Franziska Boenisch is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security , where she co-leads the SprintML lab for Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning. Her research lies at the intersection of privacy-preserving machine learning and trustworthy ML , with a focus on differential privacy , model inversion attacks , and privacy risks in federated learning . She completed her PhD at Freie Universität Berlin and was a postdoctoral fellow at the Vector Institute for Artificial Intelligence under Prof. Nicolas Papernot. Her work has been recognized with awards such as the Academics Rising Start Award (3rd Prize) and the GI Junior-Fellow honor. Her research spans a wide range of topics including memorization in diffusion models , watermarking generative models , membership inference attacks , and privacy-preserving federated learning . She has published extensively in top-tier venues like ICML , NeurIPS , ICLR , and CVPR . She is actively involved in the academic community, serving as an Area Chair for NeurIPS , Track Chair for ACM AsiaCCS , and co-organizing workshops at ICML . She is currently hiring PhDs, postdocs, and research interns for her group.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
Dr. Chunyan Lai is an Associate Professor at the Department of Electrical and Computer Engineering, Concordia University. Her research focuses on electric drives, motor control, power electronics, electrified vehicles, and vehicle-to-grid solutions. She contributes to both graduate and undergraduate education through courses such as Controlled Electric Drives and Hybrid Electric Vehicle Power Systems . Research Emphasis : Electric motor drives and control systems, electrified transportation, power electronics innovations, and energy management strategies. Publications : Specializes in sensorless control techniques for Permanent Magnet Synchronous Motors (PMSM), thermal management in electric machines, and advanced energy trading frameworks for smart grids. PhD Opportunities : The Power Electronics and Energy Research (PEER) Group under Dr. Lai offers positions for developing efficient motor drives for EVs and grid-connected power converters. Collaboration : Industry-adjacent research with requirements for professional communication, patent development, and technical dissemination.
Prof. Dr.-Ing. Elisabeth Clausen is a Professor and Director of the Chair and Institute for Advanced Mining Technologies at RWTH Aachen University. She holds key roles in the Specialist Group for Raw Materials and Disposal Technology, serves as a rectorate representative, and leads the Commission for EU Research Funding. Her research spans Underground mining automation Acoustic emission diagnostics Sustainable mining systems Space resource extraction Advanced sensor technologies Her recent publications focus on autonomous mining machinery, underground communication systems, and acoustic emission analysis across 15+ studies from 2013–2025, with particular emphasis on Ultra-wideband positioning Thermographic detection Crack monitoring in planetary gearboxes Explosive atmosphere safety Mineral processing diagnostics Digitalization trends Prof. Clausen contributes to mining education reform through initiatives like CDIO™ and has developed innovative learning spaces in underground mines. She coordinates international educational labs and integrates sustainability into mining engineering curricula, with publications on Adaptive ventilation systems Mining education frameworks Future-proof mineral extraction Entrepreneurial mindset in engineering
Carey E. Priebe is a Professor in the Department of Applied Mathematics and Statistics at the Whiting School of Engineering, Johns Hopkins University. He maintains strong affiliations with multiple research centers including the Johns Hopkins University Center for Imaging Science, the Mathematical Institute for Data Science, and the Human Language Technology Center of Excellence. His academic career spans several decades with significant contributions to statistical methodology and theory. Dr. Priebe's research focuses on computational statistics, statistical pattern recognition, and statistical inference for high-dimensional and graph data. His work bridges theoretical statistics with practical applications in areas such as brain connectome mapping, network analysis, and image processing. He has made significant contributions to spectral graph theory, graph matching, and vertex nomination, with applications ranging from neuroscience to national security. His publication record demonstrates consistent contributions to statistical methodology, with a notable emphasis on graph-based statistical methods. His research trajectory shows increasing focus on network data analysis, particularly in the last decade, with applications to brain mapping and connectome analysis as evidenced by his NSF BRAIN Initiative grant and Nature publication. 2013 Erskine Fellow (University of Canterbury) 2011 McDonald Award for Excellence in Mentoring and Advising 2010 ASA SDNS Distinguished Achievement Award 2009 Erskine Fellow (University of Canterbury) 2008 National Security Science and Engineering Faculty Fellow 2008 Pond Award for Excellence in Teaching NSF BRAIN EAGER grant recipient (2014) Professor Priebe has supervised an extensive number of doctoral students whose work spans statistical methodology, network analysis, and machine learning. His students have secured positions at prestigious institutions including academia (University of Wisconsin, Boston University), government research labs, and major technology companies (Microsoft, Facebook, Amazon). His research has been supported by significant grants from NSF, DARPA, and other agencies focused on national security applications and fundamental statistical methodology development. He maintains active collaborations across multiple disciplines and institutions, as evidenced by his numerous conference presentations and visiting appointments including at The Alan Turing Institute and The Isaac Newton Institute. His work bridges theoretical statistics with practical applications in neuroscience, security, and data science.
Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .
Dr. Koustuv Saha is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), leading the OnCARE lab. He holds a PhD from Georgia Tech and a B.Tech from IIT Kharagpur. His research focuses on computational social science, social computing, and ethical AI applications in mental health and wellbeing. His work bridges computer science with psychology, sociology, and public policy to address societal challenges. Education: PhD in Computer Science (Georgia Tech, 2021), B.Tech in CSE (IIT Kharagpur, 2012). Previous roles include Senior Researcher at Microsoft Research Montreal (FATE group) and industry research experience in Silicon Valley. Research interests include wellbeing sensing technologies, algorithmic fairness, and large language models’ societal impacts. Recent work examines caregiver mental health, deceptive wellness apps, and AI ethics in content moderation. His studies combine causal inference, NLP, and multimodal data analysis. Publications span top venues like CHI, CSCW, ICWSM, and JMIR. Notable awards include Georgia Tech’s Outstanding Dissertation Award (2022) and Snap Research Fellowship (2020). He advises on AI governance and collaborates with policymakers, clinicians, and industry. OnCARE lab explores human-centered AI for societal good, with projects on mental health support systems, ethical tech design, and algorithmic transparency in health contexts. Current focus includes caregiver AI tools, LLM-based empathetic systems, and workplace wellbeing interventions.
Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
James Aspnes is the Harold W. Cheel Professor of Computer Science at Yale University, specializing in distributed algorithms and randomized methods. He holds a PhD from Carnegie Mellon University and degrees from MIT. His research focuses on distributed systems, peer-to-peer networks, and sensor networks, emphasizing tools for efficient data management and fault-tolerance. Education: PhD (CMU, 1992), SM & SB (MIT, 1987) Affiliations: Yale since 1993, IBM Almaden Research Center (1992–1993) Research interests include distributed algorithms, randomization, and applications in biology and economics. Notable contributions include skip graphs, population protocols, and consensus algorithms. He has received the ACM-EATCS Dijkstra Prize (2020) and Dylan Hixon Prize (2000). Publications span distributed computing, algorithms, and cryptography. Recent work explores consensus protocols and privacy in population models. Grants include NSF awards totaling over $2M. Active in editorial roles (Algorithmica, Distributed Computing) and conference organization (PODC 2005, DCOSS 2007).
Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.
Dr. Han Du is an Associate Professor in the Department of Psychology at the University of California, Los Angeles (UCLA). He holds a PhD from the University of Notre Dame and leads the Du Research Lab. His methodological expertise includes Bayesian statistics, longitudinal data analysis, structural equation modeling, meta-analysis techniques, and machine learning applications in psychological research. Dr. Du's substantive research applies quantitative methods to developmental, clinical, cognitive, educational, and health psychology. His recent publications focus on transgender adolescent stress assessment, LGBTQ+ mental health in military contexts, social network interventions for HIV prevention, and minority stress theory applications. He teaches advanced statistical methods and supervises graduate students in quantitative psychology.
Saurabh Bagchi is a Professor at Purdue University, West Lafayette, USA. He holds a PhD in Computer Science from the University of Illinois Urbana-Champaign (2001). His research focuses on distributed systems security, networking, and embedded systems. Key areas include IoT security, cyber-physical systems resilience, and machine learning applications in edge computing. Bagchi's work spans theoretical and applied domains, addressing challenges in distributed algorithms, fault tolerance, and secure communication protocols. His contributions to firmware analysis, serverless computing optimization, and anomaly detection in industrial IoT systems have been widely recognized. He has published over 300 papers in top-tier conferences and journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and CVPR. He collaborates with researchers in academia and industry to advance resilient networked systems, including projects funded by NSF and industrial partnerships. His lab explores cutting-edge topics like federated learning security, edge computing architectures, and game-theoretic approaches to cyber defense.
