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Wengong JinView profile

Assistant Professor

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +5 more

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

Ambuj K. SinghView profile

Professor

• Machine Learning
• Social Networks
• Network Science
• +7 more

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

• Immunology
• Translational Immunology
• Computational Immunology
• +4 more

Professor Mark Coles serves as Professor of Immunology and Lead for Industrial Strategy and Entrepreneurship at the Kennedy Institute of Rheumatology, University of Oxford, holding concurrent roles as Kennedy Trust Senior Research Fellow, Official Fellow at Reuben College, and Affiliate Faculty at the Wolfson Centre of Mathematical Biology. His interdisciplinary work bridges immunology, computational modeling, and translational research to accelerate therapies for immune-mediated inflammatory diseases. His academic journey began with a BSc in Microbiology from Cornell University (1992), followed by a PhD in Molecular and Cell Biology at UC Berkeley under David Raulet, and postdoctoral training with Dimitris Kioussis at the National Institute of Medical Research. At the University of York (2006-2017), he pioneered stromal immunology research and co-founded the York Computational Immunology Laboratory. Coles' research centers on stromal and systems immunology, with core expertise in stromal cell biology, inflammatory disease mechanisms, and mathematical modeling of immune responses. He champions 3Rs-based approaches (Replacement, Reduction, Refinement) to reduce animal testing through in silico models, integrating spatial single-cell biology with multi-scale computational frameworks to dissect immune function in human tissues. His 2025 publications reveal a cohesive research trajectory applying interdisciplinary methods across diverse disease contexts—from cardiac fibrosis and vaccine responses to arthritis pathogenesis and CAR-T cell therapy—unified by focus on stromal-immune crosstalk and quantitative modeling to identify therapeutic targets. Key honors include: Fellow of the Royal Society of Biology Kennedy Trust Senior Research Fellow As former Director of Graduate Studies (2017-2024) at the Kennedy Institute, Coles mentored numerous graduate students while co-leading major initiatives including the Arthritis Therapy Acceleration Program and Human Cell Atlas medicalization. His entrepreneurial impact spans three co-founded ventures: Simomics Ltd (in silico disease modeling), Lightox Ltd (phototherapy for oral cancer), and Mestag Therapeutics (fibroblast-targeted therapies for cancer/inflammation). He directs the Laboratory of Stromal and Systems Immunology and Oxford Mathematical Immunology Group, fostering collaborations with Christopher Buckley, Calliope Dendrou, and Eamonn Gaffney to develop Quantitative Systems Pharmacology models that translate mechanistic insights into patient therapies.

Melody AlsakerView profile

Associate Professor

• Medical Imaging
• Applied Inverse Problems
• Mathematical Modeling
• +3 more

Melody Alsaker is an Associate Professor in the Department of Mathematics at Gonzaga University, where she has held this position since January 2016. Her research focuses on medical imaging and applied inverse problems, particularly in the field of electrical impedance tomography (EIT). She specializes in mathematical modeling, algorithm design, and biomedical image processing, with applications in pulmonary and thoracic imaging. Her work emphasizes improving EIT reconstruction techniques using the D-bar method, incorporating spatial priors, and developing real-time solutions for clinical applications. Notable contributions include the ACE1 EIT system for thoracic imaging and studies on stroke classification, air trapping in lungs, and surrogate measures of pulmonary function in children with cystic fibrosis. Alsaker's research bridges mathematics and engineering, addressing challenges in medical imaging accuracy and computational efficiency. Her collaborations span disciplines, including biomedical engineering, respiratory physiology, and clinical medicine.

• Machine Learning
• Probabilistic Inference
• Decision Making
• +1 more

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

• Neuroscience
• Synaptic Plasticity
• Neural Circuit Formation
• +4 more

Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.

John A. Copland, IIIView profile

Professor

• Cancer Biology
• Molecular Carcinogenesis
• Translational Research
• +3 more

Dr. John A. Copland III is a Professor of Cancer Biology and Biochemistry & Molecular Biology at Mayo Clinic in Jacksonville, Florida. He leads the Cancer Biology and Translational Research Laboratory, focusing on molecular mechanisms of carcinogenesis, tumor progression, and development of targeted cancer therapies. Education: PhD in Physiology & Endocrinology (Medical College of Georgia), MS in Endocrinology (Medical College of Georgia), BS in Chemistry (Columbus College), with postdoctoral training at University of Texas Medical Branch. Research interests center on: Identifying tumor suppressor genes (e.g., RhoB, TBR3, GATA3) and oncogenes (e.g., FOXO3a, SCD1, NPTX2). Developing patient-derived xenografts and live cell models for personalized medicine. Designing SCD1 inhibitors via in silico modeling for clinical trials. Recent publications highlight his work on SCD1 inhibition in leukemia and thyroid cancer ImmunoPET imaging of thyroid tumors CRISPR-identified drug synergies in cholangiocarcinoma Patient-specific combination therapies using xenograft models

Roberto A. ChicaView profile

Professor

• Protein Engineering
• Computational Protein Design
• Biocatalysis
• +5 more

Roberto A. Chica is a Full Professor in the Department of Chemistry and Biomolecular Sciences at the University of Ottawa, Faculty of Science. His research focuses on computational and experimental protein engineering, particularly in designing novel enzymes and fluorescent proteins for biotechnological applications. He develops advanced algorithms for protein design and investigates enzyme dynamics using molecular modeling and structural biology approaches. Key research interests include biocatalysis, structural biology, and the application of computational methods to engineer proteins with tailored functions. His lab integrates experimental protein chemistry with computational simulations to understand catalytic mechanisms and design proteins for industrial and biomedical uses. Recent work emphasizes ensemble-based computational enzyme design, exploring how conformational landscapes influence catalytic efficiency. His articles highlight advancements in artificial enzyme creation, substrate specificity modulation, and fluorescent protein optimization. Chica’s contributions bridge fundamental biochemistry with applied innovations in protein engineering. Notable achievements include the design of bright red fluorescent proteins via computational approaches and the development of biosensors for protein expression monitoring. His research has implications for drug discovery, biocatalytic synthesis, and personalized medicine.

Outi Salo-AhenView profile

Senior Lecturer

• Pharmaceutical Chemistry
• Molecular Modeling
• Drug Design
• +5 more

Senior Lecturer Outi Salo-Ahen is affiliated with Åbo Akademi University's Faculty of Natural Sciences and Engineering , Department of Pharmacy. Her research focuses on computational pharmacology, drug design, and pharmaceutical chemistry, particularly targeting chemokine receptors (CCR5/CXCR4) and transient receptor potential channels (TRPA1) for therapeutic applications. Doctor of Pharmacy (2006, University of Kuopio/UEF) MSc in Pharmaceutical Chemistry (2001, UEF) BSc in Pharmacy (1999, UEF) University Pedagogy Modules 1-5 (2012-2015) Her work contributes to UN Sustainable Development Goals through education and pharmaceutical innovation . Recent research trends include: Antimicrobial resistance solutions TRPA1 channel modulation Nanotechnology-enabled drug delivery Multi-target HIV-1 inhibitors 3D printing of biocompatible materials Computational analysis of nucleic acid frameworks She actively supervises doctoral projects, serves on assessment panels, and leads collaborations like Nordic Pharmaceutical Translation and Innovation. Her 60+ publications demonstrate expertise in molecular modeling and drug discovery.

• Respiratory Drug Delivery
• Pulmonary Drug Delivery
• Inhalation Aerosols
• +5 more

Professor Hak-Kim Chan of the Sydney Pharmacy School at the University of Sydney is a world-renowned expert in respiratory drug delivery, particularly pulmonary aerosols and inhalation therapies. With over 480 publications and 17,580+ Google Scholar citations, he has pioneered advancements in powder formulation , in silico modeling , and clinical applications of inhalation technologies. His work includes the development of FDA-approved diagnostics like Aridol™ (inhaled mannitol for asthma) and Bronchitol™ (for cystic fibrosis), and groundbreaking research on inhaled bacteriophage therapy to combat antibiotic-resistant respiratory infections. Education: BPharm (University Medal, 1983), PhD (1988), DSc (2009) from University of Sydney Professional Experience: Postdoc at University of Minnesota (1988–89), Scientist at Genentech Inc. (1992–95) Leadership: Executive Editor of Advanced Drug Delivery Reviews , Fellow of AAPS and RACI His research spans in vitro production methods, computational modeling of inhaler design, and in vivo imaging of aerosol deposition. Current projects focus on nanomedicine , phage therapy , and combating superbugs via inhalation routes. He has secured significant recognition for his work, including NHMRC case studies highlighting public health impacts. Professor Chan has supervised numerous researchers, including PhD student Grace YAU studying pulmonary probiotic delivery . His team's 10 patents (7 as first inventor) reflect practical innovations in dry powder inhalers , antimicrobial formulations , and drug stabilization technologies.

• Extreme Environmental Physiology
• Human Adaptation to Stressful Environments
• Environmental Health
• +3 more

Edward Ashworth is a Postdoctoral Research Associate at the Sydney School of Health Sciences, University of Sydney. He is affiliated with the Thermal Ergonomics Laboratory and the Heat and Health Research Incubator. His research focuses on human adaptations to extreme environments, including heat, altitude, hyperbaric conditions, and space-related stressors. Key projects include improving sleep quality in hot environments and enhancing physical work capacity for outdoor workers. Edward's research interests span environmental physiology, with a focus on thermal tolerance, radiation injury mitigation, and the physiological effects of extreme conditions. He has contributed to studies involving nitrogen kinetics tracking using PET imaging, hyperbaric oxygen therapy, and robotic surgery applications. Awards: Young Investigator Award (2023), Art of Science Contest 1st Prize (2024), Australia Space Biology Summit 1st Place (2022), and Sporting Blue (Auckland University of Technology). Associations: The Physiological Society and American Physiological Society. His work integrates clinical trials, statistical research design, and translational approaches to address challenges in environmental health, cardiovascular disease, and healthy ageing. Current projects aim to optimize thermal tolerance strategies for military and occupational settings.

Ian BradleyView profile

Assistant Professor

• Environmental Engineering
• Water Treatment
• Wastewater Management
• +3 more

Ian Bradley is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York. His research focuses on creating sustainable biological processes to address needs in engineered and natural systems for water and wastewater treatment and resource recovery. Education: PhD in Environmental Engineering, University of Illinois at Urbana-Champaign (2017) MS in Environmental Engineering, University of Illinois at Urbana-Champaign (2011) MS in Civil Engineering (Structures), University of Illinois at Urbana-Champaign (2010) Research Interests: Dr. Bradley specializes in microalgal-based nutrient recovery, wastewater surveillance for public health monitoring, PFAS degradation using nanomaterials, and sustainable resource recovery systems. His work integrates biological processes with environmental engineering to optimize wastewater treatment efficiency and develop predictive models for water quality and health outcomes. Publications: His recent research includes advancements in microalgal cultivation (EcoRecover process), wastewater-based epidemiology for SARS-CoV-2 tracking, and computational enzyme design for PFAS remediation. These studies demonstrate interdisciplinary expertise spanning environmental engineering, biotechnology, and public health analytics.

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +4 more

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

Zeynep AltintasView profile

Professor

• Biosensors
• Bioinspired Materials
• Molecular Imprinting
• +6 more

Prof. Zeynep Altintas is a Full Professor (W3) at the Faculty of Engineering, Kiel University, where she holds the Chair of Bioinspired Materials and Biosensors within the Institute of Materials Science since 2022. She leads cutting-edge research at the intersection of materials science, biosensing, and computational design of functional materials for biomedical applications. Her research focuses on developing novel biosensing platforms using in silico designed functional materials for medical diagnostics, environmental monitoring, and food safety analysis. She has pioneered approaches in epitope-mediated imprinting, nanoMIP biosensors, and lab-on-a-chip sensing technologies. Her work bridges computational modeling with experimental validation to create high-affinity synthetic receptors for disease biomarkers. Prof. Altintas has received numerous prestigious accolades including the Life Outstanding Investigator Award for Women (2022), The Aventis Life Sciences Bridge Award with 100,000 euros prize money (2021), and recognition on Stanford University's Top 2% Scientists List (2021 and 2022). These awards reflect her significant contributions to advancing biosensor technology and materials science. She serves in editorial roles for high-impact journals including Biosensors and Bioelectronics (Elsevier), Scientific Reports (Nature), and Micromachines (MDPI). Her research has been supported by competitive funding including a Marie Curie Individual Fellowship (2016-2018) and various British Council travel grants. She has organized international scientific events and served on multiple conference committees. Prof. Altintas leads the Biomaterials and Biosensors Working Group at Kiel University, which is actively engaged in projects related to biomagnetic sensing, materials for brain applications, and cooperative actuator systems for nanomechanics. Her research group collaborates internationally across Europe, Turkey, and the UK, addressing critical challenges in healthcare diagnostics through interdisciplinary approaches.

Xin ZhouView profile

Research Fellow

• Computational Biology
• Health Informatics
• Cardiovascular Disease Modeling
• +3 more

Dr. Xin Zhou is an Oxford-Bristol Myers Squibb Fellow at the Department of Computer Science, University of Oxford. Her research integrates computational modeling, clinical data, and experimental findings to investigate cardiac disease mechanisms and develop human-based simulations for drug evaluation. BSc and MSc in Life Sciences, Beijing Normal University DPhil in Computational Biology, University of Oxford Her work focuses on multi-scale cardiac modeling , particularly in ischemic heart disease and heart failure, exploring ionic currents, tissue conduction, and organ-level dynamics. She develops electromechanical simulations to study cardiac alternans and arrhythmic risks, translating these into clinical applications for patient stratification and pharmaceutical testing. Recent publications emphasize in silico clinical trials , sex-specific cardiometabolic analysis, and Purkinje network modeling. Collaborative efforts with clinicians and pharmaceutical partners highlight her translational approach to regulatory science. Model of the Year 2024, BioModels EPSRC Impact Acceleration Account Microsoft Research Project Award Recognition Award, University of Oxford She supervises PhD and MSc students in computational cardiology, while serving on the editorial board of Frontiers in Physiology . Her current projects involve digital twinning and predictive cardiac safety models to reduce animal testing reliance.