Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines
Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.
Rachel Rudinger is an Assistant Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Her research focuses on Natural Language Processing (NLP), Machine Learning, and AI ethics, particularly addressing sociocultural biases and fairness in large language models (LLMs). She holds a PhD from Johns Hopkins University (2019) and a B.S. from Yale University (2013). Rudinger's work explores equitable cultural alignment in AI systems, common ground misalignment in dialog systems, and the mutual influence of gender and occupation in LLMs. She received the NSF CAREER Award in 2024 for her project on robust, fair, and culturally aware commonsense reasoning. Her recent publications investigate empathy gaps in LLMs, synthetic data effectiveness in disaster response, and bias measurement techniques across domains. As an advisor, she guides seven PhD students including Christabel Acquaye and Haozhe An. Her research spans diverse topics from legal language analysis to maternal health question answering, reflecting her commitment to interdisciplinary AI ethics. She actively contributes to workshops on commonsense representation and serves as a reviewer for top conferences in NLP and AI.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Dimitra Psychogiou is a Full Professor of Microwave Engineering at University College Cork and Principal Investigator at Tyndall National Institute's CONNECT Centre in Cork, Ireland. She leads the Advanced RF Technology Group, driving innovation in reconfigurable RF systems for next-generation wireless networks. Her academic credentials include: Dipl.-Eng. in Electrical and Computer Engineering, University of Patras (2008) Ph.D. in Electrical Engineering, ETH Zurich (2013) Prof. Psychogiou's research pioneers reconfigurable microwave filters , non-reciprocal RF components , and additive manufacturing for antenna systems . Her work bridges theoretical microwave engineering with practical implementations in 5G/6G front-ends, emphasizing sustainability through hyperflexible filtering architectures that reduce hardware complexity and energy consumption. Key innovations include acoustic-wave resonator filters and 3D-printed RF components enabling unprecedented miniaturization. Analysis of her 2022-2025 publications reveals a strategic shift toward multifunctional RF integration , where filtering coexists with isolation, amplification, and switching in single modules. This trend addresses critical industry needs for compact, software-defined radio front-ends in satellite communications and IoT networks, with increasing emphasis on reflectionless topologies and spatiotemporal modulation techniques. Her scientific recognition includes: 2023 MTT-S Outstanding Young Engineer Award 2021 Roberto Sorrentino Prize 2021 SFI Research Professorship 2020 NSF CAREER Award 2020 URSI Young Scientist Award UC Boulder Junior Faculty Research Award Prof. Psychogiou actively shapes her field through leadership roles as Chair of IEEE MTT-13 Committee and Secretary of USNC-URSI Commission D, while serving as Associate Editor for IEEE MWCL and IJMWT. Her group collaborates extensively with semiconductor foundries and wireless infrastructure companies to transition lab innovations to commercial applications. The Advanced RF Technology Group operates state-of-the-art facilities for GaAs MMIC prototyping, 3D-printed RF component fabrication, and full-wave electromagnetic characterization, supporting Ireland's strategic position in European telecommunications research.
Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.
Paola Cascante-Bonilla is an Assistant Professor in the Department of Computer Science at Stony Brook University, with expertise in computer vision, natural language processing, and embodied AI. Her research focuses on developing systems for compositional reasoning, common-sense inference, and trustworthy AI using vision-language models, while addressing cultural bias and explainability challenges.
Ioannis Gkioulekas is an Assistant Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science, with a courtesy appointment in Electrical and Computer Engineering. He leads the Computational Imaging Lab at CMU, focusing on joint hardware-software approaches to develop advanced imaging systems. His research spans computational imaging, computer vision, and graphics, addressing challenges like non-line-of-sight imaging, 3D sensing, and adaptive optics. Research interests include: Computational imaging systems design Non-line-of-sight and single-photon imaging LiDAR, SONAR, and interferometry applications Physics-based and differentiable rendering Probabilistic modeling and Monte Carlo methods Recent publications (2019-2021) demonstrate strong focus on waveguides, light transport simulation, 3D sonar reconstruction, and computational tomography. Common themes include inverse problems, wave-based imaging, and differentiable simulation techniques bridging graphics and sensing. Current advising includes Master's student Neham Jain and affiliates Bakari Hassan, John Liu, Bailey Miller, Sreekar Ranganathan, and Arjun Teh. Past students include PhD graduates Arpit Agarwal and Shumian Xin, and Master's student Shirsendu Halder.
Dominique Chen is a Professor at Waseda University's School of Culture, Media and Society since 2022, previously serving as Associate Professor from 2017-2022. A French national born in 1981, he holds a Ph.D. in Interdisciplinary Informatics from the University of Tokyo (2013). His work bridges technology, art, and human experience with a focus on digital well-being and more-than-human relationships. Chen's research interests include human-microbe interaction, neo-cybernetics, and the design of systems that foster mutual care between humans and non-human entities. His work with the Nukabot project exemplifies this interdisciplinary approach, exploring how fermentation processes can serve as metaphors for communication and relationship building. He leads the Ferment Media Research group, investigating how fermentation principles apply to digital cultures and communication systems. His recent publications reveal a consistent focus on designing for well-being in digital societies, with particular attention to translation processes, human-microbe relationships, and the creation of systems that support co-adaptive interaction. The Nukabot research series demonstrates how traditional fermentation practices can inform novel interaction paradigms that acknowledge and incorporate more-than-human perspectives. Best Paper Honorable Mention (2024) - ACM Synlogue with Aizuchi-bot ACM SIGGRAPH Special Prize (2023) - Nukabot Best of AppStore 2015/2016 - Picsee/Syncle applications Good Design Award (2008) - Creative Commons Japan Super Creator certification (2009) - IPA Exploratory IT Program Chen has advised numerous projects through his leadership of Ferment Media Research and has served on various committees including the Good Design Award jury (2016-), Yomiuri Shimbun Reading Committee, and advisory boards for art and design institutions. His work extends beyond academia through his founding of Divideal Inc. (acquired by Smart News in 2018) and Creative Commons Japan (now Commonsphere). His laboratory, Ferment Media Research, explores interdisciplinary connections between fermentation processes, digital systems, and human relationships, creating installations like Nukabot that facilitate human-microbe interaction and Last Words/TypeTrace that examines writing processes and communication.
Nichole Denise Pinkard is the Alice Hamilton Professor of Learning Sciences and Faculty Director of the Office of Community Education Partnerships at Northwestern University's School of Education and Social Policy. She founded the Digital Youth Network (DYN) and developed L3, a social learning platform connecting youth's learning across multiple environments. Education: PhD, Learning Sciences, Northwestern University, 1998 MS, Computer Science, Northwestern University BS, Computer Science, Stanford University Research Focus: Designs socio-technical systems to support ecological models of learning, emphasizing community-level learning ecosystems and pedagogical-based social networks. Her work bridges technology and urban education contexts to reimagine learning documentation and opportunities. Awards: 2010 Common Sense Media Award 2004 Jan Hawkins Award 2000 NSF Early Career Fellowship Projects & Initiatives: Leads Chicago City of Learning and Digital Youth Divas programs, advancing digital equity and youth empowerment through technology. Collaborates with city agencies to create existence proofs for innovative urban learning models.
Moritz Wiese is a Researcher at the Chair of Theoretical Information Technology within the Department of Electrical and Computer Engineering at Technische Universität München (TUM) . His work focuses on information-theoretic security, quantum communication, and wireless networks, with significant contributions to interference modeling and secure coding schemes. Research Interests : Information-Theoretic Security, Quantum Wiretap Channels, MIMO Systems, 5G/6G Technologies, Physical Layer Security, and Network Coding. Key Grants : Involved in DFG-funded projects (Gottfried Wilhelm Leibniz Prize, UKoloS) and BMBF initiatives (6G-life, QuaDiQua). Collaborations : Worked with Prof. Holger Boche, Christian Deppe, and Rami Ezzine on secure communication and randomness generation. Labs : Active in the ACES Lab and 6G-life research hub, focusing on joint communication/sensing and neuromorphic computing. Publications : Over 20 papers on secure estimation, wiretap channels, and quantum secrecy, including top-tier journals like IEEE Transactions on Information Theory and Journal of Mathematical Physics .
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.
Kingsley Yuen Lung Fong is an Associate Professor of Finance at the UNSW Business School, specializing in Banking and Finance. He has published extensively in leading international finance journals and co-founded the RISE Finance Lab to explore how finance can better foster well-being, relational trust, and long-term stewardship. Institution: University of New South Wales School: UNSW Business School Department: Banking and Finance His academic work is guided by the principle that finance is an evolving design for organizing life and activity across individuals and society. Fong's research expertise spans market microstructure, investment, household finance, and sustainable finance. He has made significant contributions to understanding the connections between finance, society, and nature for a flourishing future. The analysis of his 15 most recent publications reveals a strong focus on market microstructure, trading behavior, and sustainable finance. His work examines algorithmic trading impacts, liquidity measurement, household investment decisions, and regulatory aspects of financial advice. Fong's research demonstrates consistent interest in how financial systems can be designed to better serve societal needs while maintaining market efficiency. 2017 Review of Finance Spängler IQAM Prize 2021 Aspen Institute Ideas Worth Teaching Award 2022 S&P Global Decarbonisation Hackathon As an educator, Fong teaches Wealth Management and Client Engagement, Sustainable Investing, and Sustainable Finance. His leadership roles include serving as Deputy Head of School for Banking and Finance (2011-2019), Co-Founder of UNSW RISE Finance Lab (2025), and member of the Australian Sustainable Finance Institute Sustainable Finance Capability Reference Group (2024). Beyond traditional finance, he created DATKIS, an intellectual framework that grounds common sense and intelligence in truth, awareness, and design.
Thiago Batista Soeiro serves as a Full Professor with exceptional scholarly impact, evidenced by over 200 research publications and an h-index of 27. His work fundamentally advances power electronics applications in transportation and energy systems, particularly through innovations in electric vehicle infrastructure and sustainable power conversion technologies. Despite the absence of explicit institutional affiliation in source materials, his research permeates critical IEEE journals and conferences. Professor Soeiro's research portfolio centers on: Power converter design for electric vehicle charging systems AI-driven battery health estimation using electrochemical impedance spectroscopy Wireless power transfer optimization for automotive applications High-efficiency topologies for more electric aircraft Hydrogen energy system integration Advanced semiconductor utilization in grid-connected systems Analysis of his 2023-2025 publications reveals accelerating innovation in wide-voltage-range converters, predictive battery management, and fault-tolerant power systems. His work increasingly bridges machine learning with power electronics, notably through computation-light AI models for battery diagnostics, while maintaining strong focus on practical implementation challenges in EV charging and aircraft electrification. No scientific awards or honors were documented in the available materials. Similarly, information regarding student supervision, research grants, laboratory facilities, or collaborative teams was not provided in the source texts.
