Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu
Nima Fazeli is an Assistant Professor of Robotics at the University of Michigan (2020–Present), holding courtesy appointments in Computer Science & Engineering (CSE) and Mechanical Engineering. He directs the Manipulation and Machine Intelligence (MMint) Lab, focusing on enabling dexterous robotic manipulation through multimodal representation learning, tactile sensing, and model-based reasoning. His work integrates mechanics, perception, controls, and planning to achieve autonomous interaction with uncertain environments. Education: PhD, MIT (2019); MSc, University of Maryland (2014); BSc, Amirkabir University of Technology (2011) Research interests emphasize embodied intelligence , including visuo-tactile fusion, contact dynamics modeling, and cross-modal learning. Recent work explores tactile shadows, deformable object manipulation, and language-guided robot control. His research is supported by the NSF CAREER grant and National Robotics Initiative, with applications in manufacturing, assistive robotics, and space systems. Publications span topics like tactile sensing hardware (e.g., GelSlim 4.0), visuo-tactile implicit representations (ViTaSCOPE), and failure recovery policies (Racer). His team’s work has been featured in outlets like The New York Times and BBC. Key Awards: NSF CAREER Grant (2024) Teaching includes Introduction to Robotic Manipulation . Collaborations involve cross-disciplinary projects with mechanical, electrical, and biomedical engineering groups.
Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
Richard M. Murray is the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1985), M.S. from UC Berkeley (1988), and Ph.D. from UC Berkeley (1990). He has served in academic roles from Assistant Professor (1991–1997) to his current endowed professorship. He chaired the Engineering and Applied Science division (2000–2005) and Biology and Biological Engineering (2020–2024). His research focuses on feedback control in biological and autonomous systems, synthetic cells, and networked control systems. Collaborators include experts in robotics, synthetic biology, and systems biology. Key awards include the IEEE Control Systems Award and election to the National Academy of Engineering. His educational contributions span courses on control systems, robotics, and bioengineering. Current research projects include the Developer Cell initiative (Sloan Foundation), layered testing for autonomous systems (AFOSR), and microbiome-based environmental solutions (CHARMME, ARO). He advises numerous graduate students and postdocs, with notable alumni in academia and industry. Labs include facilities in Keck and Steele laboratories at Caltech. His work bridges control theory, synthetic biology, and autonomous systems to address societal challenges like environmental monitoring and safe autonomy.
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.
Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).
Dr. Mo Rastgaar is a Professor at Purdue Polytechnic Institute, Purdue University. He holds a PhD in Mechanical Engineering from Virginia Tech (2008) and completed a postdoctoral fellowship at MIT's Newman Laboratory for Biomechanics and Human Rehabilitation. He leads the Human-Interactive Robotics Lab (HIRoLab), focused on assistive and rehabilitation robots for enhanced mobility, particularly lower-extremity devices. His research emphasizes understanding agile gait dynamics through human experiments and modeling. Research interests include assistive robotics, cyber-physical systems, dynamics, and control systems. Notable awards include the 2014 NSF CAREER Award. He has secured grants such as the 2019 NRI Collaborative Grant on robotic ankle prosthetics and 2020 grants for undersea infrastructure. Dr. Rastgaar's work bridges biomechanics, robotics, and clinical applications, advancing prosthetic designs and human-robot interaction. Key contributions include developing steerable powered ankle-foot prostheses and exploring multi-robot systems for underwater exploration. His labs integrate interdisciplinary approaches to solve complex mobility challenges, emphasizing both technical innovation and real-world clinical impact.
Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.
Per Krusell is Professor of Economics at Stockholm University and holds the Torsten and Ragnar Söderberg Chair at the Institute for International Economic Studies (IIES). He is a Fellow of the Econometric Society and served as President of the European Economic Association in 2020. He is currently the Secretary of the Prize Committee for the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. Education: PhD in Economics, University of Minnesota Research Interests: Per Krusell’s research spans macroeconomics , inequality , climate economics , labor markets , and political economy . He has made foundational contributions to understanding incomplete markets, wealth distribution, optimal taxation, and the macroeconomic effects of climate change. His work often integrates dynamic stochastic general equilibrium (DSGE) models with heterogeneous agents to study long-run outcomes and policy impacts. Publications Trend: His recent publications (2022–2025) reflect a strong focus on climate policy , integrated assessment modeling , and macro-labor dynamics . Key themes include carbon taxation, labor supply responses to productivity growth, fiscal multipliers in heterogeneous-agent settings, and the macroeconomic consequences of vaccination and epidemic control. Awards & Honors: Fellow of the Econometric Society Torsten and Ragnar Söderberg Chair in Economics President, European Economic Association (2020) Secretary, Nobel Prize Committee in Economic Sciences Advising & Grants: While specific student names are not listed, his leadership roles in global economic associations and policy committees indicate extensive academic advising and grant involvement in macroeconomic policy and climate economics. Labs & Teams: He is affiliated with the Institute for International Economic Studies (IIES) at Stockholm University, a leading research institute in macroeconomics and international economics.
Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.
Larry Pileggi is the Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He is also the Department Head of CMU’s ECE Department and has held prior roles at Westinghouse Research and Development and the University of Texas at Austin. His work bridges academic research and industry innovation, with co-founding ventures like Fabbrix Inc., Extreme DA, and Pearl Street Technologies. Dr. Pileggi earned his Ph.D. in Electrical and Computer Engineering from CMU (1989), following an M.S. (1984) and B.S. (1983) in Electrical Engineering from the University of Pittsburgh. His research focuses on three core areas: secure integrated circuit hardware (mitigating supply-chain threats), integrated circuits design methodologies (supporting sub-20nm CMOS and heterogeneous technologies), and power systems simulation (developing robust grid analysis tools like SUGAR). He has been recognized with numerous awards, including the prestigious 2023 Phil Kaufman Award for contributions to electronic system design, and is an IEEE Fellow. His academic leadership includes fostering maker initiatives and interdisciplinary research programs to address future challenges in energy and computing systems. Pileggi’s advising record includes over 47 Ph.D. students, many of whom collaborate with him in industry ventures. Current and past grants support his work on resilient power grids and novel memory technologies, reflecting a commitment to both theoretical and applied research. His lab, the Pileggi Lab, develops cutting-edge solutions for energy and integrated systems, emphasizing scalable simulation, secure hardware design, and next-generation memory architectures. Collaborations span institutions like ETH Zurich and industry partners in EDA and semiconductor sectors.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
