جست‌وجوی استاد و پژوهشگر

Saikat Duttaمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University. He is affiliated with the Software Engineering Group and focuses on the intersection of Software Engineering and Machine Learning . His work aims to enhance the reliability of ML-based systems while applying ML techniques to solve software engineering challenges. PhD in Computer Science from University of Illinois Urbana-Champaign (Summer 2023) Postdoctoral Researcher at University of Pennsylvania Bachelor's in Computer Science and Engineering from Jadavpur University Research Interests: Dr. Dutta's research spans several key areas: Automated test generation and debugging for ML/DL libraries Using AI/ML for automated software engineering tasks Improving performance of regression tests in ML libraries Static and dynamic analysis for probabilistic programming Article Trends: His recent publications emphasize: Automated testing of ML systems Security vulnerability detection using LLMs Probabilistic program analysis Neurosymbolic learning frameworks Flaky test management in stochastic environments Stochastic regression test optimization Scientific Awards: Meta AI LLM Evaluation Research Grant (2025) Mavis Future Faculty Fellowship (2022-23) Facebook PhD Fellowship (2020-22) 3M Foundation Fellowship (2019-2020) Advising & Grants: Dr. Dutta actively recruits PhD students and postdocs. He leads research projects supported by grants from Meta AI and participates in program committees for top conferences like ICSE and ISSTA. His lab focuses on neurosymbolic systems and ML-based software verification.

Haochen Liمشاهده پروفایل

استادیار

• Environmental Fluid Dynamics
• Computational Fluid Dynamics
• Physics-informed Machine Learning
• +۷ مورد دیگر

Haochen Li is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, within the College of Engineering. He leads the multidisciplinary Water Infrastructure Laboratory (Ψ Lab), which focuses on advancing urban water infrastructure through high-fidelity computational fluid dynamics (CFD), physical modeling, and physics-informed machine learning (ML). Education: PhD in Environmental Engineering, University of Florida, 2019 MS in Mechanical Engineering, University of Florida, 2019 MS in Civil Engineering, University of Florida, 2015 BS in Coastal Engineering, Hohai University, 2013 His research centers on environmental fluid dynamics , particularly multiphase and multiphysics flows in urban water systems. He investigates turbulence, particulate matter transport, pathogen fate, and chemical dynamics using advanced CFD simulations, volumetric particle image velocimetry (PIV), and AI-driven models. His lab develops open-source tools like InterAdsFoam for adsorption systems and integrates ML with CFD to optimize infrastructure design, retrofit, and regulatory frameworks. The recent publications reflect a strong trend toward hybrid CFD-ML frameworks for water infrastructure, with applications in clarifier design, stormwater basin optimization, and real-time sensing. His work emphasizes model validation, scalability, and practical deployment, including web-based tools for engineers. Scientific Awards: Rudolph Hering Medal, ASCE, 2023 Editor choice, Journal of Environmental Engineering ASCE, 2021 Editor choice, Journal of Environmental Engineering ASCE, 2020 Graduate School Fellowship, University of Florida, 2015 Academic Achievement Award, University of Florida, 2013 Haochen Li actively advises researchers and students in his lab, including Kai Liu, Mohamed Shatarah, and Ahmed Abdelmeguid. His team works on AI-empowered reactive flows, physics-informed ML, and CFD applications in energy and environmental systems. He has served as a reviewer for top journals and is a member of the ASCE/EWRI Computational Fluid Dynamics Committee. His lab is equipped with state-of-the-art HPC platforms and physical modeling facilities for experimental validation.

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

• Nonlinear Dynamics
• Model Reduction
• Data-Driven Modeling
• +۴ مورد دیگر

George Haller is a Professor at the Department of Mechanical and Process Engineering at ETH Zurich . He leads the Institute of Mechanical Systems and holds the Chair in Nonlinear Dynamics . His research focuses on: Nonlinear dynamical systems theory Data-driven model reduction Spectral submanifolds (SSMs) Coherent structure identification in fluids and solids Control of complex nonlinear systems His recent work emphasizes equation- and data-driven modeling across solids, fluids, and control systems . Key contributions include: SSMTool - a MATLAB package for nonlinear model reduction SSMLearn - open-source software for data-driven modeling Transport barrier detection algorithms with oceanographic applications Scientific accolades include: 2025 Lyapunov Award (ASME) 2023 Stanley Corrsin Award (APS) Fellowships: ASME, APS, SIAM External Member, Hungarian Academy of Sciences His group has trained notable alumni: Thomas Breunung (Assistant Professor, University of Wisconsin-Madison) Shobhit Jain (Assistant Professor, Delft University of Technology) Mattia Serra (Assistant Professor, UCSD) Publications span Nonlinear Dynamics, Nature Communications , and Physical Review Fluids , with a 2025 book Modeling Nonlinear Dynamics for Equations and Data (SIAM Press). Current projects include: Reduced-order modeling of fluid-structure interactions Control of soft robots via nonlinear dynamics Identifying material barriers in turbulence

• Genomic Instability
• DNA Replication and Repair
• Computational Biology
• +۵ مورد دیگر

Dr Michael Boemo is an Assistant Professor at the University of Cambridge, holding dual appointments in the Department of Pathology and Department of Genetics. He leads research at the intersection of computational biology, DNA replication, and cancer genomics, developing machine learning tools to analyze replication stress and genomic instability. Academic Background: BA in Mathematics (Rutgers University), PhD in Physics (University of Oxford) Research Focus: Genomic instability in cancer, DNA replication/repair defects, computational modeling using machine learning and high-performance simulations Teaching: Lectures in Natural Sciences Tripos (mathematical biology, genetics, systems biology), module organizer for cancer biology and biological modeling His research group leverages nanopore sequencing and AI to map replication fork dynamics, revealing how stalled forks generate mutations in cancer cells and pathogens. Recent work examines extrachromosomal DNA replication vulnerabilities and transcription-replication conflicts. Dr Boemo collaborates across computational biology and cancer research domains, with publications spanning journals like Nature Methods, Cell, and PLoS Computational Biology. His lab develops tools such as DNAscent for replication fork analysis and explores therapeutic targeting of replication stress.

Jason Heinمشاهده پروفایل

دانشیار

• Mechanistic Organic Chemistry
• Automated Reaction Analysis
• Self-Driving Laboratories
• +۷ مورد دیگر

Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.

Martin Hollerمشاهده پروفایل

استاد

• Mathematics of data science
• Machine learning
• Generative models
• +۷ مورد دیگر

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

Yan Chenمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Computer Science Education
• Real-Time Data Analysis
• +۳ مورد دیگر

Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.

• Nanobiotechnology
• Biophysics
• Colloid and Interface Science
• +۴ مورد دیگر

Assoc. Prof. Dietmar Pum is a leading researcher at the Institute of Biophysics, University of Natural Resources and Life Sciences, Vienna (BOKU). With a career spanning over three decades, he has pioneered work on S-layer proteins as nanobiotechnological tools and biomimetic surface functionalization. His research bridges microbiology, biophysics, and nanomaterials science. Education & Career Doctorate (1984) and Habilitation (1992) at Vienna University of Technology Awarded FEBS (1987) and EMBO (1982) fellowships Assoc. Univ. Prof. since 1997, Deputy Head of Biophysics Institute since 2014 Research Focus Dr. Pum's work centers on leveraging S-layer proteins for nanoscale fluid mechanics , molecular imprinting , and biohybrid materials . His projects explore: Self-assembly of 2D protein crystals Functionalization of carbon nanotubes Biomimetic sensor development Applications in diagnostics and environmental technology Publication Trends His recent publications (2025-2019) highlight interdisciplinary advancements in: Nanoscale biofluid dynamics Gold nanoparticle synthesis via microbial enzymes Archaeal S-layer characterization Protein-directed nanomaterials Biosensor platforms Lipid quantification via machine learning Scientific Recognition Philip Morris Research Prize (1998) Cardinal Innitzer Prize (1992) ÖAGM Prize (1986) Peer reviewer for Nature Materials , Advanced Materials , and Biophysical Journal Advising & Collaborations Dr. Pum has supervised over 15 theses, mentoring students in topics ranging from Caenorhabditis elegans imaging to nanomembrane fabrication. He leads international collaborations under EU Horizon and FWF grants, including the Stimuli Responsive Materials project (2020-2025). Labs & Networks As a core member of BOKU's Center for NanoBiotechnology and the Ludwig Boltzmann Institute for Molecular Nanotechnology , he develops bioinspired nanomaterials and contributes to global programs in molecular self-assembly.

Aravind Machiryمشاهده پروفایل

استادیار

• Software Security
• Embedded Systems
• Program Analysis
• +۹ مورد دیگر

Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.

Christopher John Rozellمشاهده پروفایل

استاد

• Computational Neuroengineering
• Artificial Intelligence
• Neuroscience
• +۷ مورد دیگر

Christopher John Rozell is the Julian T. Hightower Chaired Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology's College of Engineering. He serves as Executive Director of the Institute for Neuroscience, Neurotechnology & Society (INNS) and directs the Sensory Information Processing Lab (SIPLab). His research bridges computational neuroscience, machine learning, and neurotechnology, with clinical applications in treatment-resistant depression and neuromodulation therapies. Education: B.S.E. in Computer Engineering & B.F.A. in Music, University of Michigan (2000) M.S. and Ph.D. in Electrical Engineering, Rice University (2002, 2007) Postdoctoral Scholar, Redwood Center for Theoretical Neuroscience, UC Berkeley Research Focus: Dr. Rozell's interdisciplinary work spans computational neuroengineering, theoretical neuroscience, and artificial intelligence. He develops data analysis tools inspired by neural processing and creates therapeutic neurotechnologies. Key areas include: computational psychiatry (developing DBS therapies for depression), neural dynamics modeling, brain-computer interfaces, and societal impacts of neurotechnology. His lab focuses on high-dimensional data analysis, neural coding principles, and scalable neuromodulation approaches. Publication Trends (2023-2025): Recent works concentrate on deep brain stimulation mechanisms for depression, computational modeling of neural/autonomic dynamics, and machine learning applications in neuroscience. Dominant themes include biomarker discovery for treatment response, neural interoception modulation, probabilistic modeling of latent states, and brain-computer interface taxonomy. Clinical translation of neurotechnology is a consistent focus across publications. Awards & Honors: Elected AIMBE Fellow (2025) NIH BRAIN Initiative Photo/Video Award (2024) Congressional Panelist for BRAIN Initiative 10th Anniversary (2024) Sigma Xi Best Faculty Paper (2024) Neuro Open Science International Prize (2022) W. Howard Ector Outstanding Teacher Award (2019) McDonnell Foundation 21st Century Science Award (2014) NSF CAREER Award (2014) Leadership & Training: Dr. Rozell co-founded Neuromatch, Inc. to build global computational neuroscience communities. He advises Motif Neurotech and the Institute of Neuroethics. His mentees have received prestigious fellowships (Schmidt, Fulbright, NIH K99/R00) and hold leadership positions across academia and industry. Research is supported by NIH BRAIN Initiative, NSF, and private foundations. Labs & Initiatives: Directs the Sensory Information Processing Lab (theoretical neuroscience/neuroengineering) and the Institute for Neuroscience, Neurotechnology & Society (addressing ethical/societal implications). Neuromatch promotes open, accessible computational neuroscience training globally.

Joseph Cassadyمشاهده پروفایل

استاد

• Animal Science
• Food Animal Genetics
• Genomics
• +۲ مورد دیگر

Dr. Joseph Cassady serves as the South Dakota Corn Endowed Dean of the College of Agriculture, Food and Environmental Sciences at South Dakota State University (SDSU). Previously, he held the rank of Professor and Head of the Department of Animal Science at SDSU (2013–2022) and North Carolina State University (2001–2013). His research focuses on food animal genetics and genomics, particularly improving production efficiency in swine and beef cattle. Education: B.S. in Animal Science (Iowa State University, 1993), M.S. and Ph.D. in Animal Science (University of Nebraska-Lincoln, 1995 and 1999). Professional roles include past-president of the National Swine Improvement Federation and executive director of the Beef Improvement Federation (2009–2015). Research emphasizes genetic and genomic advancements to enhance livestock productivity, with notable work on heat stress mitigation, swine behavior, and genomic selection in cattle. Awards include the 2019 Harold and Barbara Bailey Award for Academic Leadership and recognition for service to the Beef Improvement Federation. Administrative responsibilities include leading the College of Agriculture, Food and Environmental Sciences, overseeing accreditation processes, and participating in industry boards such as the South Dakota Poultry Industries Association and American Angus Foundation. Key research themes span animal genomics, livestock environmental adaptation, and precision agriculture. Over 150 peer-reviewed publications address topics like swine feeding behavior, cattle genetics, and genomic tools for improving meat quality and production efficiency.

Martin Wellsمشاهده پروفایل

استاد

• Statistical Inference
• Bayesian Methods
• Machine Learning
• +۱ مورد دیگر

Martin Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in Social Statistics, Clinical Epidemiology, and Industrial Labor Relations. Since joining Cornell in 1987, he has developed methodologies spanning Bayesian inference, tensor analysis, and machine learning applications in biomedicine and finance. His research integrates statistical theory with computational innovations, particularly in high-dimensional modeling and quantum-inspired algorithms. Recent work focuses on geometric approaches to tensor decomposition, misclassification correction methods, and phylodynamic models incorporating dormancy effects. Professor Wells teaches statistical methodology across disciplines including law, medicine, and biology, adapting analytical frameworks to diverse research contexts. His interdisciplinary collaborations extend to Weill Medical College and the School of Industrial and Labor Relations.

Li Yangمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Li Yang is an Assistant Professor in the Department of Information Technology , part of the Faculty of Business and Information Technology at Ontario Tech University. His research focuses on applying AI and machine learning to cybersecurity, particularly in intrusion detection and anomaly detection for 5G/6G networks and IoT systems. He holds a PhD in Electrical and Computer Engineering from Western University (2022), and has held roles such as Vice Chair of IEEE Computer Society, London Section (2022–2023). Education: PhD in Electrical and Computer Engineering, Western University (2022) Master of Science, University of Guelph (2018) Bachelor of Engineering, Wuhan University of Science and Technology (2016) Research Interests: His work spans cybersecurity, machine learning, deep learning, AutoML, model optimization, network automation, IoT security, intrusion detection, and adversarial machine learning. He develops frameworks for concept drift adaptation and online learning to enhance cybersecurity measures, with a focus on trustworthy AI and defense strategies against adversarial attacks. Awards: Graduate Student Award for Excellence in Research (2022) Graduate Symposium Award for Best Presentation (2022) Mitacs Accelerate Fellowship (2021) OC2 Lab Industrial Research Excellence Award (2020) Ranked in Stanford/Elsevier's Top 2% Scientists (2024) Grants & Involvement: Li Yang has contributed to conferences like IEEE GlobeCom and IEEE CCECE, and authored patents such as 'Convenient primary-secondary barrels' (2009). His work has garnered thousands of citations and GitHub stars, emphasizing practical applications of AI in cybersecurity.

Aaron Hoskinsمشاهده پروفایل

استاد

• Pre-mRNA splicing
• Spliceosome assembly
• Single-molecule fluorescence microscopy
• +۳ مورد دیگر

Aaron Hoskins is a full-time Professor of Biochemistry and Chemistry at the University of Wisconsin–Madison, where he leads an active research program focused on pre-mRNA splicing, spliceosome assembly, and single-molecule biophysics. He is affiliated with the Department of Biochemistry and the Hoskins Group laboratory, located in the Biochemical Sciences Building. Education: B.S., 2000 – Purdue University Ph.D., 2006 – Massachusetts Institute of Technology Postdoctoral Fellow, 2006–2011 – Brandeis University and UMass Medical School His research centers on understanding the molecular mechanisms of pre-mRNA splicing and spliceosome assembly in eukaryotes. Using single-molecule fluorescence microscopy, his lab investigates how the spliceosome recognizes RNA targets, how ribonucleoproteins are assembled, and how splicing fidelity is maintained or disrupted in disease. His work integrates genetics, chemical biology, and biophysical approaches to dissect spliceosome dynamics and to develop new tools for studying RNA processing. Aaron Hoskins has published over 80 peer-reviewed articles since 2004, with recent work appearing in RNA , eLife , Structure , and Cell Chemical Biology . His research trends include the structural dynamics of spliceosomal snRNPs, cancer-associated mutations in splicing factors, and the development of splicing inhibitors as potential therapeutics. His lab also explores translational applications, including the use of humanized yeast strains for drug screening. He is supported by multiple NIH grants (R01 GM053007, R01 GM112735, R01 GM081648) and has collaborated extensively with UW-Madison colleagues David Brow and Samuel Butcher. His lab is equipped with custom-built fluorescence microscopes for single-molecule imaging and is actively training the next generation of scientists in RNA biology and biophysics.