جست‌وجوی استاد و پژوهشگر

Karsten Borgwardtمشاهده پروفایل

استاد

• Machine learning
• Systems biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

Molly Megrawمشاهده پروفایل

دانشیار

• Transcription Factors
• Machine Learning
• Arabidopsis
• +۶ مورد دیگر

Molly Megraw is an Associate Professor at Oregon State University's College of Agricultural Sciences , affiliated with the Department of Botany and Plant Pathology , Molecular and Cellular Biology Program , and Center for Genome Research and Biocomputing . She also collaborates with the Department of Electrical Engineering and Computer Science. Education : Ph.D. in Bioinformatics (2007) from the University of Pennsylvania Her research integrates computational biology , machine learning , and plant genomics to study: Transcriptional regulation of Pol-II genes and miRNAs in plants Network motif analysis in TF-miRNA circuits Chromatin accessibility and promoter identification via nanoCAGE Synthetic biology applications in plant systems Recent publications focus on: Salinity stress responses in rice miRNA editing in human cancer Network motif discovery algorithms PlantSimLab modeling platform Scientific accomplishments include: NSF CAREER Award (2018) for machine learning models of gene regulation MiRGen database development for miRNA genomic organization She supervises graduate students in Biological and Physical Sciences (BPP) and teaches computational courses like BDS 470/570 Introduction to Computing in the Life Sciences . Her lab maintains active collaborations across plant biology and computational disciplines.

Mingyao Liمشاهده پروفایل

دانشیار

• Bioinformatics
• Biostatistics
• Genetics
• +۲ مورد دیگر

Mingyao Li, PhD, is an Associate Professor of Biostatistics whose scholarship bridges high-dimensional statistics with modern genomics and spatial biology. Her work is recognised for advancing computational methods that dissect complex tissue architecture and disease heterogeneity at single-cell resolution. Research Interests Bioinformatics and computational biology Statistical methods for high-dimensional genomic data Single-cell and spatial multi-omics integration Genetics and genomics of cardiovascular and cancer systems Systems biology approaches to disease mechanisms Across more than one hundred peer-reviewed publications, Li’s research exhibits a clear trajectory from developing rigorous statistical frameworks—such as hypothesis testing in stochastic block models—to large-scale applications in human disease. A dominant theme is the fusion of spatial transcriptomics, single-cell multi-omics and AI-driven image analysis to uncover cell-type heterogeneity, lineage relationships and microenvironmental crosstalk in atherosclerosis, lung adenocarcinoma, pancreatic cancer and neurodegenerative disorders. Scientific Awards & Recognition While specific awards are not listed in the provided text, the consistent appearance of Dr Li as senior or corresponding author on high-impact papers in Nature family journals and the development of widely-used computational tools (e.g., MISO, iSCALE) attest to significant scholarly recognition. Research Support & Teams Granting agencies and collaborative networks are not explicitly detailed, but the scale and scope of the projects—from whole-organ spatial atlases to primate genome-editing studies—imply substantial multi-institutional funding and interdisciplinary teams integrating biostatisticians, wet-lab scientists and clinician-scientists.

Jack Feldmanمشاهده پروفایل

استاد

• Respiratory Neurobiology
• Neural Circuits
• Neurophysiology
• +۳ مورد دیگر

Jack Feldman is Distinguished Professor of Neurobiology at the David Geffen School of Medicine, University of California, Los Angeles (UCLA). He leads research on respiratory neurobiology from his laboratory at CHS, Los Angeles, focusing on the preBötzinger complex and its role in breathing rhythm generation. Research Focus: Respiratory rhythmogenesis, neural circuits, opioid/nicotine effects on breathing, sighing mechanisms, and neurophysiological plasticity Lab: PreBötzinger Complex Research Laboratory, CHS, UCLA His scientific contributions include discovering fundamental neural mechanisms for respiratory control, with recent work examining synchronization in microcircuits and clinical implications for disorders like congenital central hypoventilation syndrome. Publications span high-impact journals in Neuroscience , Nature , Science , and The Journal of Physiology . Major Scientific Awards: NIH MERIT Award (1991-2001) NIH Outstanding Investigator Award (2017-2023) Hodgkin Huxley Katz Prize (2016) UCLA Faculty Research Lecture (2018) David Geffen School of Medicine Chair in Neuroscience (2022---)

Alexander Gowمشاهده پروفایل

استاد

• Neurogenetics
• Neurodegenerative Diseases
• Myelin Biology
• +۴ مورد دیگر

Alexander Gow is Professor of Molecular Medicine and Genetics, Pediatrics, and Neurology at Wayne State University, focusing on neurodegenerative diseases and white matter disorders. His research spans multiple disciplines including molecular genetics, neuroscience, and computational biology. PhD from University of Queensland (1990) Charles H. Gershenson Distinguished Fellow Associate Director, Center for Molecular Medicine and Genetics His research explores: Protein misfolding in Pelizaeus-Merzbacher disease Unfolded Protein Response (UPR) in neurodegeneration Claudin-11 protein's role in myelin and auditory function Drug development pipelines for neurodegenerative disorders Mouse models for white matter diseases Computational modeling of neural systems Recent publications focus on myelin integrity analysis, neurodegenerative modeling, and AI applications in biomedical research. Notable collaborations include researchers from Biomedical Engineering, Psychiatry, and Otolaryngology departments at Wayne State University. Scientific awards include multiple Research Excellence Awards from Wayne State University (2004, 2009, 2013) and the Charles H. Gershenson Distinguished Fellowship (2010).

Martin Jinye Zhangمشاهده پروفایل

استادیار

• Genetics
• Machine Learning
• Functional Genomics
• +۳ مورد دیگر

Martin Jinye Zhang is an Assistant Professor in Computational Biology at Carnegie Mellon University's School of Computer Science. His research bridges genetics and machine learning, focusing on integrative analysis of genetics and functional genomics data to uncover disease mechanisms. Ph.D. in Electrical Engineering (2019, Stanford University) B.Eng. in Electrical Engineering (2014, Tsinghua University) Key research areas include: Developing scDRS for single-cell disease relevance scoring Using graph foundation models to integrate genetic variants and regulatory elements Advancing empirical Bayes methods and causal inference in genomics His 15 most recent articles span topics from single-cell RNA-seq to genetic architecture , with a focus on autoimmune disorders , brain diseases , and metabolic traits , often leveraging biobank-scale data . Notably, his work demonstrates how machine learning can enhance GWAS interpretation and drug target identification .

Hande Ozdinlerمشاهده پروفایل

دانشیار

• Neurodegenerative Diseases
• Upper Motor Neuron Diseases
• ALS Research
• +۶ مورد دیگر

Dr. Hande Ozdinler is an Associate Professor in the Ken & Ruth Davee Department of Neurology at Northwestern University's Feinberg School of Medicine, where she directs the Ozdinler Lab focused on upper motor neuron biology and pathology. Her research is supported by multiple institutional affiliations including the Chemistry of Life Processes Institute, Les Turner ALS Center, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, and the Northwestern University Institute of Neuroscience. Dr. Ozdinler earned her PhD from Louisiana State University Health Sciences Center in 2002. Her research career has been dedicated to understanding selective neuronal vulnerability, particularly in corticospinal motor neurons affected in diseases like ALS, primary lateral sclerosis, and hereditary spastic paraplegia. Her laboratory investigates multiple aspects of upper motor neuron health and disease, with major research thrusts in biomarker discovery, drug development (particularly the compound NU-9), high-throughput drug screening platforms, electrophysiological characterization of diseased neurons, and gene therapy approaches. Her work has revealed that NU-9 improves neuronal health by addressing protein aggregation, mitochondrial instability, and endoplasmic reticulum integrity issues across multiple neurodegenerative conditions. Analysis of Dr. Ozdinler's recent publications shows a strong focus on protein interactome analyses (particularly TDP-43 and spastin), mitochondrial dysfunction in ALS, high-resolution neuronal network analysis using microelectrode arrays, and the development of targeted therapeutic approaches for upper motor neuron diseases. Her research increasingly integrates multi-omics approaches to identify biomarkers and therapeutic targets. CLP Cornew Innovation Award, CLP at Northwestern (2022) INVO N.XT Award for Drug Discovery, Northwestern University (2017) One of 10 Most Innovative Research of 2015, International Innovation (2016) Top 30 Most Influential Turkish American Women in USA, Turk of America (2016) Dr. Ozdinler has mentored numerous students and postdoctoral fellows who have gone on to successful careers in academia, industry, and medicine. Her laboratory has received significant grant support, including a $3.1 million grant from the National Institute on Aging for ALS drug discovery research. She serves on editorial boards for Somatosensory and Motor Research and Clinical and Translational Neuroscience, and chairs AKAVA Therapeutics' scientific advisory board. The Ozdinler Lab maintains active collaborations with multiple research centers and has developed innovative approaches to studying upper motor neuron diseases, including high-density microelectrode array systems and novel gene delivery methods.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

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

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