Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.
Ashwin Sachdeva is a Clinical Senior Lecturer in Urology at the Division of Cancer Sciences, University of Manchester. His research focuses on prostate cancer, clinical trials, translational research, and cancer metabolism. He has contributed to major studies like the STAMPEDE trial, investigating treatments for metastatic prostate cancer. His work aligns with UN Sustainable Development Goals related to health and well-being. Education: BMed and BSurg from Newcastle University; PhD on mitochondrial alterations in prostate cancer progression; MSc in Interdisciplinary Medicine & Engineering from the University of Manchester. Professional qualifications include The Royal College of Surgeons of England membership. Research interests include androgen deprivation therapy, novel hormonal therapies, and biomarker development. He has authored over 50 peer-reviewed articles, with recent work addressing cardiovascular risks of androgen receptor inhibitors and penile cancer management guidelines. Awards include the 2022 Prostate Cancer Foundation Young Investigator Award, 2024 European Urology Impact Award, and 2015 The Urology Foundation Research Scholarship Medal. He actively contributes to clinical trials, guideline development, and media discussions on prostate cancer advancements. Labs/Teams: Involved with Manchester Cancer Research Centre and Cancer Digital Futures initiatives. Collaborates internationally on oncology research and clinical trials.
Vivek Rao serves as Associate Dean for Master's & Professional Programs and Executive Director of the Design and Technology Innovation Master's Program at Duke University's Pratt School of Engineering. He is also an Executive In Residence in Engineering Graduate and Professional Programs, teaching core courses including Design Ethics & Social Innovation and Design Innovation Studio while leading the development of Duke's new Masters of Engineering program. He earned his B.S. (2007), M.S. (2012), and Ph.D. (2018) in Mechanical Engineering from the University of California, Berkeley. His research spans Engineering Design Theory and Methodology , Product Design Innovation , and Emerging Technologies in Design , with significant focus on Sociotechnical Systems , Human-Computer Interaction , and Sustainability applications in water infrastructure. His interdisciplinary work integrates business innovation frameworks with engineering design principles. Analysis of his 15 most recent publications (2021-2024) reveals a dominant trend in AI-enhanced design tools, cybersecurity integration, and human-centered methodologies across disaster response, global health, and virtual collaboration contexts. These works demonstrate consistent interdisciplinary collaboration bridging engineering, business, and social sciences through journals like Design Studies and Journal of Mechanical Design. No scientific awards were documented in the source material. Rao has secured substantial external and internal grant funding during his postdoctoral research at UC Berkeley and maintains an active consulting practice with clients ranging from pre-seed startups to the US Department of Defense. His pedagogical approach emphasizes project-based learning, as evidenced by his development of foundational courses for Berkeley's Masters of Design program and Duke's new initiative. As Executive Director of Duke's Design and Technology Innovation program, Rao leads curriculum development and industry partnerships focused on translating design theory into real-world technological solutions, particularly in infrastructure and sustainability domains.
Steve Oudot is a Senior Researcher (Directeur de Recherche) at Inria where he leads the GeomeriX research group, and serves as an Adjunct Professor at École Polytechnique. His research focuses on topological and geometric approaches to data analysis. Research Interests: Primarily works on persistence theory and its connections to homological algebra and representation theory, topological data analysis with applications to statistics and machine learning, multimodal time series analysis, and manifold learning/sampling theory. His research bridges theoretical mathematics with computational applications in data science. Publication Trends: Recent works (2024-2025) focus on multiparameter persistence theory, stability analysis of topological descriptors, and computational methods for persistence module decomposition. His publications demonstrate strong emphasis on theoretical foundations with algorithmic implementations for geometric data analysis. Student Advising: Currently supervising 3 PhD students (Michel, Li, Mordacq) and has graduated 7 doctoral students since 2014. Alumni now hold positions in academia (Lacombe at LIGM, Carrière at Inria) and industry (Berkouk at CNIL, Solomon at Deep Detection). Teaching: At École Polytechnique, teaches courses on topological data analysis (INF556), algorithms for data analysis (INF442), and computational geometry/topology (MPRI). Also taught at international schools in Luxembourg (2018), TUM (2016), and La Marsa (2016).
Dr. Sandra Anusiewicz-Baer serves as Acting Chair at Heidelberg University of Jewish Studies, focusing on Jewish Religious Teaching, Education, and Didactics. She is a lecturer at the School of Jewish Theology at the University of Potsdam and actively contributes to pedagogical committees, including The Renaissance Hub and the Network of Jewish University Lecturers. PhD in Educational Science (2012-2016, Humboldt University) Continuing Education in Cultural Management (University of Music and Theatre Hamburg) Magistra Artium in Education, Judaic Studies, and Islamic Studies (Free University of Berlin) Her research explores Jewish identity construction, Holocaust education, and the role of religious schools in diaspora communities. She has developed both digital and analogue teaching content, focusing on the interplay between tradition and modernity. Recent publications and editorships examine religious education across cultures, gender perspectives in DDR Jewish history, and innovative pedagogical frameworks. Her work emphasizes experiential learning, digital engagement, and interfaith dialogue. Special Prize of Humboldt University (2017) Ernst Ludwig Ehrlich Study Foundation scholarship (2012-2016) DAAD scholarship (1998-1999) Pedagogies of Peoplehood Research Fellowship (2023-2024) She has led major educational initiatives, including strategic rabbinic training development, digital format creation for Jewish education, and international interreligious encounters. Her work continues to shape Jewish educational policies and practices in Germany and Europe.
Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.
Patricia J. Applegate, MD, is a Professor in the Cardiology Division at Loma Linda University School of Medicine. She earned her Doctor of Medicine from the University of Southern California in 1980. Her research bridges clinical cardiology and translational medicine, with a focus on echocardiography, cardiac surgery outcomes, intraoperative monitoring, and neuroprotective strategies following cardiac arrest. Research interests span: Cardiac Imaging & Diagnostics: Advanced echocardiography techniques for valvular diseases and cardiac tumors Resuscitation Science: Neuroprotection and hydrogen therapy in cardiac arrest models Surgical Innovation: Valve replacement strategies and intraoperative hemodynamic monitoring Translational Research: Bench-to-bedside approaches in critical care cardiology Her publications demonstrate consistent focus on improving diagnostic accuracy and therapeutic outcomes in cardiology. Recent work (2016-2023) emphasizes intraoperative monitoring technologies and novel case reports, while earlier foundational research (1987-2014) established expertise in echocardiography and cardiac arrest management. Collaborative work frequently appears in multidisciplinary journals spanning cardiology, anesthesiology, and critical care.
Eric Bulson is a Professor of English at Claremont Graduate University, affiliated with the Department of English in the School of Arts and Humanities. He holds a PhD in English & Comparative Literature from Columbia University and has previously taught at Columbia, Yale, and Hobart and William Smith Colleges. His educational background includes: PhD, English & Comparative Literature, Columbia University M.Phil., English Literature, Columbia University MA, English Literature, Columbia University BA, Humanities, Union College Bulson's research centers on modernism, James Joyce, critical theory, digital humanities, and world literature. He is known for integrating computational methods into literary analysis, particularly in his work on Ulysses and little magazines. His interests span spatial representation in novels, visual storytelling, narrative theory, and the global circulation of modernist print culture. He has authored and edited several influential books in these areas. His recent publications reflect a strong trend toward data-driven literary criticism and interdisciplinary approaches, combining traditional close reading with quantitative analysis. Themes such as spatiality, media forms, and transnational modernism recur across his work, especially in studies of Joyce, Woolf, and Pound. His scientific awards and fellowships include: National Endowment for the Humanities Fellowship (2018–2019) American Council of Learned Societies Fellowship (2012–2013) New York Public Library Fellowship (2011) Whiting Foundation Fellowship (2003–2004) Fulbright Foundation Fellowship (2000–2001) Bulson has advised graduate students in English and comparative literature at CGU, though specific names are not listed. He has secured competitive research funding through major fellowships, supporting his work on modernist print culture and computational literary analysis. His teaching includes seminars on Dante, Pound, and Joyce, as well as courses on narrative theory, the world novel, and visual storytelling. He is currently working on a comparative study of Ezra Pound and F.T. Marinetti, extending his exploration of avant-garde networks. Bulson is actively involved in research initiatives that bridge literary studies with digital tools, contributing to the evolving field of digital humanities. His work often engages with collaborative and interdisciplinary frameworks, particularly in analyzing the spatial and media dimensions of modernist literature.
Massoud Zolgharni is a Professor of Computer Vision at the School of Computing and Engineering, University of West London. He joined UWL as a Senior Lecturer in 2018 and was promoted to Associate Professor in 2020. Previously, he held academic positions at the University of Lincoln and was a Research Associate at Imperial College London. He is a Fellow of the Higher Education Academy and leads the MSc Artificial Intelligence and PhD programs at UWL. BSc in Mechanical Engineering, Amirkabir University of Technology Master’s and PhD, Brunel and Swansea Universities, UK His research focuses on computer vision and medical imaging , particularly in automated cardiac imaging using AI and deep learning . His work aims to develop low-cost, non-invasive techniques for echocardiography and cardiovascular diagnostics. He has published extensively in journals such as Computers in Biology and Medicine , IEEE Transactions , and European Heart Journal . Recent publications (2023–2025) highlight his leadership in AI-driven echocardiography, including left ventricle segmentation, Doppler analysis, and multimodal seizure detection. His research integrates deep learning, signal processing, and real-world clinical validation, establishing a strong interdisciplinary profile bridging computing and medicine. Scientific Awards : Fellow of the Higher Education Academy He has secured major grants from the British Heart Foundation , including a £1.5M Programme Grant (2022–2027) on AI integration in echocardiography. He serves on university research committees, acts as Critical Reader for the School, and organizes the monthly research seminar. He teaches across a wide range of computing and AI programs, contributing to curriculum development and research supervision.
Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.
Bing Liu serves as Director of Applied Research at Scale AI and Adjunct Professor in the Computer Science and Engineering department at the University of California, Santa Cruz. Previously, he held leadership roles at Meta (GenAI and Reality Labs), Google Research, and Capio.ai (acquired by Twilio), with expertise spanning generative AI, NLP, and spoken dialogue systems. His educational background includes: Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University B.Eng in Electrical and Electronic Engineering (First Class Honors) from Nanyang Technological University, Singapore Exchange studies at KTH Royal Institute of Technology, Sweden Liu's research focuses on advancing large language models, dialogue systems, and reinforcement learning for conversational AI. His work bridges theoretical innovation with industrial-scale applications, particularly in zero-shot learning, multilingual capabilities, and evaluation frameworks for generative AI. He has pioneered techniques in dialogue state tracking, knowledge-enriched task-oriented systems, and continual learning to address catastrophic forgetting in neural dialogue models. Analysis of his 15 most recent publications reveals a clear trajectory toward industrial-scale generative AI: early work (2018-2021) established foundational methods in task-oriented dialogue systems, while recent publications (2022-2024) focus on LLM post-training, multimodal integration, and evaluation benchmarks like Humanity's Last Exam. Key thematic clusters include cross-lingual transfer, knowledge grounding in dialogue, and robust evaluation frameworks for frontier models. As an educator, Liu guides graduate research at UC Santa Cruz in LLM and multimodal AI while serving in critical conference roles including Publication Chair for ACL 2024 and Area Chair for ACL 2023's Large Language Models track. His industry leadership includes building Scale AI's 40+ member research team and driving Meta's Llama3 development, managing multimillion-dollar data roadmaps that scaled the Meta AI Assistant to 700M MAU. Liu directs Scale AI's applied research lab focused on GenAI data and evaluation, having previously built Meta's NLU team for AR/VR voice assistants deployed across Portal, Oculus, and Ray-Ban Smart Glasses. His current work centers on creating evaluation leaderboards adopted by top AI labs and developing the data engine for next-generation generative models.
Joseph B. Zwischenberger, MD, is a Professor of Surgery and Pediatrics at the University of Kentucky College of Medicine , with affiliations to the Saha Cardiovascular Research Center , CVRC - Affiliated Faculty , and the Surgery/Cardiac MD/PhD Program Mentor . His clinical and research focus spans cardiothoracic surgery , ECMO technology , and critical care innovations , particularly in artificial organ development and postoperative pain management . Education: MD from University of Kentucky Residency: University of Michigan, Ann Arbor Fellowships: Cardiac Division (NIH), Cardiothoracic Surgery, and Critical Care (University of Michigan) Certifications: American Board of General Surgery (Vascular and Thoracic Surgery) His research explores ECMO program development , respiratory failure mechanisms , and congenital heart defect support systems , emphasizing multidisciplinary collaboration in medical device innovation and pediatric cardiovascular care . Key trends include postoperative critical care protocols , tracheal replacement technologies , and surgical education reform to enhance resident autonomy. Notable scientific contributions include leadership in JACC Scientific Expert Panel on ECMO and foundational work on transhiatal esophagectomy for esophageal carcinoma. His 2023 Annals of Thoracic Surgery article addresses ICU governance models, while his 2024 Ann Thorac Surg Short Rep case study highlights device complication analysis. As an MD/PhD program mentor , he trains future physician-scientists and collaborates with the Saha Cardiovascular Research Center on translational studies. His work has shaped thoracic surgical protocols, including epidural placement guidelines and Fontan procedure optimization .
Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.
Florentin Wörgötter is a faculty member at the Department for Computational Neuroscience , Georg August University of Göttingen, Germany. His research bridges robotics , computational neuroscience , and machine learning , focusing on action prediction, neural networks, and human-robot interaction. Key Research Areas : Action segmentation, semantic decomposition of manipulation sequences, 3D object reconstruction, and sensor fusion for infant movement classification. Recent Trends : Combining task-dependent learning with optimal path search, using foundation models for graph-based action recognition, and improving CNN interpretability through influence functions. He collaborates extensively with researchers like Minija Tamosiunaite , Tomas Kulvicius , and Poramate Manoonpong , contributing to journals such as NeuroImage , Robotics and Autonomous Systems , and IEEE Transactions on Neural Networks . His work often integrates deep learning , semantic reasoning , and biologically inspired models for robotic applications.
Associate Professor Judith Laister at the University of Graz's Institute for Cultural Anthropology and European Ethnology explores urban anthropology through aesthetic and multimodal frameworks. Her work interrogates intersections between art and science, focusing on participatory art in public spaces, gender justice, and anthropology in the Anthropocene. She leads the ELF - Elisabeth List Fellowship Program and co-founded the nice* network for gender-responsive urban interventions. Key research themes: Urban place-making, representational critique, relational art, and translational concepts Affiliated with initiatives like Green Belt Graz and We Earthlings! study projects Her teaching includes foundational courses in European Ethnology and urban anthropology, alongside roles in curriculum development and equal opportunities advocacy. Publications often bridge art theory and ethnographic practice, emphasizing experimental methodologies and public engagement.
