Robert MacCurdy is an Assistant Professor at the Department of Mechanical Engineering, University of Colorado Boulder . He leads the Matter Assembly Computation Lab (MACLab) focused on automating robot design and fabrication. His research bridges computational design and advanced manufacturing to create "robots that walk out of the printer." The lab develops tools like OpenVCAD , an open-source volumetric multi-material geometry compiler.
Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.
David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.
Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).
Colleen Conley is a Clinical Professor in the Department of Psychology at Loyola University Chicago, specializing in clinical psychology with a focus on adolescent and emerging adult mental health. She directs the IMPACT (Improving Mental-health and Promoting Adjustment through Critical Transitions) Lab, which conducts research on psychological well-being during developmental transition periods. Dr. Conley earned her Ph.D. from the University of Illinois at Urbana-Champaign and her B.A. from Lawrence University. Her research program blends observational studies, meta-analyses, and intervention development to improve well-being, particularly during critical life transitions like the college years. Her work examines psychological trajectories in adolescence and emerging adulthood, with special attention to gender issues and LGBTQIA+ well-being. Her research interests center on developmental psychopathology, examining the interplay between individuals and their contexts over time. She investigates how biological, psychological, cognitive, and social factors interact to influence psychological distress and well-being. Much of her work focuses on college student mental health, including trajectories of well-being during the transition to college and interventions to address mental health challenges. Analysis of her recent publications reveals a strong focus on college student mental health, with emphasis on developmental trajectories, intervention effectiveness (particularly peer-led and technology-delivered approaches), and the impact of transition periods on psychological functioning. Her work increasingly incorporates digital interventions and examines moderators of intervention effectiveness across diverse student populations. Research Mentoring Award Program recognition for mentoring award-winning students Heckler Summer Research Fellowship recognition for mentoring fellowship recipients Dr. Conley mentors numerous graduate students in the clinical psychology program, with many completing dissertations on topics related to mental health interventions, student well-being, and developmental transitions. Her IMPACT Lab collaborates with university staff, administrators, and other researchers to develop practical interventions that address student mental health needs. Current projects include Honest, Open, Proud (HOP) for stigma reduction, Wellness Advising with Motivational Interviewing (WAMI), Students Taking on Effective Post-Graduate Skills (STEPS), and Supportive Accountability Methods for Mentoring Young Adults (SAMMY-A). The lab also partners with commercial entities like Headspace for mindfulness research.
Chi-Ming Yang is a Professor of English at the University of Pennsylvania's School of Arts & Sciences, where she has established herself as a leading scholar in literary and visual culture studies with a focus on race, empire, and East-West cultural exchanges. Her interdisciplinary approach bridges British, American, Asian, and Latin American studies while examining historical periods from the early modern era through the 18th century to contemporary contexts. Educational Background: Ph.D. in English from Cornell University B.A. in Comparative Literature from Stanford University with emphases in Spanish, Chinese, and English Professor Yang's research interrogates the intersections of race, visual culture, and imperial histories through multiple disciplinary lenses. Her work explores how objects, images, and texts function within global networks of power and representation, with particular attention to transpacific and transatlantic connections. She has developed significant expertise in chinoiserie, material culture, and the visual dimensions of racial formation across historical periods. Her recent publications demonstrate a growing interest in ecological perspectives on race and empire, particularly evident in her 2021 article 'The Song of the Rice Bird,' which innovatively examines plantation slavery through avian migration patterns. Her scholarship consistently bridges historical analysis with contemporary concerns about representation, power, and cultural exchange. Professional Recognition: 2022 Best Advisor Award in English, Sponsored by the English Undergraduate Advisory Board Professor Yang maintains an active advising role, having chaired doctoral dissertations including Jacob Myers' 'Noxious Life: Figuring Vermin in the Natural Histories of the Anglophone Caribbean' (2025) and Cassidy Holahan's 'Dramatizing the Novel: Transmedial Exchange in the Long Eighteenth Century' (2023). She regularly participates in departmental workshops and events, including the 2024 conversation with Sharon Holland exploring Black feminist perspectives on animal life in relation to Octavia E. Butler's work. Her teaching portfolio reflects her interdisciplinary approach, with recent courses including 'Science Fictions of Octavia E. Butler,' 'Orientalisms,' '18th-Century British Poetry,' and 'Arts of Abolition and Liberation,' demonstrating her commitment to connecting historical scholarship with contemporary social justice concerns.
Professor Agnieszka Zabłocka-Kos is a faculty member at the Institute of Art History, University of Wrocław, specializing in Central European architectural history and urbanism. Her work focuses on 19th-20th century architecture, with particular emphasis on Wrocław/Breslau, including post-war reconstruction, national identity construction, and sacred space design. Professor, University of Wrocław Specialist in 19th-20th century Central European architecture Director of postdoctoral internships (NCN FUGA III) Recipient of multiple Rector's awards for research and teaching Research Themes: Urban development in Central Europe, political architecture in Poland/Germany, sacred architecture of the 19th century, post-war modernism, industrial architecture revitalization. Her students have produced award-winning theses on topics like Wrocław urban blocks, Silesian architects, and post-industrial adaptations. Scientific Contributions: Notable works include the edited volume Architecture in the 'long' 19th century (2019), multiple articles on Breslau's architectural conflicts, and international projects on Central European sacred architecture. She has supervised 33 master's and 5 doctoral theses, with several student works published as books. Dettloff Prize for dissertation (1992) Rector's awards for research and didactic achievements International projects with universities in Berlin, Freiburg, and Leipzig Advisor for Wrocław's Monument Office and City Development Bureau Her teaching integrates interdisciplinary approaches, linking art history with political, economic, and social contexts. She promotes student research popularization through conferences, exhibitions, and publications.
Andre Levchenko is the John C. Malone Professor of Biomedical Engineering at Yale University, with secondary appointments in the Department of Neurosurgery and affiliations with the Cancer Signaling Networks, Immunology, and the Yale Program in Neurodevelopment and Regeneration. His research focuses on systems biology, signal transduction, and cell-cell communication, utilizing microfluidics and computational modeling to study cancer progression, stem cell behavior, and neurological disorders. PhD, Columbia University MEng, Moscow Institute of Physics and Technology Levchenko's work explores how cells process dynamic signals to make critical decisions, particularly in glioblastoma migration, organoid development, and cardiovascular tissue engineering. His lab develops innovative microfluidic platforms and mathematical models to dissect multicellular communication and signaling networks. Recent publications highlight his contributions to understanding YAP-driven cancer invasion , NOTCH signaling in angiogenesis , and metabolic regulation of hypoxia responses . He has pioneered methods for organoid modeling and single-cell analysis , advancing precision in biological signaling studies. Scientific Awards : Computational Molecular Biology Post-Doctoral Fellowship (Burroughs Wellcome Fund) National Academies Keck Futures Conference Invitee Distinguished Guest Lecturer, University of Virginia American Asthma Foundation Early Excellence Award Fellow, American Institute for Medical and Biological Engineering Levchenko leads the Levchenko Lab at the Yale Systems Biology Institute, collaborating with institutions like Mayo Clinic and Yale Cancer Center. His research has received recognition in Faculty of 1000 and multiple journal highlights.
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Dr. Gregor Schuhknecht is a Researcher and incoming Max-Planck Research Group Leader at the Max Planck Institute for Brain Research in Frankfurt, Germany. Starting December 2025, he will lead the Brain Algorithms and Circuits Group, focusing on computational neuroscience and neuronal circuitry in larval zebrafish. His work bridges experimental and computational approaches to understand brain algorithms enabling flexible behaviors. Education PhD in Neuroscience (2014–2019), Institute for Neuroinformatics, University of Zurich and ETH Zurich MSc in Biology (Neuroscience) (2012–2019), ETH Zurich BSc in Biosciences (2009–2012), Heidelberg University His research investigates how synaptic circuitry implements computational algorithms for sensorimotor processing, evidence accumulation, and behavior. Experimental approaches include functional imaging, optogenetics, connectomics, and computational modeling. The group will explore neuronal circuits across scales, from synapse to behavior, with a focus on biophysical properties and dynamic environmental adaptation. Recent publications highlight correlative light/electron microscopy, developmental activity-free circuit formation, and neocortical synapse analysis. Scientific Awards Meselson Prize (2024) Aspirational Neuroscience Award (2023) Best Publication Award (2021) Swiss National Science Foundation Fellowships (2021, 2019, 2018) Gregor Schuhknecht's lab will welcome graduate and postdoctoral researchers starting December 2025, focusing on experimental and computational systems neuroscience. He previously collaborated with Harvard University's Department of Molecular and Cellular Biology and the Department of Moritz Helmstaedter for connectomic analyses.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Prof. Dr. Kenan Aycan is a Professor of Anatomy at Ahi Evran University's Faculty of Medicine, Department of Basic Medical Sciences, where he has served since 2019. He also holds the position of Department Head since 2020. Previously, he worked at Erciyes University as a School Director from 2011-2016. His academic career spans over four decades with significant contributions to anatomical sciences. Dr. Aycan earned his PhD in Basic Medical Sciences from Ege University's Faculty of Medicine (1983-1986) following his Bachelor's degree in Science from Ege University's Faculty of Science (1970-1975). He also holds a Certificate of Use of Experimental Animals from Erciyes University (2008). His research focuses on anatomical morphology, vascular structures, and developmental processes. Dr. Aycan has pioneered anatomical techniques including the 'Aycan's method' for corrosion preparations. His work spans comparative anatomy across various species, morphometric analyses of anatomical structures, and investigations into teratogenic effects and protective agents. He has extensively studied the foramen magnum using golden ratio principles, vascular anatomy of reproductive organs in ruminants, and auditory ossicles in sheep. Analysis of his recent publications (2021-2025) reveals consistent focus on anatomical methodology development, morphometric studies of key anatomical structures, vascular anatomy investigations, and research on developmental processes and teratology. His work often employs plastic injection and corrosion techniques, CT imaging, and comparative approaches across human and animal models. Dr. Aycan has mentored numerous graduate students, serving as primary advisor for over 20 Master's and PhD theses covering diverse anatomical topics from vascular variations to developmental studies. His collaborative network includes researchers like Tufan Ulcay, Burcu Kamaşak, and others across Turkish institutions.
