جستجوی استاد راهنمای دانشگاهی

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

• Programming Languages
• Compilers
• Domain-Specific Languages
• +۷ مورد دیگر

Anna Vilanova is a Full Professor in Visual Analytics at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), and is associated with the Electrical Engineering department's Signal Processing Systems. Previously, she served as Associate Professor at TU Delft (2013-2019) and Assistant Professor at TU/e (2002-2013). Her research focuses on Visual Analytics for high-dimensional data , explainable AI , and biomedical applications including Diffusion Weighted Imaging, 4D Flow, and Pangenomics. Education: Doctorate in Computer Graphics & Visualization (2001) Master in Computer Science (1997), Universitat Politècnica de Catalunya Research Highlights: Vilanova leads work on Visual Analytics systems for biomedical data, with recent publications in Diffusion MRI modeling , Tractography visualization , Explainable AI frameworks , and Pangenomic variant analysis . Her work bridges dimensionality reduction , uncertainty visualization , and medical imaging applications. Scientific Contributions: NWO-Veni grant (2005): "Visualization of global tensor information for diffusion tensor imaging" NWO-Aspasia grant (2013) Best Poster Award EuroVis (2025) Best Demo/Poster Awards (2022) Leadership & Service: Vilanova serves on the IEEE VIS Steering Committee , was EUROGRAPHICS President (2019-2022), and contributes to conferences like IEEE Visualization and EG-EuroVis . She co-founded the EAISI Health research initiative at TU/e.

• Visual Analytics
• Explainable AI
• Biomedical Image Analysis
• +۶ مورد دیگر

Abigail Johnson, PhD, RD is an Assistant Professor in the Division of Epidemiology & Community Health at the University of Minnesota. She also serves as the Associate Director of the Nutrition Coordinating Center (NCC) and is a Member of the Masonic Cancer Center (MCC). Dr. Johnson earned her PhD in Nutrition and completed her Registered Dietitian (RD) training at the University of Minnesota, where she also obtained her BS in Nutrition Science and Biology. Her research explores the relationships between diet and the human gut microbiome in health and disease using novel computational methods for dietary data. Her current focus areas include: analysis of diet and microbiome after dietary interventions, methods development for dietary data analysis and visualization, and understanding the interactions between foods, microbes, and fungi during different stages of development. She is particularly interested in how diet and the microbiome interact to influence chronic diseases including prediabetes, diabetes, and cancer. Dr. Johnson's research demonstrates significant trends in microbiome analysis, dietary assessment, and computational methods. Her work increasingly focuses on personalized nutrition approaches, longitudinal microbiome dynamics, and the integration of multi-omics data to understand diet-microbe-host interactions in disease contexts. Her scientific contributions include: Development of methods for dietary data analysis and visualization Research on diet-microbiome interactions in chronic diseases Studies on how immigration and dietary changes affect the gut microbiome Investigations into personalized diet-microbiome associations Dr. Johnson actively mentors students and researchers in the fields of nutritional science, microbiome research, and computational biology. Her work has been supported by various research grants focused on understanding the complex relationships between diet, the microbiome, and human health.

• Microbiome
• Nutrition
• Obesity
• +۶ مورد دیگر

Dimitrios (Dimitris) Anastasiou is a Senior Group Leader at The Francis Crick Institute in London, UK, specializing in cancer metabolism research. Previously, he served as a Group Leader at the Medical Research Council National Institute for Medical Research (NIMR) starting in 2012 before transitioning to the Crick Institute in 2015. His research career includes postdoctoral work and an Instructor position at Beth Israel Deaconess Medical Center and the Department of Systems Biology, Harvard Medical School under Lewis Cantley, where he focused on metabolic reprogramming in cancer. University College London, UK - BSc Molecular Biology (2001) University of Basel, Basel, Switzerland - PhD in Biochemistry (2006) Anastasiou's research centers on understanding how cancer cells generate energy and utilize nutrients differently from normal cells. His laboratory conducts detailed analyses of metabolic pathways in cancer, investigating how tumor cells rewire their metabolism to support rapid growth and evade the body's defenses. His work spans biochemistry, proteomics, computational systems biology, human physiology, and tumor biology, with particular emphasis on identifying metabolic vulnerabilities that could be targeted for cancer therapy. His innovative approaches include developing chemical 'sensors' to monitor metabolic changes in cancer cells over time as tumors develop. Analysis of his recent publications reveals a consistent focus on metabolic regulation in cancer, particularly regarding glycolysis, hypoxia response, amino acid metabolism, and nucleotide biosynthesis. His work frequently examines enzyme regulation (particularly PKM2), metabolic adaptation to environmental stressors, and the intersection between metabolism and signaling pathways. A notable trend is his exploration of how metabolic enzymes function beyond their traditional roles, influencing cellular signaling and gene expression in cancer contexts. Anastasiou has made significant contributions to understanding metabolic reprogramming in cancer, particularly regarding pyruvate kinase M2 regulation, hypoxia responses, and nutrient utilization in tumor microenvironments. His work bridges basic biochemical mechanisms with potential therapeutic applications, focusing on identifying metabolic vulnerabilities in cancer cells that could be exploited for treatment. As a Senior Group Leader at the Crick Institute, Anastasiou leads a research group investigating how metabolism contributes to disease, particularly cancer. His laboratory utilizes a range of techniques including metabolomics, bioinformatics, structural biology, and high-throughput screening to study metabolic pathways. The group's work aims to identify fundamental differences between metabolic pathways in tumors and healthy tissue to discover new therapeutic targets against cancer.

• Cancer Metabolism
• Metabolic Reprogramming
• Biochemistry & Proteomics
• +۳ مورد دیگر

Christopher Grainge is a Conjoint Associate Professor at the University of Newcastle's School of Medicine and Public Health, and a Staff Specialist in Respiratory and General Medicine at John Hunter Hospital. Previously, he served as a Consultant Physician and Senior Lecturer at the University of Southampton. His extensive clinical background includes service in the Royal Navy, specializing in diving and remote medicine, and deployments to Antarctica and Southern Iraq. Dr. Grainge maintains active research collaborations with institutions worldwide, focusing on respiratory medicine. Dr. Grainge completed his undergraduate training at Imperial College, University of London, with an intercalated BSc. He earned his PhD from the University of Southampton in 2011, with research published in the New England Journal of Medicine. His thesis examined the effect of repeated bronchoconstriction on airways in asthma. He also holds membership in the Royal College of Physicians and was awarded the Diploma in the Medical Care of Catastrophes, specializing in remote and refugee medicine. Dr. Grainge's research focuses on the role of mechanical forces in asthma pathophysiology, environmental dusts in lung disease development, and platelet antagonists' effects on allergen challenge. His work demonstrates that mechanical forces play a crucial role in determining long-term changes in human airways. He leads investigations into how environmental exposure to inhaled particles leads to conditions like constrictive bronchiolitis, and explores novel therapeutic pathways for asthma through platelet activation inhibition. His research has significant clinical implications for difficult asthma and interstitial lung disease management. Dr. Grainge's recent publications reflect strong trends in interstitial lung disease research, particularly idiopathic pulmonary fibrosis, and asthma mechanisms. His work increasingly incorporates advanced technologies like artificial intelligence for disease stratification and deep learning for predicting disease progression. Collaborative research with international teams examines biomarkers for disease progression and novel treatment approaches for fibrotic lung diseases, with a particular focus on quantitative CT analysis and patient-reported outcome measures. Leatherdale Prize for Clinical Teaching: Finalist (2013) Military and Civilian Health Partnership Awards Winner (2011) University of Southampton Translational Medicine Research Prize (2011) Michael Arthur Clinical Research Prize (2014) British Lung Foundation Travel Fellowship (2011) Gilbert Blane Medal (2010) Colt Foundation Prize: Finalist (2010) Asthma UK Travel Fellowship (2009) Dr. Grainge has received significant research funding from the British Lung Foundation, the Southampton Marine and Maritime Institute, the Institute for Life Sciences, and the Gerald Kerkut Foundation. His projects investigate the effects of physical and environmental stress on lung disease, mechanisms underlying lung disease development following environmental particle exposure, and potential therapies. He has collaborated extensively with research teams across Australia, the UK, the Netherlands, and South Africa, contributing to the training of numerous students and junior researchers through his supervision and mentorship roles. Dr. Grainge collaborates extensively with the Respiratory Research group at the University of Southampton, the Asthma and COPD Research Group at the University of Groningen in the Netherlands, and the Microbiome group at the South Australia Medical Research Institute. His current work at the Hunter Medical Research Institute (HMRI) focuses on mechanical forces within airways and their impact on acute and long-term lung changes in patients with airway diseases. He is also involved in the Australian IPF Registry, contributing to multicenter studies on idiopathic pulmonary fibrosis progression and treatment outcomes.

• asthma
• respiratory physiology
• airway mechanics
• +۶ مورد دیگر

Wendy R Gordon serves as an Associate Professor in the Structural Biology and Biophysics department at the University of Minnesota Medical School. Her research program bridges structural biology, biophysics, and translational medicine with significant funding from NIH sources including the National Cancer Institute and National Institute of General Medical Sciences. Dr. Gordon leads multiple active research projects focused on mechanobiology, protein engineering, and cancer therapeutics. Dr. Gordon's research interests center on understanding molecular mechanisms of cell surface receptors, particularly the Notch signaling pathway, and developing innovative tools for studying cellular mechanics. Her work spans structural biology approaches to characterize protein-DNA interactions, mechanotransduction mechanisms, and receptor regulation. She has developed novel technologies including HUH-Tags for protein-DNA labeling and tension sensors for measuring cellular forces. Her research has significant implications for cancer immunotherapy, particularly in engineering protein modulators of Notch activation for CAR T-cell therapy. Analysis of Dr. Gordon's recent publications reveals a strong focus on three interconnected research themes: 1) molecular mechanisms of Notch signaling and receptor regulation, 2) development of innovative tools for protein-DNA conjugation and cellular mechanotype measurement, and 3) applications of these technologies to cancer biology and immunotherapy. Her work demonstrates increasing integration of structural biology with functional cellular assays and therapeutic applications, particularly in tumor microenvironment and immunotherapy contexts. Dr. Gordon currently leads or collaborates on seven major research projects totaling millions in NIH funding. Her active grants include: LUMICKS C-trap for Mechanistic Studies of Biological Systems (as PI), Engineering Protein Modulators of Notch Activation for CAR T-cell immunotherapy (as CoI), Viral vector technology for cell type specific gene delivery (as CoI), Decoding mechanotransduction mechanisms of cell-surface receptors (as PI), and the Center for Multiparametric Imaging of Tumor Immune Microenvironments (as CoI). These projects reflect her expertise spanning structural biology, mechanobiology, and translational applications. Dr. Gordon's laboratory operates at the intersection of structural biology and cellular mechanobiology, with strong collaborations across the University of Minnesota campus including with cancer researchers, bioengineers, and clinicians. Her team develops and applies cutting-edge technologies including single-molecule techniques, protein engineering approaches, and high-throughput cellular mechanotype measurement systems to address fundamental questions in receptor biology with therapeutic implications.

• Structural Biology
• Biophysics
• Notch Signaling
• +۷ مورد دیگر

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +۷ مورد دیگر

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).

• Data Mining
• Machine Learning
• Reinforcement Learning
• +۹ مورد دیگر

Duong Nguyen serves as an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research integrates operations research, artificial intelligence, economics, and engineering to develop mathematical models for decision-making in large-scale networked systems including cloud/edge computing, smart grids, and crowdsourcing. He directs the NEMO research group focused on building intelligent multi-agent platforms through optimization and market design. His educational credentials include: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2020) M.Sc. in Telecommunications from INRS, University of Quebec (2014) B.Sc. in Electronic and Telecommunications from Hanoi University of Science and Technology (2011) Dr. Nguyen's research spans Operations Research, Artificial Intelligence, Decision-Making, Market Design, and Optimization with applications in edge computing, power systems, and network economics. His work emphasizes robust algorithms for uncertain environments and secure multi-agent platforms, recently expanding into quantum machine learning and privacy-preserving distributed systems. Current projects address decentralized federated learning, dynamic pricing, and EV charging network design. Analysis of his publication record reveals consistent focus on distributed optimization techniques for edge/cloud systems, with increasing integration of game theory and quantum computing. His work demonstrates strong methodological innovation in handling spatio-temporal uncertainty while addressing practical challenges in energy flexibility and secure genomic computation. His scientific recognition includes: Finalist for Best Student Paper Award at American Control Conference (ACC) 2024 Finalist for Best Paper Award at International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) 2023 Dr. Nguyen actively mentors Ph.D. students including Jiaming Cheng and Long Vu, with student-led research achieving significant recognition. His NEMO group collaborates with institutions including ETH Zurich on projects spanning autonomous driving, edge AI, and quantum optimization. Current research directions emphasize fair resource allocation, privacy-preserving learning, and dynamic pricing frameworks for next-generation networked systems.

• Operations Research
• Artificial Intelligence
• Decision-Making
• +۱۰ مورد دیگر

Guimu Guo is an Assistant Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His research focuses on parallel and distributed computing techniques for large-scale graph mining problems, with applications in bioinformatics and transportation engineering. Ph.D. in Computer Science from University of Alabama at Birmingham M.Sc. in Computer Science from Tongji University Dr. Guo has published extensively in top-tier venues like VLDB, ICDE, and IEEE BigData. His work spans graph mining algorithms, parallel computing, and interdisciplinary applications in transportation and genomics. He actively mentors PhD and Master's students, offering fully funded positions. Key research trends include: Advancing GPU-accelerated graph decomposition techniques Developing distributed frameworks for subgraph querying and task concurrency Exploring parallel algorithms for frequent pattern mining and clique-like subgraphs Scientific Recognition: NSF CRII Award UAB Outstanding PhD Student Award Alabama GRSP Awards (Rounds 15 & 16) Teaching spans from foundational object-oriented programming to advanced graduate courses in parallel programming. His lab group has produced significant contributions to subgraph mining, transportation simulation, and genome assembly systems.

• Parallel Computing
• Graph Mining
• GPGPU
• +۲ مورد دیگر

Yuri Pritykin is an Assistant Professor at Princeton University, affiliated with the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science. He also holds cross-appointments in Molecular Biology, the Omenn-Darling Bioengineering Institute, and the Center for Statistics and Machine Learning. Pritykin earned his Ph.D. in Computer Science from Princeton University (2014), alongside MSc and Ph.D. in Mathematics from Lomonosov Moscow State University. His research lies at the intersection of applied statistics, machine learning, and functional genomics, focusing on integrative analysis of multi-dimensional biological data. Research Interests: Decoding regulatory genomics in immune cells, CRISPR tool development (GuideScan2), single-cell and spatial multi-omics for immunology and cancer, post-transcriptional regulation, and cell-cell interaction profiling (uLIPSTIC technology). Awards: NSF CAREER Award (2023) NIH New Innovator Award (2022) Recognized by Princeton Ludwig Institute, Rutgers Cancer Institute, and AACR Teaching: Courses in computational biology, genomics, and machine learning applications in life sciences. His lab actively collaborates with immunologists and genomicists, seeking interdisciplinary scientists at all career stages.

• Computational Biology
• Genomics
• Machine Learning
• +۵ مورد دیگر

Bruce Jacob is a Keystone Professor and Full Professor in the Department of Electrical & Computer Engineering at the University of Maryland College Park's College of Engineering. His research primarily focuses on memory systems design and exascale computing architectures, with significant contributions to DRAM simulation and high-performance computing systems. Dr. Jacob received his A.B. in Mathematics from Harvard University (1988), followed by his M.S. and Ph.D. in Computer Science & Engineering from the University of Michigan (1995 and 1997 respectively). His research interests include memory systems design, exascale computing architectures, embedded systems, circuit integrity, and algorithmic composition. His recent publications demonstrate a strong focus on next-generation memory technologies, particularly ReRAM and advanced DRAM architectures. His work bridges the gap between theoretical modeling and practical implementation, with significant contributions to memory system simulation through projects like DRAMsim. His research shows a consistent trajectory toward solving the memory bottleneck problem in high-performance computing systems. Named Fellow, IEEE (2021) Multiple University of Maryland Research Leader awards (2006, 2010, 2012, 2016, 2017) Clark School of Engineering Keystone Professor (2006) National Science Foundation CAREER Award (2000) University of Maryland Award for Teaching Excellence (2004) Dr. Jacob has led significant research initiatives including the University of Maryland Exascale Systems Research and Memory-Systems Research groups. He has developed important computational artifacts such as DRAMsim (a public-domain DRAM-system simulator) and BioBench (a set of bioinformatics workloads). His work has influenced both academic research and industry practices in memory system design.

• Memory Systems Design
• High-Performance Computing
• Exascale Systems Architecture
• +۷ مورد دیگر

Lauren Weiss, PhD is a Professor of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a faculty member at the UCSF Weill Institute for Neurosciences. Her research focuses on understanding the genetic architecture of autism spectrum disorder through genome-wide genetic data analysis and human induced pluripotent stem cell (iPSC) models. Dr. Weiss's laboratory investigates the genetic mechanisms by which DNA variants influence autism risk, examining questions about copy number vs. SNP variation, rare vs. common variation, gene-sex interaction, gene-gene interaction, and gene-environment interaction. Her team uses rich genetic datasets to identify susceptibility loci and the physiological pathways these risk loci implicate. Additionally, they employ iPSC models to study known mutations or copy number variants predisposing to autism, first identifying the effects of genetic risk variants and then determining whether these effects can be modified at the cellular level by environmental or pharmacological agents. Analysis of Dr. Weiss's recent publications reveals a strong focus on sex differences in autism genetics, the role of specific copy number variants (particularly 16p11.2 and 22q11.2), maternal environmental factors during pregnancy, and the integration of multi-omics data to understand neurodevelopmental pathways. Her work bridges basic genetic research with potential clinical applications for improving understanding, prevention, diagnosis, and treatment of autism and related traits. Dr. Weiss has secured significant research funding as Principal Investigator on multiple NIH grants, including R01MH114924 (Decoding the Genetics of Sexual Dimorphism in Autism Spectrum Disorders), R01MH107467 (Utilizing eQTL networks to gain biological insight into multigenic CNVs), and DP2OD007449 (Dissecting Epistasis and Pleiotropy in Autism towards Personalized Medicine). Her laboratory offers research opportunities for students interested in analytical genetics projects related to gene-environment effects, gene-sex effects, gene-gene effects, and the relationship between ASD and brain size. Dr. Weiss actively collaborates with numerous researchers across institutions, particularly on large-scale genomic studies of autism and other neurodevelopmental disorders. Her work has contributed significantly to our understanding of the complex genetic architecture underlying autism spectrum disorder and related conditions.

• Autism Genetics
• Genetic Architecture
• Copy Number Variants
• +۵ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

• Causality
• Causal Representation Learning
• Causal Discovery
• +۴ مورد دیگر

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +۵ مورد دیگر