Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.
Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.
Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.
Dagfinn Aune serves as a Research Fellow at the Department of Epidemiology and Biostatistics within the School of Public Health, Faculty of Medicine at Imperial College London. He concurrently holds positions as Associate Professor at Oslo New University College and Senior Researcher at the Cancer Registry of Norway in Oslo. His work centers on large-scale epidemiological investigations into diet, obesity, and chronic disease mechanisms across international populations. Education: PhD in Epidemiology, Norwegian University of Science and Technology (2016) MSc in Nutrition, University of Oslo (2008) Research interests focus on the interplay between dietary patterns, adiposity, physical activity, and smoking with chronic disease outcomes. His work examines how these factors influence cancer risk (particularly breast, gastrointestinal, and hematological malignancies), pregnancy complications, diabetes progression, and mortality across diverse populations. He employs systematic review methodologies and analyzes data from major cohorts including EPIC and Norwegian registries. Publication trends reveal consistent emphasis on obesity-cancer relationships, with recent work (2023-2025) expanding into metabolomic mediators, cardiometabolic interactions, and rare cancer epidemiology. His research frequently utilizes systematic reviews, meta-analyses, and multinational cohort studies to establish evidence for cancer prevention guidelines. Scientific Awards: No awards specified in source materials Advising and grant activity centers on major collaborative projects including the World Cancer Research Fund's Continuous Update Project, the EPIC study, and South American case-control research. While specific student mentorship isn't documented, his leadership in systematic review protocols suggests substantial training responsibilities. His grant portfolio includes funding from WCRF International and Norwegian research councils for large-scale epidemiological analyses. Labs and teams include the Centre for Translational Nutrition and Food Research at Imperial College London, where he collaborates with nutrition scientists and biostatisticians. He maintains active roles in the EPIC consortium and WCRF International's CUP Global Programme, coordinating multinational analyses of diet-cancer relationships through the Cancer Registry of Norway.
Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.
Osbert Bastani is an Associate Professor at the Department of Computer and Information Science, University of Pennsylvania, leading the trustml@Penn research group. He is affiliated with the ASSET , PRECISE , and PRiML centers, and the PLClub research group. His research focuses on Trustworthy Neurosymbolic Systems , Synthesizing Neurosymbolic Programs , and Machine Learning for Programmer Productivity , with applications in verification, fairness, and human-AI collaboration. He received the NSF CAREER Award in 2023. His recent publications (2024-2025) emphasize AI Safety , LLM Robustness , and Algorithmic Fairness , including work on adversarial robustness, conformal prediction, and program synthesis. Students he has advised include Sagnik Anupam, Stephen Mell, Jason Ma, Shuo Li, and others. Awards: NSF CAREER Award (2023)
Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.
Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).
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
Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. She leads the Stanford Accelerate research group, focusing on domain-specific hardware architectures and agile hardware-software co-design. Her work emphasizes high-performance, energy-efficient accelerators for emerging technologies. Education: B.Tech. from IIT Delhi (2011), M.S. and Ph.D. from MIT (2013/2018). Postdoctoral experience includes NVIDIA Research (2018) and Amazon Visiting Academic (2023–present). Awards include the Sloan Research Fellowship (2024), NSF CAREER Award (2023), and Terman Faculty Fellowship (2018). Research Interests: Domain-specific architectures, near-memory computing, design productivity, and machine learning acceleration. Her group develops frameworks like AHA (Agile Hardware) for efficient accelerator design and compilers. Awards: Over 10 major awards, including best paper recognitions at VLSI, MICRO, and JSSC. Active roles as Associate Editor for IEEE JSSC and Program Chair for IEEE Hot Chips (2020). Advising & Teaching: Supervises multiple PhD and MS students in hardware design, CGRAs, and ML accelerators. Teaches VLSI design courses (EE271/272/372) and oversees independent studies in embedded systems and chip design. Labs & Collaborations: Affiliated with Stanford PORTAL Center, AHA Center, and SystemX Alliance. Projects include MINOTAUR (edge AI accelerator), Amber (CGRA-based SoC), and EMBER (RRAM macros).
Deborah Frances Smith is a Professor affiliated with the Centre for Immunology and Infection. She has held former roles including Head Of Department, Biology Pro-Vice-Chancellor for Research, and Special Adviser to the Vice Chancellor in the Vice Chancellor's Office. She is also associated with CNAP (Cancer and Neuroscience Academic Programme) and the Jack Birch Unit. Her research focuses on leishmaniasis, parasite biology, and drug/vaccine development. Key projects include leading the 'C2D2 research 1a & 2b' (2012–2012) to establish sandfly colonies for leishmaniasis studies and co-investigating an integrated crystallization facility (2013–2018). She secured the Wolfson Research merit Award (2009–2010) from the Royal Society. Her work spans academic leadership, collaborative research, and translational medicine targeting neglected tropical diseases.
Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.
Eric Wong is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, affiliated with the ASSET Center and leading the Brachio Lab. His research focuses on robust and reliable machine learning, including model debugging, adversarial robustness, and explainable AI. He holds a PhD from Carnegie Mellon University (CMU), advised by J. Zico Kolter, and completed a postdoc with Aleksander Madry. His work bridges theory and practice, addressing challenges in model interpretability, safety, and scalability. Teaching includes CIS 5200 (Machine Learning), CIS 3333 (Mathematics for Machine Learning), and a specialized course on debugging ML pipelines. Notable contributions include the FIX Benchmark for interpretable features and defenses against LLM jailbreaking attacks. He received an Amazon Research Award in 2024 and has published extensively in top conferences like ICML, NeurIPS, and ICLR. Affiliations: University of Pennsylvania (CIS), ASSET Center, Brachio Lab Education: PhD in Machine Learning (CMU), Postdoc at MIT Key Projects: FIX Benchmark, DOLPHIN framework, SmoothLLM defense Awards: Amazon Research Award (2024) Lab Focus: Safe AI, model debugging, neurosymbolic learning
Prof. Dr. Frank Ulbrich is a Lecturer and Programme Director for the BSc Business Administration at the Lucerne School of Business (HSLU). He holds a PhD in Business Administration from the Stockholm School of Economics. His academic career spans roles at universities in Sweden, the UK, Canada, and Switzerland, alongside IT & Management consulting experience. Research focuses on management education, project management, information management, and organizational change. He has published extensively in areas like shared services, digital transformation, and curriculum development. Notable awards include multiple Emerald Literati Awards and a Fellowship from the Higher Education Academy. Education: PhD in Business Administration (Stockholm School of Economics) Affiliations: Institute of Business and Regional Economics at HSLU Teaching Subjects: Academic Methods, Higher Education Management, Project Management, Digital Transformation Recent presentations include discussions on bridging academia-industry gaps, curriculum integration of SDGs, and data-driven learning strategies. His work emphasizes aligning education with labor market needs and advancing evidence-based teaching practices.
Professor Elaine Fahey is a leading scholar in EU law and global governance at the City Law School, City St George's, University of London, where she serves as Professor of EU Law and Deputy Head of Department (academic programmes). She is co-convener of the Institute for the Study of European Law (ISEL) and the UACES James Madison Trust EUFUtures initiative (2022–2025). Her academic career includes visiting professorships at Washington College of Law (American University), Keio University, and NYU, as well as fellowships at the European University Institute and the University of Graz. LLB, Trinity College Dublin LLM, Sidney Sussex College, University of Cambridge PhD, Trinity College Dublin Barrister-at-Law, Honorable Society of King’s Inns, Ireland Attorney-at-Law, State of New York Senior Fellowship, Higher Education Authority UK Her research focuses on the intersection of EU law with global governance, digital regulation, transatlantic relations, and the Area of Freedom, Security and Justice. She explores how the EU navigates legal frameworks beyond the nation-state, particularly in digital trade, cybersecurity, and data protection. Her work critically examines the EU’s role as a global regulatory actor and its interactions with international institutions. Her extensive publication record includes the monograph The EU as a Global Digital Actor (Hart, 2022) and numerous articles on digital sovereignty, data transfers, and transatlantic cooperation. Recent research analyzes the EU–US Trade and Technology Council (TTC), the evolution of the Privacy Shield, and strategic litigation in digital rights. Her work reveals a strong trend toward understanding how soft law, institutional design, and judicial decisions shape EU external relations and digital governance. Erasmus+ Jean Monnet Chair in Law & Transatlantic Relations (2019–2022) Emile Noël Fellow, NYU Law School Max Weber Fellow, European University Institute Senior Land Steiermark Fellow, University of Graz Senior Fellowship, Higher Education Authority UK Professor Fahey has supervised multiple PhD students, including Isabella Mancini, Giulio Kowalski, and Christos Karetsos, on topics such as algorithmic collusion, digital markets, and investment law. She has led significant research projects, including the ESRC-funded TRILATTRADE grant and the Marie Curie ITN on EU and Transatlantic Investment and Trade (EUTIP). She previously held leadership roles as Associate Dean (International) and interim Associate Dean (Research) at the City Law School. She is an elected member of the EU Studies Association (EUSA) Executive (2021–2025). She has also contributed to public policy through briefings for the Welsh Parliament and reports on CJEU litigation in Ireland. She is associated with research initiatives such as the Institute for the Study of European Law (ISEL) and has collaborated on interdisciplinary projects on Facebook regulation, transatlantic relations, and EU technological sovereignty. Her work bridges legal scholarship, policy, and international academic collaboration.
