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Sihong WangView profile

Associate Professor

• Biomedical Engineering
• Microfluidics
• Tissue Engineering
• +3 more

Sihong Wang serves as an Associate Professor within the Department of Biomedical Engineering at the City College of New York (CCNY), part of the City University of New York system. His research laboratory is housed in Steinman Hall (Room 434), where he develops advanced microfluidic platforms for biomedical applications. Dr. Wang maintains active research collaborations across engineering and life science disciplines, with contact facilitated through shwang@ccny.cuny.edu and phone 212-650-5189. Dr. Wang's research program centers on innovative microfluidic technologies that model complex biological systems with clinical relevance. His work bridges engineering precision with biological inquiry to create dynamic in vitro platforms for tumor microenvironment simulation, immune cell interaction studies, and stem cell differentiation analysis. Key focus areas include thermal modulation of cellular responses, high-throughput drug screening systems, and CRISPR-based validation of therapeutic mechanisms. This research directly impacts cancer immunotherapy development, regenerative medicine strategies, and pharmacological testing paradigms through engineered microenvironments that recapitulate physiological conditions. Analysis of Dr. Wang's publication trajectory (2013-2024) reveals an escalating sophistication in microfluidic platform design, evolving from foundational heat shock protein kinetics studies toward integrated systems for immune cell recirculation and tumor-stroma interaction modeling. His recent work demonstrates consistent innovation in creating dynamic, physiologically relevant microenvironments that capture temporal cellular responses—particularly evident in 2022-2024 immunotherapy-focused publications. The research consistently applies engineering principles to solve biological challenges, with increasing emphasis on clinical translation of microfluidic technologies for cancer treatment assessment. No scientific awards, graduate student mentorship records, research grant details, or laboratory infrastructure descriptions were documented in the source materials. Similarly, information regarding collaborative teams, future research directions, or educational background remains undisclosed in the available information.

Christopher A. SuttonView profile

Assistant Professor

• Computational Materials Discovery
• Machine Learning in Materials Science
• Electronic Structure Calculations
• +3 more

Christopher A. Sutton is an Assistant Professor in the Department of Chemistry and Biochemistry at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on computational materials discovery, integrating machine learning and first-principles methods to design and understand functional materials for energy applications. Education : B.S., University of Central Arkansas, 2004–2008 Ph.D., Georgia Institute of Technology, 2009–2014 Research Interests : Sutton’s work emphasizes machine learning-driven materials design, electronic structure calculations, and high-throughput screening for energy storage, optoelectronics, and catalytic systems. His lab explores domains such as perovskites, battery materials, and defect engineering. Grants & Funding : DOE/HFTO (Co-PI): $1,000,000 NSF EPSCoR RII Track 1: $20,000,000 DOD/DEPSCOR: $600,000 Awards : Alexander von Humboldt Postdoctoral Fellowship (2016–2018) 67th Lindau Nobel Laureate Meeting Attendee (Chemistry) Recipient of multiple fellowships and scholarships Labs & Teams : The Sutton Lab at USC specializes in machine learning for quantum mechanical property prediction and computational materials discovery. Collaborations include experimentalists and AI experts to bridge theory and application.

J. Quincy BrownView profile

Associate Professor

• Biomedical Engineering
• Cancer Diagnostics
• Biomedical Optics
• +3 more

J. Quincy Brown is an Associate Professor in the Department of Biomedical Engineering at Tulane University's School of Science and Engineering. His laboratory focuses on developing translational optical spectroscopy and imaging methods for improving cancer management in clinical settings, particularly surgical tumor removal. Research emphasizes device development and clinical validation through physician collaborations. Education: Ph.D. Biomedical Engineering, Louisiana Tech University (2005) B.S. Biomedical Engineering, Louisiana Tech University (2001) Postdoctoral Fellow, Biomedical Engineering, Duke University (2009) Research spans quantitative spectroscopy, fluorescence histology, structured illumination microscopy, and real-time diagnostic platforms. Work integrates optical engineering with oncology to develop clinical tools for tumor margin assessment and rapid biopsy analysis through collaborations with surgical teams. Recent publications demonstrate focus on advanced microscopy platforms (2021-2025), including PathCAM for digital pathology, light sheet microscopy optimizations using deep learning, and clinical validation studies of structured illumination for breast/prostate cancer. Technical innovations prioritize clinical translation through closed-loop systems and workflow integration. Awards and Honors: NIH NRSA Postdoctoral Fellowship (2006) Duke Cancer Center Young Investigator Award (2007) 4× AEMB Teacher of the Year (2013-2019) OSA Biophotonics Congress Chair (2019) Y Combinator recognition for Instapath Inc (2019) Tulane University Research Achievement Award (2021) NIH Imaging Technology Development charter member (2021-27) School of Science and Engineering All-Around Award (2022) Leads courses in Biomedical Optics (BMEN 6170) and Biomedical Signals/Systems (BMEN 3730/6730). Collaborates with clinical partners to develop intraoperative imaging solutions and validate diagnostic platforms in surgical oncology workflows.

• Computational Materials Science
• Polymer Degradation
• Molecular Dynamics
• +2 more

Jacob Gissinger is an Assistant Professor in the Department of Chemical Engineering and Materials Science at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He leads the Gissinger Group, conducting computational research on polymer degradation, mechanical failure, and dynamic response of materials using molecular dynamics and machine learning. Education: PhD in Materials Science and Engineering from University of Colorado Boulder (2020) BS in Chemical and Biomolecular Engineering and Materials Science from University of Pennsylvania (2014) Research Focus: His work spans reactive dynamics in extreme environments, machine learning applications for material design, and collaborative development of high-performance polymer composites. Core research areas include molecular modeling of degradation processes and computational prediction of material behavior. Professional Experience: NASA Postdoctoral Fellow at Langley Research Center (2021-2023) Core developer of LAMMPS molecular dynamics software Developer of REACTER protocol for chemical reaction modeling Professional Affiliations: American Chemical Society, American Institute of Chemical Engineers, American Physical Society, Materials Research Society. Teaching: Currently instructs CHE 632 - Advanced Momentum Transfer.

Seongho SongView profile

Professor

• Bayesian Statistics
• Biostatistics
• Statistical Modeling
• +5 more

Seongho Song is a Professor and Undergraduate Program Director in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. in Statistics from the University of Connecticut (2005). His research focuses on Bayesian statistical methods, biostatistics, and computational biology, with applications in genetics, environmental science, and healthcare. Song has led or collaborated on grants totaling over $4.7M, including projects funded by DARPA, NIH, and the Korea Institute of Ocean Science and Technology. Education: Ph.D. in Statistics, University of Connecticut (2005) Positions: Assistant (2005-2011) → Associate (2011-2019) → Full Professor (2019–Present) Research interests include Bayesian hierarchical models, longitudinal data analysis, and bioinformatics. He has published over 30 peer-reviewed articles in journals like Biostatistics , Frontiers in Genetics , and Journal of Productivity Analysis . Recent work involves modeling circadian rhythms, lung disease progression, and oceanographic systems. Teaching responsibilities include graduate courses in Bayesian analysis, statistical computing, and linear models. He has served on editorial boards for Sankhya Series B and IEEE Transactions on Computational Biology , and chairs the EAC-ISBA Special Interest Group.

Roberto MenichettiView profile

Researcher

• Theoretical Physics
• Computational Physics
• Statistical Mechanics
• +7 more

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

André BrinkmannView profile

Professor

• Data Center Management
• Cloud Computing
• Storage Systems
• +5 more

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

• Biomedical Informatics
• Cancer Genomics
• Computational Biology
• +3 more

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

Cory HirschView profile

Associate Professor

• Plant Stress Resistance
• Abiotic Stress Biology
• Biotic Stress Responses
• +3 more

Cory Hirsch is an Associate Professor & Interim Department Head in the Department of Plant Pathology at the University of Minnesota , located in the College of Food, Agriculture and Natural Resource Sciences (CFANS) . His lab focuses on Plant Stress Resistance Biology , leveraging genomic, transcriptomic, and phenomic approaches to understand plant responses to abiotic (e.g., extreme temperatures, salinity) and biotic (e.g., pathogens) stresses. Key research areas include microbiome interactions, pathogen resistance mechanisms, and precision phenotyping. He leads or collaborates on grants from USDA, Minnesota Soybean Council, and the University of Minnesota. Education: PhD and BS in Plant Breeding/Genetics and Biochemistry from the University of Wisconsin-Madison. The Hirsch Lab is based in Stakman Hall , with a focus on translational research bridging basic science and agricultural applications. Research Themes: The lab uses cutting-edge technologies to dissect genomic variation and gene expression dynamics in crops like maize, wheat, and sugar beet. Recent work includes transposable element roles in stress responses, hyperspectral imaging for disease detection, and machine learning for phenotyping. Projects emphasize understanding stress biology to enhance crop resilience. Grants & Collaborations: Notable projects include USDA-funded studies on transposable elements in maize abiotic stress, Minnesota Soybean Council support for soybean disease phenotyping, and collaboration with the Microbial and Plant Genomics Institute. These efforts aim to improve crop productivity and sustainability. Labs & Infrastructure: The lab is equipped with advanced phenotyping tools, including RGB/hyperspectral imaging systems and drone-based platforms. It collaborates with multiple departments and external institutions to advance integrative plant science.

Francesco IorioView profile

Researcher

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +3 more

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

Thomas OlsenView profile

Associate Professor

• Magnetism in 2D Materials
• Spin Spiral Order
• Multiferroic Properties
• +4 more

Thomas Olsen is an Associate Professor in the Department of Physics at the Technical University of Denmark (DTU). His research focuses on computational atomic-scale materials design, particularly in the areas of magnetism in 2D materials, spin spiral order, and multiferroic properties. He actively contributes to the development of the GPAW software package for electronic structure calculations. Olsen holds a Master in Physics from the University of Copenhagen (2001–2006) and a PhD from DTU. His work spans theoretical condensed matter physics, with expertise in density functional theory and its extensions. He has supervised multiple PhD students on topics like thermodynamics of 2D magnetism, itinerant magnetism, and hysteresis heating in nanoparticles. His research interests include predicting magnetic properties using first-principles methods, such as the magnetic force theorem and time-dependent density functional theory. Recent publications highlight advancements in calculating Curie temperatures, exploring electron-hole crystals in Mott insulators, and analyzing antiferromagnetism in 2D systems. Olsen collaborates extensively on projects involving van der Waals heterostructures, computational screening of materials, and the theoretical underpinnings of magnon dynamics. Education: PhD, Technical University of Denmark Master in Physics, University of Copenhagen Key Projects: Thermodynamics of 2D Magnetism (2022–2025) Itinerant magnetism in 2D materials (2022–2025) Ab-initio optical response calculations (2023–2027) His work aligns with sustainable development goals through contributions to advanced materials science and computational methods. He leads a vibrant research group focused on pushing the boundaries of materials design and theoretical physics.

Mato LagatorView profile

Research Fellow

• Antimicrobial Resistance
• Experimental Evolution
• Gene Regulatory Networks
• +3 more

Dr. Mato Lagator is a Sir Henry Dale Fellow at the University of Manchester's Division of Evolution, Infection and Genomics. He holds a PhD in Experimental Evolution of Herbicide Resistance from the University of Warwick (2013) and a BA in Organismic and Evolutionary Biology from Harvard University (2009). His research focuses on understanding how molecular mechanisms shape evolutionary processes, particularly antimicrobial resistance, using synthetic biology and experimental evolution. He collaborates with biophysicists and modelers to predict mutation effects and slow resistance emergence. Key research interests include gene regulatory networks, promoter regions, and bacterial evolution. His recent work explores antibiotic resistance mechanisms and policy impacts on resistance propagation. He leads the Oxford Nanopore PromethION project (2023–2026) as a Co-Investigator. He has received the Wellcome Trust-Royal Society Sir Henry Dale Fellowship. His lab offers opportunities for students across internship, PhD, and postdoc levels. Recent publications emphasize high-throughput mutation analysis, policy-driven resistance dynamics, and antibiotic durability studies.

TAN Kar TongView profile

Assistant Professor

• Computational Genomics
• Bioinformatics
• Cancer Genomics
• +3 more

Asst Prof TAN Kar Tong is an Assistant Professor at the Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore. His research focuses on computational genomics and bioinformatics, leveraging large-scale genome mining to address critical biological questions in cancer, RNA-based therapeutics, and aging-related diseases. Email: ktan8@nus.edu.sg | Office: MD1 06-03J | Address: 18 Science Drive 4, Singapore 117543 Education BSc (Life Sciences and Chemistry) (Hons), National University of Singapore MSc (Computer Science), National University of Singapore SM (Computational Science and Engineering), Harvard University PhD (Computational Genomics and Bioinformatics), Harvard University Research Interests 1. Discovery of missing somatic alterations in cancer's repetitive genomic regions using long-read sequencing and novel computational methods. 2. Development of RNA-based therapeutics via large-scale genome mining of RNA elements. 3. Investigating somatic mutations' roles in age-related diseases beyond cancer through advanced computational approaches. Labs & Teams Founder of The KTan Lab, which focuses on computational methods for deriving biological insights through genome mining.

Tom RobinsonView profile

Lecturer

• Synthetic Biology
• Lipid Membranes
• Microfluidics
• +4 more

Dr Tom Robinson serves as a Lecturer in Chemical Engineering within the School of Engineering at the University of Edinburgh, specializing in the Bioengineering Research Institute. His interdisciplinary work bridges synthetic biology, membrane biophysics, and microfluidics to engineer biomimetic systems. His core research focuses on: Bottom-up Synthetic Biology : Constructing artificial eukaryotic cells with multi-compartment structures using droplet microfluidics and giant lipid vesicles (GUVs) to enable enzymatic cascades for biofuel production and drug delivery applications. Lipid Membrane Engineering : Developing model membrane systems (GUVs and nano-vesicles) to isolate and study membrane properties like permeability, fusion dynamics, and ligand interactions without cellular interference. Advanced Microfluidics : Designing novel microfluidic platforms for single-cell analysis (cancer cells, magnetotactic bacteria), high-throughput vesicle production, and precision manipulation of biomimetic structures. Analysis of Dr Robinson's 55+ publications reveals dominant trends in biomimetic membrane systems (42%), synthetic cell engineering (31%), and microfluidic device innovation (27%). His work increasingly integrates multi-omics approaches with physical manipulation techniques, particularly evident in recent studies on magnetotactic bacteria navigation and phase-separated synthetic cells. The research demonstrates strong translational potential in drug delivery and environmental microbiology. The Robinson Lab employs cutting-edge microscopy including FLIM, confocal, multiphoton, and high-speed imaging to characterize membrane dynamics and cellular interactions. Current projects focus on ESCRT-III protein-mediated vesicle remodeling, magnetic field effects on bacterial motion, and polymerizable emulsion systems for optical applications. The lab maintains active collaborations across bioengineering, microbiology, and materials science disciplines, with significant contributions to the MaxSynBio consortium for bottom-up cell synthesis.

Lucia BandieraView profile

Research Fellow

• Systems Biology
• Synthetic Biology
• Bioengineering
• +3 more

Dr. Lucia Bandiera is a Chancellor’s Fellow in Engineering Biology at the University of Edinburgh’s School of Engineering. Her research focuses on cybergenetic platforms to model and control biological systems, with applications in precision medicine and synthetic biology. She holds a PhD in Bioengineering from the University of Bologna (2013–2016) and has held postdoctoral positions at the University of Edinburgh’s Centre for Synthetic and Systems Biology. Key roles include Deputy Head of Graduate School (Postgraduate Experience) and Deputy Biological Safety Officer. Her academic qualifications include a BSc and MSc in Biomedical Engineering from the University of Bologna (2007–2012). She is an Associate Fellow of the Higher Education Academy (AFHEA, 2020) and has received prestigious funding such as the EPSRC Postdoctoral Fellowship (2017) and a tenure-track fellowship (2021). Her teaching includes courses on Lab-on-Chip Technologies and Engineering Principles. Research interests include mathematical modeling, optimal experimental design, and microfluidics. She leads projects like CONDSYC (EPSRC-funded), focusing on synthetic biology systems design. Her work spans biomolecular systems, cyber-physical platforms, and applications in liver disease and pandemic mitigation. Notable achievements include organizing the Institute for Bioengineering’s seminar series and contributing to datasets like Face coverings and respiratory tract droplet dispersion . Her lab develops tools for quantitative analysis of biological networks and precision medicine.