating expertise in advanced photonic systems and quantum technologies. Her key research interests include: Designing topological photonic structures for robust light control Nonlinear optical phenomena in metasurfaces and nanophotonic systems Multipolar light-matter interactions in dielectric nanostructures Machine learning applications for optimizing photonic systems Recent work highlights include broadband infrared imaging with silicon metasurfaces, identification of topological lattice properties via machine learning, and exploration of Floquet-engineered materials. Her publications reflect interdisciplinary collaborations in nanophotonics, quantum optics, and applied physics. Dr. Smirnova has secured significant funding through ANU initiatives and leads a research group advancing photonic technologies. Her work integrates theoretical modeling with experimental validation, producing impactful contributions to optical engineering and materials science.

Phillip TaylorView profile

Assistant Professor

• Molecular Simulations
• Soft Materials
• Polymer Physics
• +3 more

Phillip Taylor is an Assistant Professor in the Department of Chemical Engineering at the University of Virginia. His research focuses on computational materials science, polymer physics, biomaterials, and peptide self-assembly, using molecular simulations and machine learning to engineer soft materials such as hydrogels and nanoparticles. Education: B.S.E. and M.S.E. in Chemical & Biomolecular Engineering from the University of Pennsylvania; Ph.D. in Chemical & Biomolecular Engineering from the University of Delaware (under Professors Arthi Jayaraman and April M. Kloxin). Postdoctoral research at Sandia National Laboratories' Center for Integrated Nanotechnologies with Drs. Gary Grest and Mark Stevens. Research interests include bio-inspired polymers for medical applications (e.g., drug delivery, tissue engineering), dynamic covalent hydrogels, and machine learning-driven coarse-grained models for self-healing materials. His work emphasizes computational guidance for synthesizing responsive soft materials. Awards: UVA Rising Scholars Postdoctoral Fellowship (2022) Chemical Engineering Teaching Fellowship (University of Delaware, 2022) AIChE 8B Biomaterials First Place Graduate Talk Award (2021) Best Talk Prize (MRS Spring Meeting, 2021) Lab activities: Taylor Lab develops biomaterials and soft materials via computational design. Collaborates on hydrogel synthesis and stimuli-responsive nanoparticles with controllable viscoelastic properties.