Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.
Keunhyun (Keun) Park is an Assistant Professor of Urban Forestry at the University of British Columbia (UBC), affiliated with the Department of Forest Resources Management . He also holds an Adjunct Professor position at Utah State University in the Department of Landscape Architecture and Environmental Planning. Education: BSc and MSc in Landscape Architecture from Seoul National University; PhD in Urban Planning and Design from the University of Utah Research Lab: Faculty lead of the Urban Nature Design Research Lab ( under_lab ) His research focuses on designing healthy, just, and resilient cities through urban nature , with particular emphasis on: Environmental justice and equitable access to urban green spaces Human behavior in public spaces using drone/sensor/VR technology Smart growth urban design impacts on public health and ecological systems Recent publications demonstrate expertise in GIS applications , pedestrian behavior analysis , and urban planning across 20+ studies from 2013-2025. Collaborations include the Vancouver Park Board , Metro Vancouver , and Wasatch Front Regional Council .
Ryo Suzuki is an Assistant Professor at the ATLAS Institute within the University of Colorado Boulder's Computer Science department. His research focuses on innovative intersections of Human-Computer Interaction (HCI), Augmented Reality (AR), and robotics. He explores systems that blend AI, haptics, and shape-changing interfaces to create enriched user experiences. Key areas of investigation include embedding interactivity into static educational materials (e.g., textbooks), developing AI-driven AR tools for procedural instruction, and creating shape-changing robotics for tactile feedback. His work often emphasizes practical applications in education, remote collaboration, and creative industries. Recent projects include MapStory (LLM-driven map animation), RealityEffects (3D volumetric video augmentation), and HoloDevice (holographic cross-device collaboration).
Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology
Heiko Schuldt is a Full Professor of Computer Science at the University of Basel and leads the Databases and Information Systems (DBIS) group. His research spans databases, transaction management, cloud data systems, digital libraries, and multimedia retrieval, with a focus on distributed systems, data streams, and service-oriented architectures. He studied at the University of Karlsruhe (KIT) and received his PhD from ETH Zurich in 2001. From 2003-2006, he served as an associate professor at UMIT, Austria. Education: University of Karlsruhe (Computer Science), ETH Zurich (PhD, 2001) Research Interests: His work integrates databases, cloud computing, and multimedia retrieval, emphasizing scalable systems for lifelog data, sports analysis, and VR/AR environments. Projects include vitrivr, Polypheny-DB, and StreamTeam. Recent Publications: Trends focus on VR/AR multimedia retrieval, cross-modal analysis, and polystore systems. Key contributions include open-source frameworks for video/image retrieval and immersive analytics. Advising: Supervised over 50 theses in areas like mixed reality, polystore optimization, and gesture-based interfaces.
Joo Heung Yoon, MD is an Assistant Professor of Medicine in the Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine at the University of Pittsburgh School of Medicine. His research develops machine learning models for predicting hemodynamic instability in critical care settings, with applications extending to space medicine environments. His educational background includes: MD from Catholic University of Korea, Seoul, South Korea (2002) Internal Medicine Internship at Maimonides Medical Center - SUNY Downstate (2007) Internal Medicine Residency at New York Medical College (2009) Research Fellowship at Massachusetts General Hospital / Harvard Medical School (2011) Research Fellowship at Beth Israel Deaconess Medical Center / Harvard Medical School (2014) Fellowship in Pulmonary and Critical Care Medicine at University of Pittsburgh School of Medicine (2017) Dr. Yoon specializes in identifying hidden pathologic patterns through machine learning, developing prediction models for shock, hemorrhage, and tachycardia using large-scale clinical data. His work bridges critical care medicine with AI, focusing on real-world ICU implementation through alert systems and user interfaces. He actively explores microgravity applications, aiming to create feasible prediction algorithms for resource-constrained space missions where timely high-stake decisions are critical. His publication trend (2018-2020) reveals consistent advancement in hemodynamic prediction models, transitioning from theoretical frameworks to practical implementation strategies. These works integrate supervised ML and deep neural networks to address circulatory shock, hemorrhage identification, and instability surrogates, demonstrating strong interdisciplinary collaboration between clinical medicine and engineering. Notable awards include: SCCM Gold Snapshot Award (2019) ATS Abstract Award (2018) Excellence in Clinical Service Award (2010) Partners in Excellence Award (2009) Richard D. Levere Teaching Award (2008) As Principal Investigator for NIH K23 grant GM138984 (2020-2025), Dr. Yoon leads research on machine learning-driven shock prediction models. He mentors medical students and house staff daily in the ICU, specializing in cardiopulmonary physiology teaching. His grant portfolio focuses on therapeutic strategies for circulatory shock in critically-ill patients, with strong industry-academic partnerships. Based at UPMC Montefiore, Dr. Yoon collaborates with Carnegie Mellon University's Machine Learning School and Pitt Engineering to develop clinical decision support systems. His team is designing graphic user interfaces for spaceflight applications where resource limitations demand highly efficient predictive analytics for hemodynamic crises.
Haochen Li is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, within the College of Engineering. He leads the multidisciplinary Water Infrastructure Laboratory (Ψ Lab), which focuses on advancing urban water infrastructure through high-fidelity computational fluid dynamics (CFD), physical modeling, and physics-informed machine learning (ML). Education: PhD in Environmental Engineering, University of Florida, 2019 MS in Mechanical Engineering, University of Florida, 2019 MS in Civil Engineering, University of Florida, 2015 BS in Coastal Engineering, Hohai University, 2013 His research centers on environmental fluid dynamics , particularly multiphase and multiphysics flows in urban water systems. He investigates turbulence, particulate matter transport, pathogen fate, and chemical dynamics using advanced CFD simulations, volumetric particle image velocimetry (PIV), and AI-driven models. His lab develops open-source tools like InterAdsFoam for adsorption systems and integrates ML with CFD to optimize infrastructure design, retrofit, and regulatory frameworks. The recent publications reflect a strong trend toward hybrid CFD-ML frameworks for water infrastructure, with applications in clarifier design, stormwater basin optimization, and real-time sensing. His work emphasizes model validation, scalability, and practical deployment, including web-based tools for engineers. Scientific Awards: Rudolph Hering Medal, ASCE, 2023 Editor choice, Journal of Environmental Engineering ASCE, 2021 Editor choice, Journal of Environmental Engineering ASCE, 2020 Graduate School Fellowship, University of Florida, 2015 Academic Achievement Award, University of Florida, 2013 Haochen Li actively advises researchers and students in his lab, including Kai Liu, Mohamed Shatarah, and Ahmed Abdelmeguid. His team works on AI-empowered reactive flows, physics-informed ML, and CFD applications in energy and environmental systems. He has served as a reviewer for top journals and is a member of the ASCE/EWRI Computational Fluid Dynamics Committee. His lab is equipped with state-of-the-art HPC platforms and physical modeling facilities for experimental validation.
Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.
Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.
Yintong Huo is a tenure-track Assistant Professor in the Department of Computer Science at Singapore Management University (SMU), School of Computing and Information Systems. He joined SMU in early 2024 after completing his PhD at The Chinese University of Hong Kong (CUHK) under Prof. Michael R. Lyu. His academic journey includes a Bachelor's degree from the University of Electronic Science and Technology of China. Education: PhD in Computer Science and Engineering, The Chinese University of Hong Kong (2024) Bachelor's degree, University of Electronic Science and Technology of China Huo's research focuses on intelligent software engineering , particularly empowering AI models (especially LLMs) for software development, testing, and operations. His work spans AI4SE, LLM4SE, AIOps, code intelligence, and multimodal software engineering . Two flagship projects define his current research: LogPAI - an open-source AI platform for automated log analysis adopted by leading tech companies, and WebPAI - a multimodal intelligence project for automatic webpage development. His research addresses critical challenges in software reliability, log analysis, and UI code generation through innovative applications of AI. His recent publications reveal a strong trend toward multimodal approaches in software engineering , combining vision and language models for UI code generation, and increasingly sophisticated applications of LLMs for log analysis and software reliability. Huo's work demonstrates exceptional impact, with multiple papers accepted at top-tier venues including ASE, ICSE, and FSE with high acceptance rates (e.g., 9.5% for ASE'25). Scientific Awards: ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022, for LogPAI with 3k+ GitHub stars and 70k+ downloads) ACM SIGSOFT CAPS Travel Grants (ASE'23, ICSE'24, FSE'24) Nomination for Best Teaching Assistant Award (2022) National Scholarship (2019) Huo actively mentors students at multiple levels, currently supervising PhD students Shi Ying Chang and Dan Huang (co-supervised with Prof. David Lo), research engineer Minxing Wang, and visiting students including Shiwen Shan. His undergraduate mentee Truong Hai Dang will intern at Apple Inc. He maintains strong industry connections, with his LogPAI project adopted by world-leading tech companies. Huo serves on program committees for major conferences including ASE'25, ICSE'26, and FSE'26, and is recruiting fully-funded PhD students and research assistants for projects in AI4SE and multimodal software engineering. Huo leads the LogPAI and WebPAI research initiatives, which have evolved into substantial open-source projects with significant industry adoption. His team focuses on practical applications of AI in software engineering, with particular emphasis on reliability and usability in real-world systems. The research environment benefits from SMU's strong position in software engineering research, where the university ranks No. 2 globally in Software Engineering according to CSRankings (2020-2025).
Nicholas Dalton is a Senior Lecturer in the Department of Computing and Communications at Northumbria University, Newcastle. With a PhD in Engineering from University College London (2010), he has taught at prestigious institutions including UCL and The Open University across foundational to postgraduate levels, specializing in supporting neurodivergent students. His research bridges architecture and computing, focusing on large-scale user interfaces (e.g., public displays, VR, multitouch systems) and space syntax analysis. Current projects include combating obesogenic work environments through body movement tracking at standing desks and redesigning diabetes management interfaces. A founding member of NORSC (Northumbria Social Computing), he also explores neurodiversity advocacy and pedagogical innovations in computing education. His work appears in venues like the CHI Conference and Space Syntax Symposia. Education: PhD Engineering (UCL, 2010) Research Themes: HCI, Architecture-Computing Intersection, Neurodiversity Professional Affiliations: RSA Fellow, BCS/IET/ACM Member Publications span technical journals and platforms like The Conversation , translating complex topics for broader audiences.
R. Jayakrishnan , a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering , University of California, Irvine, is a leading researcher in transportation systems engineering. Ph.D., University of Texas, Austin, Civil Engineering, 1992 M.S., University of Texas, Austin, Civil Engineering, 1987 B.S., Indian Institute of Technology, Madras, India, 1985 His research focuses on dynamic traffic assignment , urban traffic simulation , and real-time information systems to improve congested traffic corridors. He is developing advanced dynamic simulation-assignment models for urban traffic networks. Recent publications highlight his contributions to: Crowdsourced delivery optimization using decomposition heuristics Eco-driving algorithms with V2I communication Multi-furniture placement applications via augmented reality Subscription mobility services cost-benefit analysis Agent-based lane-changing coordination systems These works demonstrate his interdisciplinary approach combining transportation engineering, optimization algorithms, and emerging technologies like AR and connected vehicles.
Erik Scheme is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB), and serves as Associate Director of the Institute of Biomedical Engineering (IBME). He holds a PhD and is a Professional Engineer (PEng). His roles include advising the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship, emphasizing innovation in biomedical technologies and healthcare systems. His research focuses on advanced human-machine interaction through biomedical engineering, with a strong emphasis on myoelectric prosthetics, wearable sensors, and machine learning applications. Key areas include improving neuroprosthetic control via incremental learning, gait analysis using underfoot pressure sensors, and developing robust EMG-based gesture recognition systems. His work bridges clinical needs with technological innovation, addressing challenges in rehabilitation, activity monitoring, and user-centric design. Recent publications highlight advancements in adaptive control systems, sensor fusion, and ethical data practices in healthcare. His contributions span both theoretical frameworks (e.g., self-supervised learning models) and applied technologies (e.g., gold-plated 3D-printed electrodes). Dr. Scheme collaborates across disciplines, integrating robotics, signal processing, and clinical validation to create impactful solutions. His lab, affiliated with IBME, actively explores emerging areas like exhaled breath analysis for disease detection and federated learning in healthcare data analytics.
Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.
