Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.
Peter A. Tass is a Professor of Neurosurgery at Stanford University's School of Medicine, where he leads the Tass Lab within the Department of Neurosurgery. His research focuses on developing groundbreaking neuromodulation techniques designed to impact the course of neurological diseases including Parkinson's disease, stroke, epilepsy, and tinnitus. The Tass Lab is part of several prestigious Stanford initiatives including Bio-X, the Wu Tsai Human Performance Alliance, the Maternal & Child Health Research Institute (MCHRI), and the Wu Tsai Neurosciences Institute. MD from Universities of Ulm and Heidelberg, Germany (1989) PhD in Physics from University of Stuttgart, Germany (1993) Diploma (master's degree) in Mathematics from University of Stuttgart, Germany (1993) Habilitation thesis in Physiology from RWTH Aachen University, Aachen, Germany (2001) Dr. Tass's primary research interests center around computational neuroscience approaches to understanding and treating neurological disorders. His lab pioneers neuromodulation techniques based on thorough computational modeling that employs dynamic self-organization, plasticity, and other neuromodulation principles to produce sustained therapeutic effects after stimulation. He specifically focuses on developing stimulation methods that cause sustained neural desynchronization by unlearning abnormal synaptic interactions. His work spans both invasive techniques like deep brain stimulation and non-invasive approaches such as vibrotactile and acoustic stimulation. Current projects involve developing novel therapies for Parkinson's disease, epilepsy, tinnitus, and other neurological conditions using comprehensive computational neuroscience methods derived from non-linear dynamics, statistical physics, and numerics. Analysis of Dr. Tass's recent publications reveals a strong focus on coordinated reset stimulation techniques, neural network modeling with plasticity mechanisms, and computational approaches to brain stimulation. His work consistently bridges theoretical computational neuroscience with clinical applications, particularly for Parkinson's disease treatment. A significant portion of his recent research examines how stimulation parameters, sequences, and timing affect long-lasting desynchronization effects in neural networks. His publications demonstrate an interdisciplinary approach combining physics, mathematics, neuroscience, and clinical medicine to develop novel therapeutic interventions. Member of the European Academy of Sciences and Arts (2012) Nicolaus August Otto Innovation Prize (2011) German Innovation Award in Medicine (2011) Rapid Response Innovation Awards from The Michael J. Fox Foundation (2009, 2010) Runner-up for the German future prize (2006) Erwin Schrödinger prize (2005) Fritz Winter prize (2000) Dr. Tass actively mentors a diverse team of researchers including staff scientists, postdoctoral fellows, clinician-scientists, and students. His lab currently includes researchers with backgrounds in physics, computational neuroscience, biomedical engineering, and clinical neurology. The lab is involved in multiple clinical trials, including studies on coordinated reset spinal cord stimulation and vibrotactile coordinated reset stimulation for Parkinson's disease. His research is supported by various funding sources including foundations focused on neurological disorders and innovation in medical technology. Dr. Tass collaborates extensively with both internal Stanford researchers and external collaborators worldwide. The Tass Lab at Stanford is a multidisciplinary research group comprising physicists, neuroscientists, engineers, and clinicians working together to develop novel neuromodulation therapies. The lab team includes staff scientists like Justus Kromer (theoretical physicist), postdocs like Daniel Ehrens and Kanishk Chauhan, clinician-scientists like Tina Munjal, and clinical research coordinators. The lab maintains active collaborations with Stanford colleagues across departments including Kwabena Boahen, Vivek P. Buch, and Jaimie Henderson, as well as external collaborators like Alexander Neiman and Kęstutis Pyragas. Current research directions include developing non-invasive vibrotactile treatments for Parkinson's disease, acoustic coordinated reset therapy for tinnitus, and responsive deep brain stimulation for conditions like loss-of-control eating.
Erin Bell is a Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire . She holds a Ph.D. in Structural Engineering from Tufts University and has extensive experience in structural health monitoring, finite element modeling, and infrastructure sustainability. B.C.E., Georgia Institute of Technology M.S., Civil Engineering, Tufts University Ph.D., Structural Engineering, Tufts University Her research focuses on structural health monitoring, bridge condition assessment, and integrating AI techniques like artificial neural networks and deep reinforcement learning for infrastructure asset management. Recent work includes equitable maintenance strategies for aging bridges in flood-prone zones and tidal energy conversion for sustainable bridge monitoring systems. Key trends in her publications include the application of machine learning to structural analysis, finite element model calibration, and climate change adaptation in transportation infrastructure. She has led projects on deep reinforcement learning for bridge scour maintenance, modal-based uncertainty quantification, and multi-scale modeling of steel bridges. Grants and Collaborations : Erin Bell has secured funding from the National Science Foundation (NSF) , US Department of Energy (DOE) , and New Hampshire Department of Transportation . Notable projects include the Living Bridge initiative for tidal energy-powered smart infrastructure and statewide data exchange systems for bridge condition assessment.
Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.
Anne Staples is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech, leading the Laboratory for Fluid Dynamics in Nature (FINLAB). Her research focuses on fluid mechanics in biological systems, medical fluid dynamics, and bioinspired engineering, leveraging computational modeling and microfluidic technologies to innovate in healthcare and engineering. Education: B.S. in Mechanical and Aerospace Engineering, Cornell University (2000) M.Eng. in Mechanical and Aerospace Engineering, Princeton University (2001) Ph.D. in Mechanical and Aerospace Engineering, Princeton University (2006) Postdoctoral Researcher at the Naval Research Laboratory (2006–2008) Research Interests: Her work spans bioinspired microfluidics, medical device design, and fluid dynamics in biological systems. Notable projects include developing pulse-driven micropumps for drug delivery and studying insect respiratory systems to inform engineering solutions. Publications: Over 50 peer-reviewed articles, focusing on topics like microfluidic systems, insect-inspired flow control, and hemodialyzer modeling. Recent work emphasizes wearable drug delivery and biomechanical innovations. Awards & Service: NIH Trailblazer Award (2024) Virginia Tech Dean’s Fellow (2023–present) Editorial Board Member, PLOS ONE and Scientific Reports (2021–present) Fulbright Scholar (2016) Grants & Collaborations: Leads a NIH-funded project to develop lightweight drug delivery devices. Collaborates with statisticians and biomedical engineers to simulate and optimize prototypes. Active in interdisciplinary teams at Virginia Tech and Georgia Tech. Labs & Teams: Directs the FINLAB, which integrates computational modeling, experimental microfluidics, and biological principles to address challenges in healthcare and environmental engineering.
Professor Jun Huang is a faculty member in the School of Chemical and Biomolecular Engineering at the University of Sydney, where he holds the rank of Professor and is Director of the Laboratory for Catalysis Engineering. He is also a Domain Leader for Materials at the nanoscale at Sydney Nano Institute and a member of several interdisciplinary institutes, including the China Studies Centre and Sydney Institute of Agriculture. His research focuses on catalysis engineering, with an emphasis on developing sustainable processes for renewable fuels, pollutant treatment, and greenhouse gas mitigation. Huang has held prestigious awards such as the Australia Research Council Future Fellowship (2022) and the Sydney Accelerator Fellowship (2018). Education: Huang earned his PhD from the University of Stuttgart (2008) and completed postdoctoral research at Georgia Institute of Technology and ETH Zurich. He joined the University of Sydney in 2010 as a Lecturer, advancing to Senior Lecturer, Associate Professor, and Professor. Research Interests: Huang's work centers on catalyst design for green chemical processes, including biomass conversion to biofuels, wastewater treatment, and CO2 utilization. He emphasizes sustainable manufacturing and environmental impact reduction through innovative catalytic systems. Current Projects: These include catalytic transformation of hydrocarbons/CO2/biomass, nano-catalysts for renewable energy, and advanced NMR spectroscopy for catalysis analysis. Collaborative projects involve anti-cancer therapies and drug pharmacology studies. Awards: Over 15 awards, including the 2021 ACS Sustainable Chemistry & Engineering Lectureship and 2017 Vice-Chancellor’s Research Excellence Award. Teaching: Huang instructs courses such as CHNG2801 (Conservation Processes), CHNG3802 (Industrial Systems), and advanced chemical engineering topics. He supervises PhD/Master students in catalysis and sustainable engineering. Labs/Teams: Leads the Catalysis Engineering Lab and collaborates with Sydney Nano Institute on nanomaterials research.
Eralp Demir is a Post-Doctoral Researcher at the Department of Engineering Science, University of Oxford. His research focuses on materials mechanics, crystal plasticity, and finite element methods. He holds a PhD from RWTH Aachen University and has conducted research at institutions including Carnegie Mellon University, Max Planck Institute, and Cornell University. His current work involves developing the OXFORD-UMAT framework for fusion energy materials in collaboration with UKAEA. He specializes in in-house finite element code development and commercial software integration (e.g., Abaqus, MSC Marc). His expertise spans computational materials modeling, microstructural analysis, and experimental validation using techniques like 3D XRD. Education: PhD in Engineering Science, RWTH Aachen University Advanced Studies at Carnegie Mellon University (Mechanical Engineering), Cornell University (MAE), and others Research Interests: Crystal plasticity modeling, fusion energy materials, finite element method development, microstructural mechanics, and additive manufacturing. His work bridges computational simulations with experimental techniques to understand material behavior under extreme conditions. Labs/Teams: Collaborates with the Tarleton Research Group at Oxford and UKAEA on fusion energy projects. Active in developing open-source tools for material modeling.
Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.
Professor Thierry Langer is a Full Professor of Pharmaceutical Chemistry at the University of Vienna’s Faculty of Life Sciences (Department of Pharmaceutical Sciences). He leads research in computational drug design, with a focus on pharmacophore modeling, 3D-QSAR analysis, and AI-driven molecular design. His work bridges theoretical and experimental chemistry, addressing targets like viral proteases (e.g., SARS-CoV-2), GABA receptors, and dopamine transporters. Research interests include: Pharmacophore-guided drug discovery for anti-viral and CNS therapies Development of next-generation computational tools (e.g., PharmacoMatch, QPhAR) Protein-ligand interaction modeling using neural networks and graph-based algorithms Recent studies focus on: Inhibitors for herpesvirus nuclear egress complexes, AI-optimized antivirals, and dopamine transporter inhibitors for cognitive enhancement. His lab collaborates on projects like the NeuroDeRisk initiative to de-risk neurotoxic compounds. Publications emphasize drug repurposing, metabolic pathway analysis, and scalable synthesis methods for promising drug candidates.
Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.
Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.
Gustav Eje Henter is an Assistant Professor at KTH Royal Institute of Technology, holding roles as the Head of Research at Motorica AB and a Core Team Member of the Wallenberg Research Arena (WARA) for Media and Language. He is the Secretary of the ISCA SynSIG (Special Interest Group on Speech Synthesis) and a Co-Organiser of the GENEA Workshops on Embodied Agents' Non-Verbal Behavior. His research focuses on speech synthesis, gesture generation, and multimodal interaction, with contributions to TTS systems, neural networks, and embodied AI. He leads Digital Futures, a cross-disciplinary research center addressing societal challenges through digital technologies. This center is a collaboration between KTH, Stockholm University, and RISE. His work spans foundational research to industrial applications, emphasizing ethical AI, privacy in voice conversion, and human-robot interaction. Key research themes include causal reasoning in LLMs, adversarial privacy techniques, and benchmarking frameworks like the GENEA Leaderboard. He has organized international workshops (GENEA 2021-2024) and contributed to standards in TTS evaluation methodologies. His technical innovations include HiFi-Glot for formant synthesis and Matcha-TTS for fast waveform generation. His research integrates audio, gesture, and motion synthesis with deep learning, addressing challenges in spontaneous speech synthesis, multimodal coherence, and listener perception. He advocates for rigorous evaluation practices and open challenges to advance the field's reproducibility and real-world applicability.
Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning
Prof Daniel Innerarity serves as a Part-time Professor and Chair in AI & Democracy at the Florence School of Transnational Governance, European University Institute. He is simultaneously Professor of Political and Social Philosophy at the University of the Basque Country and the Ikerbasque Foundation for Science in Spain, and director of the Instituto de Gobernanza Democrática. His academic career spans multiple continents with previous positions including Robert Schuman Visiting Professor at EUI, Fellow of the Alexander von Humboldt Foundation at University of Munich, visiting professor at University of Paris 1-Sorbonne, professorship at Georgetown University, and visiting fellow at Max Planck Institut for International and Public Law at Heidelberg. Innerarity's research focuses on the critical intersection of democracy and emerging technologies, particularly artificial intelligence. His scholarship examines how complex democratic systems can be designed and maintained in the 21st century, with special attention to the epistemic challenges posed by AI systems. He investigates European integration through the lens of consent theory, arguing for democratic legitimacy in transnational governance structures. His publications demonstrate a consistent concern with how digital technologies reshape democratic processes, citizen participation, and political representation in contemporary societies. Scientific Awards: Miguel de Unamuno Essay Prize 2003 National Literature Prize in the Essay category Espasa Essay Prize Euskadi Essay Prize Prize for Humanities, Culture, Arts and Social Sciences from the Basque Studies Society/Eusko Ikaskuntza (2008) Príncipe de Viana Culture Prize (2013) Innerarity maintains an extensive international academic network across European and North American institutions. His work bridges theoretical political philosophy with practical governance challenges in the digital age, particularly focusing on how democratic institutions can maintain legitimacy and functionality amid rapid technological change. His leadership of the Instituto de Gobernanza Democrática demonstrates his commitment to translating theoretical insights into practical governance frameworks.
Shion Guha is an Assistant Professor at the University of Toronto's Faculty of Information, cross-appointed to the Department of Computer Science. He directs the Human-Centered Data Science Lab and is affiliated with the Schwartz Reisman Institute for Technology and Society and the Data Sciences Institute. His research focuses on integrating technical methodologies with critical social science approaches to address algorithmic biases in public sectors like child welfare, healthcare, and policing. Key roles include coordinating the Human-Centred Data Science concentration in the Master of Information program and advising national policies on AI ethics. Education: PhD in Information Science and Statistics, Cornell University (2016) MS in Information Science, Indian Statistical Institute (2010) Bachelor of Business Administration, Jadavpur University (India) Research Interests: Human-Centered Data Science, AI ethics, public policy, healthcare systems, algorithmic accountability, and marginalized communities' interaction with technology. His work emphasizes participatory design and real-world impact, addressing high-stakes decisions in public services. Recent Trends in Articles: Focus on participatory AI design in public sectors, algorithmic harms in child welfare, and cultural biases in NLP tools. His studies explore the intersection of technical systems and societal values, advocating for equity and transparency in algorithmic decision-making. Awards: Way-Klingler Early Career Award (2019) Connaught New Researcher Award (2021) Schwartz-Reisman Institute Faculty Fellowship (2023–2025) Grants & Advising: Secured funding from NSERC, CIFAR, and others for projects like the Responsible AI for Health Systems (RAIHS) framework. Advises PhD students through cross-departmental programs and collaborates with organizations like Parkview Health and the ACLU. Current supervisees include Ramaravind Kommiya Mothilal and Seh Young Moon. Labs & Initiatives: Leads the Human-Centered Data Science Lab, contributing to interdisciplinary research on AI ethics. Active in policy advocacy, including work with the Canadian government’s AI Policymakers Expert Group and the CIFAR AI Solutions Network.
