جست‌وجوی استاد و پژوهشگر

Jackie Chit Kit Cheungمشاهده پروفایل

دانشیار

• Natural Language Processing
• Natural Language Generation
• Automatic Summarization
• +۱۲ مورد دیگر

Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.

Asaf Cidonمشاهده پروفایل

دانشیار

• Software Systems
• Storage Systems
• Machine Learning
• +۴ مورد دیگر

Asaf Cidon is an Associate Professor at Columbia University, jointly affiliated with the Department of Electrical Engineering and Computer Science, and a member of the Data Science Institute. His research focuses on software systems , storage , large-scale machine learning , and cybersecurity . Stanford University - PhD in Electrical Engineering Stanford University - MS in Electrical Engineering Technion - BS in Computer and Software Engineering His work in distributed storage systems has been commercialized by companies such as Facebook, Tibco, and Rubrik. He has led projects like Sentinel and Forensics during his industry career. Recent publications highlight advancements in software-based radiation protection (ASPLOS'26), PCIe pooling with CXL (HotOS'25), and AI phishing detection (IMC'25). These reflect his expertise in system architecture , security , and networking . Scientific recognitions include best paper awards at OSDI, Usenix Security, CIDR, and ATC, along with NSF CAREER and ARO Young Investigator Awards . His papers Cookie Monster (SOSP'24) and Chablis (CIDR'24) received notable accolades. He has mentored numerous PhD and Master's students , including Edward Guo, Harry Wang, and Teng Jiang, many of whom now hold roles at Google, Meta, Amazon, and academic institutions. His lab at Columbia is actively recruiting CS and EE PhD students. Prior to Columbia, he founded and led the startup Sookasa to acquisition and served as Senior Vice President of Email Protection at Barracuda Networks , managing a $200M business with 100 engineers.

Mathieu Salzmannمشاهده پروفایل

عضو هیئت علمی

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.

• Machine Learning
• Deep Learning
• AI Safety
• +۱۲ مورد دیگر

Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.

Aditya Parameswaranمشاهده پروفایل

دانشیار

• Database Systems
• Human-Computer Interaction
• Data Analytics
• +۳ مورد دیگر

Aditya Parameswaran is an Associate Professor in the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley. He co-directs the EPIC Data Lab and the Police Records Access project, focusing on simplifying data science at scale through human-in-the-loop systems, LLM-powered tools, and scalable data systems. His research spans database systems, human-computer interaction, and machine learning, with notable contributions in tools like Lux, Modin, and DataSpread. Education : PhD in Computer Science from Stanford University (2013) BTech in Computer Science and Engineering from IIT Bombay (2007) Research Interests : Parameswaran's work centers on empowering end-users with intuitive data tools. Recent projects include LLM-powered systems for document processing (DocETL, TWIX), proactive data systems, and benchmarking frameworks. He emphasizes democratizing data science through low/no-code solutions and improving production ML workflows. Articles Trends : His recent work (2023–2025) prioritizes LLM integration into data systems, focusing on robust pipelines, assertion generation (SPADE), and debugging tools (RAGGY). Earlier contributions include visualization recommendation (Lux), scalable dataframes (Modin), and spreadsheet optimization (DataSpread). Awards : Recipient of the VLDB Early Career Award (2019), Sloan Research Fellowship (2020), NSF CAREER Award (2017), and multiple best paper/demonstration awards at top venues like SIGMOD and VLDB. Advising & Grants : Guides over 20 PhD/postdoc alumni, many now in academia (e.g., Madelon Hulsebos at CWI) and industry leadership roles. Active in securing grants (e.g., NSF, Army Research Office) and industry partnerships (e.g., Snowflake, LangChain). Labs/Teams : Leads the EPIC Data Lab, focusing on agentic data systems, and co-founded Ponder (acquired by Snowflake). Collaborates on the Police Records Access initiative, building transparency tools for public records.

• Database Management Systems
• Self-Driving Database Architectures
• Transaction Processing Systems
• +۴ مورد دیگر

Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.

Steve Mussmannمشاهده پروفایل

استادیار

• Data-centric machine learning
• Active learning
• Experimental design
• +۲ مورد دیگر

Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.

Reza Shokriمشاهده پروفایل

دانشیار

• Data Privacy
• Trustworthy Machine Learning
• Security
• +۷ مورد دیگر

Reza Shokri is a Dean's Chair Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research lies at the intersection of data privacy, security, and trustworthy machine learning, with a focus on quantifying privacy risks and developing robust, fair, and interpretable models. PhD in Computer Science, EPFL His research interests center on data privacy and trustworthy machine learning , particularly in the context of deep learning and federated systems. He investigates how machine learning models memorize training data, leading to privacy leakage, and designs frameworks to audit and mitigate such risks. His work bridges theoretical guarantees with practical applications, emphasizing the trade-offs among privacy, fairness, robustness, and utility. His recent publications (2023–2025) reveal a strong trend in analyzing privacy in large language models (LLMs), membership inference attacks, federated learning, and fairness. These works are published in top venues such as NeurIPS, ICML, ICLR, CCS, and FAccT, highlighting his leadership in both AI and security communities. Notable scientific awards include: Asian Young Scientist Fellowship (2023) Intel Outstanding Researcher Award (2023) Best Paper Award, ACM FAccT (2023) IEEE S&P Test-of-Time Award (2021) Caspar Bowden Award for Privacy Enhancing Technologies (2018) NUS Presidential Young Professorship (2019–2023) VMware Early Career Faculty Award (2021) He has advised numerous PhD and Master’s students, many of whom have contributed to high-impact publications. He has also received research grants from major industry partners including Meta, Google, Intel, and VMware. He leads the Data Privacy and Trustworthy Machine Learning Lab at NUS and has served on program committees for top conferences such as IEEE S&P, ACM CCS, and FAccT, including co-chairing roles at HotPETs and Shadow PC of IEEE S&P. He has delivered tutorials at ICML and CCS on privacy auditing in machine learning. His lab focuses on developing tools and frameworks—such as the ML Privacy Meter—for assessing and improving the privacy properties of machine learning models, with applications in regulatory compliance and secure AI deployment.

Theo Damoulasمشاهده پروفایل

استاد

• Probabilistic Machine Learning
• Bayesian Statistics
• Digital Twins
• +۵ مورد دیگر

Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.

Suchi Sariaمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Health Informatics
• Probabilistic Methods
• +۶ مورد دیگر

Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.

Arvind Narayananمشاهده پروفایل

استاد

• AI Policy
• Societal Impact of Digital Technologies
• Fairness in Machine Learning
• +۶ مورد دیگر

Arvind Narayanan is a Professor of Computer Science at Princeton University and Director of the Center for Information Technology Policy (CITP). His research focuses on the societal impact of digital technologies, particularly artificial intelligence, with emphasis on policy implications, fairness, and privacy. He leads interdisciplinary efforts connecting technical research with real-world policy challenges. Dr. Narayanan earned his Ph.D. from the University of Texas, Austin in 2009. His academic journey has established him as a leading voice in the critical examination of AI systems and their societal consequences. Narayanan's research spans multiple domains where technology intersects with society. His work on AI includes critical analysis of AI capabilities versus marketing claims (AI Snake Oil), fairness in machine learning systems, and the reproducibility crisis in ML-based science. In privacy research, he led the Princeton Web Transparency and Accountability Project which uncovered how companies track users online, developing the OpenWPM tool used in over 100 studies. His early work demonstrated fundamental limits of de-identification techniques and how machine learning reflects cultural stereotypes. His recent publications reveal a consistent focus on demystifying AI capabilities while identifying genuine opportunities and risks. Narayanan's work bridges technical computer science with policy relevance, emphasizing the importance of evidence-based approaches to AI governance. His research increasingly addresses the limitations of prediction systems, the challenges of evaluating AI systems, and the need for transparency in foundation models. Presidential Early Career Award for Scientists and Engineers (PECASE) Privacy Enhancing Technologies Award (twice recipient) Privacy Papers for Policy Makers Award (three-time recipient) TIME's inaugural list of 100 most influential people in AI 2025 Graduate Mentoring Award Narayanan is recognized as an exceptional mentor, receiving Princeton's Graduate Mentoring Award in 2025. His policy engagement extends to congressional testimony, advisory roles, and frequent media commentary. He has secured significant research funding supporting his work on web transparency, AI policy, and cryptocurrency analysis. His research group has produced influential tools like OpenWPM for web privacy studies and contributed to foundational textbooks on cryptocurrencies and fairness in machine learning. At Princeton, Narayanan leads the Web Transparency and Accountability Project, a major research initiative that has conducted large-scale measurements of online tracking across millions of websites. He also co-founded and directs the CITP's AI Policy Initiative, which brings together researchers from multiple disciplines to address pressing AI governance questions. His work frequently involves collaboration with social scientists, legal scholars, and policymakers to develop practical solutions to technology governance challenges.

Yiping Luمشاهده پروفایل

استادیار

• Machine Learning
• Scientific Machine Learning (AI4Science)
• Numerical Algorithms
• +۷ مورد دیگر

Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford

Ralf Haefnerمشاهده پروفایل

استادیار

• computational neuroscience
• perceptual decision-making
• causal inference
• +۳ مورد دیگر

Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +۲ مورد دیگر

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

Jiang Huمشاهده پروفایل

استاد

• VLSI Design Automation
• Hardware Security
• Machine Learning Applications
• +۳ مورد دیگر

Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.