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Stefano ErmonView profile

Associate Professor

• Machine Learning
• Generative AI
• Computational Sustainability
• +7 more

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

• Machine Learning
• Probability
• Mathematical Physics
• +6 more

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Yee Whye TehView profile

Professor

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +4 more

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +2 more

Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.

Luca CarloneView profile

Associate Professor

• Robotics
• Autonomous Systems
• Computer Vision
• +6 more

Luca Carlone is the Boeing Career Development Associate Professor in the Department of Aeronautics and Astronautics at MIT and a Principal Investigator at the Laboratory for Information & Decision Systems (LIDS) . He leads the SPARK Lab , focusing on developing certifiable perception algorithms for autonomous systems. PhD in Mechatronics (Polytechnic University of Turin, 2012) Research spans robotics, computer vision, and optimization Research Interests : Certifiable Perception algorithms for high-integrity systems High-level Perception (geometric, semantic, physical understanding) Efficient Perception methods for resource-constrained robots Scientific Contributions include: 2024 Outstanding Systems Paper Award (RSS) 2023 IEEE Transactions on Robotics King-Sun Fu Award 2021 NSF CAREER Award 2020 AIAA Advising Award 2019 Amazon Research Award Advising : Teaches graduate courses like Visual Navigation for Autonomous Vehicles and Robotics: Science and Systems . Collaborates with institutions including JPL, Caltech, and KAIST through the DARPA SubT Challenge.

Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Nadia FigueroaView profile

Assistant Professor

• Robot Learning from Demonstration
• Human-Robot Collaboration
• Dynamical Systems
• +5 more

Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), and is a core faculty member at the GRASP Lab. Prior to Penn, she was a Postdoctoral Associate at MIT’s CSAIL and earned her PhD in Robotics from EPFL under Prof. Aude Billard. Her research focuses on physical and perceptual adaptive intelligence for robots, enabling fluid collaboration with humans in dynamic environments. Key applications include robot learning from demonstration , human-robot co-manipulation , safe navigation in human-centric spaces , and rehabilitation robotics . Her work integrates machine learning control theory artificial intelligence biomechanics psychology with guarantees of stability, safety, and robustness . Recent publications highlight advancements in reactive collision avoidance dynamical system learning intent estimation EEG-driven assistive control origami-based reconfigurable robots across platforms like autonomous vehicles and humanoid robots. She has authored a 2022 textbook on dynamical systems for robot control and received the Presidential Assistant Professorship at Penn.

Michael KaessView profile

Associate Professor

• Robotics
• Computer Vision
• SLAM (Simultaneous Localization and Mapping)
• +6 more

Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).

Andrew StuartView profile

Professor

• Applied and Computational Mathematics
• Bayesian Inverse Problems
• Data Assimilation
• +6 more

Andrew Stuart is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), joining in 2016. He previously held faculty positions at the University of Warwick (1999–2016), Stanford University (1992–1999), and Bath University (1989–1992). He earned his PhD from the University of Oxford's Computing Laboratory in 1986. Professor Stuart's research focuses on applied and computational mathematics , particularly Bayesian inverse problems , data assimilation for dynamical systems , and stochastic modeling . His work bridges mathematical theory, algorithm development, and applications in geophysics, materials science, and biological systems. His recent publications emphasize operator learning , machine learning for PDEs , and uncertainty quantification . Key areas include ensemble Kalman methods , Gaussian processes , and neural operators for solving and learning from complex systems. Scientific awards include the Vannevar Bush Faculty Fellowship and election to the Royal Society of Great Britain . He advises graduate students in applied mathematics, computational science, and geophysics, including Edoardo Calvello , Hojjat Kaveh , and Florian Wolf .

Shoudong HuangView profile

Professor

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +4 more

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

Amitabha BagchiView profile

Professor

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +7 more

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

Niki KilbertusView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Ethics in AI
• +5 more

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

Huan ZhangView profile

Assistant Professor

• Trustworthy Machine Learning
• Formal Verification of Machine Learning
• AI Safety
• +4 more

Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.

Vineeth N BalasubramanianView profile

Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +8 more

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +6 more

Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI