Prof. Dr. Alexander Stroh-Steckelberg is Professor of Political Science with a focus on African Politics & Development Policy at the University of Bayreuth since 2022. He serves as Deputy Head of the Bayreuth International Graduate School of African Studies (BIGSAS) until 2025 and has held prior roles as Junior Professor and Senior Fellow at BIGSAS (2015–2021). His academic career includes research associate positions at the GIGA German Institute of Global and Area Studies (2006–2015) and teaching roles at the Universities of Abomey-Calavi, Heidelberg, and Lüneburg. Stroh-Steckelberg’s research spans comparative democracy studies , judicial politics , electoral systems , political party dynamics , and development policy in Africa, with regional expertise in Francophone West Africa and Rwanda . His work emphasizes institutional diffusion, informal networks in courts, and the interplay between formal and informal governance structures. Recent publications reveal a focus on judicial appointment policies , electoral manipulation , and local governance challenges across Africa. He contributes to methodological advancements in comparative area studies and network analysis , particularly in non-Western democracies. Stroh-Steckelberg is actively involved in academic communities including the American Political Science Association , German Political Science Association , and Association for African Studies in Germany .
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.
Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under the Berkeley Artificial Intelligence Research (BAIR) umbrella and serves on the advisory board of Wonder Dynamics. Her research focuses on the intersection of computer vision, computer graphics, and machine learning, with a particular emphasis on 4D reconstruction of dynamic scenes, neural radiance fields (NeRF), and systems that model human-environment interactions from 2D visual data. Education : Ph.D., Computer Science (2017), University of Maryland, College Park BA, Mathematics and Computer Science (2012), New York University (NYU) Her work aims to build systems that can capture, perceive, and understand complex 3D/4D worlds from photographs and videos, enabling applications in scene reconstruction, motion analysis, and generative modeling. She has pioneered techniques for scaling NeRFs across GPUs (NeRF-XL), developing open-source tools like nerfstudio and gsplat. Her recent publications focus on topics like self-occluded avatar recovery (SOAR), decentralized diffusion models, and 4D reconstruction of articulated objects for robotics. Kanazawa's research has been recognized with prestigious awards including the IEEE CS TCPAMI Young Researcher Award (2024) , Sloan Research Fellowship (2023) , and Google Faculty Research Award (2021) . Her lab has trained numerous students who now hold positions at leading institutions and companies like Anthropic, Meta Reality Labs, and Luma AI. Key Scientific Awards : IEEE CS TCPAMI Young Researcher Award (2024) Sloan Research Fellow (2023) Hellman Fellow (2022) Bakar Fellows Spark Award (2022) Google Faculty Research Award (2021) Her KAIR lab collaborates extensively with industry partners and academic institutions, including the Max Planck Institute and Google Research. She has served as an advisor for PhD students and postdocs who now lead teams at UC Berkeley, MIT, Stanford, and Luma AI, while her teaching includes graduate courses like CS 280A (Computer Vision) and CS 294-173 (Learning for 3D Vision).
Karl Gustafsson is a Professor at Stockholm University within the Department of Economic History and International Relations. His work bridges security studies , collective memory , and digital research methods in international relations. Current Role: Professor of International Relations, Stockholm University Previous Positions: Post-Doc at Lund University, Researcher at Swedish Institute of International Affairs Visiting Appointments: University of Tokyo, Osaka University, Keio University, Academia Sinica, Nordic Institute of Asian Studies Research Focus: Karl specializes in memory politics , ontological security , and anxiety-driven international dynamics , particularly in Sino-Japanese relations and digital platforms . His studies explore how historical narratives and online memory construction influence state identity and policy. Awards & Grants: Recipient of the Wang Gungwu Prize (2014) and Stockholm University Association’s award (2011). Holds Swedish Research Council and Marianne & Marcus Wallenberg Foundation grants for projects on apologies in international politics and digital memory in East Asia . Publications: His work appears in journals such as International Studies Quarterly , European Journal of International Relations , and Memory Studies , addressing topics like narrative power in East Asia, anxiety’s role in policy change , and digital humanities applications in IR.
Bo Li is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. Her research focuses on trustworthy machine learning, emphasizing robustness, privacy, and security in AI systems. She leads the Secure Learning Lab (SL²), exploring adversarial attacks and defenses across digital and physical domains. Key contributions include foundational work on adversarial examples, robust learning frameworks, and privacy-preserving techniques. Her academic roles include advisory board positions at the Center for Artificial Intelligence Innovation (CAII) and membership in the Information Trust Institute (ITI). She collaborates with institutions like the Advanced Digital Science Center (ADSC) and the Quantum Information Science and Technology Center (IQUIST). Notable recognitions include the IJCAI Computers and Thought Award (2022), MIT Technology Review's 35 Innovators Under 35 (2020), and multiple best paper awards. Recent work addresses AI safety through frameworks like ShieldAgent and AutoRedTeamer, aiming to enhance system resilience against adversarial threats. Her research spans theoretical guarantees, practical defenses, and ethical AI deployment. Students advised include Chulin Xie, Linyi Li, and Boxin Wang, who have received prestigious fellowships such as the IBM PhD Fellowship and Rising Stars in ML Awards. Advising: Guides PhD students in adversarial ML, privacy, and security. Labs/Teams: Secure Learning Lab (SL²), collaboration with ALERT program. Grants/Funding: NSF CAREER Award, Amazon/Google Faculty Awards, and industry partnerships.
Tülay Adali is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She has held this position since 1992 and was named a Distinguished University Professor in 2015 for her contributions to statistical signal processing and machine learning. Currently serving as Editor-in-Chief of the IEEE Signal Processing Magazine, she has also held leadership roles in IEEE committees and conferences. Her research focuses on statistical signal processing, machine learning, and their applications in medical imaging and data fusion. Dr. Adali earned her Ph.D. in Electrical Engineering from North Carolina State University in 1992. Her work integrates foundational signal processing techniques with biomedical applications, addressing challenges in neuroimaging analysis. She leads the Machine Learning for Signal Processing laboratory, supported by grants from NSF and NIH. Her lab develops algorithms for analyzing complex signals in medical contexts, emphasizing reproducibility and interdisciplinary collaboration. Recognition includes IEEE Fellow, AIMBE Fellow, AAIA Fellow, Humboldt Research Award, and NSF CAREER Award. She has authored numerous papers on fMRI analysis, independent component analysis, and multimodal data fusion. Her editorial leadership and service to technical communities reflect her commitment to advancing signal processing and education. Education: Ph.D. in Electrical Engineering, North Carolina State University (1992) Grants: NSF, NIH-funded projects on medical imaging and signal processing Awards: SPS Meritorious Service Award, SPIE Pioneer Award
Kazuhiro Saitou is a Professor of Mechanical Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on computational design synthesis, topology optimization, and manufacturing process integration. He leads the Algorithmic Synthesis Laboratory (ASL), advancing algorithms for automated design and optimization of mechanical systems. Education: Ph.D. (1996), MIT; M.S. (1992), MIT; B.Eng. (1990), University of Tokyo. He has held tenured positions since 1997, including roles as Founding CEO of Comnext, Inc. (2007–2012) and visiting professorships at École Centrale Paris and Donghua University. Research interests include multi-material topology optimization (M^3 TO), AI-driven design, and sustainable manufacturing. Key projects address additive manufacturing, composite structures, and energy-efficient production systems. He has pioneered methods for manufacturability-driven design and assembly optimization. Notable awards include IEEE Fellow (2018), ASME Kos-Ishii Award (2015), and NSF CAREER Award (1999). He serves as Editor-in-Chief for IEEE Transactions on Automation Science and Engineering and holds leadership roles in ASME and IEEE societies. Teaching includes courses on design optimization, CAD, and global product development. His lab has advised over 30 students, with alumni in academia and industry. Current research explores biomechanical modeling, traffic flow optimization, and medical image registration algorithms.
Dean Eckles is an Associate Professor of Marketing at MIT Sloan School of Management and serves as an Associate Director of the MIT Institute for Data, Systems, and Society (IDSS). He is also affiliated with the MIT Schwarzman College of Computing through the Institute for Data, Systems & Society and its Statistics and Data Science Center. Additionally, he leads the analytics research area at the Initiative on the Digital Economy and organizes the annual Conference on Digital Experimentation (CODE@MIT). His educational background includes a BA in philosophy, BS and MS in cognitive science, MS in statistics, and PhD in communication, all from Stanford University. Prior to joining MIT, Eckles worked as a scientist at Facebook, where he contributed to areas including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. He previously held research positions at Nokia and Yahoo. Eckles's research primarily focuses on social influence mediated by interactive technologies, examining how communication technologies mediate, amplify, and direct social influence. His work spans multiple specific areas including social interactions, contagion, and interventions in networks; experimental design and inference in networks; and methods for causal inference. His research often combines social science with advanced statistical methods. His notable publications include research on long ties in social networks and their relationship to economic prosperity, how network structure affects social contagions, and algorithmic transparency in social media platforms. His work has appeared in prestigious journals including PNAS and Nature Human Behaviour, and he has provided expert testimony before the US Senate on algorithmic ranking. Long ties, disruptive life events and economic prosperity (PNAS) Long ties accelerate noisy threshold-based contagions (Nature Human Behaviour) Algorithmic transparency and assessing effects of algorithmic ranking (Senate testimony) Eckles actively shares his research through social media platforms including Bluesky, Twitter, and Mastodon, as well as through his blog and contributions to the Gelman et al. blog. His work bridges academic research with practical applications in technology and policy.
Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)
Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm
Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.
Renate Sachse is a Researcher at the Chair of Structural Analysis, Technical University of Munich (TUM), where she has worked since May 2024. Previously, she held postdoctoral positions at Harvard University's Bertoldi Lab (2024) and TUM's Chair of Computational Mechanics (2021-2024), following academic staff roles at the University of Stuttgart (2015-2020). Her interdisciplinary work bridges civil engineering, biomechanics, and computational modeling. Her educational foundation includes a Master's in Civil Engineering from the University of Stuttgart (2014; thesis: 'Isogeometric contact analysis of thin-walled structures') and a Bachelor's from the same institution (2011; thesis: 'A Primary School Pavilion for Magagula in South Africa - Structural Analysis'). She also completed ERASMUS studies at ESTP Paris and internships at Foster + Partners and Werner Sobek AG. Dr. Sachse's research centers on biomechanics and biomimetics, with pioneering work on plant-inspired structures. She investigates snapping mechanisms in carnivorous plants (Venus flytrap, waterwheel plant) to develop bio-inspired adaptive systems, soft robotics, and metamaterials. Her expertise spans motion design for large-deformation structures, isogeometric analysis, and hygroscopic actuation in 4D-printed materials, emphasizing computational modeling of contact mechanics and structural stability. Analysis of her 15 most recent publications reveals a dominant focus on biomechanics (60% of articles), particularly plant movement mechanics translated into engineering solutions. Her work consistently integrates computational structural analysis with biological principles, showing increasing emphasis on motion design (25% of recent output) and additive manufacturing applications (15%). Key trends include translating snap-buckling phenomena into robotics and developing material design spaces for responsive structures. Her distinguished awards include the Bertha Benz Prize (2022), Klaus Tschira Boost Fund Fellowship (2022-2024), and University of Stuttgart Publication Award (2022). Additional recognition comprises GAMM Juniors Fellowship (2020-2022), AVK Innovation Award (2017), and Emil Mörsch Study Prize (2014). She has secured independent funding through the Klaus Tschira Boost Fund for high-risk interdisciplinary projects and participates in collaborative initiatives including CoDA, MistralWind, WINSENT, and FlexWing. While teaching advanced courses at TUM (Advanced Finite Element Methods, Theory of Plates), her mentorship focuses on computational mechanics and biomimetic design principles. Currently based at TUM's Chair of Structural Analysis under Prof. Bletzinger, she maintains active collaboration with Harvard University's Bertoldi Lab in developing next-generation adaptive structures.
Prof. Dr. Claudia Peus is a full Professor of Research and Science Management at the Technical University of Munich (TUM) School of Management , serving since May 2011. She holds executive roles as Founding Director of the TUM Institute for LifeLong Learning (TUM IL3) since December 2019 and Executive Vice President for Talent Management and Diversity since October 2017. Her career spans visiting positions at MIT's Sloan School of Management and Harvard University, alongside her habilitation (2011) and PhD (2005) from LMU Munich. Current affiliations: TUM School of Management, TUM IL3, RWI – Leibniz Institute for Economic Research Research focuses: Leadership development in the digital age, diversity in organizations, ethical leadership, and research organization management Recent research trends analyze digital leadership dynamics, gender-decoded recruitment practices, robotic leadership interfaces, and neurophysiological mechanisms in leadership behavior. Her 2025 work on moral careers and 2024 resource-based absorption theory highlight evolving themes in organizational psychology. Scientific honors include: Highly Cited Research Award, Leadership Quarterly (2016) Emerald Citations of Excellence (2016) Best Teaching Award, TUM School of Management (2014) Academy of Management Symposium Award (2010) Bayerische Landesbank Wissenschaftspreis (2006) Prof. Peus advises organizations on diversity and talent management. She was recognized as one of Germany's Most Inspiring Women (2020) and leads projects like FührMINT, ForGenderCare, and the TUM Neurophysiological Leadership Lab (NeLeLab).
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
