Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)
Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.
Michael D. Ernst is a Professor in the Computer Science & Engineering department at the University of Washington's College of Engineering. His research aims to make software more reliable, more secure, and easier (and more fun!) to produce. Previously, he was a tenured professor at MIT and a researcher at Microsoft Research. Ernst's primary technical interests are in software engineering, programming languages, type theory, security, program analysis, bug prediction, testing, and verification. His research combines strong theoretical foundations with realistic experimentation, with an eye to changing the way that software developers work. He focuses particularly on programmer productivity and developing practical tools that can be integrated into developers' workflows. Analysis of his recent publications (2018-2025) reveals a continued focus on verification techniques, program analysis, and testing methodologies. His work spans from theoretical foundations of type systems to practical applications of NLP for test generation and LLMs for test oracle creation. A consistent theme is developing lightweight, modular approaches that can be practically applied in real-world development environments. Scientific Awards: ACM Fellow (2014) John Backus Award (2009) NSF CAREER Award (2002) ACM SIGSOFT Impact Paper Award (2013) 8 ACM Distinguished Paper Awards across multiple conferences ECOOP 2011 Best Paper Award Microsoft Academic Search ranked #2 in software engineering research (2013) Ernst has received significant research funding including the NSF CAREER Award, supporting his work on program analysis and verification techniques. His research combines theoretical rigor with practical impact, often resulting in tools that are adopted by the software engineering community. He actively collaborates with researchers across institutions and has served in leadership roles for major conferences in programming languages and software engineering. His research group develops practical tools that address real challenges in software development, with a focus on making verification and analysis techniques more accessible to working developers. Current projects include applying machine learning techniques to software engineering problems while maintaining strong theoretical foundations.
Florian Muijres is an Associate Professor and Chairholder at the Experimental Zoology Group, Wageningen University & Research, where he leads the Animal Flight Lab. His research focuses on the biomechanics, aerodynamics, and flight control of natural flyers such as insects, birds, and bats, with applications in bio-inspired robotics and ecological solutions like mosquito traps and flapping-wing drones. He holds a PhD from Lund University (Sweden) and conducted postdoctoral research at the Dickinson Lab, University of Washington (USA). Research Interests: Merging experimental and computational methods, his work explores primary research on flight mechanics (e.g., mosquito evasion, butterfly gliding) and applied studies (e.g., drone design, pollinator behavior in greenhouses). His lab uses advanced videography and robotic models to study flight dynamics under real-world conditions. Labs & Teams: The Animal Flight Lab collaborates with biologists, physicists, and engineers to investigate flight adaptations in mosquitoes, bumblebees, and pied flycatchers. Projects include developing high-efficiency traps and analyzing flight performance in complex environments.
Professor Tan Hun Tong is a Professor in the Division of Accounting at the Nanyang Business School, Nanyang Technological University (NTU), holding the UOB Chair in Banking. He is also Director of the Centre for Accounting & Auditing Research (CAAR). His academic journey includes a B. Acc (Honors) from NUS, M.A. in Psychology, and Ph.D. in Business Administration (Accounting) from the University of Michigan. Research focuses on judgment and decision-making in accounting, leveraging psychological theories and experimentation. Key interests include preparers, users, and intermediaries of accounting information, and institutional/environmental influences on financial judgments. His editorial roles include Editor-in-Chief of Accounting, Organizations and Society , and Editor of Journal of International Accounting Research . Recent work spans critical audit matters, investor decision-making, and auditing standards, reflecting themes in behavioral accounting and regulatory impact. Awards: 2021 ABO Notable Lifetime Contribution Award Public Administration Medal (Silver, 2016) Nanyang Award for Research (2008) Long Service Awards (2015, 2019) Advising and grants: No formal advisee list provided. Active in editorial and AAA committees, emphasizing professional standards and research advocacy. Labs/Teams: Leads CAAR, focusing on accounting and auditing research. Engaged in interdisciplinary projects merging psychology with financial decision-making.
Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.
Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.
Thomas C. Killian serves as Dean of the Wiess School of Natural Sciences and Professor of Physics and Astronomy at Rice University, where he joined the faculty in 2001 after completing his PhD at MIT and a postdoctoral fellowship at NIST. His leadership roles include Deputy Speaker of the Faculty Senate, Chair of the Physics and Astronomy Department, and Associate Dean of Strategic Planning, with significant contributions to academic governance and resource allocation for research and education missions. He earned his AB in Physics from Harvard University (1991), an M.Phil in Physical Chemistry from Cambridge University as a Marshall Scholar (1993), and a PhD in Atomic Physics from MIT (1999). His research focuses on matter at temperatures near absolute zero, exploring quantum degenerate atomic gases, ultracold plasmas, and Rydberg systems to uncover fundamental physical laws with applications in quantum computing, precision timekeeping, and astrophysical modeling of white dwarf stars. His pioneering experimental work includes producing atomic Bose-Einstein condensates and developing techniques for generating the coldest neutral plasmas ever observed, bridging microscopic quantum behavior and macroscopic astrophysical phenomena. This research program investigates strongly interacting systems where quantum effects dominate classical physics. Notable honors include: Fellow of the American Physical Society David and Lucille Packard Foundation Science and Engineering Fellowship Alfred P. Sloan Research Fellowship Professor Killian co-founded nano3D Biosciences (now ChemoSen3D) to commercialize 3D bioprinting technology for drug discovery and personalized medicine, demonstrating translational impact beyond academia. His laboratory at Rice operates cutting-edge facilities for ultracold matter research, collaborating with institutions like UT MD Anderson Cancer Center on interdisciplinary projects that merge atomic physics with biomedical applications.
Professor David Abbink is a Full Professor of Haptic Human-Robot Interaction at Delft University of Technology, holding a joint appointment between the Department of Cognitive Robotics in the Faculty of Mechanical Engineering and Industrial Design Engineering since November 2023. He founded the Delft Haptics Lab and co-founded the Cognitive Robotics Department in 2017. Abbink leads the transdisciplinary research and innovation centre FRAIM, which was awarded the prestigious NWO Stevin Premie (Dutch Nobel Prize equivalent) in June 2024. Trained as a mechanical engineer specializing in biomechanics, Abbink's research focuses on human behavior adaptations when interacting with autonomous systems. He has published over a hundred scientific articles on human-robot interaction, haptics, shared control, tele-operation, driver assistance systems, and sensorimotor control. His research has been funded by industry partners (Nissan, Boeing, Renault), RVO (Brightsky project 2022-2026), and the Dutch Science Foundation NWO through personal grants (VENI 2010-2014, VIDI 2015-2019). Abbink's recent work centers on worker-robot relations as an academic focus, collaborating with organizations like Erasmus Medical Centre for nursing work, Schiphol and KLM for baggage handling, and KLM Engine Repair Services for maintenance work. He also serves as scientific director for the Centre for Meaningful Human Control, launched in October 2024. His work bridges engineering, social sciences, and practical applications to responsibly shape the future of work with emerging robotic capabilities. NWO Stevin Premie (2024) Best IEEE SMC journal paper on Cybernetics (2019) Top 25 scientific talents according to New Scientist (2015) Best teacher of Faculty 3mE (2013, 2014) Best teacher of Department of BioMechanical Engineering (seven consecutive years) Abbink has supervised over 110 MSc students and 11 PhD students. His educational contributions include developing the Master Programme in Robotics at TU Delft and receiving international recognition for his course 'The Human Controller.' He is also a prominent science communicator, featured on national television, radio, and major Dutch newspapers, and has delivered lectures at venues like The Royal Institution and Lowlands Festival. Despite his academic commitments, Abbink maintains a drummer persona, having recorded four albums and performed over 400 shows across three continents between 1999-2014.
Desmond Elliott is an Associate Professor and Villum Young Investigator at the Department of Computer Science, University of Copenhagen. His research focuses on vision-language models, multilingual and multimodal processing, with particular emphasis on tokenization-free language modeling approaches. He leads a research group actively working on pixel language models and cross-lingual multimodal understanding. University of Copenhagen, Department of Computer Science Villum Young Investigator Associate Editor for JAIR (2025-2028) Senior Area Chair for ACL 2025 Elliott's research spans vision-language integration, multilingual NLP, and multimodal machine learning. His work explores how language models can operate directly on visual pixels without traditional tokenization, enabling more seamless integration of vision and language processing. He investigates compositional generalization in multimodal systems, retrieval-augmented image captioning, and cross-lingual transfer in vision-language tasks. His group develops methods for low-resource language processing and creates benchmarks for evaluating multimodal systems across diverse cultural contexts. His recent publications demonstrate strong trends in pixel-based language modeling, synthetic dataset generation through retrieval augmentation, and multilingual vision-language processing. The work spans theoretical advances in model architectures and practical applications in areas like medical text analysis, food culture understanding, and social media content moderation. His research often bridges computer vision and natural language processing with a focus on making these technologies accessible across diverse languages and cultures. Best Paper Honorable Mention at CVPR Visual Concepts Workshop 2025 Best Long Paper Award at EMNLP 2021 Area Chair Favourite paper at COLING 2018 Elliott actively supervises student projects in BSc and MSc programs related to his research interests. His research has received substantial funding from Google (2024-2025), Facebook (2022-2024), Villum Foundation (2021-2026), Novo Nordisk Foundation (2019-2024), and European Union (2023-2026). He regularly recruits postdocs for projects including the Danish Foundation Models project and the Responsible AI for the People Project. His group holds regular meetings on Tuesdays from 13:00-14:00 in IF G.03, with an active mailing list for announcements. The research environment appears collaborative, with frequent co-authorship across institutions and regular participation in major NLP and computer vision conferences.
Jann Spiess is an Associate Professor of Operations, Information & Technology at Stanford University's Graduate School of Business and holds a courtesy appointment as Associate Professor of Economics in the School of Humanities and Sciences. He is also a Center Fellow at the Stanford Institute for Economic Policy Research and a Faculty Affiliate of the Golub Capital Social Impact Lab. PhD in Economics (Harvard University, 2018) AM in Economics (Harvard University, 2015) MPP in Public Policy (Harvard University, 2013) MASt in Mathematics (University of Cambridge, 2011) BSc in Mathematics (Technical University of Munich, 2010) Jann's research integrates machine learning with econometric methods to advance causal inference and data-driven decision-making . He explores high-dimensional and robust causal inference, synthetic control methods, and algorithmic fairness, while addressing challenges in human-AI collaboration and policy design. His publications span econometrics, behavioral economics, and data science, focusing on experimental design, robust statistical techniques, and applications of machine learning to public policy. Key themes include replicable inferences from big data, human-AI interaction, and ethical algorithmic design. Philip F. Maritz Faculty Scholar, 2021–22 David A. Wells Prize for best dissertation, Harvard Economics, 2018 Restud Tour, 2018 Jann's work bridges microeconometric methods , statistical decision theory , and mechanism design to enhance analytical frameworks for data-driven policy. He has contributed to robust inference in panel data and synthetic control methods, alongside studies on nudges for vaccination and financial aid renewals. As Faculty Affiliate at the Golub Capital Social Impact Lab, Jann collaborates on projects applying data science to social policy challenges, merging technical rigor with societal impact.
Brandon Weissbourd is an Assistant Professor in the Biology department at the Massachusetts Institute of Technology (MIT) and holds a joint appointment as an Investigator at the Picower Institute for Learning and Memory. He joined MIT in 2023 after completing a postdoctoral fellowship in the lab of David Anderson at the California Institute of Technology (Caltech). Prior to that, he earned his PhD in Biology from Stanford University in 2016 under the mentorship of Liqun Luo, and a BA in Human Evolutionary Biology from Harvard University in 2009. His research interests encompass systems neuroscience, evolutionary biology, and molecular biology. He uses jellyfish models, such as Clytia hemisphaerica, to study the evolution and functional mechanisms of nervous systems. His work combines computational techniques like single-cell RNA-seq and advanced microscopy with traditional genetic and anatomical approaches to dissect neural circuits and their roles in behaviors like feeding and social interaction. Additionally, he has explored serotonin and noradrenaline systems in mammals, focusing on their heterogeneity and functional connectivity. Recent publications emphasize the utility of non-traditional model organisms for evolutionary studies and underscore his expertise in computational methods for neurobiological analysis. Earlier work includes groundbreaking studies on the dorsal raphe serotonin system and basal forebrain circuits governing sleep-wake cycles. No scientific awards or honors have been explicitly mentioned in the provided text. Weissbourd’s academic trajectory reflects a strong emphasis on interdisciplinary research, merging evolutionary, molecular, and systems-level perspectives to understand neural systems across species. His advising record is not detailed here, though he has been affiliated with prestigious research labs during his training. Current affiliations include the MIT Biology department and the Picower Institute, where he likely contributes to collaborative projects in systems and evolutionary neuroscience. Weissbourd’s work is grounded in experimental models such as Clytia medusa and mouse brain studies, enabling him to investigate both ancient nervous system architectures and modern mammalian neural pathways. His lab’s focus on functional genomics and circuit mapping positions him at the forefront of studies on neural diversity and evolutionary innovation.
Dr Glenda Cooper is Reader in Journalism Studies and Head of the Department of Journalism at City St George's, University of London , where she leads a team of 25 staff, 50 visiting lecturers, and over 500 students from 53 countries. She holds a PhD in Journalism from City and an MA in English Language and Literature from Oxford, and has played a pivotal role in elevating the BA Journalism programme to 1st place nationally in the Guardian and Complete University Guide (2023), as well as 1st for Graduate Prospects (Sunday Times 2022). Education: MA in Academic Practice, City, University of London (2022) PhD in Journalism, City, University of London MA in Creative Writing, City, University of London BA (Hons) English Language and Literature, St Hilda's College, University of Oxford PG Diploma in Newspaper Journalism, City University of London Glenda's research focuses on crisis and humanitarian reporting , journalism ethics , live journalism , and the intersection of media and politics, particularly through the legacy of Alistair Cooke’s Letter from America . She has led major funded projects with the British Academy, ESRC, Google Digital News Initiative, and HEIF. Her current research explores AI’s impact on journalism through the DMINR project and innovative storytelling formats via the News on Stage initiative and the Contemporary Narratives Lab . Her recent publications span topics such as AI in newsrooms, humanitarian scandals (#AidToo), refugee representation, and digital narrative innovation. These works reflect a strong interdisciplinary trend, combining journalism studies with media technology, ethics, performance, and political communication. The articles indicate a growing focus on digital transformation, ethical challenges in sourcing, and the evolving role of journalists in public discourse. Scientific Awards and Recognition: Laurence Stern Fellow at the Post (2001) Guardian Research Fellow at Nuffield College, Oxford (2006–2007) AURORA Role Model for Future Women Leaders in Academia (2018) Glenda has supervised PhD students on topics ranging from political communication in Istanbul to the manosphere's influence on journalism. She has been instrumental in curriculum development, notably designing a core ethics module for final-year journalism students. She also serves as Reviews Editor for Journalism: Theory, Practice and Criticism , UK Director of the European Journalism Observatory, and Board Member of The Conversation UK . She co-founded and co-presented the Knowhow podcast, bridging academia and media. She co-directs the Contemporary Narratives Lab and co-founded the News on Stage project, which merges journalism with theatre through live events such as News Cabaret and News on the Street . Her play Aid Memoir , based on her PhD research, was performed at the Pleasance Theatre and featured in the ESRC Festival of Social Science.
Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.
Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.
