Steve Chase is a Professor at Carnegie Mellon University , affiliated with the Biomedical Engineering , Electrical and Computer Engineering , Neuroscience Institute , and Robotics Institute departments. His research spans Computational Neuroscience , Neural Engineering , and Systems Neuroscience , with a focus on neural circuits, motor control, and brain-computer interfaces (BCI). Research Areas: Sensation & Perception, Methods Development, Diseases & Disorders, Physiological & Anatomical Methods. Lab Highlights: Development of the RotaWheel, memory trace studies in the motor cortex, and investigations into BCI stabilization and learning dynamics. Scientific Contributions: His lab has published extensively in journals like Neuron , Nature Computational Science , eLife , and PNAS , with notable works on neural activity patterns, dimensionality reduction in calcium imaging, and sensory constraints on motor cortex modulation. Students and postdocs in his lab have received awards, including the CNBC best paper award.
Allen J. Scott is a Distinguished Research Professor at the University of California, Los Angeles, with a joint appointment in the Department of Public Policy (School of Public Affairs) and Department of Geography. He has held prestigious positions at UCLA since 1994 and previously at the University of Toronto (1969–1994). His career spans over five decades, focusing on urban geography, regional development, and cultural economy. Education : BA (1961) from Oxford University; MA and PhD (1962, 1965) from Northwestern University. Research Interests include: Creative cities and cognitive-cultural capitalism Political economy of urbanization and globalization Industrial clusters and labor-intensive economies Human capital distribution in metropolitan areas Cultural landscapes and peripheral development Publication Trends (2005–2014) emphasize: Globalization's impact on low-technology industries Cognitive-cultural capitalism in urban contexts Human capital dynamics in metropolitan hierarchies Celebrity culture and urban glamour Creative field theories and urban policy implications Scientific Awards : 1986 Guggenheim Fellow 2003 Vautrin Lud Prize 2005 Meridian Book Prize 1999–2013: Chaires d’Excellence, Fellowships, and Doctor Honoris Causa 2008 Carol and Bruce Mallen Lifetime Achievement Award 2003 ISI Highly Cited Author
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
John D. Murray is the Gregg L. Engles Associate Professor of Psychological and Brain Sciences at Dartmouth College and an Adjunct Associate Professor of Psychiatry at Yale School of Medicine. He holds a PhD in Physics from Yale University (2013) and a BS in Physics and Mathematics from Yale (2006). His research focuses on computational neuroscience and computational psychiatry, with secondary appointments in Physics and Neuroscience at Yale until 2023. His work integrates computational modeling, neuroimaging, and systems neuroscience to study decision-making processes, cortical organization, and psychiatric disorders. Collaborators include prominent researchers like Dr. John Krystal and Dr. Anticevic. Research interests include hierarchical brain organization, neuroimaging analysis techniques, and pharmacological effects on neural circuits. His lab (Murray Lab) develops computational tools like PsychRNN for cognitive task modeling. Notable contributions include linking transcriptomic data to neuroimaging patterns and modeling LSD’s effects on brain topography. He has been featured in YaleNews and Nature Communications for innovations in mapping mental illness variability and neural circuit dynamics. Grants and collaborations span translational neuroscience, addiction, and PTSD research through partnerships with Yale’s Center for Biomedical Data Science and VA National Center for PTSD. His interdisciplinary approach bridges physics, computer science, and clinical psychiatry to advance understanding of brain function and dysfunction.
**Dr. JIN Chen** is an **Associate Professor** in the **Department of Information Systems and Analytics** at the **National University of Singapore (NUS) School of Computing**. He holds a PhD in Industrial Engineering and Management Science from Northwestern University (U.S.) and joined NUS in 2018 after postdoctoral work at the Wharton School, University of Pennsylvania. His research focuses on **online platforms**, **information systems operations**, and **digital transformation**, with a particular interest in addressing challenges like fake information dissemination and platform competition. His work bridges **operations management** and **information systems**, emphasizing practical applications in e-commerce and fintech. **Education**: B.Sc. (Shanghai Jiao Tong University) M.Sc. & Ph.D. (Northwestern University, U.S.) **Research Interests**: Platform design and competition Information bundling and pricing strategies Consumer search behavior Right-to-repair policies and environmental impacts **Awards & Recognition** (selected): Finalist, MSOM Service Management SIG Best Paper Award (2024) Nomination for NUS Young Researcher Award (2022–2024) Winner, INFORMS ENRE Young Researcher Prize (2022) Teaching Excellence Awards (2021–2023) **Key Projects**: Optimal information structure design to combat fake orders/reviews Environmental and welfare implications of right-to-repair laws Strategic bundling and pricing in multi-service platforms
Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.
Dr. Jeremy Guest is the Levenick Professor in Civil and Environmental Engineering (and courtesy appointment in Chemical & Biomolecular Engineering) at the University of Illinois Urbana-Champaign (UIUC). He serves as Associate Director for Research at the Institute for Sustainability, Energy, and Environment (iSEE) and leads the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). His research focuses on advancing circular bioeconomies through sustainable sanitation systems, resource recovery from wastewater, and biofuel/bioproduct development from lignocellulosic biomass. Guest's work emphasizes Quantitative Sustainable Design (QSD) methodologies to bridge engineering innovation with policy and investment decisions. Education: PhD in Environmental Engineering (University of Michigan, 2012); MS (Virginia Tech, 2007); BS (Bucknell University, 2005). Research interests include wastewater treatment innovation, nutrient recovery, bioenergy systems design, and sustainable agriculture practices. Key contributions include the EcoRecover phosphorus recovery process, the QSDsan modeling platform, and leadership in CABBI's $262.5M DOE-funded initiative. Guest has secured funding from NSF, US EPA, USDA, and the Bill & Melinda Gates Foundation. Recognitions include the NSF CAREER Award, 2021 James J. Morgan Early Career Award, and the Paul L. Busch Award. He advises over 15 graduate students and leads multiple high-impact projects like the NEWgenerator sanitation system and oilcane biorefinery integration. Lab/Group: Guest Research Group focuses on experimental, computational, and policy-oriented research to solve global sustainability challenges. Current projects address algae-based wastewater treatment, fecal sludge management in low-income regions, and precision fermentation systems development.
Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Justin A. Weibel is a Professor of Mechanical Engineering at Purdue University, affiliated with the School of Mechanical Engineering. He directs the Cooling Technologies Research Center (CTRC), a National Science Foundation Industry/University Cooperative Research Center. His research focuses on advanced electronics cooling, phase-change transport, additive manufacturing for thermal components, and machine-learning-driven design optimization. He has led projects funded by DARPA, ONR, ARPA-E, and industry partners, advancing cooling solutions for high-power electronics and energy systems. Research interests span thermal management, heat transfer, micro/nano-scale engineering, and sustainable energy. Key contributions include topology optimization for heat sinks, two-phase flow modeling, and embedded cooling systems for electric motors. His work integrates computational methods with experimental validation. Grants & Programs: DARPA TGP/ICECool, ONR NEPTUNE, ARPA-E ASCEND/COOLERCHIPS, SRC CHIRP Labs: Cooling Technologies Research Center (CTRC) Future Work: Expanding additive manufacturing applications, improving thermal efficiency in electrified transport, and advancing AI-driven thermal system design. Awards: Fellow of ASME (2023) Outstanding Faculty Mentor (2022) Multiple best paper awards from IEEE ITherm, ASME, and SEMI-THERM conferences
Sushmita Roy is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Computer Sciences and the College of Letters and Science. Her research focuses on developing computational methods in statistical machine learning to understand gene regulatory networks in living cells, particularly under environmental, developmental, disease, and evolutionary contexts. She explores bulk and single-cell genomic data integration to study processes like cell fate specification, host-microbe interactions, and diseases such as cancer and neurodevelopmental disorders. Her work emphasizes three key areas: inference of genome-scale transcriptional networks, evolutionary analysis of regulatory networks, and 3D genome organization dynamics. Roy’s lab collaborates across disciplines, leveraging genomic data from plant and mammalian systems. She has contributed to methodologies for analyzing chromatin accessibility, single-cell profiling, and network-based models of pathogen systems. Her affiliations include Wisconsin Institutes for Discovery, and she is a leader in computational biology and systems genomics research.
Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).
Dr. John Francis Clinton is the Director of Seismic Networks and Head of the Earthquake Monitoring Section at the Swiss Seismological Service (SED), ETH Zurich. He leads the Marsquake Service for NASA's InSight mission and oversees Switzerland's broadband and strong-motion seismic networks. His expertise spans earthquake early warning systems, seismic instrumentation, and glacial seismology. Clinton is also a Co-Investigator on the Mars InSight mission and involved in international projects like EPOS and RAMSIS. Education PhD in Civil Engineering (Minor in Geophysics), California Institute of Technology (2004) MSc in Civil Engineering, California Institute of Technology (1998) BEng in Civil Engineering, University College Dublin (1997) Research Interests Dr. Clinton focuses on real-time seismology, seismic instrument design, structural health monitoring, and glacial seismology. His work bridges engineering applications with geophysical data analysis, particularly in earthquake early warning systems and induced seismicity studies. He collaborates internationally on projects such as the Valais Near Fault Observatory and Nicaragua’s Earthquake Early Warning development. Publications Overview His 15 most recent papers (2011–2015) highlight advancements in seismic network optimization, Marsquake detection algorithms, and glacial icequake mechanisms. Key themes include improving early warning accuracy, understanding subglacial dynamics, and validating high-rate GPS for structural monitoring. Awards & Memberships Member, Swiss Academy of Sciences (since 2008) Member, IRIS Quality Assurance Advisory Committee (since 2013) Chair, European Integrated Data Archives (EIDA) (2013–2015) Labs & Teams Clinton directs the SED’s Earthquake Monitoring team and collaborates with ETH Zurich’s Geophysics Masters Program. His group manages Switzerland’s seismic networks and leads the Marsquake Service, which analyzes InSight lander data for Martian seismic events.
Yan Liu is a full professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), serving as Director of the USC Machine Learning Center within the Viterbi School of Engineering. He holds courtesy appointments in the Ming Hsieh Department of Electrical Engineering and the Quantitative and Computational Biology Department. Before joining USC in 2010, he was a research staff member at IBM's T.J. Watson Research Center. He earned his M.S. and Ph.D. from Carnegie Mellon University. His research focuses on machine learning for time series, physics-informed AI, and interpretable models, with applications in healthcare, sustainability, and social media. Notable projects include developing AI for surgical training, analyzing misinformation on social platforms, and predicting cancer treatment outcomes. He has held leadership roles in top conferences like ICLR and ACM KDD, and serves as Associate Editor-in-Chief of TPAMI and Board Member of ICLR. Education: Ph.D., Carnegie Mellon University Affiliations: USC Machine Learning Center, Viterbi School of Engineering Service: General Chair (ICLR 2023, ACM KDD 2020), Program Chair roles across multiple conferences His lab, the Melady Group, emphasizes foundational ML advancements and interdisciplinary applications. Recent work includes physics-aware neural networks and time-series foundation models.
Nathan Schine is an Assistant Professor at the University of Maryland, specializing in quantum physics and quantum information science. He leads the Schine lab, which explores controlled coherent dynamics and engineered dissipation in quantum systems, particularly using optical cavities coupled to tweezer-trapped cold atoms. His research bridges atomic physics, quantum optics, and condensed matter physics. Education: B.A. in Physics, Williams College (2013) Ph.D. in Physics, University of Chicago (2019) Research interests focus on quantum many-body systems, optical cavities, and applications such as quantum information processing and ultra-coherent atomic clocks. The lab’s work includes developing state-of-the-art strontium tweezer array apparatuses for precision metrology and quantum simulation. Recent publications highlight advancements in Dicke state preparation, optical pumping of quantum Hall states, and cavity-enhanced measurements. Advising and grants involve mentoring graduate students and postbaccalaureate researchers, including Shardul Rao and Siddharth Taneja. The lab collaborates with groups like AMPED, QuICS, and RQS at UMD. Members include postdoctoral researchers and graduate students working on theoretical quantum optics and experimental setups. Labs/Teams: The Schine lab integrates atomic, optical, and condensed matter physics approaches to address fundamental and applied questions in quantum science.
