Ronald C. Lasky is a Professor of Engineering at Dartmouth College's Thayer School of Engineering and a Senior Technologist at the Indium Corporation. His academic roles include teaching courses such as ENGM 187: Technology Innovation and Entrepreneurship and ENGS 155: Intermediate Thermodynamics . Dr. Lasky holds a BS in Engineering Physics from Cornell University (1970), an MS in Applied Mathematics from Binghamton University (1974), and a PhD in Materials Science from Cornell University (1986). His research focuses on process optimization, electronic assembly, materials science, and environmental compliance. Notable contributions include work on lead-free solder assembly and Lean Six Sigma methodologies. He received the Member of Technical Distinction Award from the SMTA in 2021. Dr. Lasky actively engages with industry, exemplified by his collaboration with Galanz in China, exploring modern manufacturing practices and infrastructure. His insights on global supply chains and technological advancements highlight his dual academic and professional expertise.
Prof. Walter Leal Filho is a distinguished academic and researcher holding professorial positions at Hamburg University of Applied Sciences (Germany) and Manchester Metropolitan University (UK). He is the founding director of the European School of Sustainability Science and Research (ESSSR) and the Inter-University Sustainable Development Research Programme (IUSDRP). With over 30 years of editorial experience, he has authored over 500 publications, focusing on sustainability, climate change, and environmental management. His research interests span Climate Change, Urban Resilience, Natural Resource Management, and Sustainability Education. He emphasizes interdisciplinary approaches to address global challenges, such as space debris mitigation and bio-based materials for sustainable packaging. His work bridges policy, education, and practical solutions for sustainable development. Prof. Leal Filho’s recent articles highlight trends in sustainable urban planning, space sustainability, and ecosystem conservation in the Amazon. He has been recognized as a Top Cited Scholar 2024, reflecting his impactful contributions. With 1,200+ co-authors and a global network, he collaborates widely, fostering international research and education initiatives. As director of ESSSR and IUSDRP, he leads programs advancing sustainability science and interdisciplinary research. His academic leadership and prolific publications underscore his role as a pivotal figure in global sustainability efforts.
Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.
Asaf Cidon is an Associate Professor at Columbia University, jointly affiliated with the Department of Electrical Engineering and Computer Science, and a member of the Data Science Institute. His research focuses on software systems , storage , large-scale machine learning , and cybersecurity . Stanford University - PhD in Electrical Engineering Stanford University - MS in Electrical Engineering Technion - BS in Computer and Software Engineering His work in distributed storage systems has been commercialized by companies such as Facebook, Tibco, and Rubrik. He has led projects like Sentinel and Forensics during his industry career. Recent publications highlight advancements in software-based radiation protection (ASPLOS'26), PCIe pooling with CXL (HotOS'25), and AI phishing detection (IMC'25). These reflect his expertise in system architecture , security , and networking . Scientific recognitions include best paper awards at OSDI, Usenix Security, CIDR, and ATC, along with NSF CAREER and ARO Young Investigator Awards . His papers Cookie Monster (SOSP'24) and Chablis (CIDR'24) received notable accolades. He has mentored numerous PhD and Master's students , including Edward Guo, Harry Wang, and Teng Jiang, many of whom now hold roles at Google, Meta, Amazon, and academic institutions. His lab at Columbia is actively recruiting CS and EE PhD students. Prior to Columbia, he founded and led the startup Sookasa to acquisition and served as Senior Vice President of Email Protection at Barracuda Networks , managing a $200M business with 100 engineers.
Sung-Kyu Lim is a Professor and the Motorola Solutions Foundation Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, where he serves as director of the GTCAD Laboratory. His educational background includes: B.S. in Computer Science from the University of California, Los Angeles (UCLA) in 1994 M.S. in Computer Science from UCLA in 1997 Ph.D. in Computer Science from UCLA in 2000 Professor Lim's research focuses on advancing VLSI design automation through physical design methodologies, 3D circuit integration, quantum circuit layout, micro-architecture exploration, and reconfigurable circuit optimization. His work bridges theoretical graph theory with practical electronic design automation challenges to improve circuit performance and scalability. His notable scientific distinctions include: NSF CAREER Award (2006) Advisory Board Member of ACM SIGDA (since 2003) Technical Program Committee roles for ICCD, ISPD, ISCAS, ASPDAC, and GLSVLSI conferences He leads the GTCAD Laboratory at Georgia Tech, driving innovation in computer-aided design tools for next-generation integrated circuits and systems.
Rakesh Kumar is a Professor and John Bardeen Faculty Scholar in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign. His work focuses on computer architecture, system-level design automation, and low-power computing. PhD in Computer Engineering from University of California, San Diego BS in Electrical Engineering from IIT Kharagpur His research spans all layers of the computing stack, with key contributions to flexible computer systems , waferscale computing , error-resilient architectures , and approximate computing . He has pioneered work on voltage-reliability tradeoffs and peak power management techniques. Recent publications highlight trends in space microdatacenters , printed microprocessors , and neural graph accelerators . His work on plastic chips was recognized as one of the three biggest semiconductor headlines of 2022 by IEEE Spectrum. IEEE Fellow (2024) ISCA Influential Paper Award MICRO Test-of-Time Award ICCAD Ten Year Retrospective Most Influential Paper Award Best Paper Awards at CASES, SELSE, HPCA He has received teaching accolades including the Stanley H. Pierce Faculty Award and Ronald W. Pratt Outstanding Teaching Award . His research group explores hardware-software co-design for emerging applications in AI, IoT, and sustainable computing.
Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.
Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.
Michael J. Aziz is the Gene and Tracy Sykes Professor of Materials and Energy Technologies at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He serves as Area Chair for Materials Science and Mechanical Engineering and is a Faculty Associate at the Harvard University Center for the Environment. His research focuses on electrochemical engineering for energy and environmental applications, including redox flow batteries, carbon capture, and sustainable energy technologies. Aziz leads the Aziz Group, which develops grid-scale energy storage solutions and innovative methods for CO₂ removal. He holds equity in Quino Energy, a startup commercializing his battery research, and serves as Chief Scientist and Board Member. His work bridges fundamental materials science with practical engineering, emphasizing ClimateTech solutions. Key contributions include aqueous organic redox flow batteries, quinone-based carbon capture systems, and wearable energy storage devices. Education & Affiliations: Affiliated with SEAS since joining Harvard, his academic roles include coordinating the Graduate Consortium for Energy and Environment (2009–2018). His lab (Materials Science Group) is located at McKay 504, with administrative support from Sabrina Azinheira. Research Interests: Aziz's group investigates electrochemical energy storage, CO₂ capture via electrochemical systems, and novel materials for sustainable technologies. They employ advanced techniques like operando electrochemical fluorescence microscopy to study porous electrode dynamics and battery degradation mechanisms. Their work emphasizes scalability and real-world applicability, such as grid-scale battery infrastructure and decarbonization strategies. Recent Trends in Publications: Aziz's recent work emphasizes carbon capture innovations (e.g., acid-base concentration swing methods), hydrogen storage under ambient conditions, and electrochemical synthesis of industrial chemicals like hydrogen peroxide. His group also develops open-source tools like RFBzero for battery modeling and explores bioinspired materials (e.g., self-gelling hydrogel batteries). Awards & Recognition: While no personal awards are explicitly listed in the text, his team members (e.g., Dawei Xi) have received accolades such as the 2025 Carbon Future Young Investigator Award. Aziz's contributions have been recognized through industry partnerships and startup ventures. Advising & Industry Impact: Aziz advises PhD students focusing on electrochemical systems (e.g., Jordan Sosa, Tommy George). His industry engagement includes licensing intellectual property to Quino Energy, which achieved a manufacturing milestone in 2024 for grid-scale battery systems. His research bridges academia and industry, addressing climate challenges through technological innovation. Labs & Teams: The Aziz Group includes interdisciplinary researchers from electrochemistry, chemical engineering, and materials science. Collaborators include institutions like MIT and industry partners. Current projects target next-gen batteries, CO₂ removal systems, and scalable energy storage solutions.
Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.
Hao Liu is an incoming Assistant Professor of Machine Learning at Carnegie Mellon University and currently works as a research scientist at Google DeepMind. Previously, he completed his Ph.D. in Computer Science at UC Berkeley under the supervision of Pieter Abbeel. He also spent two years part-time at Google as part of the Google Brain team. His educational background includes: Ph.D. in Computer Science from UC Berkeley Hao Liu's research focuses on solving intelligence through deep learning, neural networks, and innovative learning objectives. His work spans multiple areas including large language models, reinforcement learning, world models, and attention mechanisms for long context processing. He has made significant contributions to making transformer models more efficient and capable of handling extremely long sequences through techniques like Ring Attention and Blockwise Transformers. His recent publications demonstrate a strong focus on extending the capabilities of language and vision models, particularly in handling long sequences and multimodal data. Key themes include attention optimization, tokenization efficiency, and alignment techniques. His work bridges theoretical advances with practical implementations for real-world AI systems, with multiple papers at top conferences including NeurIPS, ICML, and ICLR, often receiving spotlight or oral presentations. Hao is actively involved in open-source AI research, having contributed to projects like Koala and OpenLLaMa, which aim to make advanced language models more accessible to the research community. His work on RingAttention has been implemented as a Python package available on GitHub, demonstrating his commitment to practical implementations and community sharing.
Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as the Director of the 3D Systems Packaging Research Center. His research focuses on heterogeneous integration of microsystems, including 2.5D and 3D ICs and packaging technologies, with significant contributions to advanced cooling systems, electrical and photonic interconnects, and biosensor integration with CMOS. Dr. Bakir's research interests span heterogeneous microsystem design and integration, advanced cooling and power delivery for emerging architectures, electrical and photonic interconnect technologies, biosensor technologies, and nanofabrication. His work addresses critical challenges in next-generation electronics, enabling polylithic integration that concatenates heterogeneous ICs of various functionalities while mimicking monolithic-like densities. His research particularly focuses on co-design of thermal technologies, power delivery networks, and signaling networks for silicon nanoelectronic systems. His recent publications demonstrate strong trends in fused-silica stitch-chip technology for heterogeneous integration, with particular emphasis on RF and mm-wave applications, power delivery for AI accelerators, and thermal management solutions. His work bridges electrical engineering, materials science, and thermal management to solve critical bottlenecks in computing performance and efficiency. 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Society Outstanding Young Engineer Award 2012 National Academy of Engineering Frontiers of Engineering Symposium Invited Participant 2015 IEEE CPMT Society Distinguished Lecturer 2014 Best Paper of the IEEE Transactions on Components Packaging and Manufacturing Technology More than 25 conference and student paper awards Twelve issued US Patents Dr. Bakir leads the Integrated 3D Systems Lab (I3DS) at Georgia Tech, which is actively researching advanced packaging, interconnects, electrical and thermal design, and system integration. His team has received significant recognition for their work, including multiple best paper awards from major conferences like ECTC, IITC, and CICC. The lab is currently seeking postdoctoral researchers and research faculty to advance next-generation electronics through collaborative research. His lab focuses on enabling the next phase of Moore's Law through polylithic integration, which concatenates heterogeneous ICs of various functionalities (digital, analog, photonic, and mm-wave) using advanced off-chip '2.5D' and '3D' heterogeneous interconnects and packaging. This work impacts applications in high-performance computing, machine learning, edge intelligence, autonomous vehicles, augmented/virtual reality, and healthcare.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Danick Briand is a Senior Scientist at the Soft Transducers Laboratory within the Microsystems for Space Applications Group (LMTS) at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on MEMS and Microsystems for environmentally friendly technology , integrating flexible and printed electronics with applications in energy harvesting , smart sensing systems , and advanced gas sensing . Research Themes : Environmental sensors using microsystem technology Green microtechnologies and micromanufacturing Ultra-low energy MEMS Energy-saving and harvesting systems Recent Publications : Developed transient biodegradable sensors and microwave sensing technologies using printed and degradable materials Explored flexible piezoelectric systems and wearable sweat analyzers for biomedical applications Advanced inkjet-printed biosensors and eco-friendly fabrication methods Labs & Collaborations : Soft Transducers Laboratory (EPFL) Laboratory for Microsystems (LMTS)
