Nathan Martin is an Associate Professor of Music Theory at the University of Michigan, School of Music, Theatre & Dance. He joined in 2015 after postdoctoral fellowships at Columbia, Harvard, Katholieke Universiteit Leuven, Hochschule für Musik Freiburg, and Yale. He holds a PhD from McGill University’s Schulich School of Music (2009). His research focuses on the history of music theory and musical form analysis, particularly the works of Jean-Philippe Rameau and their reception among Enlightenment thinkers like Jean-Jacques Rousseau. He integrates intellectual history with practical analytical methods, emphasizing both contextual and emic perspectives. Recent interests include adapting empirical social science and linguistics methods to study musical form. Key publications include articles on Rameau’s harmonic concepts, Rousseau’s musical writings, and global music theory history. His 2014 article on Rameau’s supposition/suspension theories earned the Society for Music Theory’s Outstanding Publication Award. He co-edited Formal Functions in Perspective (2015) and served as co-editor of Music Theory & Analysis until 2019. Professional highlights include the Edward T. Cone Membership at the Institute for Advanced Study (2018–2019) and a 2023 visiting researcher role at the Max Planck Institute for Empirical Aesthetics. His work bridges historical analysis with contemporary empirical approaches, fostering interdisciplinary dialogue in music theory.
Maxim Kontsevich has been a permanent professor at the Institut des Hautes Études Scientifiques (IHÉS) since 1995, holding the AXA-IHES Chair for Mathematics . He is also Professor at Rutgers University (one month per year since 1997) and was Professor at University of California, Berkeley (1993–1995). Born 25 August 1964 in Khimki, former USSR, he became a French citizen in 1999. Education: Ph.D. in Mathematics, University of Bonn, March 1992. Research Interests: Prof. Kontsevich’s work lies at the intersection of algebraic geometry , mathematical physics , and quantum theory . He introduced revolutionary ideas in mirror symmetry , non-commutative geometry , and deformation quantization , creating new algebraic structures that have found applications far beyond their original contexts. His current ERC Synergy Grant “ReNew Quantum” (2019-2025) explores recursive and exact new quantum theory . Publications & Trends: Across more than 50 influential papers, Kontsevich has consistently pushed the boundaries of enumerative geometry , motivic integration , and topological field theory . His recent work delves into symplectic aspects of homological algebra , motivic Donaldson-Thomas invariants , non-archimedean geometry , and integrable systems , revealing deep connections between geometry, algebra, and physics. Scientific Awards & Honors: Fields Medal (1998) Crafoord Prize (2008) Shaw Prize in Mathematical Sciences (2012) Breakthrough Prize in Fundamental Physics (2012) Breakthrough Prize in Mathematics (2014) ERC Synergy Grant “ReNew Quantum” (2019) AMS Moore Prize (2025) Doctor Honoris Causa: Aarhus University (2014), Universität Wien (2015), Syddansk Universitet (2023) Member of Académie des Sciences, Institut de France, Academia Europaea, National Academy of Sciences (foreign), London Mathematical Society (honorary) Grants & Leadership: As Principal Investigator of the ERC Synergy Grant “ReNew Quantum” (€10 million, 2019-2025), Kontsevich coordinates an international team advancing exact methods and resurgence in quantum field and string theories. Editorial Service & Community Roles: He serves on the editorial boards of Compositio Mathematica , Publications Mathématiques IHÉS , Journal of Noncommutative Geometry , and several other leading journals, shaping the direction of contemporary mathematical research.
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Edward H. Adelson is the John and Dorothy Wilson Professor of Vision Science at MIT, affiliated with the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans computer vision, human vision science, and robotics, with a focus on artificial touch sensing and tactile robotics. He has pioneered technologies like the GelSight tactile sensor, enabling high-resolution touch sensing for robots surpassing human skin sensitivity. Adelson holds a PhD in Experimental Psychology from the University of Michigan (1979) and a BA in Physics and Philosophy from Yale University (1974). His career includes roles at MIT since 1987, progressing from Associate Professor to Professor and later the Wilson Chair. He contributed to early vision theories, including the plenoptic function and motion energy models, and has been recognized with prestigious awards like the Helmholtz Prize (2013) and Rank Prize (1992). His research interests include material perception, optical sensing, and the integration of vision and touch. Key innovations include the plenoptic camera, layered representation techniques for motion analysis, and tactile sensors for robotics. Adelson has authored over 300 publications and holds numerous patents in imaging, vision, and robotics. Awards and honors include membership in the National Academy of Sciences and the American Academy of Arts and Sciences. His work bridges neuroscience and engineering, advancing both fundamental understanding and practical applications in robotics and computer vision.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University within the Language Technologies Institute (with courtesy appointment in Robotics Institute). His research bridges Natural Language Processing , Robotics , and Embodied AI , focusing on language grounding, theory of mind, and multimodal interaction. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Postdoctoral Experience : USC ISI, University of Washington, Allen Institute for AI Industry Appointments : Microsoft Research, Meta AI His research emphasizes embodied language systems and social intelligence in AI . Recent projects include WebArena for autonomous agents, SOTOPIA for social reasoning, and HomeRobot for open-vocabulary manipulation. He leads the REAL Center (Robotics, Embodied AI, and Learning) to foster interdisciplinary collaboration. Key scientific awards include selection for the DARPA ISAT Study Group (2024). He teaches courses like "Talking to Robots" and "Multimodal Machine Learning" while serving as area chair/editor across NLP, Robotics, and ML communities.
Tae-Ho Lee is an Associate Professor in the Department of Psychology at Virginia Tech, with affiliated appointments in the School of Neuroscience and Translational Biology, Medicine, and Health. His research focuses on affective and cognitive neural development across the lifespan, neurodegeneration, and family-based neural dynamics. PhD in Brain and Cognitive Science, University of Southern California M.A. in Clinical Psychology, Korea University B.A. in Psychology, Korea University Dr. Lee’s work explores brain connectome dynamics, dyadic neural concordance in families, and age-related attentional control. He employs neuroimaging techniques to study how familial and environmental factors shape emotional and cognitive outcomes in adolescents and older adults. Recent publications highlight trends in longitudinal studies , parent-child neural similarity , and functional connectivity in emotion regulation . Key areas include autism spectrum disorder, substance misuse risk, and the role of socioeconomic factors in brain development. Rising Star , Association for Psychological Science (2020) Dr. Lee is not currently accepting students and leads the Affective Neurodynamics and Development (AND) Lab at Virginia Tech.
Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.
Jianjing Kuang is an Associate Professor of Linguistics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. As Director of the Penn Phonetics Laboratory, they lead research in phonetics, laboratory phonology, and tonal language studies. Kuang holds a Ph.D. from UCLA (2013) and specializes in the interplay between production and perception in speech, particularly focusing on tonal systems, prosody, and cross-linguistic fieldwork. Their work integrates behavioral experiments, corpus studies, and computational modeling to explore phonological contrasts, voice quality, and sound change. Key affiliations include MindCORE and the Center for East Asian Studies. Research interests include multidimensional cues in tone processing, glottal articulations, mapping production-perception relationships, and prosodic sentence processing. Kuang has conducted fieldwork on languages such as Yi, Q’anjob’al, and Mandarin. Their studies address topics like tonal splitting in Yi, cue-changing in Korean stops, and prosodic patterns in Mayan languages. Recent work explores voice quality’s role in pitch perception and prosodic boundary detection. Awards and grants are not explicitly listed, but Kuang’s extensive publications (over 50 peer-reviewed papers) reflect sustained academic impact. They advise graduate students in phonetics and phonology, with active collaborations in computational linguistics and speech technology. The Penn Phonetics Laboratory, under Kuang’s direction, emphasizes interdisciplinary research bridging experimental and theoretical phonetics.
Dr. Akhilesh Jaiswal serves as Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison, where his research pioneers device-circuit co-design for next-generation computing systems. His work focuses on enabling extreme-edge intelligence through processing-in-pixel technology, in-memory computing architectures, and bio-inspired neuromorphic systems. His academic credentials include: PhD in Nano-electronics from Purdue University (2019) MS from the University of Minnesota (2014) Bachelor of Technology from Shri Guru Gobind Singhji Institute of Engineering and Technology (2011) Dr. Jaiswal's research program centers on revolutionizing edge computing through hardware innovations that integrate sensing and processing. His device-circuit co-design approach leverages alternate state variables to create energy-efficient systems for real-time applications, with particular emphasis on retina-inspired sensors and photonic memory architectures. This work bridges semiconductor physics with AI acceleration needs, targeting applications from autonomous systems to biomedical devices. Analysis of his 2023-2025 publications reveals dominant themes in photonic SRAM-based in-memory computing (40% of output), retina-inspired motion processing (30%), and secure hardware architectures (20%). His team consistently develops novel bitcell designs that enable XOR logic execution within memory arrays while maintaining compatibility with CMOS fabrication processes. Recent work shows increasing focus on biomedical applications of processing-in-pixel technology. His distinguished recognition includes: Three consecutive ISI Exploratory Research Awards (2020-2023) IEEE Brain Community Best Paper Award (2022) 27 issued US patents with multiple pending applications Nomination for USC Moore Inventor Fellowship (2022) Dr. Jaiswal actively mentors graduate researchers through ECE 790/890/990 courses while securing exploratory funding through ISI and Keston Foundation awards. His patent portfolio demonstrates exceptional translational impact, with industry game-changer classifications from USPTO. The 2022 VLSI-SoC nomination and multiple research highlights in major outlets validate his contributions to hardware security and neuromorphic vision sensors. His laboratory develops integrated hardware platforms combining magnetic tunnel junctions, photonic memory, and CMOS image sensors to create unified processing-in-sensor systems. Current projects include retina-inspired motion segmentation for event cameras and electro-optic frequency transducers for quantum computing interfaces, with strong industry collaboration through patent licensing.
Dr. Wim Biemans is an Associate Professor at the Faculty of Economics and Business of the University of Groningen, specializing in Innovation Management and Marketing. He holds a PhD from Eindhoven University of Technology (1989) and has over 25 years of experience in research, teaching, and consulting. His work focuses on service innovation, B2B marketing, customer involvement in innovation, and the marketing-sales interface. He has authored over 100 peer-reviewed articles and books, including the best-selling Dutch B2B marketing textbook. His research has been recognized with awards like the Thomas P. Hustad Best Paper Award. He serves as Triage Editor for the Journal of Product Innovation Management and editorial board member for Industrial Marketing Management and Journal of Business & Industrial Marketing. Biemans teaches at the University of Groningen and multiple international institutions, including guest professor roles in Europe, Asia, and the U.S. He also designs executive programs for managerial training in innovation management and customer value creation. His recent research explores humor’s impact on creativity, digital tools in sales-marketing integration, and bridging technology-to-product development gaps. He emphasizes end-to-end value creation through intra-organizational collaboration and has published on the Valley of Death ecosystem challenges in innovation commercialization.
Rebecca Schulman is an Associate Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School of Engineering, Johns Hopkins University. She holds secondary appointments in Chemistry and Computer Science and is affiliated with multiple interdisciplinary institutes, including the Institute for NanoBioTechnology, the Hopkins Extreme Materials Institute, the Chemistry-Biology Interface Program, the Center for Cell Dynamics, and the Laboratory for Computational Sensing and Robotics. She currently co-directs the Passport to Future Technology Leadership program for PhD students. Research Interests: Schulman's research lies at the intersection of DNA nanotechnology, synthetic biology, and smart materials. Her group develops intelligent, adaptive biomolecular materials and nanostructures by integrating concepts from materials science, biochemistry, circuit design, and soft matter physics. The team focuses on engineering dynamic self-assembly processes using DNA to create reconfigurable materials, molecular circuits, and autonomous soft micro-robots. Key themes include self-healing nanostructures, feedback-regulated crystallization, programmable hydrogels, and synthetic genetic networks for materials control. Publication Trends: Her recent publications demonstrate a consistent focus on using DNA-based chemical reaction networks to program spatial and temporal behavior in materials. The work spans from fundamental mechanisms like catalytic polymerization and crystal growth regulation to applications in soft robotics, self-wiring circuits, and synthetic pattern formation. The research is highly interdisciplinary, combining synthetic biology with materials engineering to achieve life-like functionalities in non-living systems. Scientific Awards: AIMBE Fellowship Award Vannevar Bush Faculty Fellowship Award Hartwell Individual Biomolecular Research Award President’s Early Career Award in Science and Engineering (PECASE) DARPA Young Faculty Award DARPA Directors Fellowship NSF CAREER Award Turing Scholar Award DOE Early Career Award Advising and Grants: Schulman mentors graduate students and leads a vibrant research group focused on next-generation biomolecular engineering. Her work is supported by major federal grants, including the NSF CAREER, DOE Early Career, DARPA, and the Vannevar Bush Fellowship—a prestigious Department of Defense award for basic research. She is actively involved in training future leaders through programs like the Passport to Future Technology Leadership. Labs and Teams: The Schulman Lab at Johns Hopkins is a multidisciplinary team working on DNA-powered materials and molecular programming. The lab is embedded within several collaborative centers, enabling strong cross-departmental and cross-institutional research. Their work combines experimental biochemistry with theoretical modeling to design and implement complex molecular systems.
Professor Daniel Segrè is a faculty member at Boston University, holding the title of Professor of Biology, Bioinformatics, and Biomedical Engineering. His research focuses on systems biology, microbial ecology, and metabolic engineering, with an emphasis on understanding complex biological networks and their applications in bioenergy and biomedicine. Segrè leads the Segre Lab ( segrelab.bu.edu ), where theoretical and computational approaches are applied to study metabolism, microbial interactions, and synthetic biology. Segrè earned his PhD from the Weizmann Institute of Science, Israel. His work bridges fundamental science and applied engineering, addressing topics such as microbial community dynamics, metabolic pathway design, and environmental microbiome applications. Research Interests: Systems biology of metabolism, evolution of biochemical networks, microbial interactions, bioinformatics, and environmental microbiome engineering. His lab develops computational models (e.g., COMETS) to simulate microbial ecosystems and design synthetic microbial communities for climate change mitigation and bioenergy production. Teaching: Courses include BE 777 (Computational Genomics), BF 821 (Bioinformatics Seminar), and BF 571 (Dynamics and Evolution of Biological Networks). These courses reflect his expertise in integrating computational methods with biological systems analysis.
Yimin D. Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at Temple University's College of Engineering, where he leads the Advanced Signal Processing (ASP) Lab. His research spans statistical signal processing, array processing, radar, wireless communications, and convex optimization. Research Interests: Dr. Zhang's work focuses on cutting-edge signal processing techniques including compressive sensing, sparse arrays, time-frequency analysis, and robust beamforming. These are applied to radar systems, satellite navigation, assisted living, and wireless networks. His research addresses core challenges in target localization, direction-of-arrival estimation, and spectrum-efficient joint radar-communication systems. The recent publications highlight a strong trend in exploiting sparsity, virtual arrays, and deep learning to enhance resolution and robustness in radar and communication systems. Themes include multi-frequency processing, low-rank matrix recovery, and optimized OFDM waveforms for dual-function systems. Scientific Awards: 2016 IET Radar, Sonar and Navigation Premium Award 2017 IEEE Aerospace and Electronic Systems Society Harry Rowe Mimno Award 2018 IEEE Signal Processing Society Young Author Best Paper Award (coauthor) Advising and Grants: Dr. Zhang has served as Principal or Co-Principal Investigator on over $6 million in research funding from the NSF, AFRL, ONR, and DARPA. While student advisees are not listed, his leadership of the ASP Lab suggests active mentorship of graduate researchers. He contributes extensively to the academic community as an associate editor for IEEE Transactions on Signal Processing and editor for Signal Processing journal, and serves on key IEEE technical committees. Labs and Teams: He directs the Advanced Signal Processing (ASP) Lab at Temple University, which focuses on developing novel algorithms for real-world applications in radar, communications, and navigation. Previously, he led the Wireless Communications and Positioning Lab and the RFID Lab at Villanova University.
Paolo Gardoni is the Alfredo H. Ang Family Professor and an Excellence Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, with additional professorial appointments in Industrial & Enterprise Systems Engineering and Biomedical & Translational Sciences. He also serves as Director of the MAE Center and Editor-in-Chief of Reliability Engineering & System Safety. Education Ph.D. in Civil Engineering, University of California, Berkeley (2002) M.A. in Statistics, University of California, Berkeley (2001) M.Eng. in Structural Engineering, University of Tokyo (1997) Laurea (BS+MS equivalent) in Structural Engineering, Politecnico di Milano (1997) Research Interests Gardoni’s scholarship integrates probabilistic methods with large-scale infrastructure systems to advance reliability, risk, and life-cycle analysis. His work quantifies the performance of deteriorating systems under natural and anthropogenic hazards, models societal impacts of disasters, and develops decision frameworks for sustainable and resilient infrastructure. He also examines ethical, social, and legal dimensions of risk, and investigates optimal strategies for hazard mitigation, disaster recovery, and climate adaptation. Across more than 250 refereed journal papers, he has advanced sub-fields ranging from probabilistic mechanics and earthquake engineering to catastrophe bond pricing and engineering ethics, leveraging tools such as stochastic differential equations, Bayesian networks, and physics-informed machine learning. Awards & Honors Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure (ASCE, 2021) Best Paper Awards in ASCE Journal of Sustainable Water in the Built Environment (2019) and Geotechnical Research (2018 Telford Premium Prize) Fellowships and named professorships: Alfredo H. Ang Family Professor, Excellence Faculty Scholar, and courtesy or honorary professorships at Tsinghua, IIT Guwahati, Tongji, Jianghan, and Loughborough universities. Research Leadership & Funding Gardoni has secured over $58 million in research funding from NSF, DHS, NIST, USAID, Qatar National Research Fund, and other agencies. He directs the MAE Center—formerly an NSF Engineering Research Center—focused on multi-hazard engineering approaches, and is Editor-in-Chief of Reliability Engineering & System Safety (Elsevier, IF 9.4). He founded and formerly led the journal Sustainable and Resilient Infrastructure (Taylor & Francis) and serves on editorial boards of nine additional journals. Advising & Mentorship He has graduated 27 PhD and 35 Master’s students, many of whom now hold faculty positions worldwide. His group maintains an active pipeline of doctoral and post-doctoral researchers working on resilience analytics, infrastructure monitoring, and risk-informed decision-making. Laboratories & Collaborations He leads the MAE Center and is affiliated with the Critical Infrastructure Resilience Institute (CIRI) and the Biomedical and Translational Sciences group. International collaborations span the UK (Loughborough), India (IIT Guwahati), and China (Tsinghua, Tongji, Jianghan), fostering cross-disciplinary research in reliability and resilience engineering.
Tanya P. Garcia, PhD is an Associate Professor of Biostatistics at the Gillings School of Public Health and Research Faculty in the UNC Neurology Huntington Disease Program at the University of North Carolina at Chapel Hill . She leads the Methods for INcomplete Data (MIND) Lab , focusing on statistical methods for handling censored, missing, or incomplete data in neurodegenerative disease progression studies. Education: PhD in Statistics, Texas A&M University MS in Statistics, University of Western Ohio MS in Industrial Engineering and Operations Research, UC Berkeley Research Interests: Specializing in High-Dimensional Variable Selection , Longitudinal Data Analysis , and Neurodegenerative Disease Modeling , her work develops reproducible statistical methods for Huntington's disease progression, improving clinical trial design and biomarker identification. Scientific Awards: American Statistical Association Fellow (2024) Landis Award for Outstanding Mentorship (2024) Roy R. Kuebler Award (2024) Gertrude M. Cox Award (2024) Leadership & Mentorship: Director of the MIND Lab, Chair-Elect of the Biometrics Section of ASA, and Tyson Academic Leadership Fellow (2023–2024). Her lab alumni have secured prestigious positions at institutions like Wake Forest University and Baylor University.
