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Kilian Q. Weinberger is a Professor of Computer Science at Cornell University's College of Engineering, focusing on Machine Learning, Deep Learning, and AI applications. He has held previous roles as Associate Professor at Washington University in St. Louis and Research Scientist at Yahoo! Research. His research spans metric learning, resource-constrained learning, Gaussian Processes, and advancements in 3D perception for autonomous systems. Education : Ph.D. in Machine Learning (University of Pennsylvania), BA in Mathematics and Computing (University of Oxford) Key Research Areas : AI in Science, Computer Vision, Autonomous Vehicles, and Neural Network Efficiency His recent work emphasizes interpretable machine learning, large language models, and multimodal applications. Awards include NSF CAREER (2012) Daniel M Lazar '29 Teaching Award (2016) Ann S. Bowers Excellence Award (2024) ACM and AAAI Fellow (2024) He teaches advanced courses like CS6784 (Cornell) and has mentored numerous PhD students across institutions. Current affiliations include the Sloan Research Fellowships Selection Committee since 2024.

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994

• Federated Satellite Systems
• CubeSat Constellations
• Spacecraft Systems Architecture
• +3 more

Andrei Kutuzov is an Associate Professor in the Language Technology Group (LTG) within the Department of Informatics at the University of Oslo. He serves as the Norwegian on-site manager of the High-Performance Language Technology (HPLT) project and has made significant contributions to computational linguistics and natural language processing. His research primarily focuses on computational linguistics and natural language processing, with specialized expertise in semantic change detection, diachronically aware language models, distributional semantics, and large language models. Kutuzov has been instrumental in developing Norwegian language resources including NorBERT, NorELMo models, and the very large-scale NORA.LLM generative models. He created WebVectors, a web service for exploring neural distribution models for Norwegian and English texts. Analysis of his recent publications reveals a strong focus on semantic change modeling, multilingual dataset development, and Norwegian language technology. His work spans from theoretical linguistic analysis to practical applications in language modeling, with significant emphasis on low-resource and Nordic languages. Kutuzov's research demonstrates a consistent trajectory toward improving language models' understanding of semantic evolution and developing robust evaluation frameworks for Norwegian language processing. Norwegian Artificial Intelligence Research Consortium (NORA) award as Distinguished Early Career Researcher (2022) Kutuzov teaches several advanced courses including IN5550 - Neural Methods in Natural Language Processing (2019-2025) and IN3050 - Introduction to Artificial Intelligence and Machine Learning (2024-2025). He has received research funding through the HPLT project which focuses on developing high-performance language technologies. His laboratory work centers around the Language Technology Group at UiO, where he collaborates on developing Norwegian language resources and models, with particular emphasis on diachronic semantic analysis and multilingual capabilities.

• Computational Linguistics
• Natural Language Processing
• Semantic Change Detection
• +6 more

Eldan Cohen serves as an Assistant Professor of Industrial Engineering within the Department of Mechanical & Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. His academic journey includes a PhD from the same department followed by a postdoctoral fellowship in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence. His educational background is detailed as follows: PhD in Mechanical & Industrial Engineering, University of Toronto Postdoctoral Fellowship in Computer Science, University of Toronto and Vector Institute for Artificial Intelligence Dr. Cohen's research centers on machine learning, deep learning, heuristic search, and optimization with strong emphasis on interpretable and human-compatible AI systems. His work bridges theoretical advancements with practical applications in healthcare (e.g., patient-physician interaction analysis, surgical safety diagnostics), automated planning, natural language processing, and software engineering. Recent projects develop interpretable clustering methods for medical data and optimization techniques for constrained sequence generation. Analysis of his 2023-2025 publications reveals a concentrated focus on healthcare AI applications, particularly using large language models for clinical text analysis and diagnostic support systems. Significant work also addresses interpretable machine learning for medical imaging, diverse plan selection in optimization, and constrained sequence generation in domains like vehicle routing. No major scientific awards or fellowships are documented in the available information. As an academic advisor, Dr. Cohen mentors graduate students in mechanical and industrial engineering, guiding research in optimization and machine learning. His OptiMaL research group fosters collaboration between computer science and industrial engineering to solve real-world decision-making challenges through human-centered AI approaches. The Optimization and Machine Learning (OptiMaL) research group, led by Dr. Cohen, serves as the primary hub for developing scalable, interpretable AI solutions for complex healthcare, planning, and engineering problems, with active projects in medical diagnostics and automated planning systems.

• Machine Learning
• Deep Learning
• Optimization
• +6 more

Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.

• Machine Learning
• Extreme Classification
• Optimization Techniques
• +5 more

Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.

• Affective Computing
• Software Engineering
• Natural Language Processing
• +5 more

Arjun (Raj) Manrai is an Assistant Professor at Harvard Medical School and a faculty member in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital. He earned an A.B. in Physics (Harvard, Highest Honors) and a Ph.D. in Bioinformatics and Integrative Genomics from Harvard-MIT. His research focuses on improving medical decision-making through computational approaches in clinical genomics, algorithmic bias mitigation, and healthcare AI ethics. Key projects include race-free kidney function equations, genetic variant classification, and semi-supervised learning for medical imaging. Education: B.A. in Physics, Harvard University (Highest Honors) Ph.D. in Bioinformatics and Integrative Genomics, Harvard-MIT Division of Health Sciences and Technology Research interests span AI-driven diagnostics, health equity, and reproducibility in biomedical studies. His work has been featured in New England Journal of Medicine , JAMA , and highlighted by the New York Times and NPR. Notable contributions include advancing race-free diagnostic algorithms, addressing biases in clinical genomics, and advocating for transparent AI systems in healthcare. The Manrai Lab collaborates widely to translate computational methods into clinical practice.

• Clinical Genomics
• Machine Learning in Healthcare
• Algorithmic Bias in Medicine
• +3 more

Cengiz Zopluoglu is an Associate Professor in the Department of Special Education and Clinical Sciences at the University of Oregon's College of Education. His research focuses on quantitative methods in education, item response theory, computational psychometrics, and educational data science. He teaches advanced courses on psychometrics, statistical methodology, and data analysis using R. Education: PhD, 2013: University of Minnesota (Educational Psychology, Quantitative Methods) MA, 2009: University of Minnesota (Educational Psychology, Quantitative Methods) BA, 2005: Abant Izzet Baysal University (Mathematics Education, K-8) Research Interests: Zopluoglu's work emphasizes integrating machine learning and statistical models into educational measurement. He develops methods to detect test misconduct (e.g., item preknowledge) using response time and accuracy data, and explores automated scoring of open-ended responses using AI (e.g., transformers). His contributions include advancements in continuous response models, multidimensional IRT, and DETECT analysis for dimensionality assessment. Awards: 2023 Runner-up Prize in NAEP Math Automated Scoring Challenge (NCES) 2021 3rd Place in NIJ Recidivism Forecasting Challenge 2013 Graduate Student Research Award (University of Minnesota) Advising & Grants: Zopluoglu has advised on projects related to test security, automated scoring, and machine learning applications. His work often involves open-source tools like R and Stan, with a focus on reproducible research. Labs & Collaborations: He collaborates on initiatives like the Deterministic Gated Models for Test Security and the WrightRightNow automated scoring platform. His research leverages interdisciplinary approaches, blending psychometrics with computer science and data science.

• Quantitative Methods in Education
• Item Response Theory
• Computational Psychometrics
• +4 more

Tal Micah August is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois. His research focuses on Natural Language Processing, Plain Language Summarization, and Human-Computer Interaction, emphasizing audience-adaptive communication and AI-driven educational tools. Key research interests include developing intelligent writing assistants, evaluating AI-generated summaries across audiences, and leveraging large language models for legal and mathematical education. He collaborates on projects involving user-centered design and curriculum-grounded AI assessment. His work has been presented at major conferences like CHI and EMNLP, addressing topics such as interactive writing systems, plain-language metrics, and educational storytelling. No specific grants or student advisees are listed in the provided materials. Research highlights include the MathFish framework for math reasoning evaluation and studies on audience-specific plain language generation.

• Natural Language Processing
• Plain Language Summarization
• Human-Computer Interaction
• +3 more

Dr Lin Yue is a Lecturer at the University of Adelaide , affiliated with the Faculty of Sciences, Engineering and Technology and the School of Computer and Mathematical Sciences . She earned her PhD from Jilin University, with part of her doctoral studies completed as a joint PhD candidate at the University of Queensland. Past affiliations: Northeast Normal University, University of Queensland, University of Newcastle Her research focuses on Sequential Data Analysis and its applications in Medical Data Analytics, EEG Data Analysis, Brain-Computer Interfaces, Social Media Data Analytics, and Sentiment Analysis . She collaborates with academia, government, and professional organizations, supported by internal and external research grants. Dr Yue is eligible to supervise Masters and PhD students as a Co-Supervisor and contributes to advancing data mining and machine learning techniques in healthcare and time series analysis.

• Sequential Data Analysis
• Medical Data Analytics
• EEG Data Analysis
• +3 more

Ina Fiterau Brostean is an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where she leads the Information Fusion Lab. Previously, she was a Postdoctoral Fellow at Stanford University's Mobilize Center (2015–2018) and earned her PhD in Machine Learning from Carnegie Mellon University (2015). Her research focuses on hybrid systems for multimodal data integration, particularly in healthcare, aiming to develop predictive models for clinical outcomes using time series, text, and images. Key areas include disease trajectory modeling, weakly-supervised transfer learning, and adaptive representation learning. Education: PhD in Machine Learning (Carnegie Mellon, 2015), MSc in Machine Learning (Carnegie Mellon, 2012), BEng in Computer Engineering (Politehnica Timisoara, Romania, 2009). Professional roles include teaching COMPSCI 651 (Optimization in Computer Science) and organizing NeurIPS workshops on Machine Learning in Healthcare. Research interests span machine learning methodologies for healthcare applications, including interpretable models, time series analysis, and dimensionality reduction. Notable achievements include the Marr Prize (ICCV 2015) and Star Research Award (SCCM 2016). Her lab collaborates on projects like predicting Alzheimer's disease progression and surgical outcomes using Bayesian networks and deep learning. Awards and recognitions include Rising Stars Workshop (2016), Manning IALS Research Award (2019), and GE Foundation Scholar Leader Award (2007). She actively contributes to the ML4Health community through leadership roles and workshop organization.

• Machine Learning
• Artificial Intelligence
• Health Informatics
• +3 more

Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .

• Distributed Systems
• Operating Systems
• Machine Learning
• +5 more

Fengqi You is the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at Cornell University. He holds affiliations with multiple departments and graduate fields, including Chemical Engineering, Computer Science, and Sustainability. His research focuses on systems engineering, artificial intelligence, and their applications in energy systems, digital agriculture, and sustainability. You serves as Chair of Ph.D. Studies in Systems Engineering and leads initiatives like the Cornell AI for Sustainability Initiative (CAISI) and the Cornell Institute for Digital Agriculture (CIDA). Education: B.Eng. from Tsinghua University (2005), Ph.D. from Carnegie Mellon University (2009). Before Cornell, he worked at Argonne National Laboratory and Northwestern University. Research Interests Decarbonization of energy systems (e.g., photovoltaics, battery tech) Material informatics and computer-aided molecular design AI-driven solutions for agriculture and climate resilience Supply chain optimization and smart manufacturing Awards & Recognition Fellow of AAAS, AIChE, and Royal Society of Chemistry Over 25 major awards including NSF CAREER (2016), AIChE Research Excellence (2017), and the 2024 Cecil Award Grants & Leadership Recipient of NSF NRT Award for AI in Sustainability Sciences Amazon Research Award for LCA transparency using LLMs Labs & Initiatives Cornell AI for Science Institute (CUAISci) Cornell Institute for Digital Agriculture (CIDA)

• Sustainable Energy Systems
• Artificial Intelligence
• Complex Systems
• +9 more

Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.

• Applied Mathematics
• Combinatorics and Discrete Geometry
• Probability and Statistics
• +4 more

Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.

• Machine Learning
• Natural Language Processing
• Data Mining
• +3 more