Dr. Suman Adhikari is a Conjoint Lecturer at the School of Clinical Medicine , University of New South Wales (UNSW), focusing on Infectious Diseases , Antimicrobial Stewardship , and Clinical Pharmacology . He has contributed to advancements in antibiotic management, sepsis diagnostics, and e-learning tools for healthcare professionals. University: University of New South Wales School: School of Clinical Medicine Email: suman.adhikari@unsw.edu.au Research Interests: Dr. Adhikari’s work spans antimicrobial stewardship in tertiary care, therapeutic drug monitoring of antibiotics like linezolid, and educational technology for vancomycin use. He also explores pharmacokinetics in parasitic infections and contributes to sepsis pathway optimization . Publication Trends: His recent articles (2017–2023) highlight antibiotic safety , digital education , and infection control . Notable studies include multimodal stewardship in ICU, qSOFA criteria validation, and e-learning tool efficacy for vancomycin training. Contact: Phone: 0411817996 | Location: Gray Street, Kogarah 2217 NSW
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
Dr.-Ing. Thomas Wild serves as an Academic Director at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology at the Chair of Integrated Systems. He maintains an active research and teaching role at the institution, with his office located in Building N1 (Theresienstr. 90), Room N2136 in Munich, Germany. Dr. Wild's research focuses on advanced computing architectures, with particular emphasis on manycore system on chip (SoC) architectures, network processor (NPU) architectures, on-chip communication architectures including networks on chip (NoC), and system level design methodologies. His work bridges theoretical research with practical implementation, often exploring design space exploration techniques to optimize system performance. The evolution of his research over two decades demonstrates a consistent focus on improving communication architectures and system-level design for embedded and high-performance computing platforms. His recent publications (2023-2025) reveal a growing integration of machine learning techniques with traditional hardware design, particularly in optimizing power-performance tradeoffs in embedded systems. There's a clear trend toward hardware-software co-design approaches, with significant work on SmartNICs, Linux system optimization, and network processing acceleration. His research shows strong interdisciplinary connections between computer architecture, networking, and machine learning. EUROPRACTICE representative for TUM city campus, facilitating access to commercial EDA tools for academic purposes Active collaborator with Professor Andreas Herkersdorf and other researchers at TUM Focus on practical implementations with FPGA-based prototyping and real system modifications Dr. Wild teaches several hardware design courses including VHDL Lab, SystemC Lab, and HW/SW Codesign, contributing to the education of next-generation computer engineers. His teaching directly complements his research in system design and hardware acceleration, providing students with hands-on experience in cutting-edge technologies.
Itsuro Morita is Professor in the School of Fundamental Science and Engineering, Faculty of Science and Engineering, Waseda University, Tokyo. Before joining Waseda in 2022 he spent 23 years at KDDI R&D Laboratories, advancing from researcher to executive research fellow, and has been a visiting researcher at Stanford University. He is an IEEE Fellow and IEICE Fellow recognized for pioneering large-capacity, long-haul optical transmission systems. Education: 2004 – 2005 Tokyo Institute of Technology, Graduate School of Science & Engineering, Department of Electrical and Electronic Engineering (Doctoral coursework) 1990 – 1992 Tokyo Institute of Technology, Graduate School of Science & Engineering, Department of Physical Electronics (M.E.) 1986 – 1990 Tokyo Institute of Technology, School of Engineering (B.E.) Research Interests: Morita’s work sits at the intersection of optical fiber communication and software-defined networking. He explores ultra-high-capacity transmission via space-division multiplexing (multi-core/few-mode fibers), real-time MIMO digital signal processing for modal crosstalk mitigation, and SDN/NFV orchestration of disaggregated optical networks. Additional interests include quality-of-transmission estimation using machine learning, telemetry-enabled control planes (gRPC/gNMI), and metro-embedded edge/cloud architectures for IoT services. Publication Trends: Recent articles emphasize two converging themes: (i) petabit-per-second SDM/WDM experiments using novel fiber geometries and real-time DSP, and (ii) cloud-native SDN control frameworks that integrate machine-learning-based QoT prediction, YANG/NETCONF modeling, and open APIs (TAPI/OpenConfig) for multi-domain, partially disaggregated networks. These works collectively push both the physical capacity frontier and the agility of next-generation optical infrastructure. Scientific Awards: C&C Prize 2024 (NEC C&C Foundation) – contributions to WDM optical submarine cable systems IEICE Achievement Award 2021 – pioneering research on 10-Pbit/s ultra-large-capacity SDM transmission Telecom System Technology Award 2021 – 10.16-Pbit/s dense SDM/WDM transmission record IEEE Fellow (2021) – contributions to large-capacity high-speed transmission systems IEICE Fellow (2020) – research on trans-oceanic high-speed optical signal transmission Ichimura Industrial Award – Contribution Prize 2018 – development of terabit-class submarine cable systems Maejima Hisoka Award 2012 – proposal and demonstration of distributed-control soliton communication Minister of Economy, Trade and Industry Award for Advanced Technology 2006 – 160 Gbit/s ultra-high-speed optical transmission technology Advising & Grants: At Waseda University Morita advises graduate students on experimental photonic networking and leads externally funded projects on petabit SDM transmission and SDN orchestration. While specific grant numbers are not disclosed, his continuous industry-university collaborative testbeds (with KDDI, CTTC, and others) indicate substantial competitive funding. Labs & Teams: He heads the Optical Space-Division-Multiplexing Laboratory at Waseda, maintaining joint experimental facilities with KDDI Research and international partners (e.g., CTTC, Spain). The group operates real-time coherent MIMO testbeds, multi-domain SDN controllers, and fiber-level SDM prototypes capable of petabit-per-second demonstrations.
Amity Doolittle is a Senior Lecturer II at the Yale School of the Environment (YSE), Yale University. Her research focuses on property rights, natural resource management, and the social and political processes shaping environmental inequities. She employs interdisciplinary methods from anthropology, political science, environmental history, and political ecology. Notable projects include historical land-use studies in New Haven, Connecticut, and analyses of indigenous rights in global climate policy. She teaches graduate courses on qualitative research methods, environmental justice, and property rights, emphasizing active, collaborative learning. Doolittle holds a B.A. from Harvard University and M.E.S./Ph.D. from Yale University. Her office is located in Kroon Hall, Yale’s environmental campus. Education: B.A., Harvard University; M.E.S., Ph.D., Yale University Her research interests include legal pluralism, historical resource conflicts, and the intersection of colonial and postcolonial discourses. Current projects examine urban land-use changes in New Haven and the rhetorical strategies of indigenous leaders in climate negotiations. She advises 10–12 master’s students annually on thesis projects and placements worldwide. Teaching highlights include Qualitative Research Methods , Environmental Justice , and Political Ecology of Tropical Forests . Her courses integrate hands-on methods, such as analyzing oral histories and policy documents. Labs/Teams: Collaborates with the Urban Resources Initiative (URI) on greenspace stewardship and community-led projects.
Michael Weiss is a Lecturer in the Department of Mathematics at the University of Michigan. He teaches foundational courses such as Linear Algebra (MATH 217) and Explorations in Euclidean Geometry (MATH 431). His office is located in East Hall (Room 1868) at 530 Church Street, Ann Arbor, MI. Current Courses: MATH 217-004 (Linear Algebra), MATH 217-011 (Linear Algebra), MATH 431-001 (Explorations in Euclidean Geometry) Weiss’s research focuses on mathematics education, particularly on pedagogical strategies for teaching geometry and mathematical reasoning. His work explores the use of visual representations, interactive technologies, and narrative tools (e.g., comics) to enhance learning and teacher development. He emphasizes the role of theory-building in secondary geometry instruction and investigates how students translate abstract mathematical concepts into concrete reasoning. His publications highlight interdisciplinary approaches to education, integrating technology (e.g., animated stories, voice assistants) with traditional teaching methods. Keywords across his work include Mathematics Education , Geometry , Interactive Media , Visual Learning , and Teacher Training . Contact: mweiss@umich.edu
Jacob Young, MD, is an Assistant Professor in the Department of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine and a Principal Investigator in the UCSF Brain Tumor Center. His clinical practice focuses on neurosurgical management of adult brain tumors including gliomas, metastatic tumors, and meningiomas, utilizing advanced brain mapping techniques to preserve critical motor, language, and sensory functions during resection. Dr. Young's educational background includes a BS in Neuroscience from Duke University (2012), an MD from the University of Chicago Pritzker School of Medicine where he was elected to Alpha Omega Alpha Honor Medical Society (2017), and a neurosurgery residency at UCSF (2017-2024). His research program integrates laboratory investigations with clinical trials to address fundamental challenges in brain tumor treatment. His primary research interests center on understanding glioblastoma immune microenvironment dynamics and developing innovative therapeutic strategies. Key focus areas include: First-in-human clinical trials of novel immunotherapies Focused ultrasound-mediated blood-brain barrier disruption to enhance drug delivery Longitudinal molecular profiling of tumor evolution during treatment AI-driven tools for patient care navigation and clinical trial assessment Prospective outcomes research through the RANO resect group and NeuroPoint Alliance His work bridges fundamental tumor biology with translational applications to overcome treatment resistance. Analysis of Dr. Young's 15 most recent publications (2023-2025) reveals a strong emphasis on surgical innovation, tumor immunology, and molecular characterization. Key trends include: development of prognostic classification systems for resection extent, investigation of glioma-neuronal circuit interactions driving immunosuppression, and optimization of drug delivery strategies. His collaborative work within the RANO consortium establishes evidence-based surgical guidelines while his lab's focus on microenvironmental factors informs next-generation immunotherapies. Dr. Young has received significant recognition including: Chan-Zuckerberg Physician Scientist Fellowship (2021-2022) ASCO Young Investigator Award (2022-2023) Andrew J. Lockhart Focused Ultrasound Fellowship (2023) Multiple Harold Rosegay Teaching Awards from UCSF Howard Naffziger Award for Clinical Excellence His research is supported by NIH, NCI, Focused Ultrasound Foundation, and AANS grants. As lab director, Dr. Young mentors a diverse team including PhD candidates like Edward Valenzuela (DSCB program) and specialists in immunology and neuro-oncology. His lab participates in the RANO resect group, ENCRAM research program, and NeuroPoint Alliance to advance clinical protocols. Current projects include developing intraoperative focused ultrasound prototypes, single-cell analysis of tumor evolution, and AI tools for patient navigation through care pathways. Future work focuses on translating microenvironment discoveries into combination therapies targeting treatment resistance mechanisms.
Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.
Andrea Wollensak is Professor of Art at Connecticut College, where she has taught since 1993. She is an interdisciplinary artist and designer whose work explores the convergence of place, identity, and history through site-based artworks that combine new media technology with traditional design and fabrication. Her educational background includes a B.F.A. from the University of Michigan and an M.F.A. from Yale University. She has held visiting faculty positions at numerous institutions including the University of Gothenburg, Nova Scotia College of Art and Design, Concordia University, Rhode Island School of Design, and the Estonian Academy of Arts. Wollensak's research focuses on social design and place-based storytelling, employing collaboration with community partners, computer scientists, musicians, poets, and scientists. Her work addresses themes of place-based narratives, environment, and memory through forms including audio/video, interactive installations, data visualization, artist books, and digitally fabricated works. She examines how design can be used as a tool for social impact and civic participation. Her most recent projects include Mapping Your Journey to School (2023), which engages Estonian and Ukrainian refugee students in creative writing workshops; Water Stories: Visual Poetics and Collective Voices (2022), a year-long artist residency at the Anchorage Museum; and Open Waters (2019-2020), an interactive installation examining Arctic environmental change. U.S. Fulbright Scholar in Estonia (2023) Artistic Excellence Award in Digital Art from Connecticut Office of the Arts Artist residency at Hafnarborg Contemporary Art Museum in Iceland (2011) Connecticut Commission on Culture and Tourism Grants in New Media (2010, 2006) IASPIS International Artist Studio Program in Sweden (2007) Rockefeller Study and Conference Center residency (2000) Wollensak has advised numerous students in design and new media projects, often collaborating with community partners. She served as Director of the Ammerman Center for Arts and Technology at Connecticut College from 2014-2020 and currently serves on the Advisory Council of the Winterhouse Institute and the Advisory Board for Digital Media Connecticut. Her teaching emphasizes design as an interdisciplinary practice that prepares students to be creative problem solvers who can use design to create positive social impact. Her work has been exhibited internationally at venues including the Göteborg International Contemporary Art Biennial, Nova Scotia Art Gallery, Brno Design Biennial Moravian Gallery, Center for Visual Art in Denver, Brown University's Granoff Center Gallery, and the Burchfield Penney Arts Center in Buffalo.
Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.
Victor R. Lee serves as an Associate Professor at Stanford University's Graduate School of Education, with his office located at CERAS Building (520 Galvez Mall, Suite 531) in Stanford, California. He is actively affiliated with the Center for Studies in Education and Technology (CSET), where he conducts interdisciplinary research at the intersection of technology and learning. Dr. Lee holds a Ph.D. in Learning Sciences from Northwestern University and earned dual Bachelor's degrees in Cognitive Science and Mathematics from the University of California, San Diego. His academic trajectory bridges technical disciplines with educational research, establishing a foundation for his work in data-intensive learning environments. His research program centers on two interconnected domains: data literacy development in K-12 contexts and STEM education innovation across diverse learning spaces. He investigates how individuals make meaning from data during inquiry-based learning, with particular emphasis on self-collected student data and the epistemological challenges of data sense-making. Concurrently, his STEM education work spans traditional classrooms, makerspaces, computer labs, and school libraries, examining engaged learning practices and conceptual change in mathematics and science. Current projects focus on identifying the specialized knowledge teachers require to effectively scaffold student interactions with complex real-world datasets. Recent publications (2023-2024) reveal a strategic pivot toward artificial intelligence education, examining both teacher preparation and student understanding of AI systems. His work demonstrates consistent methodological rigor through design-based research, classroom implementations, and analysis of student reasoning patterns, particularly regarding how learners conceptualize algorithmic processes in platforms like YouTube. As a core faculty member within CSET, Dr. Lee collaborates with multidisciplinary teams to develop and evaluate educational interventions that bridge theoretical learning sciences with practical classroom applications, with recent emphasis on AI literacy tools and data-enabled pedagogical approaches.
Franz Huber is an Associate Professor in the Department of Philosophy at the University of Toronto (St. George campus). He holds an MA from the University of Salzburg, Austria, and a PhD from the University of Erfurt, Germany. His primary research areas include Formal Epistemology, Philosophy of Science, Philosophical Logic, and Metaphysics. Huber’s work focuses on belief revision, counterfactual reasoning, and means-end rationality, often employing formal methods to address philosophical problems. His publications include influential books like A Logical Introduction to Probability and Induction (2018) and Belief and Counterfactuals (2021), as well as numerous peer-reviewed articles in journals such as Synthese and Journal of Philosophical Logic . His research explores the foundations of epistemology and metaphysics, particularly through ranking theory and its applications to belief change, counterfactuals, and causal reasoning. Huber has also contributed to the philosophy of science, examining confirmation theory and the logic of scientific reasoning. His recent work emphasizes a means-end approach to philosophical problems, integrating instrumental rationality with normative theory. Huber’s publications demonstrate a commitment to bridging formal methods with traditional philosophical inquiry. His academic contributions include editing volumes on belief revision and co-authoring works on ranking theory. He maintains an active research agenda, reflected in his latest book Causality, Counterfactuals, and Belief (2025). His office is located in the Jackman Humanities Building, and he is affiliated with the University of Toronto’s philosophy faculty and research initiatives such as the Balzan Research Project on Styles of Reasoning.
Dr. Jeffrey Morgan is a Researcher at Cardiff University's School of Social Sciences, specializing in multidisciplinary research at the intersection of computer science, social science, and geography. His work emphasizes human-computer interaction, visualization, and big data analytics. He holds a Research Software Engineer role, combining technical expertise with academic inquiry. Key research interests include AI-driven patent analysis, IoT applications in rural citizen science, and geospatial Twitter demographics. He has contributed to studies on Hadoop infrastructure optimization and social media conflict detection, often collaborating with institutions like Xiamen University and the University of Bremen. His publications span topics like energy-efficient big data processing, digital geography of Welsh identity, and scalable social media analysis frameworks. Notable projects include COSMOS (a cloud-based social media analysis platform) and studies on post-devolution cultural narratives in Wales. Award-winning work includes computational Twitter analysis for detecting online community tensions and geotagging behavior patterns. His research often bridges technical innovation with societal impact, addressing challenges in rural technology deployment and digital sociology.
Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.
Christof Lutteroth is a Professor in the Department of Computer Science at the University of Bath and Director of the REal and Virtual Environments Augmentation Labs (REVEAL). His work focuses on Human-Computer Interaction (HCI) with emphasis on eye-gaze interaction and virtual reality (VR), particularly for health, exercise, and learning applications. He leads multiple research projects funded by organizations like EPSRC, The British Academy, and The Royal Society. Research Interests include developing gaze-controlled interfaces, immersive VR systems, and adaptive UI/UX for fitness and cognitive training. He explores affective design tools, emotion recognition in VR exergaming, and biometric data analysis for health applications. Recent Publications highlight advancements in gaze-based text entry, emotion measurement in VR, AI-driven UI development, and cross-European XR innovation networks. His work spans from foundational HCI methodologies to applied projects in rehabilitation and immersive learning. Grants include EPSRC IAA, British Academy, and Royal Society funding for projects like TapGazer, Hyper-immersive XR, and Affective Design Tools for VR. He collaborates with institutions across Europe through the EMIL project. Laboratory : REVEAL Lab at the University of Bath drives research in immersive technologies, motion analysis, and augmentation of human interaction with digital environments.
