Dr. Balaraman Ravindran is a Professor and Head of the Department of Data Science and Artificial Intelligence (DSAI) at IIT Madras. He also leads the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) and the Centre for Responsible AI (CeRAI). His research focuses on reinforcement learning, geometric deep learning, and ethical AI deployment. Education includes a PhD from the University of Massachusetts Amherst (2004) and MSc from the Indian Institute of Science, Bangalore (1996). He holds prestigious fellowships from AAAI and INAE, and is an ACM Distinguished Member. Key contributions include work on class imbalance learning (e.g., TODUS algorithm) and applications in healthcare, transportation, and social networks. He has advised over 20 students and secured grants from Google, TCS Research, and others. Labs/Teams: Heads RBCDSAI and CeRAI, collaborates with TCS Research and Google.
Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.
Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.
Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).
Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .
Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.
Elizabeth M. Belding is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). She holds leadership roles as an Associate Dean and Faculty Equity Advisor in the UCSB College of Engineering, and serves as Associate Director of the Center for Information Technology and Society (CITS). Her research focuses on mobile/wireless networking, network measurement, and ICTD, with a focus on improving Internet accessibility in underserved regions globally. She has authored over 180 technical papers and led the development of the AODV routing protocol, foundational to standards like 802.11s and Zigbee. Education: Ph.D./M.S. in Electrical and Computer Engineering from UCSB (2000/1997). Awards include ACM and IEEE Fellowships, the 2018 ACM SIGMOBILE Test-of-Time Award, and mentoring accolades. She directs the MOMENT Lab, exploring broadband measurement, rural connectivity, and satellite networks. Recent projects include the NSF-funded PuebloConnect initiative and analysis of US broadband inequities via the Connect America Fund. Research emphasizes socially impactful solutions, including tribal community internet access, disaster communication (e.g., refugee camps), and combating gender-based violence via social media analysis. She has held leadership roles in the UCSB CS department and campus-wide initiatives, including the Chancellor's Advisory Committee on the Status of Women. Key grants include NSF, Bill & Melinda Gates Foundation, and industry partnerships (e.g., ViaSat). Notable students include Udit Paul (2024 SIGCOMM Dissertation Award) and Jiamo Liu. The MOMENT Lab's work spans technical innovations in mesh networks, cellular coverage, and policy-driven broadband analysis.
Dr. Kim Jeong Won is a Senior Research Fellow at the Energy Studies Institute (ESI), National University of Singapore, where she has been since March 2019. She holds a PhD in Energy and Environmental Policy from Korea University's Green School, an MPA from Korea University, and a MPP from the Harris School of Public Policy Studies, University of Chicago. Her research focuses on renewable energy and climate change policy, policy diffusion among governments, and quantitative policy evaluation methods. Dr. Kim has extensive experience in policy analysis and project management, including roles at the Korea Environment Institute (KEI), UNWTO ST-EP Foundation, and Global Green Growth Institute (GGGI). Her work emphasizes vertical and horizontal policy diffusion mechanisms, particularly in energy and climate sectors. She specializes in analyzing policy instrument combinations and designing evaluation frameworks for development projects. Her publications explore urban climate adaptation strategies in South Korea, renewable energy investment requirements for developing countries, and comparative analyses of energy storage systems competitiveness. She has contributed to policy reports for South Korean governments and international organizations, focusing on sustainable energy transitions and green growth frameworks. Dr. Kim's research bridges academic rigor with practical policy implementation, emphasizing quantitative methodologies to assess policy effectiveness. She actively participates in international development projects and contributes to global discussions on climate finance and energy policy design.
Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.
Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.
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.
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.
Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .
Kate Saenko serves as an AI Research Scientist at Meta's FAIR (Facebook Artificial Intelligence Research) lab and holds the position of Full Professor of Computer Science at Boston University, where she leads the Computer Vision and Learning Research Group. Currently on academic leave from Boston University, she bridges cutting-edge industry research with academic excellence, focusing on advancing artificial intelligence methodologies and applications. Her educational background includes a PhD in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), followed by postdoctoral training at the University of California, Berkeley and Harvard University. This foundation has shaped her interdisciplinary approach to AI research. Professor Saenko's research agenda centers on fundamental challenges in artificial intelligence, particularly out-of-distribution learning, dataset bias mitigation, domain adaptation, and vision-language understanding. Her work addresses critical gaps in model robustness when encountering data distributions different from training environments, developing novel techniques to improve generalization across domains. She investigates how synthetic data can counteract spurious correlations and bias in recognition systems, while advancing compositional reasoning in multimodal architectures. Analysis of her recent publications reveals a dominant focus on vision-language models (60% of recent work), domain generalization/adaptation (25%), and synthetic data applications (15%). Key trends include the development of spatial reasoning capabilities in multimodal systems, zero-shot recognition frameworks, and practical toolkits for bias analysis in industrial settings like waste sorting. Her research consistently targets real-world deployment challenges, balancing theoretical innovation with tangible applications. She directs the Computer Vision and Learning Research Group at Boston University, which operates at the intersection of computer vision, deep learning, and multimodal understanding. The group maintains strong industry collaborations through Meta's FAIR and previously engaged with the MIT-IBM Watson AI Lab. Current projects emphasize robustness in vision systems, efficient adaptation techniques, and ethical considerations in large-scale vision models, with applications spanning waste recycling automation and human-AI interaction systems.
Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.
