Prof. Dr. Frank Ulbrich is a Lecturer and Programme Director for the BSc Business Administration at the Lucerne School of Business (HSLU). He holds a PhD in Business Administration from the Stockholm School of Economics. His academic career spans roles at universities in Sweden, the UK, Canada, and Switzerland, alongside IT & Management consulting experience. Research focuses on management education, project management, information management, and organizational change. He has published extensively in areas like shared services, digital transformation, and curriculum development. Notable awards include multiple Emerald Literati Awards and a Fellowship from the Higher Education Academy. Education: PhD in Business Administration (Stockholm School of Economics) Affiliations: Institute of Business and Regional Economics at HSLU Teaching Subjects: Academic Methods, Higher Education Management, Project Management, Digital Transformation Recent presentations include discussions on bridging academia-industry gaps, curriculum integration of SDGs, and data-driven learning strategies. His work emphasizes aligning education with labor market needs and advancing evidence-based teaching practices.
Xupeng Miao is a Visiting Assistant Professor in the Department of Computer Science at Purdue University, holding the Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professorship. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University's Catalyst Group under Professors Zhihao Jia and Tianqi Chen. He earned his Ph.D. in Computer Science from Peking University (2022) and a Bachelor’s degree from Northeastern University. His research focuses on machine learning systems, distributed computing, and data management, with notable contributions to systems like Hetu (a distributed deep learning framework) and innovations in large language model (LLM) serving (e.g., SpotServe, SpecInfer). He has received awards including the 2024 WAIC Yunfan Award and an Outstanding Paper Award at ACL 2024. Current projects emphasize efficient systems for generative AI, including optimizing LLM training and inference on heterogeneous hardware, fault-tolerant distributed training, and scalable graph-based machine learning. He teaches CS 59200-MLS: Machine Learning Systems at Purdue and actively mentors students in PhD/MS programs and internships. Key achievements include NVIDIA Academic Awards, IEEE Micro Top Picks recognition, and leadership in international conference roles (e.g., Artifact Evaluation Co-Chair for KDD 2025 and MLSys 2025). His work bridges system design, algorithmic innovation, and hardware-aware optimizations to advance scalable AI infrastructure.
Xiangyu Zhang is the Samuel D. Conte Professor of Computer Science at Purdue University. He specializes in AI security, software analysis, and cyber forensics, with a focus on detecting vulnerabilities in traditional software and AI systems. His research includes dynamic slicing, program profiling, and securing AI models against backdoors. He has led projects funded by DARPA, IARPA, NSF, and industry, securing over $13 million in grants. He has mentored 30+ PhD students and postdocs, many of whom hold academic positions or industry roles. His work has been recognized with awards like the ACM SIGPLAN Distinguished Dissertation Award and NSF Career Award. Education: PhD, Computer Science, University of Arizona (2006) MS & BS, Computer Science, University of Science and Technology of China (2000 & 1998) Research Interests: AI Security: backdoor detection, adversarial attacks, model integrity Program Analysis: dynamic slicing, static/dynamic debugging Cyber Forensics: log analysis, attack investigation Software Reliability: fault localization, vulnerability detection Key Projects: DARPA V-SPELLS: domain-specific program analysis IARPA TrojAI: AI backdoor detection competitions NSF-funded research on model debugging and co-reasoning of software/NL artifacts Awards & Recognition: Top performer in IARPA TrojAI competitions (13/18 rounds) ACM SIGPLAN Distinguished Dissertation Award (2006) NSF Career Award (2009) Ranked 8th globally in systems research productivity (2024) Career Contributions: Secured over $13M in research funding Guided 25 PhDs and 8 postdocs Published 89+ top-tier papers (2014–2024)
Scott Barnett is a Professor of Materials Science and Engineering at Northwestern University , specializing in ceramic thin films and electrochemical systems for energy applications. His research spans over 25 years in solid oxide fuel cells (SOFCs) and lithium-ion battery electrodes, with a focus on 3D printing, thin electrolyte deposition, and microstructural evolution under operational stress. Education: Ph.D. and B.S. in Metallurgy and Physics from the University of Illinois at Urbana-Champaign. Research Interests center on: Reversible solid oxide cells for energy storage 3D tomographic imaging of electrode microstructures Novel processing methods for ceramic materials Electrochemical stability and degradation mechanisms Recent Publications highlight advancements in: Pressurization effects on oxygen electrodes 3D printed SOFCs with enhanced performance Nanoparticle infiltration for fuel electrode stability Impedance modeling of lithium-ion batteries Scientific Awards include: Fulbright Scholar (2015) Cheng Tsang Man Endowed Professorship (2008) Office of Naval Research Young Investigator (1986) AVS Fellow (1998) Advising Legacy encompasses over 30 graduate students and postdocs, including Nicholas Geisendorfer (NASA Space Technology Fellow) and Matthew Lu (now at Tesla), with notable alumni placements at institutions like Bloom Energy and Microsoft . His lab employs advanced characterization techniques to predict long-term durability of electrochemical devices.
Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.
Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.
Ana Klimovic is an Assistant Professor at the Department of Computer Science, ETH Zürich, and Deputy Head of the Institute for Computing Platforms. She leads the Efficient Architectures and Systems Lab (EASL) and focuses on optimizing cloud computing performance and resource efficiency. Education: Ph.D. in Electrical Engineering (Stanford University), M.S. in Electrical Engineering (Stanford), B.A.Sc. in Engineering Science (University of Toronto) Previous Roles: Research Scientist at Google Brain Her research spans cloud computing, operating systems, distributed systems, and their intersection with machine learning. Key areas include cloud-native programming models, elastic storage, GPU sharing, and data pipeline optimization. The 2025-2024 articles highlight trends in serverless computing, large language model (LLM) serving, GPU optimization, and data-centric ML systems. Themes include elasticity, resource multiplexing, heterogeneous computing, and infrastructure for AI/ML workloads. Scientific Awards: Microsoft Research PhD Fellowship, Stanford Graduate Fellowship She has led research projects on ephemeral storage, distributed training automation, and cost-efficient ML preprocessing, with grants from Microsoft Research and Stanford.
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Dr. K. Max Zhang is a Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He is the director of the Energy and the Environment Research Laboratory (EERL) and a fellow at the Atkinson Center for a Sustainable Future. His research is deeply interdisciplinary, focusing on sustainable energy systems, air quality, and environmental justice, with significant impacts on policy and community development in New York and beyond. Ph.D., Mechanical Engineering, University of California-Davis, 2004 B.S., Thermal Engineering, Tianjin University, 1998 B.A., English Language, Tianjin University, 1998 Dr. Zhang’s research centers on the integration of energy and environmental systems. He investigates air pollution dynamics using advanced numerical models like CTAG, with applications in near-source pollution, indoor air quality, and environmental justice. His work on renewable energy systems includes designing sustainable solar farms and managing distributed energy resources such as heat pumps to enhance grid flexibility. He also leads a pioneering initiative to create the first statewide public IoT network in the U.S., enabling hyperlocal weather forecasting and microclimate monitoring. His recent publications reflect a strong trend toward agrivoltaics, peer-to-peer energy markets, and IoT-based environmental monitoring. These works demonstrate a consistent focus on data-driven modeling, community-scale energy solutions, and the integration of social considerations into technical systems. The keywords across his articles highlight expertise in sustainability, machine learning, air quality, and energy transition. Cornell Town-Gown Achievement Award (2022) Engaged Scholar Prize, Cornell University (2017) People's Choice Sign of Sustainability Award, Sustainable Tompkins (2016) Scientific and Technological Achievement Award, Environmental Protection Agency (2015) Fellow of the American Society of Mechanical Engineers Dr. Zhang is actively involved in mentoring students and securing research grants from agencies such as the National Science Foundation (NSF) and the New York State Energy Research and Development Authority (NYSERDA). His projects often involve interdisciplinary collaboration across eight Cornell colleges and 16 academic departments. He has led initiatives such as the Cornell Atkinson Academic Venture Fund projects and the development of a county-level energy roadmap for Tompkins County. He also teaches courses in engineering thermodynamics, future energy systems, and air quality, emphasizing experiential and community-based learning. Dr. Zhang leads the Energy and the Environment Research Laboratory (EERL) and collaborates with the Atkinson Center for a Sustainable Future. His lab functions as a hub for innovation in sustainable communities, combining advanced modeling with real-world applications. Through partnerships with community organizations, government agencies, and industry, his team develops science-driven solutions to urban and rural sustainability challenges.
Gretchen A. Macht is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island, College of Engineering. She specializes in Human Systems Engineering, with research emphasizing team performance, sustainability, electric vehicles, solar energy systems, and voting systems. Her work bridges engineering, ergonomics, and public policy. Her research interests focus on understanding and optimizing human-centered systems. She investigates team communication and performance through fuzzy cognitive maps and entropy modeling, develops sustainable design practices for products and energy systems, and analyzes user behavior in electric vehicles and solar installations. A significant portion of her research is dedicated to election systems, where she uses simulation to improve accessibility, safety, and efficiency of in-person voting, particularly during public health crises. Her recent publications span topics such as EV charging behavior, solar installation safety, voting simulation, and team dynamics. These works reflect interdisciplinary trends combining systems engineering, data analytics, ergonomics, and sustainability science. She frequently employs simulation, naturalistic data analysis, and human factors methodologies. Dr. Macht has secured substantial grant funding from organizations including the U.S. Election Assistance Commission, National Science Foundation, MIT, and Democracy Fund. She serves as Principal Investigator (PI) or Co-PI on projects related to election systems (e.g., SMILE, URI VOTES), ergonomic redesign of protective gear, and community solar development. She leads the SIS Lab and the URI VOTES initiative, fostering collaborative research on simulation and system design. Her lab supports graduate students in conducting impactful research at the intersection of engineering and societal challenges. Notable grants include: PI, MIT: “Voting in Person - White Paper” (2023) PI, US EAC: “SMILE 2.0” (2022) PI, Democracy Fund: “URI VOTES Pocket Guide Tool” (2022) Co-PI, NSF: “Election Science: Convergence Accelerator Workshop Proposal” (2021) PI, US EAC: “SMILE: Simulation Models for an Immersive Learning Environment” (2021) She has advised several graduate students, including Mehrsa Khaleghikarahrodi, Nicholas D. Bernardo Jr., and Tim Jonas, whose research aligns closely with her focus areas in EVs, elections, and team performance.
Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).
Dr. Gloria Milena Monsalve Bravo is an Advanced Queensland Industry Research Fellow and lecturer at The University of Queensland's School of Chemical Engineering, where she develops novel multiscale simulation techniques combining molecular simulations with macroscopic physics-based modeling to solve complex energy and environmental problems. Her interdisciplinary work bridges applied mathematics and engineering to improve understanding of phenomena in complex systems across chemical, biomedical, and ecological applications. Her research focuses on: Multiscale simulation techniques for complex systems Molecular simulations coupled with macroscopic modeling Gas permeation and separation in mixed-matrix membranes Uncertainty and sensitivity analysis in mathematical models Applied mathematics for engineering problems Dr. Monsalve Bravo's publication record demonstrates a strong trajectory in membrane technology and computational modeling. Her recent work has advanced understanding of gas transport in novel membrane materials, particularly mixed-matrix membranes, with applications in carbon capture and hydrogen storage. She has made significant contributions to theoretical frameworks for modeling permeation in finite-sized composite systems and developed Bayesian approaches for analyzing parameter uncertainty in sorption predictions. Her research bridges fundamental science with practical applications in energy and environmental engineering. Her scientific contributions have been recognized through research funding including: ARC Research Hub for Value-Added Processing of Underutilised Carbon Wastes (2024-2029) Tailor-made composite membranes for greenhouse gas capture (2023-2026) through Advance Queensland Industry Research Fellowships Dr. Monsalve Bravo actively mentors PhD students on cutting-edge projects related to membrane technology, catalyst development, and waste conversion. She collaborates extensively across disciplines, as evidenced by her diverse publication record spanning chemical engineering, materials science, and environmental applications.
Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division
Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.
Shane G. Henderson is the Charles W. Lake, Jr. Professor in Productivity at Cornell University's School of Operations Research and Information Engineering (ORIE). His research focuses on the intersection of optimization and simulation, with applications to complex systems such as bike-sharing networks, medical scheduling, and ambulance deployment. He emphasizes practical societal relevance, addressing challenges in healthcare, transportation, and parallel computing environments. Education: B.Sc. (Mathematics), University of Auckland (1992) M.S. (Statistics), Stanford University (1995) Ph.D. (Operations Research), Stanford University (1997) Research Interests: Structured simulation optimization, parallel computing applications, applied probability, and case-driven problem-solving. Recent work includes MRI scheduling, radiation treatment planning, and land/air ambulance systems. He advocates for methodologies that leverage convexity or quasi-convexity to enhance algorithmic efficiency and robustness. Awards and Recognition: 2016 Tau Beta Pi Professor of the Year 2014 Sonny Yau '72 Excellence in Teaching Award 2011 Hess Research & Education Award NSF CAREER Award (1999) Teaching & Service: Courses in probability, simulation, and mathematical modeling using case-based learning. Served on award committees for INFORMS Nicholson Prize, Pierskalla Prize, and Impact Prize. Organized major conferences including the 2009 INFORMS Applied Probability Society conference and multiple Winter Simulation Conferences. Currently co-edits simulation-optimization testbeds for experimental comparisons. Labs & Collaborations: Active in Cornell's collaborative research environment, focusing on interdisciplinary projects with industry partners like Motivate (bike-sharing systems). Engaged in advancing methodologies for complex systems through data-driven approaches.
