جستجوی استاد راهنمای دانشگاهی

• Discrete Mathematics
• Probability
• Theoretical Computer Science
• +۶ مورد دیگر

Prasad Tetali is a Regents' Professor at the Georgia Institute of Technology , with appointments in both the School of Mathematics and the School of Computer Science . He also holds an adjunct professor position at Emory University's Mathematics and Computer Science departments. Education: PhD in Mathematics (1991) from the Courant Institute of Mathematical Sciences at NYU; MS in Mathematics (1987) from the Indian Institute of Science; Postdoc at AT&T Bell Labs His research spans Discrete Mathematics, Probability Theory, and Theoretical Computer Science , focusing on Markov chains, Isoperimetry, Combinatorics, Computational number theory, and Algorithms. Recent work includes applications to statistical physics models and hypergraph structures. He has served as Director of the ACO Ph.D. Program since 2019 and held leadership roles like Interim Chair of the School of Mathematics. His publications reflect a blend of discrete geometry, stochastic processes, and algorithmic analysis . Key Honors: AMS Fellow (2012) SIAM Fellow (2009)

• Astrobiology
• Theoretical Physics
• Origin of Life
• +۳ مورد دیگر

Sara Walker is an Associate Professor at Arizona State University, serving as Deputy Director of the Beyond Center for Fundamental Concepts in Science, Associate Director of the ASU-Santa Fe Institute Center for Biosocial Complex Systems, and a member of the Board of Directors of Blue Marble Space. Her work bridges astrobiology and theoretical physics with a focus on the origin of life. School of Earth and Space Exploration Beyond Center for Fundamental Concepts in Science ASU-Santa Fe Institute Center for Biosocial Complex Systems Walker explores whether universal 'laws of life' exist, emphasizing how information structures physical systems. Her research spans exoplanet atmospheric chemistry, biochemical network analysis, and the application of assembly theory to quantify molecular complexity and evolutionary dynamics. Her recent publications highlight advancements in assembly theory for life detection, biochemical reaction modeling, and exoplanet habitability assessments. Key themes include chemical selection, information processing in biological systems, and the universality of biochemical patterns. Walker actively engages in public science communication through appearances at the World Science Festival, 'Through the Wormhole,' and NPR's Science Friday. She co-founded an astrobiology-themed social website and contributes to STEM education initiatives.

R.L. Lagendijkمشاهده پروفایل

استاد

• Cyber Security
• Cryptography
• Privacy-Preserving Computation
• +۴ مورد دیگر

R.L. Lagendijk serves as a Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, specializing in Cyber Security research. His academic profile demonstrates sustained leadership in privacy-enhancing technologies and cryptographic systems development across diverse application domains. His core research spans Cyber Security, Cryptography, and Privacy-Preserving Computation with specialized expertise in Differential Privacy and Algorithmic Security. Lagendijk pioneers practical implementations for sensitive data protection in supply chain logistics, healthcare diagnostics, and blockchain infrastructure, consistently bridging theoretical cryptography with real-world security challenges through innovative protocol design. Analysis of his publication trajectory since 2020 reveals concentrated advancement in differential privacy applications, particularly for trajectory data obfuscation in supply chains and bin-packing optimization in logistics. His work increasingly integrates blockchain security with AI ethics frameworks, demonstrating evolving focus toward human-centric privacy solutions in emerging technologies. His distinguished career includes recognition through significant professional honors: NAE Fellow (2023) Professor Lagendijk has guided 44 students through academic supervision while actively leading European research initiatives including H2020 IRIS, SPECIES, and SESAME projects. His editorial contributions to IEEE Transactions on Information Forensics and Security underscore his influence in shaping cryptographic standards. As a core member of TU Delft's Cyber Security research group, he drives collaborative innovation in privacy-preserving computation through both theoretical exploration and industry-engaged solutions development, maintaining active participation in national and international cybersecurity discourse.

Juan Carlos Linares Calderónمشاهده پروفایل

استاد

• Ecology
• Climate Change
• Forest Management
• +۵ مورد دیگر

Juan Carlos Linares Calderón is a Full Professor at Pablo de Olavide University in the Department of Physical, Chemical and Natural Systems. His research focuses on Mediterranean relict forests' response to climate change through multidisciplinary approaches combining dendrochronology , ecophysiology , genomics , and epigenetics . PhD in Science (2008) from University of Jaén Principal Investigator in 10+ national/international projects Research Themes Specializing in climate change impacts on forest ecosystems , Linares leads the Forests Ecology and Global Change Lab . His work spans from individual tree responses to large-scale biogeographical patterns, challenging CO 2 -induced growth paradigms and emphasizing forest management as climate modulator. Scientific Contributions Over 162 publications with 82,704 reads and 9,074 citations Highly Cited Author status (Ioannidis et al., 2020) Developed SAPFLUXNET global transpiration database Email: jclincal@upo.es

Xiaoyue Niمشاهده پروفایل

استادیار

• Flexible Electronics
• Precision Measurement
• Non-Destructive Testing
• +۴ مورد دیگر

Xiaoyue Ni is an Assistant Professor at Duke University’s Thomas Lord Department of Mechanical Engineering and Materials Science, with additional appointments in Biostatistics & Bioinformatics and Electrical and Computer Engineering. They lead the Ni Lab, developing human-oriented materials intelligence through soft electronics and digital metamaterials. PhD, California Institute of Technology (2018) Research focuses on flexible electronics , mechanical metamaterials , and machine learning to create dynamic materials that sense and adapt to human physiology. Key innovations include soft wireless sensors , liquid metal actuation , and non-invasive biomarker monitoring . Recent publications (2022–2019) highlight expertise in wearable health technology , mechanical-acoustic interfaces , and programmable materials . Collaborative work spans reconstructive surgery , athlete monitoring , and thermal expansion control .

Philip Romeroمشاهده پروفایل

دانشیار

• Protein Engineering
• Computational Biology
• Machine Learning
• +۳ مورد دیگر

Philip Romero, Ph.D., is an Associate Professor in the Department of Biomedical Engineering at Duke University. He earned his doctorate from the California Institute of Technology in 2012 and leads the Romero Lab, which relocated to Duke in 2023. His research focuses on developing computational and experimental methods for protein engineering, with applications spanning therapeutics, biocatalysis, and synthetic biology. Research Interests: Romero's work integrates machine learning, microfluidics, and high-throughput experimentation to study protein fitness landscapes. Key areas include: Self-driving laboratories for autonomous protein optimization Neural network models for predicting protein functions Therapeutic enzyme engineering (ACE2, caspases, lysins) Microfluidic platforms for deep mutational scanning His recent publications demonstrate a strong emphasis on machine learning-guided protein design, with 80% of post-2022 publications involving AI/ML methods. Therapeutic applications against infectious diseases (particularly SARS-CoV-2) and microbiome engineering represent emerging directions. Lab & Advising: The Romero Lab develops novel technologies for protein engineering, including custom gene library assembly platforms and droplet microfluidics systems. Romero mentors graduate students (e.g., Nishit, who recently defended a thesis on transcription factor engineering) and has collaborated with researchers across computational biology, metabolic engineering, and virology.

Darfiana Nurمشاهده پروفایل

مدرس ارشد

• Nonlinear time series analysis
• Bayesian inference
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Dr Darfiana Nur is an Adjunct Senior Lecturer at the University of Western Australia (UWA) , affiliated with the School of Physics, Mathematics and Computing and the Department of Mathematics and Statistics . She has a rich academic background with experience at Flinders University and Curtin University, and a strong record in teaching and research. Education: BMath – University of Gadjahmada (UGM), Indonesia (1988) MSc (Research) – University of Western Australia (1993) PhD in Statistics – Curtin University of Technology (1999) Research Interests: Her research focuses on nonlinear time series modelling , particularly Smooth Threshold Autoregressive (STAR) and regime-switching volatility models. She applies Bayesian MCMC algorithms and Hidden Markov Models (HMMs) in genetics and bioinformatics, and explores deep learning for time series forecasting. Her work spans DNA sequence modelling , Bayesian inference , and machine learning applications in complex data environments. She has published extensively in top-tier journals and contributed to both theoretical and applied statistics. Scientific Contributions: She has authored 23 journal publications , 10 refereed conference papers , and 35 non-refereed conference papers . Her recent work includes advanced statistical modelling in GARCH, BEKK, and LASSO frameworks. Supervision: She has supervised 7 PhD students , 1 MMath(Research) , 9 Masters by Coursework , and 7 Honours students across UWA, Flinders University, and the University of Newcastle. Teaching: Her teaching portfolio includes courses in Data Science , Mathematical Statistics , Time Series Analysis , and Bayesian Inference , delivered at both undergraduate and postgraduate levels.

• Bioinformatics
• Computational Biology
• Algorithms
• +۵ مورد دیگر

Travis Wheeler is an Associate Professor in the Department of Pharmacy Practice & Science at the University of Arizona. His work spans bioinformatics, computational biology, and algorithm development for genomic sequence analysis. Developed tools like HMMER and Dfam Research focuses on transposable elements, sequence alignment, and epigenetics Co-author of key works with Robert Finn, Sean Eddy, and colleagues Research Interests include machine learning applications in biological sequence annotation, drug discovery, and evolutionary genomics. His work bridges computational methods with biomedical applications. Notable Contributions : Advancing profile Hidden Markov Model (HMM) methodologies Creating community resources for transposable element research Developing alignment algorithms for biological sequences Collaborations include institutions like Institute for Systems Biology, Harvard University, and Montana State University.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Petter Brodinمشاهده پروفایل

استاد

• Systems Immunology
• Pediatric Immunology
• Immune System Regulation
• +۷ مورد دیگر

Petter Brodin is a dual-appointed academic with a Professor ship at Karolinska Institutet 's Department of Women's and Children's Health and a Senior Lecturer position at Imperial College London 's MRC London Institute of Medical Sciences. His work bridges clinical pediatrics and cutting-edge systems immunology , focusing on human immune system profiling through blood analysis. Education : MD/PhD from Karolinska Institutet, postdoc at Stanford University Research spans three major domains: Sex hormone immunomodulation - Revealing testosterone's role in suppressing autoimmune pathways while enhancing infection vulnerability through Nature studies of gender-affirming treatment Post-COVID pathophysiology - Developing antiviral treatment strategies for chronic SARS-CoV-2 reservoirs with Pfizer, focusing on severely affected patients Developmental immunology - Mapping immune-microbiome interactions in newborns and cancer immunotherapy applications in pediatric solid tumors Major awards include: 2023 Oscar Medin Prize 2024 Wallenberg Scholar 2022 VR and SSMF Consolidator Awards 2019 EMBO Young Investigator 2015 ERC Starting Grant Supervised 5 PhD/MSc students while leading a multidisciplinary team at BrodiN lab (brodinlab.com). Current clinical trials include: 15-day antiviral regimen for tissue reservoirs Immunomodulatory approaches for autoimmune profiles Active in science communication through BlueSky platform and Paris Kids Cancer Symposium leadership, emphasizing human-centric research over animal models.

Brian D. Atheyمشاهده پروفایل

استاد

• Bioinformatics
• Pharmacogenomics
• Machine Learning
• +۸ مورد دیگر

Brian D. Athey is a Professor of Computational Medicine and Bioinformatics and Psychiatry at the University of Michigan Medical School. He serves as Michael Savageau Collegiate Professor & Chair of his department, leading interdisciplinary research in pharmacogenomics, epigenomics, and machine learning applications. His work bridges biomedical informatics with clinical insights in mental health, neurology, and drug response mechanisms. Ph.D. , Biophysics, University of Michigan (1990) B.S. , University of Michigan-Dearborn (1982) Dr. Athey’s research focuses on the pharmacoepigenome, high-throughput 4D imaging, and AI-driven pharmacogenomic pipelines. His lab develops tools for patient-specific drug response prediction, chromatin structure analysis, and next-generation sequencing assays. Collaborations span institutions like Assurex Health, tranSMART Foundation, and Johns Hopkins Medical School. His publications highlight innovations in pharmacogenomics, including AI integration for variant identification, 4D nucleome modeling, and adverse event ontologies. Recent projects address bipolar disorder treatment personalization, hypertension management, and generative AI workflows to enhance academic productivity. Scientific Awards : NIH Postdoctoral Fellowship 1990-1991 NIH Postdoctoral Fellowship 1991-1993 Advisees & Collaborations : His lab mentors PhD and Masters students in bioinformatics and biostatistics, with graduates like Ari Allyn-Feuer (GSK AI Products Director) and Alex Kalinin (Broad Institute). Collaborations include NIH-funded initiatives (O’Brien Kidney Core Center, VIOLIN 2.0) and industry partnerships.

Anni Zhangمشاهده پروفایل

استادیار

• Microbiome Medicine
• Artificial Intelligence
• Microbiology
• +۷ مورد دیگر

Dr. Anni Zhang serves as Assistant Professor at Nanyang Technological University's School of Biological Sciences with a courtesy appointment at the Singapore Centre for Environmental Life Sciences Engineering. Her interdisciplinary research bridges microbiology, immunology, and artificial intelligence to develop novel therapeutic approaches. Dr. Zhang earned her PhD in Environmental Engineering from the University of Hong Kong (2018) following undergraduate studies in Environmental Biology at Nanjing University (2014). Prior to joining NTU, she completed a postdoctoral fellowship (2018-2023) and subsequent research associate position (2023-2024) at MIT's Alm Lab. Her research program focuses on Microbiome Medicine , Bacterial-Based Cancer Therapy , and Phage Therapy , utilizing AI-driven approaches to analyze microbial mechanisms. The AnniZ Lab develops computational tools for microbiome analysis while investigating antibiotic resistance gene dissemination and host-microbe interactions. Publication analysis reveals consistent contributions to Genome Biology , Cell Genomics , and Nature Communications , demonstrating expertise in bioinformatics algorithm development, CRISPR systems, and antibiotic resistance mechanisms. Her work shows increasing integration of AI methodologies with experimental microbiology. Dr. Zhang actively mentors graduate students including Qiwen (current PhD student) and Zhiqi (transitioning to PhD candidacy), with opportunities for new PhD candidates seeking research at the AI-microbiology interface. The AnniZ Lab maintains active collaborations with MIT and participates in major conferences including Gordon Research Conferences. Current projects include developing AnniBot (a specialized LLM for microbiome research) and Jeff's KnowledgeGraph for microbial data integration.

• Maize genetics
• Genomics
• Quantitative genetics
• +۵ مورد دیگر

Addie Thompson is an Assistant Professor at Michigan State University's College of Agriculture & Natural Resources , affiliated with the Department of Plant, Soil and Microbial Sciences , Plant Resilience Institute , and Plant Breeding, Genetics, and Biotechnology Program . Her expertise spans maize genetics, genomics, and phenomics, with a focus on genotype-environment interactions. B.S. from Iowa State University Ph.D. from University of Minnesota Postdoctoral work at University of Minnesota & Purdue University Research focuses on drought stress adaptation , high-throughput phenotyping , and quantitative genetics in maize and sorghum systems. Current projects include computational modeling of crop traits, field phenotyping technologies, and cross-species stress response analysis. She leads maize genetics research while participating in national initiatives like the Genomes-to-Fields Initiative , with recent publications on phenotypic plasticity, hyperspectral imaging applications, and climate-resilient breeding strategies. Her work connects molecular genetics to agricultural productivity through advanced data modeling and field experimentation. Thompson directs the Thompson Maize Lab which focuses on: Maize and sorghum genotype-environment interactions Phenomics technologies for trait analysis Development of computational breeding tools Climate resilience trait discovery Historical genetic diversity patterns Agricultural workforce development

• Machine Learning
• Deep Learning
• Bioinformatics
• +۷ مورد دیگر

Guanghua Xiao is a Professor at the O'Donnell School of Public Health with secondary appointments in the Departments of Bioinformatics and Biomedical Engineering at UT Southwestern Medical Center. His work focuses on developing computational models and algorithms for big data analysis in biomedical contexts, particularly for predicting patient outcomes and treatment responses.

Ana Lucicمشاهده پروفایل

استادیار

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +۳ مورد دیگر

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.