Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.
Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.
Associate Professor Mohsen Kalantari is a Geospatial Engineering academic at the University of New South Wales (UNSW) School of Civil and Environmental Engineering , with concurrent roles as co-founder of the startup Faramoon . His career spans roles at the University of Melbourne's Department of Infrastructure Engineering and Victorian government's land administration initiatives through DELWP. Education : PhD in Geomatics Engineering (2008, University of Melbourne), Master of GIS Engineering (2004), Bachelor of Surveying Engineering (2001) His research bridges geospatial engineering with construction automation , focusing on 3D cadastre , BIM-GIS integration , and smart cities . Recent publications show trends in underground land administration , digital twins , and LADM standard implementations . Scientific Awards : National educational recognition (2019), Victorian educational grants (2018), and prestigious fellowships (2012) As a supervisor , he guides PhD candidates in topics ranging from BIM for waste management to underground cadastral systems . His industry engagement includes partnerships with the United Nations , Open Geospatial Consortium , and Singapore Land Authority .
Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Jan Oliver Borchers is a full professor of computer science and head of the Media Computing Group at RWTH Aachen University, where he holds the endowed Chair in Media Informatics and Human-Computer Interaction (Computer Science 10). He established his research group in 2003 and pioneered modern HCI academic research and teaching in Germany, becoming a leading lab in terms of archival publications at CHI, the top international conference in HCI. Before joining RWTH, he held faculty positions at Stanford University and ETH Zurich. His research focuses on Human-Computer Interaction with particular interest in new user interfaces for soft robotics, textile user interfaces, 3D printing and personal fabrication, augmented reality, wearable and tangible computing, interfaces for software development, deceptive patterns, and interactive guides and exhibits. He opened Germany's first Fab Lab in 2009 and has been instrumental in advancing digital fabrication and maker culture in academia. His work bridges theoretical HCI research with practical applications, as evidenced by his consulting for major companies including AirBus, Apple, ARD, Bayer, Children's Museum Boston, Daimler, Handelsblatt, OTIS, Scout24, TEDx, and Deutsche Telekom. His recent publications show a strong focus on deceptive patterns in UI design, textile interfaces, and the intersection of AI with human interaction. The research demonstrates consistent innovation in tactile interaction techniques, with numerous honorable mentions at top conferences like CHI. His lab has produced significant contributions to eyes-free interaction, textile computing, and the ethical implications of dark patterns, particularly regarding children's experiences with deceptive designs. IDC 2025 Best Work in Progress for research on children's assessment of deceptive designs CHI'25 Student Games Competition Winner for The Deceptive Dungeon RWTH-Wissenschaftsnacht 2024 Best Science Slam for Usability: 4 Prinzipien guter User Interfaces Multiple CHI Honorable Mentions spanning from 2008-2021 Best Note and Best Demo awards at ITS and UIST conferences Borchers has served in numerous leadership roles including heading the Computer Science Department's Examination Board (2015-2025), coordinating the department's public relations since 2010, and organizing the weekly Faculty Lunch since 2003. He is an active member of the ACM, SIGCHI, SIGCHI Germany, and GI, and introduced the Interactivity format to the CHI conference in 2005. His PhD thesis, A Pattern Approach to Interaction Design, became the first book to bring design patterns to HCI. The Media Computing Group under his leadership has developed significant expertise in physical computing, textile interfaces, and fabrication technologies, with a strong emphasis on both theoretical contributions and practical applications. His lab continues to push boundaries in interactive technologies while maintaining a strong ethical stance on user experience, particularly regarding deceptive design patterns.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
Achim Menges is a Research Professor and Max Planck Fellow at the University of Stuttgart, where he directs the Institute for Computational Design and Construction (ICD). His work bridges architecture, engineering, and computational design, with a focus on developing innovative construction methods and materials. Menges leads the Cluster of Excellence IntCDC: Integrative Computational Design and Construction for Architecture, a major research initiative funded by the German Research Foundation. Menges' research centers on computational design, robotic construction, and biomimetic architecture, with particular emphasis on timber construction, adaptive building systems, and digital fabrication. His work integrates principles from bionics to create responsive, sustainable building systems that adapt to environmental conditions. Notable projects include the BUGA Wood Pavilion (2019), Urbach Tower, BUGA Fibre Pavilion, and the livMatS Biomimetic Shell, all demonstrating his commitment to material innovation and sustainable construction practices. His recent publications reveal a consistent focus on advancing timber construction techniques, developing bio-inspired responsive systems, and implementing robotic fabrication processes. The research shows a clear trajectory toward more sustainable, resource-efficient building methods that integrate digital design with physical construction processes. Menges' work increasingly addresses multi-story timber systems and circular construction principles. Bauwende Prize der Universität Stuttgart 2025 Menges leads numerous research initiatives through the Cluster of Excellence IntCDC, securing significant funding for projects exploring computational design and robotic construction. His work involves extensive collaboration with industry partners and interdisciplinary research teams. Menges directs the ICD research laboratory, which focuses on developing novel computational design methods, robotic fabrication processes, and innovative material systems for architecture. The institute maintains strong partnerships with industry leaders in construction technology and materials science, facilitating the translation of research into practical applications.
Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.
Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.
Vishal Ahuja is an Associate Professor and Corrigan Research Professor at Southern Methodist University's Cox School of Business, with adjunct faculty status at University of Texas Southwestern Medical Center. He focuses on decision analytic tools for healthcare improvement through operations management. PhD, University of Chicago Booth School of Business MBA, University of Chicago Booth School of Business His research interests span healthcare operations, service optimization, and AI applications in clinical decision-making. He collaborates with the Department of Veterans Affairs, Parkland Hospital, and pediatric institutions to address care quality and delivery efficiency. Recent publications emphasize predictive modeling for chronic disease management, adaptive clinical trial design, and regulatory healthcare policy. Awards include the Dlin/Fischer Clinical Research Award (2021), INFORMS Pierskalla Award (2012), and 2025 AI75 recognition for Dallas-Fort Worth AI leadership. D CEO Excellence in Healthcare Award - Outstanding Healthcare Innovator (2023) NSF Game Changer Academies for Advancing Research Innovation (2022) C. Jackson Grayson Faculty Innovation Award (2022-23) At SMU, he teaches graduate courses in operations, supply chain, and service management, integrating corporate sector experience from chemical and consumer goods industries. His work has been cited by the FDA in safety labeling changes workshops.
Remko Van Hoek serves as Professor of Practice in the Department of Supply Chain Management at the Sam M. Walton College of Business, University of Arkansas. He teaches Sourcing and Procurement courses across undergraduate, master's, and doctoral programs. Prior to joining the University of Arkansas, Dr. Van Hoek held academic positions in Europe and served as a visiting professor at Cranfield School of Management, complemented by extensive industry experience as a supply chain and procurement executive at global corporations including Nike, PwC, and The Walt Disney Company. He currently serves on the Council of Supply Chain Management Professionals (CSCMP) Board of Directors and acts as executive director of the CSCMP Supply Chain Hall of Fame hosted by the Walton College. Dr. Van Hoek's research centers on digital transformation in procurement and supply chain management, with particular focus on blockchain implementation, artificial intelligence applications, and sustainable sourcing practices. His work investigates how emerging technologies reshape procurement processes, supplier relationship management, and risk mitigation strategies. He explores innovative approaches to supplier diversity programs, ethical sourcing frameworks, and the strategic evolution of procurement from cost reduction to value creation. His research consistently bridges academic theory with industry practice through case studies from major corporations, developing actionable frameworks for practitioners facing digital disruption and sustainability challenges. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in AI-driven supply chain risk prevention, blockchain implementation for transparency, and sustainable supplier engagement. His work demonstrates consistent industry collaboration, drawing from case studies at Walmart, Moet Hennessy, and Bayer to address post-pandemic resilience challenges. A significant portion examines procurement's strategic evolution beyond transactional functions, with recurring emphasis on ethical considerations in digital transformation and the practical implementation barriers for emerging technologies in global supply networks. As a Professor of Practice, Dr. Van Hoek integrates executive-level industry experience into curriculum development and student mentorship. His industry-engaged teaching model includes guest lecturer programs connecting students with supply chain practitioners, as evidenced by his co-authored work on integrating industry insights into supply chain education. While specific grant funding details are not publicly documented, his leadership in the CSCMP Supply Chain Hall of Fame demonstrates commitment to professional development and industry-academia knowledge transfer. Dr. Van Hoek directs the CSCMP Supply Chain Hall of Fame initiative, which documents transformative contributions to supply chain management through interviews with industry pioneers and historical case studies. This platform serves as both an educational resource for Walton College students and a professional development tool for supply chain practitioners globally. The Hall of Fame preserves critical industry knowledge while highlighting contemporary innovations in supply chain strategy and technology implementation.
Pranav Rajpurkar is an Associate Professor at Harvard University, co-founder of a2z Radiology AI, and lead of the Rajpurkar Lab. His work pioneers AI systems that emulate physician-level expertise in medical tasks, with a focus on multi-modal medical AI and radiology. He joined Harvard faculty at age 25 and became Associate Professor at 30 after completing his Stanford PhD at 19. Education: Bachelor of Science (CS), Stanford University, 2015 Master of Science (CS), Stanford University, 2018 PhD (CS), Stanford University, 2021 Rajpurkar's research centers on building AI that thinks and communicates like doctors, with breakthrough work in ECG arrhythmia detection and chest X-ray interpretation. His lab develops foundational datasets (ReXGradient-160K, RadRevise) and benchmarks for medical AI, spanning computer vision, NLP, and multimodal systems. Current work focuses on generative AI for clinical workflows, voice-guided emergency assessment, and rigorous evaluation frameworks for medical LLMs. His 2025 publications reveal three dominant trends: (1) Generative medical AI for radiology report generation and editing, (2) Voice-enabled AI agents for prehospital care, and (3) Multimodal clinical monitoring systems. Key advancements include universal biomedical foundation models (UniBiomed), 3D CT segmentation from text reports, and frameworks for AI-human role separation in radiology. Scientific Awards: Forbes 30 Under 30 in Science (2022) MIT Tech Review Innovator Under 35 (2023) Nature Medicine Early-career Researcher To Watch (2022) Rajpurkar has mentored over 84,000 students through Harvard courses and Coursera's AI for Medicine program. His lab secures major NIH and NSF grants for medical AI development, with recent funding focused on multimodal emergency care systems (MC-MED) and radiologist-AI collaboration. He directs the Harvard-Stanford Medical AI Bootcamp and co-hosts The AI Health Podcast. The Rajpurkar Lab operates as a cross-institutional hub with collaborators at Stanford, MIT, and major hospitals. It drives initiatives like the MAIDA framework for global medical imaging data sharing and develops open-source tools including RadGraph for radiology report analysis. Current projects focus on voice AI for stroke assessment and generative models for non-invasive cancer management.
Prof. April Wang is an Assistant Professor in the Department of Computer Science at ETH Zurich. Her work focuses on Educational Technology, Human-Computer Interaction, and AI-driven tools for programming education. She is affiliated with the Professur Educational Technology group and teaches courses such as Human Computer Interaction and Ethics in Computing. Her research explores interactive learning environments, collaborative data science workflows, and AI-augmented educational systems. Her research interests span computational notebooks, live coding interfaces, and human-AI collaboration in software development. Notable contributions include frameworks like DBox for algorithm learning and EDBooks for interactive programming narratives. She also investigates challenges in real-time collaboration tools and automated documentation systems for data scientists. Recent work emphasizes understanding user needs in automated copilot systems and resolving conflicts in collaborative notebook environments. Her projects often bridge theoretical HCI principles with practical educational tools, aiming to enhance both instructor efficacy and student engagement. Prof. Wang’s teaching portfolio includes courses on user-centered interface design and ethical scientific practices. She actively contributes to advancing interdisciplinary computational literacy through initiatives like datAR, which uses everyday objects to teach data concepts.
