Ultrafast laser surface structuring presents a compelling avenue for finely tuning material surface properties, such as coloration, wettability, tribological properties, etc. While nanoripples, the conventional laser-induced periodic surface structures, offer control over surface coloration, their effectiveness is limited to a narrow viewing angle. Addressing this limitation requires the development of techniques for generating alternative surface structures like nanobumps. Current methods for generating nanobumps, such as the double-pulse technique, are characterized by their complexity and high costs, posing significant challenges to widespread implementation. In this investigation, we introduce a novel approach for creating periodic nanobump structures through a straightforward double-scan method. This method involves subjecting the same surface to two consecutive laser scans with differing polarization directions.

Our study delves into the formation dynamics of these intriguing nanostructures, exploring the influence of laser parameters such as peak fluence, overlapping ratio, and polarization angle between scans. We propose and validate a formation mechanism to elucidate the observed phenomena. The resultant nanobump structures exhibit superior optical characteristics compared to nanoripples, particularly in terms of enhanced visibility across multiple viewing angles. By broadening the scope of achievable surface structures, our findings significantly expand the potential applications of ultrafast laser surface structuring. This research not only advances our understanding of laser-material interactions but also paves the way for the development of innovative surface engineering strategies with implications across diverse fields.

A Double-Scan Technique for Femtosecond Laser-Induced Nanobumps

Mechanistic interpretability is one of the most exciting and important research programs in current AI. My aim is to build some philosophical foundations for the program, along with setting out some concrete challenges and assessing progress to date. I will argue for the importance of propositional interpretability, which involves interpreting a system’s mechanisms and behavior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probability) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are the central way that we interpret and explain human beings and they are likely to be central in AI too. A central challenge is what I call thought logging: creating systems log all of the relevant propositional attitudes in an AI system over time. I will examine currently popular methods of interpretability (such as probing, sparse auto-encoders, and chain of thought methods) as well as philosophical methods of interpretation (including psychosemantics and representation theorems) to assess their strengths and weaknesses as methods of propositional interpretability.

Propositional Interpretability in Humans and AI Systems

Can AI Benefit Humanity?

Algorithmic Agnotology

In this keynote, Andrew will explore how current technologies, particularly agentic workflow, are revolutionizing the development of AI products. He will also delve into the role of AI in prototyping, showcasing how AI lowers the cost of software development and accelerates the prototyping process.

AI, Agents and Applications

This talk focuses on the use of foundation models to study problems in labor economics, in particular problems relating to the progression of worker careers and wages. In Vafa et al. (2024a), we introduced a transformer-based predictive model, CAREER, that predicts a worker’s next job as a function of career history (an “occupation model”). CAREER was initially estimated (“pre-trained”) using a large, unrepresentative resume dataset, which served as a “foundation model,” and parameter estimation was continued (“fine-tuned”) using data from a representative survey. CAREER had better predictive performance than benchmarks. Athey et al (2024) considers an alternative where the resume-based foundation model is replaced by a large language model (LLM). We convert tabular data from the survey into text files that resemble resumes and fine-tune the LLMs using these text files with the objective to predict the next token (word). The resulting fine-tuned LLM is used as an input to an occupation model. Its predictive performance surpasses all prior models. We demonstrate the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs surpasses the performance of fine-tuning larger models. In Vafa et al. (2024b), we apply and adapt this framework to the problem of gender wage decompositions, which require estimating the portion of the gender wage gap explained by career histories of workers. Classical methods for decomposing the wage gap employ simple linear models, and the resulting decompositions thus suffer from omitted variable bias (OVB), where covariates that are correlated with both gender and wages are not included in the model. We explore an alternative methodology for wage gap decomposition that employs CAREER as a foundation model. We prove that the way foundation models are usually trained might still lead to OVB, but develop fine-tuning algorithms that empirically mitigate this issue. We first provide a novel set of conditions under which an estimator of the wage gap based on a fine-tuned foundation model is root-n-consistent. Building on the theory, we then propose methods for fine-tuning foundation models that minimize OVB. Using data from the Panel Study of Income Dynamics, we find that history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of history that are important for reducing OVB.

Predicting Career Transitions and Estimating Wage Disparities Using Foundation Models

The convergence of technologies—from foundation AI models to diverse sensors and actuators to ubiquitous connectivity—is transforming the nature of interactions in the physical and digital world. People have accelerated their collaborative connections and productivity through digital and immersive technologies, no longer limited by geography or language or access. Humans have also leveraged and interacted with AI in many different forms, with the advent of hyperscale AI models (i.e., large language models) forever changing (and at an ever-astonishing pace) the nature of human-AI teams, realized in this era of the AI “copilot.” Similarly, robotics and automation technologies now afford greater opportunities to work with and/or near humans, allowing for increasingly collaborative physical robots to dramatically impact real-world activities. It is the compounding effect of enabling all three capabilities, each complementary to one another in valuable ways, and we envision the triad formed by human-robot-AI teams as revolutionizing the future of society, the economy, and of technology.

Catalyzing the Future of Collaborative Human-Robot-AI Teaming

Power in the modern world - military, economic, geopolitical - is built on a foundation of computer chips. America has maintained its lead as a superpower because it has dominated advances in computer chips and all the technology that chips have enabled. (Virtually everything runs on chips: cars, phones, the stock market, even the electric grid.) Now that edge is in danger of slipping, undermined by the naive assumption that globalising the chip industry and letting players in Taiwan, Korea and Europe take over manufacturing serves America`s interests. Currently, as Chip War reveals, China, which spends more on chips than any other product, is pouring billions into a chip-building Manhattan Project to catch up to the US.

In Chip War economic historian Chris Miller recounts the fascinating sequence of events that led to the United States perfecting chip design, and how faster chips helped defeat the Soviet Union (by rendering the Russians’ arsenal of precision-guided weapons obsolete). The battle to control this industry will shape our future. China spends more money importing chips than buying oil, and they are China`s greatest external vulnerability as they are fundamentally reliant on foreign chips. But with 37 per cent of the global supply of chips being made in Taiwan, within easy range of Chinese missiles, the West's fear is that a solution may be close at hand. 

From "Chips War" to "Chiplets War"

Patterns of Effective Mentorship Among NIH Career Development (K) Awardees

The ASCEND Research Training Program (Kamangar et al., 2019) was designed to address the need for a more inclusive research environment by focusing on student-centered hallmarks of success.(Hallmarks of Success, 2019) These included: 1) Strengthening Science Identity by fostering a supportive learning environment where students felt valued as scientists which helped them develop a strong sense of belonging in the research community; 2) Building Research Self-Efficacy by equipping students with a strong sense of confidence in their ability to conduct independent research including providing opportunities to design, execute, and present research projects; 3) Enhancing Academic, Scientific, Personal, and Social Self-Concept by actively recruiting and training mentors who could provide effective guidance and support tailored to each student's needs and exposing students to diverse research areas and opportunities to interact with successful scientists. By focusing on these hallmarks, this research training program aimed to cultivate not only research skills but also the confidence and drive necessary for students from underrepresented backgrounds to become future leaders in a diverse and thriving research landscape. In this presentation, we assess the effectiveness of the ASCEND entrepreneurial research training program in terms of the hallmarks of success.  Other key academic outcomes are also examined. An annual ASCEND survey was administered to 270 students at Morgan State University.  A retrospective pre/-post design was employed to examine perception of change in key constructs over a one-year period. ASCEND Scholars were compared to a matched control sample. 
Compared to their comparison group (n = 59), ASCEND Scholars (n = 40) had significantly greater gains in Science Identity (pre/post: 70.0 and 77.2% vs. 58.9% and 89.7%, p <0.001), Research Self-Efficacy (pre/post: 65.1% and 74.8% vs. 48.1% and 84.7%, p=0.003) Academic Self-Concept (pre/post: 75.4% and 80.4% vs. 73.9% and 89.0%, p <0.001), Social Self-Concept (pre/post: 75.4% and 84.5% vs. 65.8% and 86.5%, p=0.008), and Peer Support (pre/post: 43.3% and 49.6% vs. 33.9% and 72.0%, p <0.001). Entrepreneurial research training promotes a greater sense of research confidence and efficacy in students underrepresented in biomedical and behavioral science research. This may aid in diversifying the biomedical and behavioral science research workforce via greater student identification and confidence in pursuing these fields.

Fostering the Development of Research Entrepreneurs through ASCEND

Individual Development Plans (IDP) were designed as a tool to identify academic and career goals, assess the feasibility of obtaining these goals with respect to the individuals’ skill sets, and develop a plan to acquire these skills and competencies to obtain short- and long-term academic and career goals (Edwards, et al, 2024). In this program, ASCEND Scholars were provided with a career planning process that allowed them to identify both academic and professional needs and career goals (Kamangar, et al, 2019). The IDP also assessed student self-assessments of academic, research, and professional development skills needed to achieve their goals and areas in which improvement is needed. In this presentation, we examine if IDPs help students establish their short term and long-term goals and assess perceived skills needed to actualize their goals.  IDPs were collected at baseline/year 1 and during program year 2 from 63 ASCEND Scholars. Students specified the specific skills needed in each category and their level of proficiency on a scale from 1 = lowest level of proficiency to 5 = highest level of proficiency. Paired t-tests reveal that students showed improvement in each of the areas assessed (p < .05). Academic skills: Y1:M=3.07, SD=.57;Y2:M=3.46,SD=.46; Research/Technical Skills: Y1: M=3.05,SD=.64;Y2: M=3.30,SD=.56; Professional/ Interpersonal Skills (Y1: M= 3.18,SD=.75, Y2:M= 3.66, SD=.60. Examination of long-term goals shows that student goals changed over time and reflect interests in becoming leaders in Biomedical and Social Science careers. Participation in an entrepreneurial research training model may have influenced student perceptions of their long-term career goals and skills needed to achieve them.

Cultivating Biomedical and Behavioral Science Researchers through Self-Assessment and Individual Development Plans

Research suggests that experiential learning through community outreach and leadership serves as a basis for improving academic, personal, and career outcomes for students (Goldberg, et al 2011). Training programs that include community outreach experience and opportunities for leadership reveal that participation in community fosters a sense of pride and increased self-efficacy knowing that a contribution was made to benefit the community (Battaglia, et al, 2019).  The purpose of this presentation is to highlight changes in ASCEND Scholar community engagement and leadership during their participation in the program. Data on student engagement in community-based activities and leadership roles were collected from students each year of their participation in the program. Descriptive data suggests that student engagement in community activities and leadership roles increased during their participation in the program. Engagement in an entrepreneurial training program enhanced student involvement in key campus and community leadership roles and participation.  Implications of these findings for broadening participation in biomedical and behavioral science research is addressed.

Underline digital video library

A Double-Scan Technique for Femtosecond Laser-Induced Nanobumps

Propositional Interpretability in Humans and AI Systems

Can AI Benefit Humanity?

Algorithmic Agnotology

AI, Agents and Applications

Predicting Career Transitions and Estimating Wage Disparities Using Foundation Models

Catalyzing the Future of Collaborative Human-Robot-AI Teaming

From "Chips War" to "Chiplets War"

Patterns of Effective Mentorship Among NIH Career Development (K) Awardees

Fostering the Development of Research Entrepreneurs through ASCEND

Cultivating Biomedical and Behavioral Science Researchers through Self-Assessment and Individual Development Plans

Ascending in Biomedical Research Through Leadership and Community Engagement

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