Laura Munoz is an Associate Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. She holds a BS from the California Institute of Technology and a Ph.D. from the University of California at Berkeley. Her research focuses on mathematical biology, dynamical systems, applied control theory, and cardiac electrophysiology, with a particular emphasis on understanding mechanisms underlying cardiac arrhythmias through mathematical modeling and computational methods. Her work explores topics such as ephaptic coupling in cardiac tissue, controllability of cardiac alternans, and the role of calcium dynamics in arrhythmogenesis. Recent studies include analyzing discordant alternans mechanisms and their link to ventricular fibrillation, as well as developing state estimation techniques for cardiac ionic models using Kalman filters. Munoz has published extensively in journals like Physical Review Letters , Chaos , and Computers in Biology and Medicine , and has presented at conferences such as the SIAM Conference on the Life Sciences. Munoz currently teaches courses such as Linear Algebra, Complex Variables, Mathematical Modeling I, and Mathematical Biology, emphasizing the application of mathematical tools to real-world biological systems. Her research integrates principles from applied mathematics, control theory, and computational biology to advance understanding of cardiac physiology and disease.
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
K. Rajibul Islam is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a joint appointment with the Perimeter Institute for Theoretical Physics and co-founded Open Quantum Design and Lightflow Optics Inc. His research focuses on quantum information processing, quantum simulation, and trapped ion systems, with applications in quantum computing and entanglement studies. Education: Ph.D. in Physics (2012, University of Maryland), M.Sc. in Physics (2007, Tata Institute of Fundamental Research), B.Sc. in Physics (2005, Jadavpur University). Postdoctoral research at Harvard University (2012–2015) and MIT (2015–2016). Research Interests : Quantum simulation of spin models, quantum computing with trapped ions, entanglement measurement, frustrated spin systems, and quantum materials. His lab, QITI (Quantum Information with Trapped Ions), develops scalable quantum simulators and open-access quantum computers like 'QuantumIon.' Awards : Fellow of the American Physical Society (2024), VAIBHAV Fellowship (2024), Excellence in Teaching Award (2024), Early Researcher Award (2019), and Distinguished PhD Dissertation Award (2012–13). Teaching : Courses include PHYS 701 (Graduate Quantum Physics), PHYS 234 (Quantum Physics I), PHYS 393 (Physical Optics), and PHYS 256 (Geometrical and Physical Optics). He emphasizes outreach via initiatives like Bigyan.org.in , a Bengali-language science platform. Lab and Collaborations : Active in developing trapped-ion quantum hardware, including ion trap designs, optical addressing systems, and holographic control methods. Collaborates on quantum algorithms, machine learning for quantum systems, and experimental quantum thermodynamics.
Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.
Yoel Inbar is an Associate Professor at the University of Toronto, affiliated with the Department of Psychology and the Morality, Affect, and Politics (MAP) Lab. His research explores the intersection of moral intuitions, emotions, and political/social beliefs, with a focus on disgust sensitivity and its implications. He also investigates public acceptance of emerging technologies like genetic engineering. Contact: yoel.inbar@utoronto.ca . PhD, Cornell University BA, University of California at Berkeley His research spans moral psychology, political ideology, and behavioral responses to technological innovation. Recent studies employ natural language processing to analyze morality in real-world contexts, such as political discourse and environmental attitudes. The MAP Lab emphasizes interdisciplinary approaches, integrating psychology, behavioral economics, and computational methods to study moral decision-making and its societal consequences. Alumni from the lab include researchers now at institutions like UC Berkeley, University of the Fraser Valley, and Cornell University, reflecting his mentorship of advanced psychological and behavioral science scholars.
Professor Lucy Kimbell is Professor of Contemporary Design Practices at Central Saint Martins, University of the Arts London, where she convenes the Policy Futures Studio. She is Co-Director of the Sustainable Transitions through Democratic Design Doctoral Network funded by the EU Marie Curie programme and serves as Visiting Professor at University of Ulster and Honorary Adjunct Professor at RMIT University. Her work bridges design practice, public policy, and social innovation across multiple international contexts. Professor Kimbell's educational background includes a PhD in Design from Lancaster University (2013), an MA in Computing in Art and Design from Middlesex University (1996), and a B. Engineering in Design and Appropriate Technology from the University of Warwick (1989). Her academic journey reflects a consistent integration of technical, creative, and social perspectives. Kimbell's research focuses on service design, social design, and design for policy, with increasing emphasis on sustainable transitions and democratic innovation. She explores how design methods can address complex societal challenges, particularly through transdisciplinary approaches that connect creative practice with public policy contexts. Her work examines the role of design in government, professional services, and community settings, with particular attention to AI readiness in professional service firms and antimicrobial resistance policy in India. She has pioneered frameworks for measuring design's social and environmental value, notably through the Design Value framework developed with the Design Council. Her recent publications demonstrate a clear trajectory toward understanding design's role in public policy, sustainable transitions, and democratic innovation. The research shows increasing integration of practice-based research methods with policy development, emphasizing transdisciplinary collaboration and the application of design thinking to complex societal challenges. There's a notable focus on developing frameworks for measuring design's social and environmental value and advancing democratic innovation through design practice. Notable recognitions include: AHRC Research Fellow in Policy Lab in the Cabinet Office (2014-15) Principal research fellow at University of Brighton (2013-15) Clark Fellow in Design Leadership at Said Business School, University of Oxford (2005-10) Professor Kimbell supervises transdisciplinary PhD research connecting design with public policy, with six completions to date including four UAL-KCL joint studentships. She examines PhDs internationally and serves on the supervisory board for the PhD in Service Design for Public Sector at Sapienza University, Rome. Her grant portfolio includes major projects funded by EU Marie Curie, AHRC, ESRC, and Creative Europe, totaling millions in research funding for design-led approaches to societal challenges, including the Sustainable Transitions through Democratic Design Doctoral Network (2024-28) and the AHRC Design & Policy Research Network (2022-23). She previously directed UAL's Social Design Institute (2019-2022), which brought together university expertise in design for society through capacity building, joint research, publications, and public events. Her collaborative work extends to knowledge exchange projects with Design Council, Department of Work and Pensions, Northern Ireland Social Care Council, and Bite Back 2030, demonstrating her commitment to applying design thinking to real-world policy and social challenges.
Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team
Michael S. Horn is a Professor at Northwestern University with a joint appointment in Computer Science and the Learning Sciences. He directs the Tangible Interaction Design and Learning (TIDAL) Lab and coordinates the Learning Sciences PhD Program. His work focuses on leveraging interactive technology to design innovative learning experiences, such as tangible programming languages (e.g., Tern) and music-coding platforms (e.g., TunePad). He holds a PhD from Tufts University and has collaborated with institutions like the California Academy of Sciences and the Museum of Science, Boston. Education: PhD in Computer Science, Tufts University MS in Computer Science, Tufts University BS in Computer Science, Brown University Research Interests: Dr. Horn explores how emerging technologies can create engaging learning environments. His projects include museum exhibits (e.g., DeepTree, Build-a-Tree), educational tools for computational literacy (e.g., TunePad, Strawbies), and collaborative design with educators. Recent work emphasizes integrating music and coding to foster computational thinking. Publications: His recent work addresses computational thinking in STEM education, AI-driven qualitative analysis, and hybrid music-coding practices. Themes include equitable participation, teacher professional development, and immersive AR/VR tools for learning. Awards: Recipient of the 2018 Edith Ackermann Award for innovative work in child-computer interaction. His research has led to commercial products like Osmo Coding and Kibo Robotics. Grants & Labs: Principal investigator on NSF grants (e.g., #1612619, #1451762) and collaborator with labs like the Center for Connected Learning (CCL) and the Collaborative Technology Laboratory (CollabLab).
Angela Di Fulvio is an Associate Professor and Donald Biggar Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Nuclear, Plasma, and Radiological Engineering and the Center for Digital Agriculture at NCSA. She leads the Nuclear Measurement Laboratory (NML), focusing on radiation detection technologies for nonproliferation, medical physics, and nuclear security. Her academic journey includes a Ph.D. in Nuclear Engineering and Industrial Safety from the University of Pisa (2012), preceded by M.Sc. and B.Sc. degrees in Bioengineering. Her research emphasizes neutron detection instrumentation, radiation protection in therapy, and safeguards applications. Key areas include next-generation thermal neutron detectors, boron neutron capture therapy dosimetry, and spent nuclear fuel imaging. She has pioneered work on pulse shape discrimination using commercial ASICs and developed algorithms for neutron-gamma discrimination in harsh environments. Di Fulvio’s 15+ peer-reviewed articles span advanced detection systems, Monte Carlo modeling, and machine learning for radiation imaging. Notable contributions include a physics-based forward model for spent fuel imaging and variational autoencoder-based pulse discrimination. Her work has been recognized with the Dean’s Award for Excellence in Research. Professional roles include Associate Editor of Radiation Measurements and editorial board member of Nature Scientific Reports . She chairs APS’s Instrumentation and Measurement Science group and ANS’s Nuclear Nonproliferation Policy Division. Recent courses taught include NPRE 451-452 labs, Nuclear Safeguards, and Student Research Seminars.
Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Laurent DUPONT is a Research Engineer and Co-coordinator of the IUVTT Master’s program at ENSGSI (École Nationale Supérieure de Géologie et de Sciences Industrielles), part of the Groupe INP in Nancy, France. He also serves as Scientific Manager of the Lorraine Fab Living Lab. His work focuses on user-driven innovation, smart and sustainable territories, collaborative engineering, and immersive environments. He holds a PhD in Industrial Systems Engineering from INPL (2009), a DEA (2004), and a degree in Industrial Systems Engineering (2003) from ENSGSI. Research interests include innovation lab strategies, open innovation spaces, territorial resilience, and digital twin applications in manufacturing. He leads projects like INEDIT, exploring co-design frameworks and circular economy solutions. His articles highlight trends in collaborative innovation networks, systemic impact mapping, and the integration of extended reality in industrial contexts. Key contributions include frameworks for evaluating territorial resilience and tools for monitoring innovation lab networks. He has been involved in initiatives such as the DHDA Project for resilience assessment and the Smagrinet project on smart grid training. His work bridges academia and practice through participatory methods and cross-sector collaborations. Current projects emphasize Industry 5.0, open manufacturing, and community-driven innovation.
Dr. Shirley Coyle is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU) and Programme Chair for the BSc Global Challenges. She holds a BEng in Electronic Engineering from DCU (2000) and a PhD in Biomedical Engineering from NUI Maynooth (2005). Her career includes roles as a Telecoms Engineer at Siemens, Research Fellow at the National Centre for Sensor Research, and Team Leader of Wearable Sensors in the INSIGHT Centre for Data Analytics. She also studied part-time at the Grafton Academy for Fashion Design and later founded a consultancy in wearable technologies. Her research focuses on smart garments, wearable sensors, and sustainable textiles, with applications in healthcare, sports performance, and S.T.E.A.M. integration. Key interests include developing wearable chemical sensors, energy-autonomous sensing systems, and IoT-enabled rehabilitation devices. She has pioneered work on wearable sensors for monitoring chronic diseases, athlete training, and home rehabilitation using VR. Dr. Coyle’s work spans interdisciplinary collaboration, combining biomedical engineering with textile design. Her contributions include innovations in electrospun textiles, self-powered sensors, and sensor integration with microfluidics. She has held leadership roles in DCU’s Governing Authority and promotes STEM education through design-focused initiatives.
Jonathan Shihao Ji is an Associate Professor in the School of Computing at the University of Connecticut (UConn), leading the Intelligent Systems Lab. He holds a Ph.D. in Electrical and Computer Engineering from Duke University and previously served as an Associate Professor at Georgia State University and Director of the DoD Center of Excellence (CiARE). His research focuses on deep learning applications in computer vision, NLP, robotics, and high-performance computing, with over 50 publications in top venues like CVPR, NeurIPS, and IEEE journals. He has secured grants from NSF, NIH, DoD, and industry partners including VMware and Nvidia. His work emphasizes efficient algorithms for large-scale data processing, parameter-efficient model fine-tuning (e.g., VB-LoRA), and 3D perception benchmarks for UAVs (UAV3D). Notable contributions include sparse network optimization (Dep-L0), energy-based models (M-EBM), and robust defenses against adversarial attacks (Defense-VAE). He is a Senior Member of IEEE and has developed open-source tools like Parallel Word2Vec and WordRank. Recent projects include accelerating Llama2 models on FPGAs (LlamaF) and improving text-to-image synthesis via contrastive learning. His research spans theoretical advancements and practical applications, with industry collaborations in healthcare, robotics, and embedded systems.
Geoff Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with CAIDA's AIM-SI cluster. He is also a Canada CIFAR AI Chair and faculty member at the Vector Institute. His research bridges deep learning and probabilistic modeling, focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss earned his PhD in Computer Science from Cornell University (2020), followed by a postdoc at Columbia University. He holds multiple awards, including the AISTATS Top Reviewer and NeurIPS recognitions. His work emphasizes scalable algorithms and open-source contributions, such as the GPyTorch library. Pleiss advises students in Computer Science and Statistics, including Donney Fan (PhD), Tim G. Zhou (MSc), and others. He teaches advanced courses like STAT 547U (Deep Learning Theory) and STAT 520P (Bayesian Optimization). Grants include NSERC Discovery and New Frontiers in Research funding. Pleiss collaborates on interdisciplinary projects, such as astrophysical discovery via machine learning, and actively participates in academic service and outreach. Education: PhD in Computer Science, Cornell University (2020) MSc in Computer Science, Cornell University (2018) BSc in Engineering (Computing with Applied Mathematics), Olin College (2013) Key Research Themes: Uncertainty-aware decision-making with neural networks Scalable Gaussian processes and Bayesian optimization Ensemble methods and their theoretical limitations Recent Grants: NSERC Discovery Grant (2024) New Frontiers in Research Fund (2025, co-PI) His publications span foundational theory to applied machine learning, with over 14,500 citations. He actively mentors students through research internships and advises on open-source software development. Pleiss frequently presents at top conferences and collaborates with industry partners like Microsoft and ASAPP.
